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Agent Clan is a desktop workspace that turns your Claude Code and Codex agents into a 3D team living right on your screen. Instead of working with agents as lines of text, you build an agent army on your desk — a crew of characters you pick, talk to and direct with your hands. It is made for people who already use Claude Code or Codex and want to work with those agents their own way: choose an agent, tell it what to do, and see what comes back without reaching for the keyboard. Agent Clan is an independent desktop application for Mac and Windows, powered by your own AI accounts. The product is framed around a simple frustration: picking an agent, giving it a task and reading the result should not mean reaching for the keyboard, copying and pasting, or switching between apps. Handing something over should not require a detour through a clipboard, and answering an agent's permission request should not pull you out of what you are doing. Agent Clan's answer is to keep your crew over every app, waiting in the corner as little heads, so you always know who is busy, who is done and who needs you. Because gestures work in front of any app, not only inside Agent Clan, the interaction follows you wherever you are working. The first control layer is gesture. Open your hand, then make a fist and your agents appear. Pinch and pull toward an agent to choose it, then let go to select. The same language covers answering an agent: when an agent asks for permission, you pull right to allow or left to deny. Gestures are optional — mouse and keyboard work too — but they are the heart of the experience, letting you reach characters floating over any application rather than hunting for the right window. The second control layer is voice. Choose an agent and simply talk; your words become its next task, turned into text right on your computer. Voice input needs a microphone. Agent Clan states that gesture recognition and speech-to-text run on your computer, and that it does not upload camera frames or raw recordings. The transcribed text goes to your chosen agent and is handled by that service, and agent tasks still require an internet connection. Agents wait in the corner of the screen as little heads, always on top of whatever you are working in. A ring around each head shows whether it is working, done or waiting for you, so the state of the whole crew is visible at a glance. Point at a head for its result, then allow or deny without switching apps — for example, answering “Can I run npm install?” from inside another program. You can also circle anything on your screen to hand it over to an agent. Prompts do not have to be typed at all. A picture, a file or some text can be dragged onto an agent and it becomes that agent's prompt, with no copying, no pasting and no switching apps. Structure comes from groups: put agents under a leader, pick the leader to open its group, then pull again to talk to a member, or ask a leader and its crew splits the work. This turns a large set of agents into an organized team rather than a flat list of terminals. The crew is rendered in 3D. Every agent can be given its own hair, eyes, clothes and costumes, and each terminal you open becomes one more character at a desk in the room, with the whole room typing away together. With Pro there is no limit on characters; the site illustrates the idea with 50 agents in a cabin, each in a cat costume with its own eyes and name, typing as fast as they can. Pro also unlocks mods. You tell any Claude agent in your crew what you want — sparkles when a task is done, a crown on its character, something new in the room, a small panel, a colour theme or a sound — and it writes a small mod using Agent Clan's MCP tools, looks at a picture of the result, fixes what is off, and the change appears right away with no restart. Mods stay in a Workshop folder on your computer, apart from the app, so updates do not remove them, and any of them can be turned off on the Workshop page. Codex agents cannot write mods yet. Underneath, Agent Clan is an independent desktop workspace that connects the Claude Code and Codex tools on your computer to interactive characters. You use your own agent accounts, and the original providers bill their usage; AI service fees are separate from Agent Clan itself. Gesture recognition and speech-to-text are processed locally rather than uploaded. A character can also be created from a photo by asking your own Claude Code account to suggest a character made from existing parts and colours — a step the site notes is not processed entirely on your computer. The outcomes the product points to are immediacy and awareness. You can pick an agent and give it a task without touching the keyboard, see who is busy, done or waiting in a single glance, answer a permission request in place rather than switching apps, and hand over screen content by circling it or dragging a file straight onto a character. Instead of losing your place every time an agent needs you, the crew sits over every app and comes to you. Concrete workflows follow from that. A developer running several Claude Code or Codex tasks can open terminal after terminal and watch the room work while doing something else, then allow or deny each request as it comes up. Someone reviewing material in another application can circle the thing they mean and hand it to an agent, or drag a picture, file or text block onto one. A user organizing a bigger effort can put agents under a leader and let the crew split the work. A Pro user can ask an agent to restyle the workspace or add a sound, and a user with a photo can ask Claude Code to suggest a character built from existing parts and colours. Agent Clan has a Free tier and a Pro tier, and an account is required for both. Free includes one project with up to two agents, gesture control and voice input, and 3D characters and workspaces. Pro is a one-time purchase listed at US$14 during a launch price running until Nov 30, 2026 (US$19 afterwards), includes one year of updates, and adds unlimited projects and agents, mods, and everything in Free. Pro belongs to your account and can be signed in on up to 3 computers; signing in on one more prompts the app to sign out the one used longest ago. The app runs on Mac and Windows, and defaults to mouse and keyboard if you do not want to use gestures. Agent Clan's core value proposition is stated simply on its own site: pick with your hand, ask with your voice, and keep your crew over every app so you always know who is busy, who is done and who needs you. It takes agents that normally live in terminals and turns them into a visible, reachable team you can direct without a keyboard.

NotchX is a macOS app that turns the camera notch on a MacBook into a live hub for the things you check most often: what's playing, messages, incoming calls, your next meeting, connected AirPods, the weather, and the volume and brightness you adjust throughout the day. Instead of pulling information into the middle of the screen or sending you into separate apps and settings panels, NotchX puts it around the notch itself. Closed, it keeps two small icons either side of the camera; hover, and it opens straight down and never wider than it needs to be. It is built for people who spend their day on a MacBook Air or MacBook Pro and want status and controls within a glance rather than a detour. macOS already tells you what is happening through notification banners, a large volume and brightness overlay in the centre of the display, and pop-ups from apps such as Music. NotchX starts from the notifications your Mac already shows and re-presents them in a strip of screen that is otherwise empty. The volume and brightness pop-up that normally covers the middle of your screen becomes a slim line under the notch. Checking whether your AirPods are connected no longer requires opening Bluetooth settings. Each glance becomes a smaller interruption, which matters on a laptop where screen space and attention are both limited. Music is the deepest part of NotchX. It shows the track title, the artist, play and pause, skip forward and back, and a progress bar you can drag, all beside the camera, and it works with Apple Music and Spotify. The player offers five looks — Aura, Cover Wash, Neon, Sunset and Spectrum — and thirty-five visualizers in total, seven per look. Each look has its own colours and its own animated icons beside the camera, and each song gets a different visualizer, so the notch changes character as your playlist moves on. Everything pauses with the music, so nothing keeps animating once playback stops. Messages and calls arrive from the notification banners macOS already displays. When a message comes in from Messages, Slack, Teams, WhatsApp or another supported app, the sender's name appears beside the camera together with that app's real icon; clicking it opens the message in the app itself. Calls work the same way: if FaceTime, Slack, Teams or Zoom is ringing, the caller and a ringing phone appear beside the camera, and nothing drops down over your work. You can answer or decline from there. Once you are on the call, the notch shows just your microphone state and a timer, and hovering reveals who it is and how long you have been talking — there is nothing to press. Meetings surface a few minutes ahead of time along with the icon of the app the meeting link opens in. One click joins the meeting, and a second option reminds you again in five minutes. AirPods appear beside the camera the moment they connect, with their name, so you can confirm the connection without opening Bluetooth settings. Weather is optional and off by default; turn it on with the Weather tile and, when nothing else is going on, the notch shows a live sky — sun, rain, thunder or snow — plus the temperature. Clicking it reveals today's high and low, the chance of rain and the forecast for the next five hours. Volume and brightness are handled by replacing the large macOS pop-up in the middle of the screen with a slim line under the notch: drag the line or tap the icons. Everything else is controlled from a small menu that opens when you click NotchX in the menu bar. From there you switch individual features on or off, choose a music look, and decide how much of a message the notch reveals: the full message with sender and text, the sender only with a "New message" label, or just the app icon, which is handy when you are sharing your screen. The same menu holds Open at login and Quit. NotchX works locally on your Mac and takes its information from what macOS already presents. It reads the message and call banners the system shows, which is why it asks for Accessibility access; the same permission lets it replace the volume and brightness pop-ups. Meetings ask for Calendar access instead. Music, weather and AirPods work without Accessibility permission. Everything stays on the machine: there is no account, no analytics and no message history, and nothing you read is sent anywhere. The app only goes online to check your licence, look for updates, and — if you have turned Weather on — to fetch a forecast for your approximate area. The practical benefit is that information arrives where you are already looking rather than in the middle of your work. A ringing call shows who is calling and lets you decline without a full-screen interruption; a message shows its sender without you switching apps; the next meeting is one click away; AirPods confirm they are connected; the weather is there when nothing else is happening. Because the closed state is only two small icons either side of the camera, the feature set is available without permanently giving up screen space, and because the panel opens straight down and never wider than it needs to be, it does not cover the work behind it. Typical use is a working day on a MacBook: music playing while you write, with the notch showing the track and a draggable progress bar; a Slack or Teams message sliding in with the sender's name beside the camera; a Zoom call arriving with the caller shown so you can answer or decline; the design review meeting appearing a few minutes early with a one-click join. If you share your screen often, setting messages to icon-only keeps content out of view. If you work away from a window, the Weather tile gives you the sky and the next five hours at a glance, and the slim volume line keeps adjustments out of the centre of the display. NotchX is aimed at people using MacBooks with a notch — MacBook Air and MacBook Pro models from 2021 onward — running macOS 13 Ventura or later. On a Mac without a notch it sits at the top centre of the screen instead. It is a paid app with a 14-day free trial that unlocks every feature; after that a lifetime licence costs $9.99 as a one-time payment for one Mac, including all future updates. There is no subscription and no account, and it works offline once activated. The app is signed and notarized by Apple, checkout is handled by Lemon Squeezy, and the licence key arrives by email. A licence can be moved between Macs by choosing "Deactivate this Mac" on the old machine and entering the key on the new one. Updates arrive automatically or via Check for Updates. In short, NotchX gives the notch something to do: a single, local, glanceable surface for music, messages, calls, meetings, AirPods, weather, volume and brightness, backed by a one-time $9.99 licence after a 14-day free trial, with no account, no subscription and nothing you read leaving your Mac.

seam is a native Mac app that turns the notch at the top of your Mac display into a Dynamic Island: a small, glanceable surface that shows what is happening without pulling you out of your work. From that surface you can set a focus sprint, control your music, see upcoming meetings, check battery and weather, dictate text into any app, and see when a coding agent such as Claude Code finishes. It is built for people who spend their day on a Mac and want the small, constant interruptions of checking things — track names, timers, calendar entries, battery — to cost less. The same experience is available on machines without a notch through Island mode, which floats the identical surface on iMac, Mac Mini, Studio Display and external displays. The notch takes up a permanent strip at the top of a modern MacBook display, yet most of the time it does nothing. Meanwhile, the information people check most often during a workday lives scattered across different apps: the current song is in Music or Spotify, the remaining minutes are in a timer, the next meeting is in Calendar, the battery sits in the menu bar, and the status of a long-running coding agent is buried in a terminal window. Each check is short, but together they fragment attention and pull people out of deep work. seam's premise is that the top of the screen is already inside your field of view, so the things you glance at most should live there instead of behind an app switch. Voice input has a similar problem: most dictation tools send audio to a server, which raises privacy questions and requires a connection. seam instead runs its transcription and translation locally on the Mac, so nothing leaves the device. Voice is one of the app's core capabilities: seam lets you type 4x faster with your voice by dictating into any app. It offers ultra-fast voice transcription and translation with 100% local processing, so spoken words become text directly on your Mac rather than being uploaded for processing. The privacy benefit follows directly from that design — your voice and the resulting text stay on your machine, and the app states plainly that nothing leaves your Mac. Because the processing is on-device, it also does not depend on a network connection to function. For anyone who writes messages, notes or long passages by speaking rather than typing, this turns voice into a practical everyday input method instead of an occasional experiment. Flow is the built-in focus timer. You set a sprint from your notch and work without a clock on your screen, so the remaining time is available when you glance up but never occupies the middle of your workspace. The app describes these as deep work sessions with built-in breaks, and shows a session timer that starts at 0m with a Start Session control. The point is to remove the friction of opening a separate timer app and the distraction of watching a countdown: the session lives in the notch, starts there, and stays out of the way until you need it. Now Playing brings music control into the same surface. The expanded player can show the current track, and the settings offer fine-grained control over how that behaves: display when idle shows the last track even when playback is paused, display when on media window shows an overlay when a media app is focused, and there is an option to jump to the browser tab so the now playing tab opens directly. An output device picker shows output switching inside the expanded player, song titles can scroll, a track change animation shows a peek when the song changes, and live music bars follow the sound — a visualizer that asks for audio access in order to work. Beyond music and timers, seam keeps other everyday signals in the same place: upcoming calendar events, battery status, weather, and the completion state of coding agents such as Claude Code. Not every user wants every module, so the app is built around toggles — ten features you can switch on only if you use them, including Now Playing, Calendar, Weather, Battery, Focus and related options, alongside general settings for lock screen, notifications, sound and display, and audio devices. That configurability matters because a control center only helps if it shows what you personally care about and nothing else. The overall approach is to reuse space that already exists. Instead of adding another window, another menu bar icon or another dashboard, seam expands and collapses from the notch at the top of the screen and stays quiet the rest of the time. The app is deliberately lightweight: it reports 0.1% CPU, 33 MB of memory and zero battery impact, and only wakes up when something actually happens rather than running continuously in the background. Privacy is handled through architecture rather than policy: on-device AI, no servers, no tracking, no data collected, and no internet required. On screens without a notch, Island mode provides the same floating surface so the experience is not tied to one hardware model. The benefits follow from those choices. Because the information is already at the top of the screen, checking a track, a timer or a meeting costs a glance instead of an app switch, which the app frames as saving an hour every day from the top of your screen. Voice dictation removes typing effort for people who speak faster than they type. Local processing means you can dictate sensitive content without sending audio away, and the lightweight footprint means the utility does not itself become a battery or performance problem. Typical use cases map directly to those features. During a deep work block, you start a Flow sprint from the notch, work without a clock in the middle of the screen, and take the built-in breaks. While listening to music or a playlist in Music, Spotify or a browser tab, you check and switch tracks and outputs from the expanded player and see a peek when the song changes. Before a meeting you glance at the calendar entry rather than opening Calendar. If you prefer speaking to typing, you dictate into whatever app you are already using — mail, notes, messages — and the text appears as locally produced transcription. If you run a coding agent such as Claude Code, you keep working and let the notch tell you when it finishes instead of watching a terminal. seam is aimed at Mac users — particularly MacBook Pro owners whose notch is otherwise unused, but also iMac, Mac Mini, Studio Display and external display users who get the same surface through Island mode. It is a native Mac app distributed as a direct download with a free 48-hour trial that requires no credit card. Pricing is a one-time $19.90 purchase rather than a subscription, and every license works on up to 2 Macs. The purchase includes at least a year of free updates, with a discount for early supporters if a major new version is released, and there is a 14-day money-back guarantee. In short, seam's value proposition is simple: the top of your screen is already in view, so put the things you glance at there. Music, focus sessions, meetings, dictation, battery, weather and coding-agent status arrive in a single lightweight, on-device surface, bought once instead of rented monthly, with your data staying on your Mac.

Capnot is a macOS app that adds a caption-first communication layer on top of whatever is on your screen. It places live captions, caption bubbles, drawings, and pointer tools over any app running on your Mac, so a screen demo can still make sense when the sound is off. The product is built for people who explain through screens — SaaS founders and indie developers shipping product demos, presenters, remote workers, educators, and streamers — and its stated purpose is to let you record once and explain it live, with no voice-over and no post-production. Every tool is one hotkey away from a menu bar app, and the captions and annotations appear on screen instantly in whatever recording or screen share you are already running. The website frames the core problem simply: your screen is visible, but your meaning is not. It describes three specific failures. First, the demo is done but the editing is not — you finish a walkthrough and then lose another hour adding captions, arrows, and zooms in a video editor. Second, no mic means no explanation, so a noisy room, a muted call, or a silent social post stops you from explaining what is on screen. Third, viewers lose the tiny cursor and miss the button, menu, or detail you meant to show, which breaks the explanation. Capnot's answer is to add the missing communication layer while you record, share, or present, rather than afterwards in an editor. The Caption toolkit covers everything you want to say, on screen, right now. Bubble Chat (hotkey ⌃/) creates a caption bubble anchored to your cursor; you click anywhere to plant it on screen so viewers read your message next to the thing you are talking about, with no "look at the top-left corner" confusion. Dialogue Mode (⌃.) presents like a broadcaster, showing your name, photo, and title above a polished subtitle panel at the bottom of the screen while you speak naturally; you can choose from built-in style presets or design your own, and toggle Dialogue Mode off for a clean subtitle-only look. With the broadcaster overlay off, you get the Subtitle Bar: a slim, unobtrusive, readable bar at the bottom of the screen, style-matched to your brand. Live Captions, marked Beta, let you hit the mic and speak naturally while Capnot transcribes you in real time via macOS Speech Recognition, fully on-device, so no audio leaves your Mac and there is no cloud or subscription involved. The Annotation toolkit lets you draw attention to what actually matters by overlaying your screen with drawings and shapes, with no app switch and one hotkey. The tool list includes a Move tool to reposition shapes, a Pen for freehand drawing, an Arrow to point to anything, a Line tool for straight lines, a Rectangle for box shapes, a Circle for circles and ellipses, Text for text labels, and a Counter for numbered markers — useful for walking through steps in order. A Blur Box lets you redact content, an Eraser removes shapes, Select Color picks the stroke color, and Clear All wipes the canvas. Because these annotations are drawn directly over the app you are demonstrating rather than added later in an editor, the shapes and labels land exactly where the explanation happens. The Pointer toolkit makes your cursor impossible to ignore with four tools. Spotlight Mode (⌃S) dims the screen to black except for the area around your cursor, with feathered edges that keep it natural, so your audience stops looking at the wrong part of the screen. The Laser Pointer (⌃L) shows a vivid glowing dot that trails behind your cursor with a fading streak, with adjustable color, dot size, trail duration, and glow intensity — described as looking great in recordings and live screen shares. The Cursor Highlighter (⌃H) puts a bright ring around your cursor everywhere on screen, with customizable color, size, and shape to match your brand or mood. The Magnifier (⌃M) shows a circular magnified view that follows your cursor with a crosshair and adjustable zoom level, so you can show small UI details, tiny code diffs, or compressed content without zooming your entire display. Overall the product works as an overlay and a hotkey system rather than a recording suite. Capnot installs once, follows a guided setup, and settles into your menu bar. From there, every tool has a keyboard shortcut — no menus, no mode switching, no hunting for buttons — and captions and annotations appear on screen instantly. Because Capnot sits on top of any app, it works with any screen recorder or screen share; you are not locked into Capnot's own capture workflow. The site describes the whole path as three steps: install once, press a hotkey, and you're done, with no timeline editing and no post-production. A Settings window with four tabs — General, Caption, Annotation, and Pointer — lets you change every keyboard shortcut, font size, colors, corner radius, opacity, gradient borders, and more. The stated benefits follow from that design: you finish a walkthrough without losing another hour adding captions, arrows, and zooms in a video editor; you can explain what is on screen even in a noisy room, a muted call, or a silent social post; and viewers no longer miss the button, menu, or detail you meant to show because the pointer is impossible to lose. Because explanations go into the recording live, you record once and share immediately. Because Live Captions run on-device through macOS Speech Recognition, no audio leaves the Mac. And because it is a one-time purchase, there is no subscription hanging over every demo you make. Concrete use cases named on the site map to specific audiences. SaaS founders and indie developers ship product demos without voice-over, explaining a launch, feature, or code walkthrough with captions already on screen, then recording once and sharing immediately. Presenters keep their audience reading with on-screen cues, talking points, and live captions during conference talks and demos while their slides stay clean. Remote workers communicate without a mic by typing captions teammates can see during screen shares, which suits noisy environments, muted calls, or async video. Educators highlight what they are pointing at, annotate diagrams, and caption explanations in real time to make lessons easier to follow. Streamers drop captions, circle things on screen, and spotlight content live without touching their streaming software or switching scenes. On pricing and requirements, Capnot is $12.99 as a one-time purchase with one plan that includes every Caption, Annotation, and Pointer tool — no subscription and no feature-gated Pro tier. A 7-day free trial requires no card. The license covers one Mac, updates are included, and checkout is handled by Polar, with refund requests handled according to Polar's refund policy. Capnot supports macOS 13 (Ventura) and later, and Live Captions requires macOS Speech Recognition, available on Apple Silicon and Intel Macs. The app ships as a downloadable DMG, and the site notes there is no account creation, timeline editor, or complicated recording workflow. In short, Capnot's value proposition is that your next demo can explain itself: live captions, annotations, and pointer tools added to every recording on a Mac, for a single one-time price, with no voice-over, no editing afterwards, and no subscription.

ClarifierAI for Chrome is a Chrome extension that rewrites, fixes and translates only the text you select, in any text field on the web. It is the browser version of ClarifierAI, an iOS keyboard that makes your words clearer and translates across 113 languages in any app, and it keeps the exact same prompts so your text comes out the same on phone and desktop. The extension is built for people whose writing happens in the browser — the maker describes his own work as emails, LinkedIn messages, support tickets and docs — and it brings the Improve, Clarify, Grammar, Polite, Shorten and Translate actions directly to the place where you type. The product story behind the extension is a simple one. After launching the iOS keyboard, the most common request the maker received was "I need this on my laptop too." Most of his own writing happens in the browser, and there he was back to copy-pasting between tabs and AI chats just to make one sentence sound right. ClarifierAI for Chrome was rebuilt as a Chrome extension to remove exactly that loop: no new tab, no copy-paste, and no need to paste a corrected sentence back into the field where it belongs. Because the prompts are shared with the iOS keyboard, a rewrite produced on an iPhone and a rewrite produced in Chrome should read the same way. The defining behaviour is scope. You select any part of your text in any text field, and ClarifierAI improves, translates or changes its tone right where you type. Only the part you selected is changed, and the rest of your message stays untouched. If nothing is selected, it works on the whole field instead. Reviewers singled this out: one commenter wrote that "only touching the text I selected instead of the whole field is the part I trust here," adding that every rewrite extension they had tried eventually mangled something they wanted to keep exactly as it was. There are three ways to trigger the extension. You can select text and click the ClarifierAI button, or press Alt+Shift+C, or right-click and choose "Format with ClarifierAI" from the context menu. All three routes act on the current selection inside the field you are already typing in, so the interaction stays inside your existing workflow rather than pulling you into a separate app. When nothing is highlighted, the same actions apply to the whole text field, so the extension works whether you want to touch one sentence or the entire draft. The action set covers the common editing jobs. Improve, Clarify, Grammar, Polite and Shorten are built in, and Translate handles languages. Beyond the presets, the extension supports your own custom actions — the maker's examples are "make it formal" and "explain like I'm 5" — which lets you save instructions that match how you actually write. Because the prompts match the iOS keyboard, the same action produces consistent results across phone and desktop. Translation is a first-class feature rather than an add-on. The Chrome extension translates into 119 languages, using a searchable language picker with your recent languages shown at the top so that the languages you use daily are one tap away. On iOS, ClarifierAI translates across 113 languages in any app, and the keyboard works in Gmail, LinkedIn, iMessage, WhatsApp, Slack and more. The point in both cases is the same: you translate in place instead of switching between apps or copying text into a separate translator. Changes are transparent rather than final. After ClarifierAI runs, you see every change highlighted, and you can click any single change to undo just that one — an all-or-nothing undo is deliberately avoided so you keep your own voice. Pressing the checkmark replaces the text in the field, and Command+Z undoes it. Two small extras round out the editing experience: UPPERCASE and lowercase conversion in one click, which needs no AI at all, and light and dark mode. The extension works in Gmail and most text fields on the web. Privacy is stated plainly in the launch post. The extension reads your text only when you open ClarifierAI on it, and there is no browsing history access, no tracking and no ads. That matters because the fields where people write in the browser are frequently work contexts — client emails in Gmail, internal messages in Slack — so the reading is limited to the moment you deliberately invoke the tool on a selection. The practical benefit the maker promises is speed: writing and translating your messages 10x faster, using AI directly where you type. Instead of moving a sentence out to an AI chat and back, you act on it inline, in the same field, with the rest of your draft preserved. The highlighted diff and per-change undo mean you can accept most of a rewrite while rejecting the one word that no longer sounds like you. And because nothing is selected by default, the whole-field mode still covers the case where you want a full paragraph reworked end to end. The maker lists three concrete workflows from his own use. Before sending an important email, he selects the paragraph and runs Improve. When a reply sounds too harsh, he selects that one sentence and makes it Polite. When writing to colleagues abroad, he types in his native language and translates in place. On the iOS side, a reviewer described using translation most in work chat and while shopping on Chinese marketplaces such as Taobao, and said they no longer use a separate translator because they dislike switching between apps. ClarifierAI for Chrome is listed as free and is available through the Chrome Web Store; the launch page also points to the iOS keyboard as the earlier release from the same product. The extension's supported surface is Gmail and most text fields on the web, and it inherits the prompts of the iOS keyboard so behaviour stays consistent across devices. The product is published on Product Hunt by a small maker team, with the Chrome launch following the iOS launch. In short, ClarifierAI for Chrome takes the selection you highlight in a browser field and makes it clearer, shorter, more polite, grammatically correct, or translated into one of 119 languages — without a new tab, a copy-paste round trip, or a rewrite of text you wanted left alone.

Native Tasks for Obsidian is a free native macOS task app built for people who already keep their tasks inside an Obsidian vault. Rather than asking you to maintain a second place for your to-dos, it reads the checkboxes and Tasks plugin metadata that already exist in your .md files and writes every edit straight back into those same files. The app is shaped for Wunderlist-style task work, giving you a dedicated desktop surface for capturing, scheduling, and completing tasks while your Markdown notes remain the single source of truth. It runs on macOS 14 Sonoma or later and requires an Obsidian vault containing at least one .md file, with folder access granted so the app can scan the vault. The problem it addresses is a familiar one for note-based workers: tasks written as Markdown checkboxes inside Obsidian are fast to capture but awkward to actually work through. There is no dedicated window that stays out of the way, no quick way to capture a task while you are in another application, and no simple way to move a task from a project note into a daily note. At the same time, many people are unwilling to move their tasks into a separate cloud service, because that means duplicating information, losing the plain-text record, and accepting an account, sync, and telemetry they never asked for. Native Tasks for Obsidian sits on top of the vault instead of replacing it, so the notes stay portable and readable in any editor while the day-to-day task work happens in a purpose-built Mac app. One of the defining characteristics of the product is that it is a real macOS app rather than a wrapper around a web app. The site states plainly that there is no Electron and no proprietary database, meaning the application is shaped specifically for Wunderlist-style task work on the Mac instead of being a browser engine in a window. The other half of that promise is how edits are handled: every change round-trips back to your .md files through atomic writes, with mtime conflict detection, so concurrent Obsidian saves never get clobbered. In practice this means you can keep Obsidian open in one window and the task app in another, and the two stay consistent because the app detects when a file has changed underneath it rather than blindly overwriting your note. The app is also aware of the Tasks plugin and the metadata that plugin writes into Markdown. It recognizes due dates marked with 📅, scheduled dates marked with ⏳, recurrence marked with 🔁, priority marked with 🔺⏫🔼🔽⏬, and custom statuses such as [/], [-] and [>]. Because these are read directly out of the .md file, tasks you created in Obsidian appear correctly in the app, and changes you make in the app are written back in the same format. For anyone who has already invested in the Tasks plugin, this avoids a migration: there is nothing to import and no new syntax to learn, because the app speaks the format your vault already uses. Capturing tasks is designed to be fast from anywhere on the Mac. A global hotkey and a menu-bar composer let you add a task without switching to the main window, and captured tasks land in today's daily note, with full support for the Daily Notes plugin configuration. That means quick thoughts end up in the same place your existing daily-note workflow expects them, rather than in an inbox that lives only inside the app. The app also lets you drag tasks between files and reorder them within a file, and both rewrites land in your .md files atomically, so a move from a project note to a daily note is a single safe operation rather than a delete followed by a recreate. Overall the product follows a simple methodology: the vault is the database, and the app is the interface on top of it. You point the app at your Obsidian vault, grant folder access so it can scan, and it presents your Markdown tasks in a familiar three-pane layout with a sidebar, a list, and a task detail view. Smart lists for Today, Inbox, and Completed are provided out of the box for triage. Everything is processed locally on the Mac, with no accounts, no telemetry, and no analytics; the only network calls are obsidian:// deep links that you explicitly invoke. The benefits follow directly from that approach. Your tasks are never locked into a proprietary database, because they remain ordinary Markdown in your own vault and stay readable, portable, and yours. Because the app is native and focused, it gives you a fast, dedicated place to work through tasks without opening a full note-taking environment. Because writes are atomic and conflict-aware, you can safely keep Obsidian open at the same time. And because there is no account and no telemetry, adopting it does not add a new vendor to your task data. For existing Obsidian users, the practical outcome is that the tasks they already have become something they can actually run their day from. Concrete use cases map closely to the audiences the site names. Obsidian users who already track tasks in their vault with the Tasks plugin get a dedicated native surface for those tasks. GTD practitioners get Today, Inbox, and Completed smart lists out of the box, so they can capture quickly and triage later. Wunderlist refugees get a familiar three-pane interface of sidebar, list, and detail that respects their existing Markdown notes. Day to day, that looks like firing off a task from the menu bar while working in another app, having it appear in today's daily note, then later dragging it into a project note and reordering it among that project's other tasks. On requirements and availability: the app requires macOS 14 Sonoma or later, an Obsidian vault with at least one .md file, and folder access for vault scanning. It is distributed both as a direct download and through the Mac App Store. The app itself is free to use, and Pro is a one-off £19.99 with a 7-day free trial on the direct download that requires no card. Native Tasks for Obsidian is, in short, a focused native Mac task manager for people whose tasks already live in Obsidian. It keeps the Markdown files as the source of truth, round-trips every edit safely, understands the Tasks plugin syntax, and stays entirely local and private — which is the whole value proposition in one sentence.

TraceX is an AI engineer for production issues, built to take a customer-reported problem from the first support message all the way to a proven root cause and a fix. When a support email, a Slack message, a Jira ticket or a question in the dashboard arrives, TraceX reads it, plans what to check, and then investigates the systems behind the issue: it queries the database read-only, reads the code and recent changes, and checks error and log tools such as Sentry, Datadog and CloudWatch. It then returns the root cause with a confidence level, what should have happened versus what actually happened, and the evidence behind every claim. From there it writes the fix as a pull request and drafts a reply for the customer. It is aimed at engineering and support teams running production software, and it is free while in private beta. The hard part of a production issue is usually not the fix but finding the cause. A customer writes that a refund has not arrived, and the explanation can be buried across a database state, a migration that renamed something weeks earlier, a code path that still filters on the old value, and a background worker that now finds nothing to process. In the example shown on the TraceX site, 34 refunds sit in a pending state created after October 21, migration 0142 renamed pending to awaiting_payout, and the refund worker still filters on pending, so it silently finds nothing to process. Sentry reports no errors at all, because nothing crashes; the worker simply has nothing to do. Tracing that by hand means jumping between a database console, a repository, a commit history and log tools, and then explaining the conclusion to a support team and to a customer. TraceX is built to do that work in one place, the way your best engineer would, and to show its work at every step so the answer can be trusted rather than guessed at. Every investigation moves through six visible steps: intake, triage, evidence, root cause, action and resolve. Intake accepts an issue from a support email, a Slack message, a Jira ticket or a question in the dashboard. TraceX then plans its checks and follows the data and the code until the cause is clear, querying PostgreSQL, MySQL or MongoDB through read-only credentials, reading the code and recent commits through the GitHub App, and checking Sentry, Datadog and CloudWatch. The result is a root cause presented with its confidence level, an expected-versus-actual comparison, and a citation for every finding pointing to the record, code or log line behind it. A second model checks the answer against that evidence, which is how TraceX keeps answers grounded in what it actually looked up rather than in plausible-sounding assumptions. From the root cause, TraceX moves to action. It writes the smallest change that fixes the cause and opens it as a pull request, with the diff reviewed and its risk level shown, along with validation steps written into the description. In the refund example, the fix changes a single filter from state="pending" to state="awaiting_payout" and is presented as low risk, with the validation step of running the refund worker against staging so that 34 refunds move to payout. TraceX also reviews your team's pull requests, giving reviews on every PR with one-click suggestions. Alongside the code fix, it drafts a reply for the customer, so the support conversation can close with an accurate answer instead of an apology and a promise to look into it. TraceX also answers questions about your systems in plain English: how many refunds are stuck, which customers were charged twice, or what changed in checkout since Monday. Answers come from your databases, code and logs rather than guesswork, each step is shown as it happens, and each claim is cited. When a longer answer is needed, TraceX produces spreadsheets, CSV and PDF exports. In the example conversation on the site, asking how many refunds are stuck returns 34 rows in the pending state, all created after migration 0142 shipped, with the query, the commits read and the verification against the evidence all displayed. This turns ad hoc production questions into documented, checkable answers that a support lead or engineer can act on immediately. For developers who prefer the terminal, the TraceX CLI brings the same capability into your repo. You sign in through your browser, link a repository with one command, and ask TraceX from your terminal with tracex -c for answers carrying the same evidence as the dashboard. It installs with one line on macOS and Linux. In a linked repository it also works in a coding mode that reads, edits and runs tests, asking for your approval before every change and command. The site shows the flow in practice: it edits app/workers/refunds.py, shows the removed and added filter lines, asks whether to apply the change, then asks whether to run pytest, and reports 12 tests passed. In a repository that is not linked, TraceX works as a coding agent that asks before every change. The design of the product is centred on trust. TraceX connects to your databases with read-only access and runs only read-only queries, so nothing in production is changed, and the site lists production writes at zero. Access is read-only and can be revoked at any time. Your records are not kept: there is no index of your code or data, answers retain what was looked up and where rather than the rows themselves, and exported files are deleted after 24 hours. You approve every change, because fixes arrive as pull requests for your review and the CLI asks before every edit and command. Enterprise sign-in covers SSO, two-factor sign-in, roles for admins, investigators and viewers, revocable sessions and a full audit log. You can also run TraceX on your own Anthropic or Gemini account, so AI usage is billed to you and never through TraceX. The outcome is that every customer issue can be traced to its root cause with evidence attached. Instead of an engineer spending time reproducing a report and correlating tools, TraceX returns a root cause, a confidence level and an expected-versus-actual comparison that a team can check line by line. Support gets a drafted reply built from the actual finding, engineering gets a reviewed pull request with validation steps, and the answer can be verified against the cited records, code and log lines. Because every finding cites its source and a second model checks the answer, the conclusion stands on evidence rather than on a model's best guess, which matters when the output is going to a customer and into production code. The product fits concrete day-to-day workflows. A support email saying a refund has not arrived starts an investigation that ends with a root cause and a reply ready to send. A teammate mentions TraceX in a Slack support thread asking why an order was charged twice, and gets the answer, with its evidence, right there in the thread. A Jira ticket about checkout timing out on Safari comes back with a comment explaining the root cause and a linked pull request. A support lead asks in plain English how many refunds are stuck and since when, and receives the rows, the commits read and the verification behind the number. A developer uses the CLI to fix the refund worker filter and run the tests. Teams also use TraceX to review the pull requests they open themselves. TraceX is in private beta with early engineering teams and is free while in beta, with 50 investigations a month during the private beta. It connects to 10 integrations — PostgreSQL, MySQL, MongoDB, GitHub, Sentry, Datadog, CloudWatch, Gmail, Slack and Jira — covering databases, code, logs and inboxes, all connected read-only. Setup is described as connecting a database and a repository and asking your first question in minutes, with an early access request and a sandbox available to try. The combination of read-only access, cited evidence, human-approved changes and the option to bring your own model keys makes it usable for teams that need to see exactly what the system looked at before they act on its conclusions. In short, TraceX's value proposition is simple: every customer issue, traced to the root cause. It reads the support email, investigates the database, logs and code read-only, proves the cause with cited evidence and confidence, writes the fix as a pull request and drafts the customer reply, while keeping production untouched and every change under your approval.

Pauta is a meeting transcription and summarization app for Mac and Windows that runs entirely on your own computer. It records your microphone or your system audio, transcribes the conversation while it happens, and then turns it into a note with decisions and tasks. The product is aimed at people who hold a lot of conversations — meetings, interviews, classes, appointments and voice notes — and who want a private memory of them rather than another cloud subscription. There is no bot that joins the call, no account to create and no server storing your audio. You pay once and the license is yours forever. Most meeting recorders work the same way: a bot joins the call as an extra participant, audio is streamed to a server, and the transcript lives in someone else's cloud behind a monthly fee. Pauta takes the opposite route. It records through your own computer, capturing both the microphone and the system audio, so it works in Zoom, Google Meet, Microsoft Teams and any other call with no extra guest. The website is explicit that nothing leaves your machine: Pauta servers do not receive your recordings, transcripts or summaries. Offline use is supported — the internet is only needed to validate the license, download models and, if you connect Claude or ChatGPT, to summarize with them. Capture starts in two ways. Pauta notices when a meeting starts, or you turn it on manually whenever you want, for videos and any system audio. The rule stated on the site is simple: if you can hear it, Pauta can capture it. Controls are always at hand — on the Mac the recording sits at the top of the screen in the Mac Island; on Windows the system tray offers one click to record or mark a moment. During a session you can mark a moment worth returning to and turn on stealth recording from a meeting-detected prompt. Before you record, the options drawer shows what stays on the device. Transcription happens while the conversation is happening, from either the microphone or the system audio, and the text stays on your machine without touching a server. The interface shows the transcription engine in use — Parakeet V3 in the demo screens — alongside a language setting. Speaker identification is the second layer: Pauta separates the voices in each meeting and matches them against people it already knows, on your computer. It learns each person's voice and recognizes it in later meetings, so a familiar participant is labeled by name instead of a generic speaker number. Fix a name once and it remembers; the demo shows three speakers in a recording with two already named. Summaries are produced by the AI you pick. Local Qwen 2.5 3B is the default and the recommended option: it runs on your computer, with no cloud and no account. If you prefer, you can connect Claude or ChatGPT using your own API key, in which case the text for a summary or an answer goes to that provider. The output is a structured note rather than a wall of text — the screens show a note written by AI and attached to a project, with a decisions list covering items such as the second batch closing on Friday, Rafael taking the integration by Thursday and support being warned before the holiday, plus a task count. Projects give Pauta its memory. You create a project with a name and a description, and each recording lands in the right one; when a recording ends, Pauta suggests the right project and you confirm it with one click. A project then rolls every conversation on that subject into a single summary — the example project called Batch 2 holds three recordings totaling 1h54 and keeps an overview plus a shared decisions list. On top of that, each project has an AI conversation about all of its recordings. Inside a project you can ask what has been decided so far or who ended up with the most tasks, and the answer draws on the summaries and transcripts of every recording in that project. Beyond projects, Pauta organizes recordings with tags and a no-project bucket for anything you have not filed yet, and transcripts are searchable. Two integrations extend where the material goes. Obsidian Sync turns every recording into a note in your vault, organized by project, and keeps the note current when you edit it in Pauta. The calendar sits in the menu bar: your next meeting shows next to the icon, read from Apple Calendar with no server involved — to see Google or Outlook you connect those accounts to Calendar. The website sums the workflow up as detect, transcribe, know who spoke, summarize, organize into projects and answer across meetings. The overall approach is local-first and native. Pauta is built with SwiftUI for macOS on Apple Silicon and Intel as a universal binary, and for Windows 10 and 11 on x64 and ARM64 through the Microsoft Store. Because transcription and summaries run on the computer, the pipeline does not depend on a connection, and the privacy default is visible before you record. The only outbound contacts Pauta describes are license validation, model downloads, update checks and the optional Claude or ChatGPT calls you configure yourself. For users, the outcome is a durable, private record of conversations. Decisions and tasks are captured in a note instead of being reconstructed from memory; familiar voices are named automatically; and a subject's history is queryable in one place rather than scattered across separate recordings. Because the license is a one-time purchase, the cost does not scale with usage, and every future update is included. The site lists broad use cases rather than a single vertical. Pauta transcribes any audio from the computer or the microphone: online meetings, classes, interviews, appointments and videos. It also handles voice notes, which the product description groups with meetings and interviews as material it records, transcribes and turns into summaries and action items. The demo screens show day-to-day scenarios — a project meeting on a customer batch with three speakers, a coffee with the design team, a one-on-one with Rafael, and a roadmap review pulled from the calendar. Pauta is sold as a lifetime license: $5, marked down from $15, paid once with every future update included and no subscription or annual fees. Before buying, you can try it free for 7 days with no card required. A license activates on up to 2 computers — two Macs with the Mac license or two PCs with the Windows one — and you can deactivate an old computer to free a slot. The Mac version requires macOS 13 Ventura or later; the Windows version installs and updates through the Microsoft Store. After purchase the serial key arrives on the confirmation page and by email, payment is handled by Stripe, and activation happens in Pauta's license settings. iOS and Android builds are listed as work in progress with a waitlist. Taken together, Pauta positions itself as a private, pay-once replacement for bot-based meeting recorders: your microphone and system audio transcribed and summarized on your own Mac or Windows machine, speakers recognized by voice, and every conversation on a subject gathered into a project you can question.

Wonder Dev View is an IDE for engineers who care about design, built by Wonder and introduced by co-founder and CTO Boris. It is a development environment that combines multiple Claude Code and Codex accounts, each on its own subscription, with an editable design canvas that sits right beside your code and the running app. Wonder starts with the repository you already have: you can connect GitHub or open a local project, and agents work on your machine with your team's components, conventions and tools. Its stated purpose is to be a place to shape an idea, make it work and get it into someone's hands, giving engineers more room to use their own judgment. The product responds directly to how coding agents have changed development. In the launch letter, Boris writes that agents changed how much we can build, not how much we need to understand it, and that he wanted a development tool that keeps him close to what he is making. Two things he wanted in his own IDE shaped Wonder: multiple Claude Code and Codex accounts, each on its own subscription, and an editable design canvas right beside his code and running app. He describes wanting the design loop to feel ordinary, with fewer explanations about which button you meant and more time getting the button right. The underlying stance stated on the site is that good software still needs your judgment, and that a working result is where review begins. Wonder organises work around parallel worktrees. Each task gets its own worktree, with its chats, changes, terminal and pull request together in one place, so you can explore a feature while another agent fixes a bug, or start from an existing branch. For code-only work you can stay in the full-screen coding view. On the accounts side, you can connect several Claude Code and Codex accounts, each on its own subscription, pick the account, model and effort for each chat, and optionally let both agents share saved memory. The site notes that your agent subscriptions stay separate, so multiple accounts remain distinct rather than pooled. Context and autonomy are both configurable. You can give the agent the context you would give a teammate: a file, a design, a related chat or another worktree. You then choose how much it does on its own, from asking first to letting it run. In the walkthrough shown on the site, an agent given the instruction to redesign the pricing page reads the current plans and pricing table, edits the pricing table and plans files, searches for the price formatting helper, runs the pricing tests, reports that plans now carry a yearly price, and then continues with a monthly and annual toggle plus a Save 20% badge — with a note to ask Codex to review it when done. The design canvas is the second pillar. You can attach a screen from your designer and ask the agent to build it in your repo with your existing components, or bring a screen from the repo onto the canvas and work out the details visually. You can then run the app beside the design. Selecting an element in the Preview adds it to the chat so the agent knows exactly what you mean, and you can also capture an element onto the canvas, change it there and take it back to the repo. The stated aim is that this loop feels ordinary: fewer explanations about which button you meant, and more time getting the button right. Review and shipping are built into the same surface. Because a working result is where review begins, you read the diff, run your checks and can let Codex review Claude's code, or the other way around, while you still decide what is ready. From there you open the pull request, mark it ready, address feedback or fix failing checks, with the status shown right beside the changes — the walkthrough shows a draft pull request with the changed files, a Checks panel and a View on GitHub action. On Pro you can publish the app's Preview to a link someone can open in a browser and send it with the pull request. The canvas is collaborative too, with live editing on Pro, so a designer can shape the interaction while an engineer keeps it grounded in the code. Taken together, the approach is to keep agents, code, design and review in one continuous loop anchored to your own repository. Wonder does not ask you to move your project into a new hosted environment: it connects to GitHub or opens a local project, and agents work on your machine with your team's components, conventions and tools. Each task is isolated in its own worktree with its chats, changes, terminal and pull request together, so parallel agent work stays separated by task rather than tangled in a single working copy. Design context flows in both directions — a designer's screen comes into the repo as a build instruction, or the repo comes onto an editable canvas — and the running app sits beside the design so the element you point at is unambiguous. The stated benefits follow from that loop. Engineers get a development tool that keeps them close to what they are making rather than one step removed from it. Running several agents in parallel, each on its own subscription, lets you explore a feature while another agent fixes a bug. Giving the agent a file, design, chat or worktree as context reduces the back-and-forth about what you meant. Selecting elements in the Preview rather than describing them lowers the ambiguity in design feedback. Letting one model review the diff of another adds a second pass before you decide what is ready, and the pull request, checks and status sit beside the changes so shipping is not a separate context switch. Concrete workflows described on the site include adding a refund button to an order page, where the agent finds the refund logic in the payments service, reuses an existing ConfirmButton so it matches the rest of the page, edits four files and runs a typecheck; redesigning a pricing page with a monthly and annual toggle; exploring a design like a gallery card screen on the canvas; and reviewing a diff where Codex reviews Claude's code before the pull request is marked ready. Another workflow pairs a designer shaping an interaction live on the canvas with an engineer keeping it grounded in the code, and a further one publishes a working Preview link so someone else can try the app and receive it alongside the pull request. Wonder runs on Mac and Windows and is free to start, with downloads offered for macOS. Shared canvas editing and Preview links are on Pro, while your agent subscriptions stay separate. The site lists engineers at Autentic, Deeploi, Arc, Browserbase and CodeRabbit as users. It is aimed at engineers who build with coding agents and care about design, and at teams where designers and engineers work on the same interface. Full documentation for the design features is available in the Wonder docs. The takeaway is that Wonder Dev View is a place to shape an idea, make it work and get it into someone's hands, built on the belief that agents change how much we can build but not how much we need to understand it. By putting multiple Claude Code and Codex accounts, parallel worktrees, an editable design canvas and a running app preview side by side — and closing the loop with cross-agent review and pull requests — it gives engineers more room to use their judgment from the first prompt to the pull request.

Wingmen is an agentic outreach product that reads buying signals every day, starts the conversation and follows up until there is a call on your calendar. It runs in a web app or straight from Claude with the Wingmen MCP, and it is built for founders, B2B teams and agencies. Instead of working from a list of job titles, Wingmen surfaces the moment someone needs what you sell, attaches the reason why, writes the opener from what that person actually said, and keeps following up across LinkedIn, email and voice notes on autopilot until a meeting is booked. The product is positioned explicitly against the "list of job titles" approach to prospecting. Wingmen's Lead Radar shows a daily set of signals — 24 signals, read for you every day — and each one carries a score and a one-sentence reason. In the illustrated feed, a founder who wrote "Three months of referrals, then nothing" appears as a pain-language post at 9/10; a founder coach who commented on a competitor's launch appears as a competitor engager at 7/10; a founder who posted about onboarding for fintech appears as a post author at 8/10; a new Head of Growth three weeks into the seat appears as a job change at 8/10; and a CEO hiring a first sales rep appears as a hiring post at 9/10. The point is timing: the moment someone needs what you sell, with the reason why. The largest group of signals Wingmen reads is posts and engagement. Pain-language posts catch people who wrote about the problem in their own words. Niche post engagers engage with posts about the space you work in, while topic post engagers engage with posts on the topics your buyer cares about. Competitor post engagers engage with a competitor's posts, and engagers on your posts liked or commented on what you posted. Wingmen also reads post authors — the person who wrote the post, not only the people reacting — and handles the company page to person case, where a company page posts the signal and Wingmen finds the person to write to. Event attendees cover who is going to the events your buyer goes to, and the follower pool covers people who follow you, or the voices your buyers listen to. A second group covers hiring and workforce movement. Hiring posts and job listings flag companies hiring for the problem you solve; job changes catch someone new in the seat, still making their first calls; companies hiring fast have many open roles at once, meaning the team is scaling; and growing teams show headcount climbing month over month. Company and market signals sit alongside these: website changes such as new pages, new pricing or a new product on their site; podcast mentions where they talked about the problem on a podcast; and news mentions of a launch, a press mention or a move that opens a need. Wingmen also reads advertising, technology, financial and regulatory signals. Companies running Google ads are spending on growth right now. Tech stack changes show a company that added or dropped a tool your product sits next to, and GitHub activity shows their engineers building in your space. On the financial side, funding posts indicate a company that just raised and has budget to spend, while Form D filings surface a fresh raise straight from SEC filings. When the day is quiet, cold search tops up the pipeline: cold search matches title and industry to your buyer, lookalike search finds people who look like your best-fit leads, and target accounts track movement at the accounts you name. Once a signal is caught, Wingmen runs a four-step sequence the site describes as "four wingmen, one booked call." Step one, the scout, spots the signal — a post about the problem, a new hire, a job change — the day it happens. Step two, the scorer, gives every lead a score and a reason in one sentence, and the opener is written from that reason, in your voice. Step three, the messenger, reaches out on LinkedIn first; if there is no reply, email or a voice note picks it up in the same sequence, on autopilot. Step four, the closer, brings replies into one inbox with an answer already drafted, and the meeting goes on your calendar. Wingmen can also be run from Claude. Connecting Wingmen to Claude as a custom connector lets you run outbound in plain words from the chat you already have open — asking for founders hiring their first sales rep returns leads the radar found that week, and a follow-up "yes, go" starts the agent on all of them. Two further capabilities extend outreach: voice notes are an add-on that lets you clone your voice once, after which Wingmen records a short note for each person based on their signal; and comment automations turn comments on your posts into conversations — someone comments the keyword, the DM goes out, and the thread joins your inbox. The stated outcome is a booked call rather than a list. Buyers are found daily, each with a written reason; outreach runs across LinkedIn, email and voice notes; every reply is read and sorted, ready to book; and every feature is included with no tiers, up to 600 leads a month per seat. Because the opener is written from the reason attached to the signal, the first message carries the context of what the person actually said or did instead of a generic pitch, and because the follow-up continues on autopilot across channels, a lead that does not answer on LinkedIn is still picked up by email or a voice note in the same sequence. Concrete scenarios follow directly from the signals the product reads. A seller watching for hiring signals can ask Wingmen for founders hiring their first sales rep and start the agent on everyone it found that week. A team can reach out to people who wrote about the problem in their own words, or to people engaging with a competitor's posts. A quiet day can be topped up with cold search, lookalike search or movement at named target accounts. Anyone who comments a keyword on your own posts can be turned into a DM conversation through comment automations, and voice notes let you add a personal spoken touch to each lead at volume. Wingmen is built for founders, B2B teams and agencies. The maker, Nemanja Milic, describes it as the agent he built because he sells without a sales team. Pricing is a single seat plan at $69 per month, discounted to $48.30 per month per seat with the PH30 code during the Product Hunt launch, at 30% off for life and valid until Dec 31. A 7-day free trial is available, and if you would rather have Wingmen run it for you, Concierge Outbound starts from $999/month. In short, Wingmen's value proposition is timing plus follow-through: it reads buying signals every day, explains why each lead matters, writes the opener from the signal itself, and keeps working the sequence across channels until a call appears on your calendar.

Okapi is a free GitHub App that watches the third-party APIs your app and AI agents call and opens a pull request with the fix before a breaking change hits. It is built for developers whose applications and agents depend on external APIs, where a single changed field, renamed parameter, or retired endpoint can take a production app down. Okapi describes itself as Dependabot for APIs: instead of waiting for a deprecation email or manually reading changelogs, you connect a GitHub repository and Okapi keeps your code up to date with every API it calls. The goal is simple — your app keeps working and your team stops chasing changelogs. APIs change all the time, and keeping up with every API's changelog is a hassle. Deprecation emails get lost, nobody has time set aside for the work, and no one owns it. Still, a small field change in an API response can take your whole app down — as Okapi puts it, a tiny update can bring down a big app. The problem is not that providers fail to announce changes; it is that the announcements arrive in many different places and formats, and the people responsible for a repository are busy building. Okapi exists to close that gap by watching for changes continuously so developers can focus on building rather than on maintaining connectors and reading release notes. Getting started takes one click. You connect your GitHub repository through the Okapi GitHub App, and Okapi then finds the APIs your app depends on by reading your code. It looks for the API calls the repository makes, from the big names to the niche ones that break more often, and keeps track of their changes. Installation is described as "1 click, then forget it," and the product is 100% free. Because discovery is based on the code you already have, there is no configuration list to maintain by hand and no inventory of providers to assemble: Okapi finds them in your repo for you and begins watching them. Okapi monitors each API call individually and suggests a fix before it breaks. Today it reads 1,173 API providers' own pages and tracks 841 upcoming breaking changes, according to the website. That coverage matters because deprecations and breaking changes are usually published by each provider on its own pages, in its own format, on its own schedule. By reading those pages directly, Okapi can surface an upcoming change — such as a field that will handle null values differently from a given date — and connect it back to the specific places in your code that use the affected call. The result is a continuous watch over the APIs your app depends on rather than a one-time audit. When an API call is deprecated or about to break, Okapi opens a pull request with the fix before it fails. The pull request is not a generic warning: it contains the actual change your code needs. In the Stripe example shown on the site, okapi[bot] opens a pull request titled "Update Stripe API for upcoming customer.address change", wants to merge one commit from a branch named okapi/stripe-customer-address into main, and explains in a comment that Stripe is changing how customer.address handles null values on October 15. The comment lists the effective date, the API, and the number of affected files (two), and the diff updates both files to handle the new response format. The pull request also carries release notes and a "what changed" section plus api-changes and stripe labels, so a reviewer can understand the change without hunting for context. Okapi works with any API, not only a fixed set of well-known providers. The site's examples span many languages and services: an OpenAI call updated from max_tokens to max_completion_tokens; a PubMed query changed from sort "date" to "pub_date"; a verification request that gains a verify_profile_id field; a notification URL moved to a new push endpoint; an ip-api lookup switched from HTTPS to HTTP; a Figshare download moved to the v2 API; a Mojang profile path corrected; and a Gemini configuration changed from a thinking budget to a thinking level. Each diff is small — often one or two lines — which is exactly the point: the changes that break production are frequently tiny, and they are easy to miss when nobody is watching every provider's changelog. Okapi's approach mirrors dependency automation, which is why it calls itself Dependabot for APIs. Rather than pushing commits directly, it opens a pull request for every change so a human reviews and merges it. The site states that Okapi never commits to your branches: every change comes as a pull request you review. If you do not merge the pull request, nothing changes in your code; the pull request stays open until you merge or close it. That design keeps developers in control while still removing the detective work of finding the change and the mechanical work of applying it. The stated benefits are straightforward: no more time spent maintaining connectors, and improved reliability over time. Instead of learning about a breaking change from a production incident, teams see a pull request with a fix and an explanation before the effective date. Deprecation emails that get lost no longer matter as much, because the watch is continuous and attached to the code that makes the call. Because the fix arrives as a reviewable pull request, teams can adopt it on their own schedule, inspect the reasoning, and keep their normal code review process intact. Concrete scenarios follow directly from the site's examples. A billing service that reads customer.address from Stripe can receive a pull request ahead of the October 15 null-handling change. An application calling OpenAI can be moved from max_tokens to max_completion_tokens. A literature search tool using PubMed can have its sort parameter corrected. A service calling a geolocation or media API can have a changed URL or protocol updated. Teams running AI agents that call external services can keep those agents working as providers evolve. And teams depending on niche APIs that break more often get the same monitoring as those using the largest providers. Okapi is aimed at developers whose apps and AI agents depend on third-party APIs, and at teams that do not have anyone specifically assigned to tracking provider changelogs. The access required is limited: Okapi reads your code to find the API calls it should watch, and it never commits to your branches. Removal is equally simple — uninstall the Okapi app from your GitHub settings and it stops right away. The product is 100% free, installs in one click, and works with any API, so there is no pricing tier or provider list to evaluate before trying it. In short, Okapi brings dependency-style automation to the APIs your code calls. It discovers the APIs in your repository, watches their providers for deprecations and breaking changes, and opens a pull request with the fix before the change can break your app — free, with one-click install, and with every change left for you to review.

Zerg Client, also referred to as ZTC or Zerg Terminal Client, is an AI coding agent for the terminal and the desktop. According to the product site, its core job is to plan a change, edit code, and run tests in your project. Zerg AI describes it as a coding agent harness that drives its own terminal panes: when you ask for Vim or htop, the agent opens them in panes beside it. Those panes can also hold more agents, each running on its own model. The client operates against a real project directory — the site's example is ~/acme-api on the main branch, marked clean — and shows runtime information such as "runtime: not configured" next to the version, currently ZTC v0.9.0-beta.1. Zerg Client is presented as an evaluation beta and ships both as a terminal CLI and as a desktop app. The context for Zerg Client comes from how it is packaged and what it targets. ZTC is aimed at developers who already work inside a project directory in a terminal, following a loop of planning a change, editing code, and running tests. Zerg AI frames the product as a harness rather than a single-purpose utility: the harness drives terminal panes, which means interactive programs a developer relies on — the site names Vim and htop — can be opened in panes beside the agent. That detail matters because those are full-screen terminal applications that normally live inside a shell session, so an agent that can open them keeps the developer's existing terminal tooling in view. The product is also flexible about which model powers the agent: available models depend on the sign-in route and provider access, and panes can hold more agents, each on its own model. Terminal pane orchestration is the defining capability of Zerg Client. Rather than confining the agent to a single text interface, the harness opens terminal panes beside itself and drives them. The site gives two concrete examples of what can be launched this way — Vim and htop — and the Product Hunt listing explains that you simply ask for Vim or htop and the agent opens them in panes beside it. Because these panes are ordinary terminal surfaces, they can also host additional agents, so a single session can grow into a small workspace of side-by-side agents. That structure lets a developer keep the agent's work, an editor, and a system monitor or other terminal tool visible at the same time instead of switching between separate windows. Connecting a model is handled by a dedicated setup step titled "Connect a model," which asks how ZTC should reach a model and offers two routes. The recommended route is logging in with ZergAI, which opens your browser to log in and provides a single ZergAI login for ZTC in both the terminal and the desktop app. With this route, provider keys are added once in Zerg Router rather than on the local machine, and DeepSeek is ready on Zerg Router billing with no DeepSeek key needed. Router billing also provides usage history, per-key daily budgets, and a monthly cap. The alternative route is to use your local API keys. Whichever route is chosen, models can be switched at any time with the /model command, and the client's status line reflects the current state — for example, showing "no model" until a connection is configured. Installing the terminal client is done through a package manager or a direct script. The site lists npm, bun, and curl options, with the npm command shown as npm install -g @zergai/ztc@0.9.0-beta.1. The CLI requires Node.js 20.19+ or 22.12+, and after installation you run ztc in your project directory to spin it up. A desktop app is offered as an alternative, currently available for macOS on Apple Silicon as version 0.9.0-beta.1; the Windows and Linux desktop apps are listed as coming soon and are explicitly noted as not available yet. The site also distinguishes native terminal support (macOS, Apple Silicon) from headless support (macOS, Linux, and Windows), and mentions an optional browser mirror provided as a separate ztc-web-client plugin. Overall, the workflow Zerg Client describes runs from authentication to model connection to project work. You install the terminal CLI or the desktop app, optionally run ztc auth login first to skip the prompt, then choose how to reach a model — either the ZergAI login or your own local API keys — and finally point the client at your project directory, where it can plan a change, edit code, and run tests. From there the harness takes over the terminal: it can open tools such as Vim or htop in panes beside it and place additional agents in further panes, each with its own model. The Quickstart and Benchmarks links on the site point to further documentation and performance results for users who want detail beyond the initial setup. Several benefits follow directly from that design. Because a single ZergAI login covers ZTC in the terminal and the desktop app, users do not need to maintain separate credentials for each surface. Because provider keys can be added once in Zerg Router rather than on the machine, key management is centralized, and DeepSeek works on Zerg Router billing without a separate DeepSeek key. Usage history, per-key daily budgets, and a monthly cap give visibility and spending limits on Router billing, while /model lets a user change models at any time without reconfiguring the client. And because the agent drives its own panes, the tools it launches sit beside it rather than in another window. Concrete usage scenarios come straight from the material. A developer working in a project such as ~/acme-api can ask the agent for Vim and get an editing pane beside the agent, or ask for htop and get a system monitor in a neighboring pane. The same pane structure supports running several agents at once, each on its own model, which suits work where you want different models applied side by side. The core loop — plan a change, edit code, run tests — describes day-to-day project work in a terminal. Teams that prefer centralized key handling can connect models through Zerg Router, while developers who already hold their own provider keys can use the local API keys route instead. The optional ztc-web-client plugin adds a browser mirror for those who want one. Zerg Client targets developers who work in terminals and project directories, and Zerg AI's own framing places it in developer tooling and software engineering. It runs as a terminal CLI installed through npm, bun, or curl with Node.js 20.19+ or 22.12+, and as a desktop app for macOS on Apple Silicon, with Windows and Linux desktop apps coming soon. Headless use is supported on macOS, Linux, and Windows. On cost, the site states that ZTC is free to evaluate under the Zerg Source Available License, that commercial use requires a license, and that model usage is billed separately; Zerg Router billing adds per-key daily budgets and a monthly cap. In short, Zerg Client's value proposition is an AI coding agent that works where the developer already works — the terminal and the desktop — and that plans changes, edits code, and runs tests while driving its own terminal panes. With a choice between a single ZergAI login backed by Zerg Router billing and your own local API keys, per-pane agents on separate models, and a CLI plus macOS desktop app in evaluation beta, it is built around keeping the agent and the developer's terminal tools side by side.

Clocknext is monetization and billing infrastructure built specifically for AI agents and AI-native applications. It lets teams track AI usage from their product in real time, turn that usage into billable metrics with flexible pricing plans, and monitor costs, margins, and profitability at every level. The platform is aimed at AI founders along with revenue and engineering teams who need to convert tokens, completed outcomes, and countable units into revenue. Its stated goal is to make AI billing simple again: every AI interaction has to be metered, priced, and billed, and Clocknext supplies the infrastructure for that so teams do not have to assemble it themselves. The problem Clocknext addresses is the gap between counting usage and earning from it. As the company puts it, every token is easy to count, but turning it into revenue isn't. Without the right infrastructure, teams risk losing revenue, momentum, and peace of mind. Cost visibility is a large part of that risk: a model switch made in June should not surface as a surprise in September, which is why Clocknext tracks cost as it happens, against the signal that caused it. Engineering dependency is the other friction point — metering pipelines, wallet logic, and invoicing all have to be built and maintained unless a platform owns them, and pricing changes routinely get queued behind engineering work. Clocknext is designed so that pricing moves at the speed of the business rather than the deployment calendar. Pricing is assembled from primitives that can be mixed and changed without touching the code that reports usage. Credits are a token-denominated allowance drawn from a wallet as agents work. Outcomes price the completed result rather than the compute behind it. Units cover seats, repos, integrations, and anything countable. Composites combine primitives for multi-action agents and bill them as one line. In the product's own example, 100,000 credits at $0.008 come to $800.00, 47 outcomes at $12.00 come to $564.00, 12 seats at $29.00 come to $348.00, and a voice agent composite spanning four meters comes to $410.00 — all of it landing on one plan and one invoice totaling $2,122.00. Teams start with one primitive, add the rest when needed, and change the mix freely. Payment methods are equally configurable, supporting prepaid, postpaid, hybrid, and top-ups. With prepaid, customers fund a wallet before they use anything and usage draws it down in real time, so there is no chasing payment after the work is done. Hybrid combines a commitment upfront that covers expected volume with anything beyond it billed after, with both landing on the same invoice under one plan. Postpaid meters through the month and settles once at the end, fitting the purchase order and procurement cycle enterprise buyers already run. Top-ups let customers buy more credits or outcomes on their own whenever they need them, with the balance landing in their wallet straight away. Margin control sits alongside billing. Clocknext bridges the gap between provider cost and usage with margins that keep a product's bottom line healthy in real time, showing revenue against provider cost with margin as the visible band between them. A customer view surfaces cash balance, credit allowance, units consumed, revenue, provider cost, gross margin, and month-over-month movement, alongside a monthly table of revenue, provider cost, margin, and gross margin. Because margin is tracked by account, by agent, and by model, an account that falls below target is flagged — for example, a customer showing 8.2% margin against peers at 74.9% and 66.9% — and can be repriced immediately. Changes to the plan, the rate, or the credit conversion take effect on save, with no engineering involvement. Operationally, the product is built around usage signals rather than a bespoke metering pipeline. Setup runs in three steps: install the SDK, send a usage signal, and map signals to pricing. From there, pricing packages are built by mixing credits, outcomes, and units and bundling them into composites when a product needs its own price, then published to all customers live with no deploy. Engineering teams get a drop-in usage SDK and API so there is no metering pipeline to own, while wallets and invoicing are built in. Product teams can run no-code pricing experiments, ship new plans in hours, and A/B test monetization without an engineering dependency. The stated outcomes are speed and financial clarity. Clocknext reports a time to live of two days from signup to the first invoice out the door, eight hours of billing operations saved every week on reconciliation and invoicing, and a 90% increase in gross margin after pricing moved onto the platform. Teams can catch losses immediately because cost is attributed to the signal that caused it, and they can reprice without a deploy whenever a rate or conversion no longer works. For revenue teams, the value is per-agent margin analytics, self-serve credit top-ups, and revenue and provider cost in a single view, so pricing decisions can be made against profit rather than top-line numbers alone. Concrete workflows follow from those building blocks. A team launches a product with a hybrid plan, covering expected volume with a commitment and billing overage after. A voice agent with several billable actions is modeled as a composite of four meters and billed as one line item. A revenue team notices an account whose gross margin has slipped below target and reprices that offering, with the change going live on save. Customers top up credits on their own instead of waiting on an account manager. An enterprise buyer procures on postpaid terms through a purchase order while prepaid customers fund a wallet before use. Product teams test a new rate with an A/B experiment and publish the winner to all customers. Clocknext is positioned for AI-native teams globally, including AI founders and the product, engineering, and revenue functions inside those companies, and it lists Boomerang, Facilio, Superb AI, Strivelabs, and Ninjahire among the teams using it. The platform is offered with a free start and a free trial — free up to $100K USD in billed revenue — alongside a demo booking path for teams that want to see it in action. It also publishes a guide to pricing economics for AI agents, aimed at teams refining their pricing strategy. In summary, Clocknext is monetization infrastructure for AI agents that unifies metering, flexible pricing primitives, prepaid and postpaid payments, and real-time margin analytics in one system. Its primary value proposition is straightforward: meter AI usage as it happens, price it in whatever mix of credits, outcomes, and units fits the product, change that pricing without a deploy, and see margin per customer, per agent, and per model so AI billing stops being the bottleneck between usage and revenue.

FounderMail is email for founders who ship on Cloudflare. One click puts a Worker in your own Cloudflare account, and your domain has mail — real addresses on your own domain, for you, your team and your agents. You sign up, bring your Cloudflare account and your domains, and FounderMail does the rest: a small proxy sits in your Cloudflare account, mail comes in through it, and your replies go out the same way. Cloudflare sends and receives on your account, while FounderMail runs the mailbox behind it. The inbox lives at inbox.foundermail.app and holds every mailbox you hold in one list, personal and shared side by side, with each thread labelled by the mailbox it came to. The product is built for founders and small teams already running domains on Cloudflare who want real email without paying per seat and without forwarding to Gmail. The launch description frames FounderMail against two familiar alternatives: paying per seat for hosted email, and forwarding a domain's mail to Gmail. It also addresses a newer requirement — AI agents that need to work with mail. As the description states, you, your team and your AI agents work from the same inbox, and agents get their own access instead of your password. The site summarises the promise as "Cloudflare email without the setup or the maintenance," meaning the DNS records, the relay and the mailbox are handled for you rather than assembled by hand. FounderMail also draws a hard line: it will not install on a domain that already receives mail somewhere else. It checks, and it stops, because taking over that domain's MX records would end the existing mail. That makes the product suited to domains that are new to email or are being moved deliberately. The inbox itself is organised around a single unified view. All inboxes — every mailbox you hold — appear in one list, with personal and shared addresses side by side rather than scattered across separate accounts. Each thread is labelled with the mailbox it came to, so you can tell at a glance whether a message arrived at Ada, Team or Billing. The interface is keyboard first: j and k move between items, e archives, and ⌘K reaches everything else. The mobile inbox view shows thirteen conversations from one user's three mailboxes in one list, each tagged with its destination mailbox, which illustrates how the unified list works in practice. FounderMail is explicitly built for agents. It offers a REST API, OAuth and two MCP servers: one for mail and one for the control plane. A token issued for one is refused by the other, so access to mail does not silently become access to administration. Agent seats let you give an agent a seat on any inbox, and reading and sending are separate scopes — read-only means read-only. The site answers the question "Can an agent send as me?" directly: only if you let it. An agent given read access to a mailbox can read it and cannot send from it; give it send, and it sends from that mailbox's addresses. Agents can also be pointed at the published llms.txt at https://foundermail.app/llms.txt to discover how the service works. Shared addresses and multi-domain setups are first-class. Every address — hello@, sales@, support@ — can be held by as many people and agents as you like, so a support inbox can belong to an invited teammate and a read-only triage agent at the same time. One relay per Cloudflare account covers every domain on that account, and you can bring as many accounts as you have. Mail leaves from your domain with your own DKIM, SPF and DMARC records, so your domain's sending reputation is built on your own records rather than someone else's. The setup is described in three numbered steps. First, sign up: 14 days free, no card, so you can make an account and look around before connecting anything. Second, bring your Cloudflare account and domains, and FounderMail does the rest — the proxy in your Cloudflare account handles inbound and outbound, Cloudflare sends and receives on your account, and FounderMail runs the mailbox. Third, assign inboxes to as many people or agents as you like, each with addresses on your own domain. The requirements are explicit: your domain's DNS must be active on Cloudflare, on an account with the Workers Paid plan, because sending from your account needs it. The install proves that before it touches your domain's mail. Ownership is split in a way the site spells out. Yours: your Cloudflare account, the DNS records (MX, DKIM, SPF), the relay that sends and receives, your domain and every address on it, and your domain's sending reputation. Ours: the mailbox, stored on FounderMail's infrastructure. If you leave, you can take a copy first — every mailbox as an mbox file, from Settings → Your data in the console. If your subscription ends, FounderMail keeps your mail so you can come back or take it out, and gives you at least 30 days' notice before deleting it. The site also warns that disconnecting does not put your MX records back, so you should turn off Email Routing and publish your new records first before moving to another provider. Concrete workflows follow from the address model shown on the site. A founder can hold a personal inbox such as matthew@yourdomain.com as owner, while support@yourdomain.com is held by a teammate invited by email and simultaneously by a triage agent with a seat and read-only access. Billing addresses can be shared between an owner and another invited person. Because unlimited people, agents, mailboxes, addresses and domains are included on both plans, the same pattern scales from a single founder to a team running several domains across several Cloudflare accounts, all reachable from one inbox list. Agents that need to act on incoming mail can be granted read scope without send scope, which keeps automated triage separated from the ability to reply as the domain. On pricing, both plans start with 14 days free and no card. Founder is $20 a month with 20 GB of mail; Team is $200 a month with 200 GB of mail. People, agents, mailboxes, addresses and domains are unlimited on both. Storage is a fair-use allowance measured as the 30-day average of each day's peak; each email counts at least 10 KB, a message delivered to several mailboxes counts in each, and the Trash counts until it is emptied. FounderMail emails you at 80% and 100%, and states that your mail never stops because of it. One mailbox holds about 10 GB, and the console tells you when one is filling so you can add another. There is no service level agreement; the service is provided as it is. On data handling, FounderMail commits to processing your mail only on your instructions, keeping it confidential and secure, using only the providers its privacy notice names and telling you before adding one, notifying you without undue delay if a breach affects your mail, and deleting it when you do. FounderMail's primary value proposition is ownership without administration: real mail on the Cloudflare domains you already run, with your own DNS records and sending reputation, one inbox for every mailbox you hold, and first-class seats for AI agents whose read and send permissions are kept separate. For founders already on Cloudflare, it turns an awkward email decision into a single install.

Glasshand hosts real iPhones and Macs in its own data center and rents them, month to month, to AI agents. Instead of asking an agent to imagine what a phone or a desktop looks like, you give it an actual device that it can see and operate. You connect Claude Code, Codex, Cursor, OpenClaw, Hermes, or any other agent through the Glasshand API, and the agent rents an iPhone or a Mac from glasshand.com with a prompt. From there it can watch the screen and tap, type, or click exactly the way a person would. Glasshand is aimed at people who want their agents doing real work inside Apple's ecosystem: running social accounts, building and testing native apps in Xcode, or handling everyday admin that lives behind a sign-in. The page frames the core problem as realism. Glasshand runs real iPhones and Macs, "not virtual machines, emulators, or simulators," and asks why a real device is better. The answer it gives is that a rented device works just like the iPhone or Mac you use every day: it reaches Apple's ecosystem, it can carry a real phone number, and it can be locked with a password you set yourself. That matters for the kinds of tasks that break on a simulated device, such as installing apps from the App Store, receiving a verification text at a phone number, or completing sign-in and checkout flows on hardware that behaves like the phone in a customer's hand. Control happens through a dedicated controller. The agent asks the controller to show the screen, the controller looks, and the iPhone answers with what is displayed, for example the home screen. The agent then works through steps the way a person does: see the screen, open the browser, type a search, and check the result. Glasshand illustrates this with a simple goal, "Find the weather in San Francisco," where the agent reads the screen, opens the browser, types the query, and verifies the answer. Because the loop is see-then-act, the same approach carries from a one-off lookup to multi-step work inside apps, and the agent responds to what is actually on screen rather than to a fixed script. Because the hardware is genuine, the software is genuine too. Agents download real apps, including Instagram, TikTok, WhatsApp, and more, from the App Store rather than working against stripped-down replicas. An iPhone can also be given its own phone number by adding an eSIM plan with SMS, which lets the agent receive verification texts and use iMessage. That combination is what makes account-based work possible: a verification code arriving at the device's own number is exactly the step that stalls an agent running anywhere without one. Users also set a real password that locks the device, just as they would on their own phone. The third piece is account context. Users sign in to iCloud and the apps they already use, so the agent can work with calendars, contacts, messages, and files to get tasks done with the context it needs. This is the difference between an agent that can open an app and an agent that can act inside it. Glasshand also states that the setup persists: apps, files, and sign-ins stay put between tasks until the device is released. A single rented device therefore accumulates your configuration once, and every later task your agent runs starts from a device that is already signed in and already equipped. The rental lifecycle is stated plainly. Your device starts factory-reset, ready for your apps and accounts. It is not shared with other customers, it is yours alone, and you set the password. When you release it, Glasshand erases your data and restores the device to factory settings, and the controller is wiped and reset too. Sessions are not billed by the hour or the day; devices are rented month to month, and you can cancel at any time. If you cancel, your device stays available until the end of your current billing period, then your subscription ends. Getting started is designed to happen inside the agent's chat rather than in a separate console. You tell your agent to rent an iPhone or a Mac, paste the prompt Glasshand provides, and then approve and pay when the agent asks. The same flow is offered for both device types: rent an iPhone from glasshand.com and connect so the agent can see the screen, tap, and type, or rent a Mac and connect so it can see the screen, click, and type. Glasshand lists Claude Code, Codex, Cursor, OpenClaw, and Hermes as compatible, alongside any other agent that connects through the API, and a demo can be booked as well. Glasshand names three broad jobs. Running your socials means posting on Instagram, TikTok, and LinkedIn, replying to comments and DMs, finding leads, and following up, which depends on being signed in as a real account on a real app. Building and testing apps means building native apps in Xcode on a hosted Mac and testing sign-in and checkout on a real Mac or iPhone, so the flows that usually fail only in production get exercised on real hardware instead. Working with your accounts means signing in to iCloud and the apps you use so the agent can reach calendars, contacts, messages, and files. Everyday admin sits alongside these, and the site sums up a device's purpose as running social media, building and testing native apps, and handling everyday admin. The people this suits are those already directing agents and wanting them to reach places an API alone cannot: social media teams and founders posting across Instagram, TikTok, and LinkedIn; developers who need real hardware for Xcode builds and for sign-in and checkout tests; and anyone whose calendars, contacts, messages, and files live behind an iCloud sign-in. The requirement is an agent that can call the Glasshand API, such as Claude Code, Codex, Cursor, OpenClaw, Hermes, or anything else, and a willingness to rent month to month rather than by the hour or day. The proposition is narrow and concrete: your agent gets a real iPhone or Mac, hosted and maintained by Glasshand, that it controls by seeing the screen and acting on it, that keeps your apps and sign-ins between tasks, and that is erased and factory-reset when you let it go.

BrewPage is free HTML and Markdown hosting for AI artifacts, static websites, documents, reports and files. Publish content and get an HTTPS link without required registration, a repository or a deployment pipeline.Share AI-generated HTML from ChatGPT, Claude, Codex and other assistants as a rendered page. Publish a multi-file static site, share Markdown as a readable document, or deliver a generated report or file through a link. Developers and AI agents can automate publishing through the HTTP API, MCP server, GitHub Action and public publishing integrations. JSON and key-value hosting are also available.Typical uses include sharing an interactive report with a client, sending a generated dashboard to a teammate, publishing a small static website, and turning an AI artifact into a link someone can open.BrewPage is temporary hosting: content expires. Retention is up to 30 days without a profile and up to 90 days with a free profile. Publications retain format and size limits; a free profile extends retention rather than making links permanent. The hosted service is proprietary; its public MCP server and publishing integrations have their own open-source licenses.

Dessn is a design canvas that runs your actual production codebase. Rather than working from a static mockup file, you connect an approved repository and Dessn pulls your real product pages onto a canvas where you can view every page and state, explore ideas, prototype, review, and QA with your team. It is built for product teams with an existing codebase — designers, PMs, and engineers — who want to explore and collaborate on product ideas directly in production code without opening an IDE. The stated purpose is simple: see what's live, and fix what's drifting. Dessn describes itself as sitting at the intersection of design and dev tools, with a canvas purposely built for design workflows while remaining anchored in production. The problem Dessn addresses is one that appears when product teams ship fast with AI: it becomes hard to keep track of what is actually live and hard to keep the product consistent. Design artifacts drift away from what is really shipped, and there is no shared source of truth for humans and agents to work from. Dessn's answer is to run your codebase on a design canvas that is always in sync with prod, so your team and your AI agents work from one shared source of truth. One customer quote on the site captures the reasoning directly: production code is the source of truth for design. Another frames the appeal for designers as being able to explore ideas in the context of your production codebase. The first capability group is visibility into what's live. Dessn shows every page as it exists in production, lets you visualize entire user flows, and stays always in sync with prod. On the site this is illustrated with a fictional banking app called Orbit, where pages such as /cards, /cards/freeze, /cards/replace, /cards?frozen, and /cards/replace/done are laid out as states of a single flow. A second example walks through a meeting-booking flow from an event type to a confirmed booking, on prod data. Seeing flows as they actually exist lets a team review the real product surface — including edge states like a frozen card or a completed replacement order — instead of guessing from a stale file. The second capability group is consistency. Dessn lets you audit your entire product in one prompt, automate your design system, and iterate with perfect fidelity to prod. The site shows an audit thread titled "Tables drift across 4 pages," which reports that 12 tables were audited in a product called Fern and that two patterns were found with four differences — header capitalization versus sentence case, amount right versus left aligned, row height of 40px versus 56px, and status shown as a badge versus plain text. This illustrates how the audit surfaces concrete, comparable inconsistencies across pages so the team can decide what to standardize. Combined with a design system that stays in sync and full fidelity to production, the team iterates on the real components rather than a simulation of them. The third capability group is a design-first environment for collaboration. Dessn offers direct editing, sharing and collaboration, and what it calls the best design agent. Teams can comment, branch, and iterate together, and share prototypes for reviews, user tests, or sales demos. A comments example on the site shows a thread on a finance app screen where a teammate asks whether to show which account pays each bill. Dessn also ships an MCP that lets it work inside the AI you already use: you can view your canvases and prototypes plus the code behind them, create new flowcharts, prototypes, or comments, and approve Dessn's work from your terminal, switching to Dessn when you want to polish by hand. The MCP install page shows logos for coding agents including Claude Code, Cursor, Codex, and Grok, and the site offers a one-line prompt to paste into a coding agent to set up Dessn for a project. How it works is described in three steps. First, you connect your repo in one click — the site's example is Connect GitHub. Second, Dessn compiles your codebase, a step the site describes as taking 30 minutes while it sets up your design environment. Third, you pull your product pages onto a canvas to design, review, and QA with your team. In the FAQ, Dessn adds that there is no lengthy implementation: connect an approved repository and your first page is usually ready in minutes, because Dessn learns the existing routes, components, and design system automatically. It also states that you can design and prototype in your own codebase without coding required, and that it requests read-only repository access, never accesses backends, databases, or customer records, and never writes, modifies, or pushes code back to your repository. The benefits that follow from the content are straightforward. Teams get visibility into what is actually live instead of an approximation of it. They get a shortcut to consistency, because auditing and design-system work happen against production rather than a separate source. They get design work with perfect fidelity to the real design system, which means what they explore is what ships. Collaboration improves because the canvas is shareable and commentable, and prototypes can be reused for reviews, user tests, and sales demos without rebuilding them. And because agents can operate through Dessn's MCP, human and AI work converge on the same canvases and prototypes rather than diverging. Concrete use cases described on the site include auditing a product to find inconsistencies, mapping user flows with states and personas, commenting, branching, and iterating together, and sharing prototypes for reviews, user tests, or sales demos. The illustrated examples — a card-freeze and card-replacement flow, a booking flow from event type to confirmation, a cross-page table audit, and a commented finance dashboard — show what that looks like in practice across multi-state flows and shared design review. Teams that need to QA pages against production, or want a demo environment built from real product screens, are described as working in this same canvas. Dessn is explicitly for product teams with an existing codebase: designers, PMs, and engineers. It supports React, Vue.js, Electron, and React Native apps, and you connect your codebase through GitHub or GitLab. Access can be restricted to specific repositories or projects, and for monorepos Dessn can be configured to work from the relevant project or subdirectory, with access revocable at any time. Dessn states that it does not train on your data and uses LLM providers that guarantee no training and zero data retention, and that it is fully SOC 2 Type II compliant. No credit card is required to get started. Named customers and users shown on the site include Mercury, Vidsy, Wispr, Greenlight, Color, Noyo, ngrok, Lette, Great Question, Operate, Pencil, Ethena, Planned, Raylu, Seapoint, Groom Club, Red Rover, Dataflo, Comfy, Flowglad, Ascend, and Startale, alongside testimonials from designers and product leaders describing the product as a way to work inside production code. In short, Dessn's value proposition is that design, review, and QA happen on the code that actually ships. By turning your production codebase into a collaborative design canvas that stays in sync with prod, it gives teams and their AI agents one shared source of truth — so they can see what's live, fix what's drifting, and move from idea to merge-ready product work in the same place.

LiveReacting for ChatGPT is a ChatGPT plugin that lets people create and manage live streams by typing a request into a ChatGPT chat. After installing the plugin and signing in with a LiveReacting account, a user types @LiveReacting and describes the stream they want: a 24/7 loop of videos on their YouTube channel, or a recorded video that should go live at a time they choose. ChatGPT then works with what already exists in the LiveReacting account — the videos in the library and the channels that have been connected — and builds the stream there. LiveReacting's servers send that stream to YouTube, Facebook, Twitch, LinkedIn, X or any other platform the user has connected, all at the same time, so the user's computer can be off while the broadcast runs. Running a continuous or scheduled live stream normally means opening a streaming tool, preparing the video files, building a playlist, picking destinations and starting the broadcast — a sequence of steps that has to be repeated for every new stream. LiveReacting for ChatGPT moves that work into a conversation. The product page frames the experience as asking for a stream "in your own words": type @LiveReacting, then say which videos, which channel and what time, and ChatGPT works out the steps and reports back when they are done. This matters for creators, pages and teams that publish recurring or scheduled streams and would rather describe the outcome they want than operate a streaming interface each time. Two of the plugin's core capabilities are looping videos as a 24/7 live stream and scheduling a recorded video. For a 24/7 stream, the user names the videos and the channel; ChatGPT puts them in a playlist in the order the user wants, and that playlist plays on a loop until the stream is stopped. For a scheduled stream, the user gives ChatGPT the date and time the broadcast should start, up to 28 days ahead; LiveReacting starts the stream at that time and ends it when the video ends. The scheduled mode plays the video once, which contrasts with the loop that runs continuously. Both modes are created from the same chat, so a user can move between a permanent channel and a one-off broadcast without leaving the conversation. The plugin works with videos that are already in the user's LiveReacting library, and it can add new ones from a link. A user can give ChatGPT a Google Drive link, a Dropbox link, a public link to a video file, or a video from their own YouTube channel, and ChatGPT adds that video to the library. When building a 24/7 stream from library videos, ChatGPT finds the videos in the library and checks that each one can be streamed before building the looping playlist. Videos stored on the user's own computer are handled differently: they are added to the library in the LiveReacting Studio first, after which the user can ask ChatGPT to use them. In the chat itself, ChatGPT takes a new video from a Google Drive, Dropbox or public link rather than from a file the user attaches. Multistreaming is built into the same request. A user can pick several channels for one stream, and LiveReacting sends it to YouTube, Facebook, Twitch, LinkedIn, X or any other platform connected to the account at the same time. This means a single chat request can reach several audiences at once instead of requiring a separate setup for each destination. The product page also notes a practical limit on Facebook: Facebook ends a live stream after 8 hours, so a 24/7 stream cannot run on Facebook alone. Adding YouTube or another platform to the same stream keeps it running there, which is why multistreaming matters for continuous channels. Streams are not frozen once they start. From the chat, a user can add a video to the playlist or switch to another scene while the stream is live; viewers see the change and the stream keeps running. The plugin also reports on performance. A user can ask whether a stream is live and how it is doing on each channel, and for past broadcasts ChatGPT tells them how long each one ran and its peak viewers. Together these two capabilities turn the chat into a lightweight control and monitoring surface for streams that are already broadcasting. Under the hood, the plugin connects to the LiveReacting MCP server, the same server that Claude, Claude Code, Cursor and other AI agents use. Setup is short: choose Open in ChatGPT, click Install plugin, and sign in with a LiveReacting account when ChatGPT asks; then, in any chat, type @LiveReacting and ask for the stream. ChatGPT receives access to the LiveReacting account but never sees the user's platform logins — YouTube, Facebook, Twitch and other channels are connected inside the LiveReacting account, and ChatGPT picks from the ones that were connected. The actual broadcast is produced by LiveReacting's servers and sent to the chosen platforms, which is what allows the user's computer to be switched off. The main benefit is that a stream can be requested in natural language and then runs without the user's machine being involved. Because LiveReacting's servers send the stream, the user is not tied to a computer during the broadcast. Because the plugin works with the videos and connected channels already in the account, users do not need to rebuild assets for each stream. Because the playlist can be changed and the status checked from the same chat, routine maintenance — swapping a video, checking which channels are live, reviewing how long a past broadcast ran and its peak viewers — happens in the conversation where the stream was created. The product page illustrates the workflow with example prompts. One asks to loop the videos from the LiveReacting library as a 24/7 live stream on a YouTube channel, which suits a channel that should always be broadcasting. Another asks to live stream a recorded video from Google Drive on a Facebook page today at 8 PM: ChatGPT imports the video from the link, builds a stream that plays it once, then schedules it for 8 PM on the Facebook page. A third asks to show the status of running live streams, and ChatGPT lists which streams are live and how each one is doing on every channel. The same capabilities cover multistreaming one stream to several destinations and adding a new video to a playlist mid-broadcast. The plugin is included in every paid LiveReacting plan. On the Free plan, ChatGPT can connect, but each request stops with a message that the account needs a paid plan. The page is explicit about what the plugin cannot do: it cannot build overlays, polls or trivia, which are created in the LiveReacting Studio, the browser-based stream editor — although ChatGPT can still start, schedule, stop and check a stream that has them. It cannot import a YouTube playlist or channel; users give one video link at a time in the chat, or import the whole playlist or channel in the Studio. And it cannot run a 24/7 stream on Facebook alone, because Facebook ends a live stream after 8 hours; adding YouTube or another platform to the same stream keeps it running there. In short, LiveReacting for ChatGPT turns a chat message into a running broadcast. It combines 24/7 looping playlists, scheduled pre-recorded streams up to 28 days ahead, multistreaming to the platforms a user has connected, live playlist and scene changes, and status and viewer reporting, all handled on LiveReacting's servers so the user's computer can stay off.

OpusClip 4.0 is an AI video editing tool that takes a raw take and returns a finished video. According to the company, it was built for talking videos: users drop in raw footage or paste a link, switch on options such as speech cleanup, captions and B-roll, and receive a finished video back. The product is aimed at the kind of creator who records a two-minute product update on a phone between meetings, a dentist answering patient FAQs on camera each week, or someone teaching an audience how to make high-protein meals. Its stated purpose is to give a team of one some of the range that media companies get from a room full of editors, so individuals can post often and in many formats without hiring an editor. OpusClip originally described itself as built to "slice a clean fish." Early users brought finished videos, such as a podcast episode that was already edited or a keynote someone had cleaned up, and OpusClip found the best moments and turned them into clips. That worked, and the company notes that scrolling a feed for a few minutes gives a good chance of seeing a clip that came through OpusClip. But what people bring has changed: increasingly it is a raw take that nobody has had time to edit. The company observes that many founders and operators now have to make video content because that is where their customers are, yet almost none of them enjoy editing. They film in one take because they know they will never get around to cutting it, and they post it as is; many already have a good video sitting on their phone, filmed weeks ago and never edited. For a while OpusClip kept improving clipping and left the hardest part, the edit itself, to the user; version 4.0 takes that on. Auto-edit is the headline addition. It is built for talking videos: users drop in raw footage or paste a link, then switch on speech cleanup, captions, B-roll and more, and what comes back is a finished video. The value is that the step where most solo creators stall, actually cutting the take, is handled automatically, so a recording that would otherwise sit unedited on a phone can become something publishable. Because the controls are switches rather than a timeline, the workflow stays simple for people who do not enjoy editing and do not want to learn a professional editor. Every format means one edit can become whatever the user needs that week: 60 to 90 second highlight clips, a 3 to 5 minute extended cut, or a quick lo-fi update. This addresses the company's point that people who stay top of mind post often, in a lot of formats. Recipes address repetition: most people make the same kind of video over and over, so OpusClip lets users save their setup as a Recipe, or start from one of the built-in Recipes, and the next video gets edited the same way. Recipes turn a one-off success into a repeatable style, which is useful for a weekly series where every episode should look and feel consistent without being rebuilt from scratch. Viral Hook and Motion Graphics cover two tasks the company says editors spend hours on. Viral Hook gives the first few seconds of a video a hook style chosen from a template library, targeting the opening moments that decide whether a viewer keeps watching. Motion Graphics turns what the speaker says into animated icons, numbers and charts on screen, so spoken points become visual elements without a motion designer. Together they add polish that usually requires specialist work, applied to the talking video being edited. AI Producer lets people who already work in ChatGPT or Claude edit from there with an OpusClip plugin: the user shows it a style they like and it edits the footage to match. This meets creators inside tools they already use rather than requiring a separate editing session. Clipping has not gone away. The company says it is still what OpusClip is best known for and is still getting better, just one part of the product now, and it describes turning long videos into clips and publishing them to all social platforms in one click. The overall approach is to cover the whole path from raw take to every format a creator posts, rather than only the moment-finding step. A user brings footage or a link, selects cleanup, caption and B-roll options, optionally applies a saved Recipe or a hook template, and receives a finished video; the same edit can then be output as a short highlight, an extended cut or a quick update. For those already working in a chat assistant, the plugin moves the editing instruction into ChatGPT or Claude, where the user shows a style and the footage is edited to match. The stated benefits follow that workflow. The edit no longer stalls, so recorded footage actually gets published instead of sitting on a phone; a solo operator gets some of the range of a full editing team; Recipes keep styling consistent across repeat videos; Viral Hook templates handle the opening seconds; Motion Graphics add animated icons, numbers and charts without an editor; and one edit stretches across multiple formats. The company frames 4.0 as a starting point, with more planned around the edit in later releases. Concrete scenarios named in the content include a founder recording a two-minute product update on a phone between meetings; a dentist answering patient FAQs on camera every week; someone teaching 3,000 followers how to make high-protein meals; podcast episodes and cleaned-up keynotes being clipped into their best moments; weekly series that reuse a saved Recipe; and quick lo-fi updates versus 60 to 90 second highlights or 3 to 5 minute extended cuts from the same edit. The company stresses that a big audience or an editor is not required. Target users are founders, operators, dentists, educators and small-audience creators who make talking videos themselves. Pricing details in the content: a Free Forever Plan, described as creating and posting one short video every day for free; a Pro monthly plan; and a launch offer of 40% off the first month of Pro (monthly plan) via a coupon code. The page also shows related OpusClip tools: OpusSearch, which uses AI to surface the exact clip you need from hours of footage in seconds, and Agent Opus, which runs research, scriptwriting, storyboarding, motion, voice and edit. The company states that OpusClip is used by more than 20 million creators and brands. OpusClip 4.0's takeaway is simple: it moves beyond clipping to editing the raw take itself, so one recording can become a hook-led short, an extended cut and a quick update without a timeline, an editing skill, or a team. For anyone whose good videos are stuck unedited on a phone, that is the difference between having footage and publishing it.

Orphex MCP is a unified Model Context Protocol gateway that connects a digital marketing stack to the AI assistants teams already work in. It links supported advertising, analytics, commerce, subscription and app growth accounts, then brings that performance data into conversations with Claude, ChatGPT, Microsoft Copilot, Grok, Cursor, VS Code and Gemini CLI. Orphex describes itself as a connection layer between many platforms and one AI assistant, with tools to read performance and request supported changes. The product is built for data-oriented marketing teams that want to ask questions about their own account numbers and get answers from their connected sources, rather than opening yet another dashboard or export. Marketing analysis normally lives across a patchwork of systems. Ad platforms hold spend and conversions, analytics tools hold acquisition and on-site behavior, commerce platforms hold products and sales, and warehouses hold still other tables. The Orphex site frames the everyday problem as tab switching and repeated exports: teams move between multiple platforms to answer a single question, then rebuild context by hand. Orphex places the AI assistant at the start of that workflow instead of at the end of an export. The company states its aim is to move AI beyond QA and empower it to analyze integrated performance and take every action a human marketer can, such as reallocating budgets, pausing unprofitable creatives or adjusting products from a single prompt. The connection layer covers a broad set of sources grouped into Advertising, Analytics, Commerce, App growth, Data warehouse, Subscriptions and Creative. Advertising sources listed on the site include Meta, Google Ads, TikTok, LinkedIn, Pinterest, Snapchat, Reddit, Microsoft Ads, Criteo, DV360, Apple Search Ads, Amazon Ads, X Ads, OpenAI Ads, AppLovin Ads, Unity Ads, Yandex Direct, Taboola, RTB House and Akakçe. Analytics coverage includes GA4, Search Console, Tag Manager, CM360, YouTube, Facebook Page, Meta Pixel, TikTok Pixel, Microsoft Clarity, Similarweb, Context dev, Amplitude and HubSpot. Commerce lists Shopify, IKAS, Ticimax, T-Soft, Akinon, Merchant Center, Meta Catalog, TikTok Catalog, Klaviyo, Insider One, SAP Emarsys, Customer.io and D·engage, while app growth covers Adjust, AppsFlyer, Singular, Google Play, App Store Connect, AppLovin MAX, Appfigures, OneSignal, Unity LevelPlay and AdMob. RevenueCat and Adapty cover subscriptions, BigQuery supplies warehouse-backed context, and Creative includes Content Automation Creative, Higgsfield and Playablex. Orphex notes that coverage and available actions vary by platform and account permissions. Those connections bring in spend, conversions and campaign performance, and the site states that a user's own account data becomes the basis for each question. On top of the data, Orphex adds a Skill Catalog of prepared reviews, action procedures and report templates that give the assistant a method to follow. The example given is a weekly performance review: read the connected sources, compare complete periods, surface the main changes and prepare the next steps. The site also mentions creative fatigue and anomaly investigations with eligible skills, executive summaries and budget reallocation proposals with sources and guardrails. Curated guidance helps the assistant choose the next step, use the right metrics and check the evidence before drawing conclusions, and Orphex is careful to note that a report or proposal does not itself change an advertising account. Every requested change stays within the user's access. The assistant prepares the change and shows a preview, and nothing runs until the user confirms it; a workspace can also require a teammate's approval. The site illustrates this with a proposal that moves $500/day from Brand Search to Performance Max while keeping total daily budget at $3,200 unchanged, and it explicitly states that the proposal does not predict a performance improvement. Orphex also says every change is logged and protected by safety guardrails. On security, the site lists GDPR and KVKK compliance for EU and Turkish data protection law, ISO 27001 audited security processes, and SSL/TLS encryption for all platform traffic with enterprise-grade network protection. Overall, the product is presented as a seven-layer stack. Connected Platforms bring in advertising, analytics, commerce and app growth accounts. Performance Data adds spend, conversions and campaign performance from those accounts. Orphex MCP is the single connection layer that exposes tools to read performance and request supported changes. The Skill Catalog supplies prepared reviews, action procedures and report templates. Curated Guidance helps the assistant pick the next step and the right metrics. Access & Approval keeps each change inside the user's permissions with a review step. AI Assistants are where the user actually works: they ask a question, explore the answer and decide what happens next. Setup is described as taking a few minutes with a single sign-in. The stated benefits follow that structure. Users start with a question instead of another export, compare the same metric across two complete periods while keeping source, dates and numbers in view, drill into the campaign behind a change, and turn a finding into a focused review before acting. Because the assistant answers from connected account data, the conversation keeps the definition of each metric visible and flags missing data. Routine work gets a head start from curated skills, so teams keep the method and skip the blank page. Orphex also states that users do not need to replace their dashboards: they can investigate a question in conversation and follow the evidence back to existing tools for a deeper view. The site's documented use cases read as concrete workflows. For advertising performance and actions, teams compare campaign spend, conversions and efficiency across complete periods, drill into ad, keyword and creative reporting, and review supported budget or status changes before approval. For analytics and customer journeys, they put GA4 acquisition and Search Console organic performance in the same discussion while keeping each source's metric definitions visible. For commerce and product performance, they connect marketing questions to products and sales in Shopify, IKAS, Ticimax or T-Soft and prepare a product performance summary. For subscriptions and app growth, they read RevenueCat project overviews, Adapty subscription charts and attribution data from Adjust and AppsFlyer. For warehouse-backed context, they bring supported BigQuery datasets into the same marketing review. And with curated skills, they run weekly performance reports, executive summaries, creative fatigue checks and anomaly investigations. Orphex targets data-oriented marketing teams, including teams managing $30M+ in monthly ad spend, and lists brands such as Paşabahçe, Arby's, Vestel, Burger King, Bilyoner, Popeyes, Karaca, Sbarro, Beymen, Usta, Getir, DeFacto, Mavi, TurkNet, SneaksUp, TatilBudur, Avansas, Altınyıldız, QNB, Edenred and Analytica House as users. Pricing runs from a Free plan at $0 with 250 reads per month, 50 writes per month, one user, one workspace, three account connections and all data sources; a Read plan at $33.32/month billed yearly at $399.90 with 2,000 reads, ten users, three workspaces and 25 connections; and an Action plan at $124.99/month billed yearly at $1,499.90 with 2,000 reads, 500 writes, ten users, three workspaces and 100 connections. Enterprise offers custom limits, contract billing and onboarding support. Yearly billing includes two months free, and every plan works with Claude, ChatGPT, Microsoft Copilot, Grok and Cursor. In short, Orphex MCP consolidates a fragmented marketing stack into one connection layer so an AI assistant can answer questions from real account data and prepare controlled changes for human approval. The value proposition is continuity: the same conversation moves from signal to evidence to a reviewed next step, while permissions, approvals, logging and compliance keep the marketer in control.

QualityLayer is a software factory that keeps you in the loop while your coding agents plan, build and review software. It is designed for people who already work with AI coding agents — Claude Code, Codex, Cursor, Antigravity, Pi, OpenCode, Grok and GitHub Copilot — and who want each change to move through a structured path instead of a single prompt. You can work side by side in the QualityLayer App or in the browser, or keep using your favourite terminal or IDE, with a CLI that connects other agents. Every change travels through seven steps: Discuss, Research, Plan, Outline, Implement, Verify and Review, before your agent handles the pull request. The product describes itself as quality by design and as the software factory with you in the loop. The product's framing is stated directly on its own site: plan before you build, agree on the plan, then review the tested change. Rather than letting a change go straight from a prompt to code, QualityLayer inserts the decisions you make — Discuss, Research, Plan and Outline — before implementation begins. After implementation, your project's checks and an independent review produce proof, and the site's stated rule is that every point needs evidence. The result is presented as a workflow where you decide, agents work and agents check, in that order across the steps. A quoted engineer says that with QualityLayer, developing software with agents finally feels like doing real engineering again, and a former Pilot Shell user says that their productivity shot through the roof. QualityLayer works with the agents you already use rather than replacing them. The supported agents listed are Claude Code, Codex, Cursor, Antigravity, Pi, OpenCode, Grok and GitHub Copilot, and the CLI connects other agents. You keep your existing login or API key, and the site states that you use the models you already pay for: Opus 5.5, Sonnet 5.5, Fable 5.1, Haiku 5.5, GPT-6.1 Sol, Grok 4.7 and Gemini 3.8 Flash, reached through those agents. Models can be chosen separately for building and for independent review — for example Opus 5.5 builds it, subagents are used when useful, Codex reads the plan, Claude Code reads the plan, or one agent does every step. Any model your coding agent offers can also be used. Before any code is written, your agent maps the files a change will touch and how they connect. The site's example change map separates 'today' from 'after changes', marking three changed files, one new file and one risk, and traces connections across a gateway, billing, meters, usage windows, a handler, a new meter and a ledger with price() in the pricing file. The map can be dragged to move and zoomed, and new, changed and risk items are colour-coded. The Plan step is where you review diagrams and clickable designs and then approve the plan, and you can comment on change maps, diagrams and clickable mockups. The stated sequence is simple: agree on the plan, then review the tested change. QualityLayer runs your project's checks and security scans, and a separate agent reviews the change against your request. Project checks are described as repeatable checks from your project: tests and types run through your own recorded commands, linters using your project's rules, code health covering complexity and duplication, and recorded proof where every point needs evidence. Independent agents cover a security review of inputs, access and secrets; an independent review by an agent that did not write the code; a design review that checks each finished design; and an optional second opinion from another coding agent. Anything still open reaches you in Review together with its evidence. The workflow runs from prompt to pull request across eight numbered stages: Discuss, Research, Plan, Outline, Implement, Verify, Review and Pull request. You decide at Discuss, Plan and Review; agents work at Research, Outline, Implement and Pull request; agents check at Verify. In Discuss you settle the open questions with your agent — the site's example asks whether 400-seat customers move to usage or stay on seats, offering three choices and a recommendation. In Research, agents trace how the code works today and what can be reused. In Outline, your agent breaks the plan into small, testable tasks. In Implement, your agent builds test first and hands independent work to other agents when useful. In Verify the agent fixes the findings, and in Review you look at the code and checks, request changes or approve, control the merge, and the agent handles failing checks and review comments. You can add your own checks, tools and skills to any step. QualityLayer runs on your computer. The site lays out four layers: where you work, with your task on the left and your agent on the right in the App or browser; your agent on your own subscription, using your existing login or API key; QualityLayer planning, answering and reviewing beside your agent, with visual plans or conversation and a CLI that connects any other agent; and your repository, where every step is a Markdown file you can read, diff and commit, including the plan, decisions and test runs. Every decision, document and test run stays with the change, so the reasoning behind a piece of work remains readable and reviewable long after it was produced. Because the plan, the decisions and the test runs are stored with the change, users get a record of why the code looks the way it does rather than only the code itself. The check results and the independent review arrive together with their evidence, so you review findings rather than repeat the work. Working beside your agent in the App or browser means you do not have to abandon the terminal or IDE you already like — the same workflow is available in either place, and other agents can be connected through the CLI. Projects also accumulate reusable lessons that can be shared through Git, so working preferences and project specifics carry forward from one change to the next. The site lists concrete prompts that show how the product is used in practice: 'Using QualityLayer, add rate limits to the export API', 'Using QualityLayer, chat about the billing refactor', 'Open the settings design in my browser', 'Scan this repository for leaked secrets' and 'Share this task with my team', all asked in plain words in any language. Connected agents can plan, design and check work together — in the example, Claude Code asks Codex to review a rate-limit change on feat/export-limits, Codex reads the change and runs its tests, replies REVISE with two items (the limit resets per process, not per account), and Claude Code fixes both items in the same session. QualityLayer addresses engineers who already use coding agents, and its FAQ asks whether it is for product managers as well as engineers, and whether it can be used on your own or only with a team. The App is available for macOS, Windows and Linux, with a Linux AppImage download listed, and for servers, WSL or containers the site points to installing the command line. Settings cover agents, plan limits and reviewers, Home shows what needs you, Memory keeps useful project lessons, Artifacts let you try a screen before it is built, and Team asks the people who know. Your own model subscription is used rather than a separate one. In short, QualityLayer is a human-in-the-loop software factory: your existing agents discuss, research, plan, outline, implement, verify and review a change, you approve the plan and the result, and independent checks provide the proof.

Render Sandboxes, currently in beta, gives every AI agent its own isolated compute, persistent state, and filesystem. In the product's own words, it is a computer for every agent. Each sandbox is a separate Linux computer that an agent can use to run commands, keep files, and return to work it left running, rather than operating inside the same environment as the application it serves. Sandboxes are designed for developers building agents and agent harnesses — the product cites Claude Code and Codex as examples of what can run inside one — as well as for teams that need a safe place to execute untrusted code. The stated purpose is to give agents real tools, files, and processes that persist between sessions, all on the same platform as the rest of the application. Agents need somewhere to work. Running a coding agent or an autonomous task loop directly on application infrastructure raises practical questions: where do the files live between runs, how do you stop one agent from reaching another's data, and what happens to compute spend when the agent sits idle waiting for input. Render Sandboxes addresses those questions by making the sandbox the unit of work. Each one is isolated, so user-submitted code, model output, and uploaded files stay separated from the app's data and secrets. Each one persists, so an agent can pick up where it left off. And each one can be suspended, so idle waiting does not accumulate compute charges. The product also targets scale: Render says you can reliably run thousands of sandboxes at once, on the same platform as the rest of your app. Sandbox lifecycle is managed with TypeScript or Python SDKs or a CLI. The documented flow is explicit: create a sandbox and set a maximum lifetime, then wait until the sandbox is ready. From there you upload files and download artifacts, moving inputs into the sandbox and pulling out the files the code generates. You can run commands and stream output, receiving stdout, stderr, and the command's exit code as it arrives rather than waiting for a batch result. The quickstart example shows the same sequence in code: create a sandbox with a timeout, upload a task file, execute a Python agent followed by a test run, capture a snapshot, and terminate. Because output streams, an agent can inspect a failure and try a fix inside the same session. Snapshots give sandboxes a starting point. You can save a snapshot of a sandbox and later fork an independent sandbox from it, running processes included — so a session that has already installed dependencies, cloned a repository, or started a service does not have to repeat that work. New sandboxes can also start from custom images or saved snapshots preloaded with the files and tools each task needs, which Render describes as giving agents a head start. Multiple agents can work from the same starting point by forking the same snapshot, and each fork is independent. This matters for parallelism: instead of provisioning a fresh environment per task and paying the setup cost every time, you snapshot a known-good state once and branch from it. Isolation is the core of the design. Each Render sandbox runs in its own Firecracker microVM, so agents can use Docker and return to processes they left running without sharing a kernel with anything else. On top of isolation, Render provides access controls. You decide which domains each sandbox can reach: outbound requests can be limited to approved domains, or outbound traffic can be blocked entirely. Render API keys can be created with only the permissions a task needs — for example, managing sandboxes without access to other services (listed as early access). Sensitive credentials can be brokered: approved API requests are authenticated without giving sandbox code the raw keys or tokens (also early access). Together these controls let teams run untrusted code without handing it the keys to production. Overall, the approach is to treat each agent's environment as a disposable, resumable machine rather than a long-lived server. Sandboxes start in under a second, even when traffic arrives in bursts, and they run on the same platform as the rest of your application, so the agent's compute sits alongside the app it is working on rather than in a separate system to be wired up. Sandboxes suspend automatically when idle, with no compute charges until your agent sends its next command, and they wake with files and memory intact. While a sandbox runs, you are billed only for the CPU it actually uses. The benefits follow from those mechanics. Agents keep their work between sessions instead of rebuilding context and environment on every run. Failures are visible and debuggable because logs, errors, and exit codes stream back. Setup cost is paid once and reused through snapshots and custom images. Demand spikes are absorbed because start times stay under a second and thousands of sandboxes can run concurrently. Spend tracks actual CPU use rather than wall-clock time spent waiting for input, since idle sandboxes are suspended. And risk is contained: untrusted code, model output, and uploaded files stay isolated from application data and secrets. Render names three use cases directly. First, run your agent inside a sandbox: give agents tools, files, and processes that persist between sessions. Second, spin up a full copy of your app: run your whole stack with Docker Compose inside a sandbox so your agents can test changes end to end. Third, safely execute untrusted code: keep user-submitted code, model output, and uploaded files isolated from your app's data and secrets. The lifecycle primitives extend these — snapshots and forks support multiple agents working from the same starting point, and streamed output supports agents that iterate on a failing test, while sub-second starts support bursty workloads where many sandboxes are needed at once. The product is aimed at developers and teams building agents and agent harnesses, and at applications that need to execute code they did not write. It fits teams already deploying on Render, since sandboxes live on the same platform, and it is exposed through TypeScript and Python SDKs and a CLI, with a quickstart that walks through installing the Render CLI, signing in through the browser, creating a sandbox, running a command, and terminating. Pricing is usage-based: sandboxes suspend when idle, there are no compute charges until the next command, and while running you pay only for the CPU actually used. Render points to its pricing page for details. Render Sandboxes packages a simple idea — a computer for every agent — into an operational one. Isolation comes from Firecracker microVMs; persistence comes from a filesystem and snapshots that survive suspension and can be forked; control comes from domain restrictions, scoped API keys, and credential brokering; and economics come from suspending idle sandboxes and billing actual CPU. As Stuart Layton, Head of Technology at FinQore, puts it in the customer quote on the page, Render Sandboxes have kept the promise of moving fast. For teams putting agents to work on real code and real files, it is a way to give them a machine without giving them the keys.

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MachineMail is an email API built for AI agents. It gives each agent a real inbox that it can sign up with, wait on, and reply from, so an agent can send and receive actual email instead of being confined to a chat window. Agents connect over MCP from any client — the site lists Claude Code, Codex, OpenCode and Copilot — or through an API. Every inbound message arrives as agent-safe text with a trust level attached, and the agent reads it over MCP wherever it runs. MachineMail is aimed at developers and teams building autonomous agents that need a working email address to complete real tasks, and it presents itself as email for both humans and bots, with no per-inbox fee. Agents increasingly need an email address to do real work: to sign up for a service, receive a one-time code, wait for a reply, or take in an invoice. Handing an agent a mailbox is risky, because everything that lands in an inbox is untrusted input written by someone else. MachineMail frames this as the core problem — the site promises that nothing in the inbox can hijack your agent and describes mail your agent can safely read. Instead of passing raw mail through, it converts each inbound message into agent-safe text, removes hidden instructions before the agent reads a word, and attaches a trust report and a trust level. A message from a lookalike domain such as paypa1-payouts.ru, for example, is presented with a failed DMARC check, a link display mismatch, hidden text that was removed, and a quarantined level. Connecting an agent takes one command. MachineMail exposes a single Streamable HTTP MCP server, and developers either sign in with OAuth from the MCP client or pass an API key when running in CI. The documented setup command adds the server to Claude Code, and the site says the same prompt works with Cursor, Codex, OpenCode and Copilot, plus any other MCP client. For agents that cannot be configured by hand, the site offers a copyable agent prompt: paste it into Claude Code, Cursor or another MCP client and the agent registers itself through auth.md, receives a sandbox inbox and one ephemeral inbox, and shows a six-digit code that the user enters at /claim to bind the agent to their organization. Addresses are created either with a single API call or automatically through a catch-all domain. Once a domain is pointed at MachineMail, any address on that domain works and the inbox appears on first delivery, which means an agent can be handed an address in advance without provisioning anything first. The site shows a mix of ephemeral inboxes with expiry timers — signup-7f3k@acme.dev expiring in 24 hours, otp-runner@acme.dev in 23 hours, invoice-intake@acme.dev in six days — alongside permanent addresses such as qa-bot-12@acme.dev and support@acme.dev. There is no per-inbox fee: the site states $0 per inbox and per trust report on every plan. The trust layer sits at the centre of the product. Every inbound message receives a trust report and is sorted into one of five levels, and the agent is instructed to act on the level rather than on anything the sender says. Verified messages can be acted on; known messages are summarised; unknown messages are read only; suspicious messages are reduced to reasons only; quarantined messages are withheld entirely. The report itself lists the evidence behind the level — the example on the site includes a failed DMARC result, a link whose display does not match its target, and hidden text removed from the message before the agent saw it. Because the level is attached before the agent reads the text, the agent never has to make its own judgement call about whether a sender is genuine. Delivery can be pull or push. Webhooks push mail into the developer's own stack: they are signed, retried for five days and replayable from the dashboard, with event types such as inbox.created, message.received, message.received.quarantined and message.bounced. Replayability lets a team re-run a delivery after fixing a handler without losing the messages that arrived during the outage. For agents that prefer to fetch, the MCP server provides wait_for_message, which holds the call open until mail actually lands, for up to a minute, so the agent does not have to poll in a loop. In the site's illustration the waiting call returns a message from no-reply@acme.dev with a verified trust level and agent-safe text containing a one-time code. MachineMail's overall approach is to handle the email plumbing and the security judgement in one place, then expose only the safe result to the agent. A trust level is not a single check but a combination of SPF, DKIM and DMARC alignment, the account's own allow and block lists, prior correspondence with the sender, link and attachment checks, and a classifier. The report lists every reason it used, so a developer can see exactly why a message was verified or quarantined and can adjust their allow and block lists accordingly. Because the same report is produced on every message, on every plan, at no extra cost, the trust decision stays consistent whether an agent is reading a one-time code, an invoice or a support request. The practical benefit is that an agent can be given a genuine email identity without also being given an attack surface. The agent gets an address it can sign up with and reply from, mail it can read as clean text rather than raw HTML, and an explicit instruction about how much to trust each message. Developers get a managed inbox they do not have to provision one by one, webhook delivery they can replay, and a return call that blocks until mail lands instead of burning cycles on polling. Ephemeral inboxes add a disposable option: addresses that expire on their own and are cleaned up by a reaper, so short-lived agents do not leave mailboxes behind. The example addresses on the site map onto concrete workflows. A signup inbox such as signup-7f3k@acme.dev lets an agent register for a service and receive the confirmation, then disappear after 24 hours. An address like otp-runner@acme.dev is built for receiving one-time codes, which is exactly what wait_for_message is shown returning. invoice-intake@acme.dev covers a longer six-day window for documents that arrive asynchronously, while permanent addresses such as qa-bot-12@acme.dev and support@acme.dev suit bots that need a stable, long-running mailbox for testing flows or handling support correspondence. In each case the agent reads agent-safe text with a trust level rather than the raw message. MachineMail is built for developers and teams running autonomous agents — particularly those working in Claude Code, Cursor, Codex, OpenCode or Copilot — who need their agents to hold a real address. Pricing starts free: the Free plan costs $0 and covers 3,000 messages a month, with paid plans starting at $9 a month and no per-inbox charge. A message counts as each inbound delivery and each outbound send, while spam, quarantined and rejected mail do not count, and neither do drafts until they send. Ephemeral inboxes are cleaned up by a reaper that deletes the inbox with its messages, raw MIME and attachments, then emits inbox.expired, and new mail before expiry pushes the deadline out. The API is described as AgentMail-compatible. In short, MachineMail gives AI agents what they have been missing — a real, low-cost email address — and pairs it with the thing an inbox full of untrusted mail actually requires: a per-message trust report, a defined level of action, and agent-safe text that has already had hidden instructions stripped out before the agent reads a single word.

One Roof is a desktop application for macOS that gives you one place to keep up with a whole team of coding agents. It works with the agent command-line tools you already use — Claude Code, Codex, Gemini CLI, Kimi and Antigravity — plus plain shells, and it is explicitly not an IDE and not a terminal. Instead, it is the place where your agents work, with everything they ask, run, learn and cost gathered into one window. It finds the agent command-line tools on your Mac and the accounts you are signed in to, and each session runs the real tool, so you can always type straight into it. The product is free and open source under the MIT licence, and runs on Apple silicon with macOS 14 or later. The problem One Roof addresses is described by the project as chorus coding: several coding agents working on the same piece of work, each doing the part it does best, with one checking another — one plans, one builds, one reviews, and a loop re-runs the tests every night. The hard part of that way of working is keeping up. As the project puts it, the agents are not the bottleneck any more, you are. Five terminals look the same, a question waits in a tab you are not looking at, something failed at 3 AM and nobody told you, and the Mac slows down without any indication of which session is to blame. One Roof was built to keep up with that situation and to keep you the one who decides. The Inbox is the centre of day-to-day work. Every ask from every session lands in one column, one block per session that needs you, with the prompt's own options shown as buttons or selectable with keys 1 to 9. When an agent is blocked until you act, its block gets a red border and moves to the top, and a finished turn waits there too with a reply box where you can paste a screenshot for the agent to read. Nothing is approved for you, and you can always answer in the terminal instead. A Decided list keeps what you answered, newest first. Alongside it, Home is a front page for the morning, built from your own sessions and loops: a lead story about the thing that matters most, a Decide now list of what waits on you with one button each, and an overnight timeline showing every run since midnight as a dot, followed by what went wrong, what to do next and what ran fine. Sessions are real terminals that keep running. Every agent session and plain shell is grouped by state — Needs you, Active, Next, Planned and Exited — and each row shows the agent, the account, the folder and a live one-line status, so you no longer have to type prefixes in front of tab names to tell five identical terminals apart. Each session can be shown as a single terminal, a grid or a wall. Sessions run inside roofd, a small background service, so they keep going when you close the app and come back with their screens when you open it again. History is a read-only record of each session's important moments: steps, findings, errors, what the agent asked and what you answered, and hand-offs between sessions. Each session's run of moments is one card in its own colour, searchable and filterable by session, agent, kind and date, and kept for 30 days. Loops handle work that repeats until a check passes. A loop gives an agent a goal and a check, such as until the tests pass or every night at 3, and One Roof runs the check first — if it already passes, no agent is started. When the check fails the agent works one round on a separate branch, in the loop's own copy of the repo rather than yours, and then the check runs again: done, or another round, up to a limit. A loop never pushes, merges or deploys on its own, and what it produces waits in the Inbox for your approval. Resources shows memory and CPU for each session, for One Roof itself and for every app on the Mac, with 10-minute charts. A browser shows its windows and tabs and you can close a tab or a window from here, and roofd warns when a session grows past 1 GB and ends it at 2 GB, before it can freeze the Mac. Several more places sit on the rail, some of them optional. Projects shows your Linear projects and tickets — teams, projects by status and each project's roadmap — only if you connect Linear with your own API key; without it, Projects shows a single Connect Linear card. Learn is a course on agentic engineering, made of a narrated Agentic Engineering deck and a timeline of papers that shaped coding agents, listenable in the app, with narration by an AI-generated voice that says so. Evals is marked as a draft in the app: it lets you run public benchmarks or build a question set from your own repo's fixed bugs, then compare agents and models on it, with every score shown alongside its range and a plain explanation of what it means, and the cost shown before anything starts. Safety is the default throughout. Each agent has one permission mode — Manual, Plan, Auto or Full — and on a new Mac every agent starts in Manual and asks before it edits a file or runs a command; you change it per agent in Settings › Permissions. Setup asks for the folder that holds your code, or offers to create ~/Projects, and One Roof never scans your home folder, so macOS does not ask about Desktop, Documents or Downloads. The app ships no keys, no tokens and no datasets: Linear, hosted models and gated benchmarks use your own accounts, only if you add them. A crash in one session ends only that session. Underneath sits the roof kit, which makes every agent read the same rules; roof install puts it in place and roof doctor checks every layer, covering a shell default that moves a new terminal opening in ~ to your workspace, agent wrappers, PATH stand-ins, a Claude hook that blocks every prompt while a Claude session is outside the workspace, home notes in ~/CLAUDE.md and ~/AGENTS.md, and a single Claude memory pin whichever folder you launch from. The practical benefit is that you keep the deciding role while the agents do the work. Questions stop getting lost in tabs you are not watching, overnight runs stop being a mystery, and you no longer need to read every agent's full output stream to know what happened. Sessions are labelled with their state, agent, account and folder so the one you need is findable, and Resources makes it clear which session is slowing the Mac. The app is neutral by design: Claude Code, Codex, Gemini CLI, Kimi and Antigravity run side by side rather than inside one vendor's app, and the bar at the foot of the window shows how much of each subscription is used so you can pick the agent with room left. Typical use follows the chorus coding idea the project describes: several coding agents on the same piece of work, each doing the part it does best and one checking another — one plans, one builds, one reviews — while a loop re-runs the tests every night. You might press ⌘N, say what should happen, pick the agent and the repo, and launch; when the agent asks something it shows up in the Inbox. Overnight, loops run and the morning Home page tells you which ones failed and what to do next. When a session's memory grows, Resources flags it and can stop a runaway session. When you want to know how a model handles your own code, you build a question set from your repo's fixed bugs in Evals. One Roof is built for people who already pay for several coding agents and want to use them together: developers running Claude Code, Codex, Gemini CLI, Kimi or Antigravity who need to keep track of what each one is doing, what it costs and what it is waiting on. It requires Apple silicon, macOS 14 or later, git and at least one agent command-line tool that is signed in. Setup finds the agents you are signed in to, and optional extras include hosted models such as OpenCode and Goose with your own API key and a spending cap, and Linear for Projects. One Roof is free and open source under the MIT licence, signed with an Apple Developer ID and notarized by Apple, and can also be built from source with Swift 6 and Xcode 26. In short, One Roof is a place to keep up with a team of coding agents and stay the one who decides: the agents do the work, and you keep up and decide.

Brink is a free menu bar app for macOS that turns the edges of a MacBook trackpad into controls. Instead of reaching for the function keys, you slide a finger along an edge: the right edge handles volume, the left edge handles brightness, the top edge adjusts the keyboard backlight, and the bottom edge handles media playback, with a double-tap in the top-right corner to mute. It is built for people who already work on a Mac laptop or with a multitouch trackpad and want a faster, more physical way to reach the settings they change most often, without leaving the keyboard or the window they are in. Volume and brightness are among the most frequently adjusted settings on a laptop, and they normally live on keyboard keys or in Control Center. Brink approaches the same job differently by treating the trackpad as a continuous surface with four usable borders. Because the controls are slides rather than key presses, the adjustment follows your finger: you can slide up or down the right edge to raise or lower volume, or slide along the left edge to change screen brightness, and the matching macOS display appears as you go. The site sums up the idea simply: your trackpad has edges, so use them. The result is a control surface that is already under your hands, requiring no new hardware and no new habits beyond a slide. The volume control occupies the right edge. Sliding up makes the Mac louder, and the display fills in sixteen steps — the same native volume indicator your volume keys show. The brightness control occupies the left edge and reflects changes on screen with the sun icon you already know from the brightness keys. On the top edge, sliding sideways controls the keyboard backlight: left dims it, right brings it back up. Because all three use the native macOS display rather than a custom overlay, adjustments look like the ones the system already shows, which keeps the experience familiar and avoids a second set of on-screen indicators to learn. The bottom edge handles media. Swiping the bottom edge skips a track, and double-tapping the top-right corner mutes. There is also an optional double-tap on the bottom edge to play or pause, which the site notes is available if you switch it on. The menu bar panel refers to Quick Mute and a Media Strip, shown alongside haptics, sensitivity and edge width. Together these gestures mean music and other media can be controlled from the same surface as volume and brightness, without hunting for the keyboard's media keys or opening a player window. Haptic ticks give the trackpad a physical response: every step of a slide taps back under your finger, and the strength can be set to Off, Light, Medium or Strong. Fine Control lets you hold ⇧⌥ while sliding so each step splits into four for finer adjustments, and Precise Sliding makes that the default behaviour. Pointer stays put means that while you slide an edge, the cursor waits exactly where you left it, so you do not lose your place in whatever you were doing. Taken together, these details make edge slides feel precise and confirmed rather than approximate. Active Area decides where a slide can begin: anywhere on the edge, or only in the bottom ¾, half or quarter, which helps avoid accidental triggers. Swap Sides puts brightness on the right and volume on the left for people whose hands prefer that arrangement. Require Fn or ⌥ means a slide only registers while a modifier key is held, so a resting thumb never changes a thing. The menu bar panel shows live levels on the edges that set them, along with haptics, Quick Mute, the Media Strip, sensitivity and edge width, so most day-to-day settings are adjustable at a glance; settings you change once, such as swapping sides, choosing an active area, requiring a key, holding the pointer, Light or Dark appearance and opening at login, live behind them. Under the hood, Brink works by posting the same media keys your keyboard sends and by holding the pointer still while you slide, which is why macOS requires Accessibility access; the site states that nothing leaves the Mac. Only a slide that starts inside the thin edge strip counts, so taps, clicks and scrolling near the edge behave as usual, and if you still catch an edge by accident you can narrow the edge width, limit the Active Area, or require Fn or ⌥. Brink is written in Rust on Tauri by Zerowing Studios, which keeps it small and light, and builds are published on GitHub. The outcome for users is fewer reach-and-press movements: volume, brightness, keyboard light and media all become one slide or tap on a surface your hands are already on. Because the gestures drive the native macOS displays and happen without moving the cursor, the change feels like part of the system rather than a separate utility layered on top. Haptics add confirmation without looking down, and the Active Area and Require Fn or ⌥ options let people tune how sensitive the edges are to their own typing and resting habits. Concrete scenarios include adjusting volume while watching a video without leaving the trackpad, dimming the screen in a dark room, nudging the keyboard backlight up at night, skipping tracks while writing, or muting instantly with a double-tap in the top-right corner. Someone using an external multitouch trackpad, or a Magic Mouse through the Magic Mouse mode that listens along the sides of the mouse, can get the same controls on a desktop setup. Brink is free, with every feature included, no account and no trial. It requires macOS 13 Ventura or later and ships as a universal app for Apple silicon and Intel, signed and notarized by Apple. It also includes Magic Mouse support, Light and Dark appearance, a liquid-glass panel, Open at Login and updates that install themselves; the download is a disk image you drag to Applications, and from then on it updates itself. In short, Brink takes an unused surface — the border of your trackpad — and turns it into a set of native-feeling controls for the settings you adjust most, at no cost and with nothing to unlock.

Nana is a native terminal multiplexer for Windows built for running multiple coding agents at the same time. Rather than keeping a single terminal tab on a single agent, nana lets you run Claude Code, Codex, OpenCode and other agents side by side, each one on its own git worktree, inside one fast native terminal. It is made for developers who already work with command-line coding agents and want to run several in parallel without losing track of what each one is doing. Nana is currently a free beta for Windows 10 and newer, needs no admin rights, can be installed from PowerShell with a single command, and is also available as a download with the source on GitHub. Running more than one coding agent on the same repository quickly gets messy. Agents edit the same files, and without isolation they step on each other's work. Nana's answer is to give each agent its own git worktree and branch, so parallel sessions never collide. The second problem is visibility: once several agents are running in different tabs, it is hard to know which one is still thinking, which one has stopped to ask a question, and which one has finished. Nana is built for many agents, not one tab, and it reports the state of every agent — working, waiting or done — so you know where your attention is needed. A third problem is continuity: closing a terminal normally kills whatever was running inside it, which is painful when an agent is in the middle of a long task. The core of the product is the worktree sidebar. Each agent gets its own worktree, and the sidebar shows the branch next to the agent using it — for example feat/auth next to Claude Code, or fix/race-pty next to Codex. From that sidebar you can switch between agents, merge their work, and clean up worktrees when you are done. Alongside it, nana detects the agents running inside your terminals and shows whether each one is working, waiting or done, with no hooks and no setup required. The status is descriptive rather than a bare spinner: it can show that Claude Code is waiting on a question such as whether to push, that Codex has finished with tests passing, or that OpenCode is still working through a task with progress like 312/492. Because state is shown per agent, you can leave several running, glance at the list, and jump only into the pane that actually needs you. Nana also brings code review into the same window. You can review code right in Nana instead of switching to another tool: view Git diffs to see exactly what changed, browse older commits, and revisit previous changes without leaving the terminal. The review area separates current changes from commit history, so you can look at the working diff of an agent's branch and then step back through the commits that produced it. Diffs are displayed in a line-by-line format with added and removed lines marked, which makes it practical to check what an agent did before merging its worktree. Combined with the worktree sidebar, this turns nana into a place where parallel agent output can be inspected and accepted rather than a set of terminals you have to trust blindly. Terminals in nana live in nana-server rather than in the window, so quitting the window does not stop your agents — they keep running in the background, and you can come back to find the work continued. The product surfaces this directly, for example with a nana-server panel showing three sessions running while updates and test commands continue in the background. Remote servers extend the same idea to other machines: you can open projects on Linux or macOS over SSH, and nana installs nana-server there. Because the terminals live on the remote server, remote sessions keep running even when you disconnect. The interface presents this as a normal machine entry — for example a local Windows nana alongside an ubuntu@build-box server running nana-server — so local and remote agents are managed from the same place. Several smaller features round out the day-to-day workflow. The Composer lets you write long prompts with files and images attached and then send them to a single terminal or to every split pane at once, which is useful for instructions like asking the agents to run the tests and fix any failures. Themes make the terminal yours: nana ships with more than 490 terminal themes and a live preview, including well-known options such as Tokyo Night, Gruvbox Dark, Nord, Dracula, Solarized Dark and Catppuccin Mocha. Extensions let you build what you need — you can add tabs, panels, commands and status bar items using any web tooling, so the terminal can be shaped around how you actually work. Underneath, nana is a native Rust application with GPU rendering and Ghostty's terminal engine, and it deliberately does not use Electron. That choice is what makes it fast and light on memory compared with running several heavyweight terminal windows. It also works with the agents you already use rather than locking you into one: Claude Code, Codex, OpenCode, Copilot, Cursor, Droid, Grok, Kiro, Pi, Oh My Pi, Antigravity and Xal are all listed as supported. The split is straightforward: the nana window is the interface you look at, while nana-server holds the terminals, so sessions can outlive the window and, when nana-server is installed on a remote machine, outlive your connection. Agent detection happens without hooks or configuration, which means an existing setup keeps working. The practical benefit is parallelism without collisions. Instead of running one agent at a time, you can put several to work on separate branches of the same project and let them proceed independently. The status list tells you where to spend attention, so waiting agents get answered and finished agents get reviewed rather than forgotten. Reviews inside the terminal mean an agent's diff can be checked and merged on the spot. Background sessions and remote servers mean long-running work is not tied to a window being open or a connection being alive. And because the app is native, all of this runs in one fast, memory-light terminal rather than several heavy ones. Typical scenarios follow the feature set. A developer might start Claude Code on a feature branch, Codex on a bug fix and OpenCode on a themes change, each in its own worktree, and watch the sidebar for whichever one needs input. Another might use the Composer to send the same instruction to every pane, then review each resulting diff from the commit history view before merging. Someone working with a Linux build box can open the project over SSH, let nana install nana-server there, and keep tests running after disconnecting. Others will simply close the nana window at the end of the day knowing the agents are still working in the background. Nana is aimed at developers on Windows who use terminal-based coding agents and want to run more than one at a time. It requires Windows 10 or newer and no admin rights. The stack is Rust with GPUI for the interface, libghostty for the terminal engine, and WebView2 for extensions. It is currently free in beta, with an optional way to buy the developer a coffee. Mobile apps for iOS and Android are listed as coming soon; they will connect straight to your server, with no cloud and no account. The takeaway is simple: nana treats coding agents as a fleet rather than a tab. One native Windows terminal, one worktree per agent, live status, in-place diffs, and sessions that survive closing the window or dropping a connection — free while in beta.

Flockbay is a free AI game maker built on Godot: a desktop app for Windows, macOS and Linux that hosts the full Godot editor with an AI agent and an MCP server built in. The agent writes GDScript and scenes into your Godot project, places props, builds rigged 3D models, finds plugins and playtests the game. The same app then goes on to cut trailers and Shorts from your real gameplay and draft your Steam page. It is built for developers making a Godot game they intend to ship and market, and it works with Godot 4.6+, GDScript and C#. Building a game is two jobs that usually live in different tools: writing and debugging the game itself, and then convincing players to wishlist and buy it. Flockbay is designed around the loop that actually finishes a game - write, run, read what Godot reported, fix, play again - and around the marketing work that follows. Instead of leaving you with code you have not seen run, its agent plays the game with input events, reads the live scene tree, the profiler and Godot's errors, fixes what it finds and plays again. You judge the result by playing it. Once the game exists, the agent moves on to store pages, trailers, websites, social posts and ads, all drafted inside the same editor. The editor is the real thing: the full Godot editor, opening in Minimal with Standard and Full one switch away, with Godot, standard and .NET, included. You choose the AI. Flockbay's own hosted AI is free to start, with a model menu that includes Demo (free to start), Auto (picks the model for each task), Claude Opus 5.5 as the recommended option, GPT-5.5 from OpenAI and Gemini 3.1 Pro from Google. Or bring your own AI: Claude Code on your Claude plan, ChatGPT through the bundled Codex engine on your ChatGPT plan, OpenRouter for 100+ models on your own key, or an agent over MCP such as Codex, Cursor or Gemini CLI. Your own agent is free - Flockbay sets no limit of its own, and your plan's limits apply. Playtesting is where the agent closes the loop. It plays with input events, reads the live scene tree, the profiler and Godot's errors, fixes what it finds and plays again. A real tool call shows an agent running `flockbay_run_headless` on a script, which quit by itself with exit code 0 and reported 0 errors and 0 warnings. The page shows a worked example: a SCRIPT ERROR in `hud._drop_note()` occurring 14 to 35 times per scripted demo run, where signing an offer after the first morning's note had gone passed the freed note to a typed argument. The agent added an `is_instance_valid(note)` guard, and nine scripted demo runs afterwards produced 0 SCRIPT ERRORs. The agent sends input with `flockbay_game_input` and captures the viewport with `flockbay_game_screenshot`. Content creation sits in the same editor. Blender is built in, so your agent models props and characters in it, and placeholder 3D models come from Tripo on your own key for you to swap out. A library of Godot add-ons lets the agent find and install plugins. ElevenLabs works with your own key for music, sound and voice, and your agent can make looping placeholder tracks in your game's style, saved into your project at `assets/Audio/Music`, for you to swap for your own. Paid plans add pictures and sound on every chat. Every change is reviewable. The command palette (Ctrl/Cmd+Shift+P) reaches every action in the window by name, and `/model`, `/compact`, `/undo` and `/review` work at the start of a message while `@` adds a file or asset. Permission modes (Ctrl/Cmd+Shift+M) cover Plan mode, Ask before edits and Edit automatically, with Full access in the menu. Review changes lists every file a turn changed with Revert per hunk, per file and for the whole turn. A transcript view switches between Normal, Thinking and Verbose, chats can sit side by side, and you can open the project in VS Code, Cursor, Rider or Zed, or in Terminal at the game's folder. Command line flags include `--open-game` and `--your-own-agent on`, version control (Ctrl/Cmd+Shift+G) commits, pushes and pulls with Git and GitHub, and Flockbay's skills and hooks can be installed for Claude Code and Cursor. What makes the approach distinctive is where the output lives. Your game stays a plain Godot 4 project on your own disk, on the official engine, not a fork, and it opens as a plain Godot 4 project in stock Godot. Games run locally on the copy of Godot bundled with the app - no streaming, no cloud rendering, no cloud building - and projects and assets are private. There are no royalties. An MCP server inside the editor exposes Godot tools to outside agents; Flockbay can print the `claude mcp add` or `codex mcp add` line, add the server to Cursor or VS Code, and a setup prompt asks your agent to check the connection and read the project before it changes anything. For users, the outcome is a game that has actually been run and repaired rather than code that has only been written, and every file the AI touched can be reviewed, reverted hunk by hunk, and opened in stock Godot or your own editor whenever you want. The marketing side produces assets you would otherwise have to make by hand: a trailer cut from your own Play sessions with music and titles, Shorts with captions, a Steam page draft with copy, tags, capsules and content survey, a website with wishlist buttons and an email signup, and ads written and cut from your gameplay. Free covers the app, Godot and your own AI, plus free hosted AI to start and one free video in Video studio. Use cases in the content show the range. Build logs document agents producing a 500-enemy horde with no enemy stuck, a six-legged walker, a physics truck on a mountain trail, a jump that feels like Celeste, a character controller for stairs and slopes, and a save that remembers everything. The team's own game, Shelf-Made Tycoon, is a cozy logistics tycoon on Godot 4.7 coming soon to Steam, used to illustrate a card that switched under the cursor, a drafted Steam page, a published website at shelf-made-tycoon.flockbay.app, a Short cut from a play session and a wishlist push before Next Fest running across Instagram, Facebook, TikTok, Reddit and YouTube at $40 a day. Market research shows another workflow: the agent compares your idea with similar games on Steam - their prices, reviews and estimated sales - reading prices and reviews from Steam and estimating units as reviews multiplied by 30 to 60. In the example, 15 comparable games for a cozy warehouse tycoon are listed, the closest match is Wilmot's Warehouse at 96% positive and $14.99, and the suggestion is to launch at $14.99 with a 10-15% launch discount, holding $19.99 until there is more content. Flockbay requires 64-bit Windows with an Intel or AMD processor, Macs with Apple Silicon, or an x86_64 Linux AppImage on Ubuntu 22.04, Debian 12 or Fedora 39 (glibc 2.35) or newer, with a GPU supporting Vulkan 1.0 or OpenGL 3.3. There is no browser version, browser editor, browser preview or mobile app. Pricing is free ($0) with free hosted AI, your own Claude, ChatGPT or agent, every editor feature and one free video in Video studio; optional plans are Hobby at $20, Indie Developer at $60 and Studio at $200 a month, and Studio adds 20 social accounts, 8 ad accounts, 25 published websites and 100 live playtest links. There is no team, per-seat or enterprise plan. Flockbay's value proposition is a single desktop app where a Godot game is written, playtested, repaired, reviewed and then marketed, with the project remaining an ordinary Godot project you own and can open in stock Godot at any time. The team states its intent plainly: it builds Flockbay for games worth playing - made with AI, played by people, and polished until they are beautiful.

Wavefunction is a coding agent built as a desktop application for macOS. It is aimed at developers who want an agent that stays focused on writing and changing code instead of growing into a general-purpose suite. The app puts one agent and one conversation on screen, with a simple project sidebar that leaves the conversation and the composer at the center. You give it a task and keep talking while it works on several tasks at once. Wavefunction is free and open source under the MIT license, and it can run on local models, your own API keys, or a ChatGPT sign-in. The product's starting point is a reaction to how coding agents have evolved. The site explains that coding agents keep turning into super apps, adding more with every release, while Wavefunction goes the other way: less is more, simpler is better, and building comes first. To make the contrast concrete, the site describes other apps as an interface crowded with tabs, sidebars, editor panels, and a terminal. Against that, Wavefunction shows a simple project sidebar with room for the conversation and composer, so there are fewer sidebars and fewer tabs and the conversation stays at the center. The stated intent is to leave more room to focus on your code. The minimal interface is a deliberate feature set rather than a stripped-down compromise. There are fewer sidebars and fewer tabs, and the conversation is the primary surface. Wavefunction does not ship a built-in browser: links open in your default browser, with the setup you already use. It also describes itself as just a coding agent, without dots, bots, built-in apps of all kinds, or a million extra features. The claim is that what remains is the set of tools needed to get things done, which keeps the app minimal, considered and fast instead of crowded. Parallel workflows are handled by splitting work across subagents inside a single conversation. You send the next task while the last one runs; each new piece of work that can run on its own gets a subagent, and nothing waits its turn. To stay parallel without collisions, every chat starts on its own branch, and subagents that could touch the same files each get a worktree. Wavefunction reviews their changes and brings the work together. You can also steer while it works: change your mind mid-task and your message reaches the subagent already on it, so it adjusts without starting over. Fork or rewind lets you start a new chat from any message, or put the code and the conversation back to how they were. Computer use is separated from your own machine. Other agents take over your mouse, keyboard and screen; Wavefunction gets a desktop of its own, a local Docker container with its own browser and files, so you keep working while it clicks, and you can watch it or take control at any time. The site states that it never moves your pointer, types on your keyboard, or records your screen, and that a file reaches your Mac only when the agent hands it over, into a folder you allowed. When a site needs your login, you sign in privately: the agent pauses and never sees what you type, and the chat stays signed in for next time. Downloads come to your Mac in the folder you choose, and files you add are ready to upload. Computer use needs Docker Desktop and a model that can read images. Models are a choice rather than a lock-in. You can sign in with ChatGPT Plus or Pro, use API keys from Anthropic, Google Gemini, OpenAI, OpenRouter or Vercel AI Gateway, or run models locally through Ollama on your Mac or your server, LM Studio, or another OpenAI-compatible server. You pick a model for each chat and switch whenever you like, so you are never tied to one provider's plans or limits. Running models locally or requiring zero data retention from a provider are the two privacy paths the site describes. Privacy is grounded in where things are stored and which services you connect. Your conversations and settings are stored on your computer. Using a local model keeps model inference local; alternatively you can configure a provider with zero data retention. Which services receive your data is determined by the models and connectors you choose. The app is open source down to the prompt, released under the MIT license, with a public GitHub repository containing backend, frontend, skills and a Tauri-based src-tauri directory alongside AGENTS.md, LICENSE and README.md. The project welcomes new contributors. Analytics surface what the agent consumes. Wavefunction tracks token usage, cache usage, and provider-reported costs in one place, so you can see what you use, what you spend, and how it changes over time. Connectors extend what the agent can reach: there are 70+ ready-made MCP connectors, plus any MCP server you add, and the catalogue shown includes Gmail, Outlook, Google, Microsoft 365, Airtable, Asana and Atlassian. Skills and instructions keep the agent aligned with existing practice: it follows your AGENTS.md and loads SKILL.md skills from .agents, .codex and .opencode folders. Overall, the product works as a conversation-first loop. You describe what you want in one conversation; Wavefunction can split work across subagents for the pieces that can run independently, each on its own branch or worktree. Follow-up instructions are accepted while they run and passed to the subagent already handling that work. Results are reviewed and brought back together for you to check. When a task needs a graphical environment, the agent works in its containerized desktop with its own browser and files, handing files back only into folders you permit. The outcomes the site emphasizes follow from those choices. You get to keep the conversation flowing instead of waiting for one task to finish before describing the next, which is why the app is described as optimized for parallel workflows. That parallelism is framed as parallel without collisions because of branches and worktrees, and mid-task changes do not force a restart. Your own computer stays under your control and stays yours while the agent works. Cost and usage visibility, plus the ability to bring your own model or run one locally, keeps spending and data handling under your control rather than a vendor's. Concrete workflows appear throughout the site. In the main example, a user asks for a billing dashboard; Wavefunction starts three subagents for the API, interface and tests, each in its own worktree. The user then asks to also add a usage chart, and a fourth subagent takes the chart while the rest keep moving. When the user says to use Stripe for the invoices, the instruction is passed to the Billing API subagent, which adjusts without starting over, and all four tasks come back ready to review. Separately, the computer-use example shows the agent working in its isolated desktop on a billing site, looking at a screen with invoices and a downloadable report, which the user can watch or take control of. Other described workflows include updating the docs from the project sidebar and relying on an existing AGENTS.md and SKILL.md setup so the agent works the way a repository already expects. Wavefunction is built for developers on macOS; the site states it is currently available for macOS only, and you download the app, connect a model, and start a conversation. Integrations come through the MCP connector catalogue and any MCP server you add, including the listed Gmail, Outlook, Google, Microsoft 365, Airtable, Asana and Atlassian, and through repository instructions and skills. Model providers include ChatGPT sign-in, Anthropic, Google Gemini, OpenAI, OpenRouter, Vercel AI Gateway, Ollama and OpenAI-compatible servers such as LM Studio. Pricing is free and open source under the MIT license; you bring your own models or provider account, and any model usage charges come from your provider. Computer use additionally requires Docker Desktop. The takeaway is a deliberately small tool with a large amount of leverage: one agent, one conversation, parallel subagents that keep working while you keep talking, a containerized desktop that leaves your Mac alone, and your choice of model, all in a free, MIT-licensed macOS app that is open source down to the prompt.

kwant is an open-source, self-hosted team of AI agents that you run on your own machine. The whole product ships as a single binary, called kwnt, which you can install on a laptop or a VPS and which brings the app and the database with it. Instead of a single chat tab, you get a group of bots, each with its own role, voice and model, that share chats, pass work to each other with @mentions and keep going on a schedule even after you close the tab. It is built for people who want an AI team that proves its work rather than simply announcing that a job is done. Most AI tools answer a question and then declare the job finished, leaving you to take the model's word for it. kwant takes a different position: chat apps and agent frameworks can do the work, but they rarely prove it. In a typical chat assistant there is one assistant per chat, one answer is picked for you, and when facts conflict you only ever see the version that was chosen. Agent frameworks can be wired up to do better, but you have to build the routing, the evaluations and the approvals yourself. kwant is built around the idea that a result should be checked, that a fact should open at the exact quote it came from, and that nothing should be paid without you. Each bot has its own role, voice and model, and knows who else is in the room. You hire bots from 27 roles, and each message goes to the bot that fits, with kwant picking the bot, the model and the effort and showing you why. When one bot finishes its part it hands the work on with an @mention: in the product's own example, TechLead takes a request to ship a pricing page and passes the layout to Designer, who then asks QA to check the layouts on a phone. Team discussions work the same way, with the lead running a short discussion and posting a single summary, while the full fold of every turn stays available to read. New teammates are added from presets such as Security, Research or Chef, and the name, role, avatar and voice fill in automatically. Verified results are the core of the product. Bots split work into cards, and a card turns Verified only when kwant runs the check itself, so "done" means it was actually checked. Web facts come with links that open at the exact quote, and no quote on the page means no claim. When two facts conflict, they wait for you: the Truth Ledger shows both and you keep one. Goals let you state what should stay true, kwant checks it, and when a goal breaks a bot offers one fix for you to approve. Smart Results turns a table or list in a reply into a comparison you can sort, a chart or a checklist, copied word for word. Risky steps are handled deliberately. Ordinary work just runs, but something risky, like a push, a network call or a change outside the bot's folder, shows you exactly what will happen first, with the full arguments in front of you and Allow once, Always allow or Deny as the options. That prompt can be switched off too. Payments always ask, even when they sit behind a plain "Continue". Autonomy is earned rather than assumed: in Flight School bots earn tiers per kind of work, only you grant them, and new bots start at Act. AutoFix, audits, Flight School and the Truth Ledger all run without setup. kwant is designed to keep going when you are not watching. Say "every morning" and the bot sets up the routine itself, with Undo, Edit and Pause on the card, or you can create one for daily, weekly, every few minutes or a cron rule. The Inbox page collects what happened while you were away on one screen: what is waiting on you, what finished, and what got stuck. Loose ends finds the reply you owe across chats, email and GitHub and drafts it, though it never sends. Letters lets you photograph a bill or notice so kwant can pull out the deadline and amounts and remind you. Accounts and spaces keep everything separated. You can use Claude, Codex or Grok logins you already pay for, API keys or your own server, and when one account runs out of usage the turn moves to the next in your order across Claude, Codex, Grok and Custom API, with the chat saying so. Spaces give work, side projects and home their own bots, chats, routines and accounts so nothing leaks between them; each space gets a name, an icon and a colour, switches with Command or Ctrl plus 1 to 9, and opens where you left it. Getting started requires Rust and Node.js to build it plus one AI account. You run make run, the web app opens on your machine, and on a server you open the link it prints once. Accounts are added by using a CLI you are already signed in to or by pasting a key, and you can add as many as you like. There are downloads for Mac, as a universal build for Apple silicon and Intel that is not yet notarized by Apple, and for Linux as a .deb for Ubuntu and Debian on x86_64 or an AppImage, plus a Docker option. kwant is AGPL-3.0 open source, at version 0.6.8 with 19 releases since October 25. Typical scenarios follow the shape of a working day. A research bot runs every weekday at 07:00, sums up three new app reviews, spots a price change on a rival's site and puts a draft reply in Loose ends for you to review the next morning. A team ships a pricing page by handing the layout to a designer and the phone check to QA. Parallel Universe load-tests up to three ways to deploy on your own server before you pick one. You approve an action from your phone away from your desk, or photograph a notice to have the November 5 deadline pulled out and tracked. kwant is aimed at people who want an AI team on their own hardware without writing code. It works with Claude Code, Codex, Grok, API keys, any MCP server, Ollama and any OpenAI-compatible server, and runs on Mac, Linux and Docker. It is free and open source, and you bring the model accounts you already have. The takeaway is straightforward: kwant gives you your own self-hosted team of AI bots that share chats, hand off work, check each other, quote their sources and ask before they spend, all from one binary on your laptop or server.

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Widgeteer is an AI tool that builds working website widgets from a single sentence of plain English. You describe what you need — the site's own examples are "a cleaning quote calculator" and "a table booking form" — and Widgeteer produces a working widget in seconds. The range covers forms, quizzes, booking tools and calculators, with the widget library also listing galleries and saying there is "plenty more". The product is positioned for busy owners of shops, blogs, agencies, clubs, clinics and side projects: people who, in the site's words, "do not have an afternoon to spend in a form builder". Its promise is summarised in the three words used across the homepage — say it, see it, sorted — with "no setup, no keys, no code" needed to get from an idea to an embedded widget. Traditional form and widget builders assume you will spend time in a configuration interface: choosing field types, wiring up settings and figuring out where submissions go. Widgeteer frames that work as the product's job rather than the user's, under the heading "No tech. No config. No code." The site describes the target customer's constraint directly: "Whatever you run, you do not have an afternoon to spend in a form builder. Say what you need and get on with your day." It also addresses the common frustration that a generated widget can be close but not quite right, noting that "your widget is never stuck the way it came out" — you can fix a word yourself or ask for something bigger. Booking and enquiry widgets are a major part of what Widgeteer builds. A showroom appointment booking widget lets customers book a visit to a showroom by appointment only, and booked slots disappear for everyone else so there are no double bookings. A Calendly-style booking widget can be edited in the browser — picking the wording and colour scheme — before being embedded on any site. Restaurants and cafes get an OpenTable-style table booking form where guests pick a date, party size and time and then confirm, with no per cover fees. For enquiries there is a Typeform-style form that asks one question at a time, built on the site for a wedding photographer, and a floating WhatsApp chat button that shows your photo, a greeting and a ready-made message so visitors tap once and the chat opens in WhatsApp. Interactive marketing and calculation widgets round out the library. A cleaning business instant quote calculator lets customers price their own clean, with bedrooms, bathrooms, extras and a weekly discount adding up live before they book in two steps. A product recommendation quiz asks five questions, scores the answers and recommends the right product, collecting an email before showing the result. A spin-to-win discount wheel collects emails, with prizes and odds you set and a code shown to copy at checkout. A before-and-after image slider lets visitors drag a handle to compare work, with tabs for showing several projects. There is also a podcast player that puts your Spotify episodes on your own website, built for health and fitness coaches. Every widget stays editable after it is generated. Editing by hand lets you change any question, answer type, message or colour, move, copy or remove questions, and split a long form into steps. Editing with AI is the shortcut for bigger changes: need a new field, an extra step or a different layout, and you say it in plain English and Widgeteer makes the change. Before anything goes live, the preview is the real widget — you can fill it in and submit it before it goes anywhere near your site. The widget library also lets you open an existing widget, edit it and use it as the starting point for your own version, which is how many of the listed templates are presented. The overall workflow is three steps. First, ask in plain English: tell Widgeteer what you want — a booking form, a quote calculator, a countdown for Friday's drop — typed "like you'd text a colleague". Second, watch it appear: it builds a working widget in seconds, and forms actually submit; if you don't like a bit, change it by hand or ask again. Third, drop it anywhere: add it as a Shopify section, a WordPress block or a snippet of embed code, on a homepage, product page or landing page. Submissions are routed somewhere useful — every form actually works, and you can get an email the moment someone submits or send it to Zapier, n8n, Make or a webhook, with Airtable, Telegram and more listed as additional actions. The site stresses there are no keys to paste and no setup screens. The practical benefit is speed and control without technical work. Because widgets are described rather than configured, the gap between knowing what you need on a page and having it live is short — the site describes widgets appearing in seconds. Because everything stays editable both by hand and by asking, a near-miss result is not a dead end. Because submissions route to email, automation tools or webhooks without API keys, the widgets are functional rather than decorative. And because the preview is the real widget, you can test the actual submission flow before your visitors encounter it. For restaurants, the table booking widget also avoids per cover fees, which the site calls out explicitly. The demo section shows widgets combined into whole sites. Fieldnote, a children's learning and therapy centre in Leeds, uses a support finder quiz, a course list, fees, team profiles, parent reviews, free call booking, video lessons, a branching enquiry form and an FAQ. Rota Transfers, an airport taxi and transfer company in Lisbon, uses a live taxi fare quote and booking, car comparison, a price list, an arrivals guide, reviews, an FAQ and contact forms. Individual widget pages show narrower scenarios: a showroom visit booking, a cleaning quote, a skincare product finder quiz, a call booking form, an email-capture wheel, a before-and-after portfolio, a restaurant table reservation, a WhatsApp chat button and a wedding photography enquiry form. Widgeteer is built for busy owners — shops, blogs, agencies, clubs, clinics and side projects — rather than for developers. Distribution matches that: build the widget on Widgeteer and paste the embed code into any site, or use the Shopify app to add it to any theme, or the WordPress plugin to add it to any page. Pricing starts with a free plan and includes a 14-day trial on Premium, with no card required to start; several widget pages note that specific widgets are free to start. Documentation is organised around tasks such as editing a widget, changing a widget with AI, previewing a widget, and sending form submissions to email, Zapier, n8n, Make or a webhook. Widgeteer's core proposition is that a working, editable, embeddable widget should be one sentence away. Describe the form, quiz, booking tool or calculator you need, adjust it by hand or by asking, test it in a live preview, then embed it on WordPress, Shopify or any other site with submissions routed to email, Zapier, n8n, Make or a webhook — no code, no keys, no setup screens.

diff-tour is a command line tool that turns a git diff into a tour you step through with the → and ← keys. Point it at a branch in any git repository and the AI coding CLI you already use writes a guided walkthrough of the change, covering what the change is for, which files to read next — including files that did not change — what a senior engineer would ask about it, what must be fixed versus what is only a nit, and where an experienced reviewer would stop and say "wait". It is built for engineers who want to keep understanding code as AI writes more of it, and it runs on your own AI coding CLI, read-only and open source. The tool starts from a question its maker kept hearing from people who teach software engineering: now that AI writes the code, what should engineers learn? People rarely write code by hand anymore, AI often reviews it too, and software moves so fast that reading every line is hard. diff-tour's answer is that understanding code matters as much as ever, and that understanding code means learning how a senior engineer thinks. That thinking is what lets an engineer give AI the right instructions, notice what really matters in a problem, design well, and keep a project moving in the right direction. Rather than teaching those habits in the abstract, diff-tour teaches them on real changes in a real repository. The core of the product is the tour itself. Each step is navigated with the → and ← keys, so a reviewer moves through the change one question at a time instead of scrolling through a wall of diff. Every step opens with what the change is for, then points to where to look next — and that includes files that did not change, which, as reviewers on the launch noted, is often where the real risk is hiding. Because the experience is a sequence rather than a summary, you work through the change in the order a careful reviewer would, rather than skimming until something looks off. diff-tour frames each step around the questions a senior engineer asks about a change: What if this runs twice? Can it be rolled back? Would the tests fail if the code broke? Those questions are asked explicitly rather than left implicit, so a reviewer has a checklist grounded in the actual diff. The tour also separates what must be fixed from what is only a nit, and calls out what looks risky but is actually fine, so attention goes to the parts that deserve it. Beyond correctness, the tour asks design questions too: does this belong here, and does it make the next change easier or harder? A distinctive part of the format is that the moments where a senior engineer would stop and say "wait" are asked as questions, so you guess first and then see the answer. Committing to an answer before seeing it is what makes the lesson stick, and it turns the diff from something you passively read into something you actively reason about. The tour also stops at risky schema changes, flagging them as deserving extra care, though the maker notes that it does not yet walk you through what is stored and how it connects — an acknowledged gap that is open for improvement through the project's issue tracker. Several design choices are about trust. diff-tour is read-only: the AI gets a copy of the files git tracks plus read-only tools, and that restriction is enforced by command-line flags rather than only by the prompt. The code on screen is read from git at the right commit and is never copied from the AI's reply, so what you see is the actual change. There is no server and no telemetry, it is MIT licensed, and it saves nothing between runs — which also means it does not track your guesses over time. It runs on your own Claude Code, Codex, Gemini, OpenCode or Copilot plan. Getting started takes one line: install with the one-line script in the README, then run `tour` in any git repository. The first run asks for a language and an AI CLI, with recommended choices already selected — pressing Enter three times starts the tour being written. There is no separate account, dashboard, or approval workflow to learn. The product is explicit that it is not for approving a change; it is for learning how a senior engineer reads one, and because it is open source you can see how those perspectives are written in internal/generate/lessons. The outcome diff-tour aims at is a reader who can look at a change and know where to look, what to ask, and what to ignore. Instead of skimming a large diff produced by an agent and waiting for something to look off, an engineer works through the runs-twice, can-it-roll-back and would-the-tests-fail questions up front. Because the questions come before the code, and because the reader guesses first, the habits of a senior reviewer get practiced rather than merely explained. Over time that is what supports giving AI better instructions, spotting what matters in a problem, and keeping a project pointed in the right direction. Typical use cases follow from that. When an agent hands back a large diff, diff-tour gives you a structured way to read it instead of skimming. When a tour reaches a risky schema change, it stops and asks for extra care, which is useful for the data-model-sensitive reviews reviewers on the launch said they still watch closely. When you are learning, the tour is practice on a real change rather than a toy example, and the design questions it raises — does this belong here, will the next change be easier or harder — give you a vocabulary for architectural discussion. The maker also agrees that reviewing AI-written changes is not only an engineer's job anymore, so the same walkthrough can be used by managers who need to follow what a change actually does. diff-tour is aimed at software engineers, at people who teach software engineering, and at anyone who needs to read AI-written changes rather than only write them. It does not ship its own model: it plugs into the AI coding CLI you already use, including Claude Code, Gemini CLI and OpenAI Codex CLI, and the launch also lists OpenCode and Copilot plans. It is a command line tool distributed under the MIT license, free to use, with installation through a one-line script and no server component. Everything it shows is read from git at the correct commit, and nothing is stored between runs. The takeaway is simple: as writing code gets cheaper, reading it well still matters, and diff-tour teaches that reading on the real changes in front of you. It turns a git diff into a short, navigable tour built around a senior engineer's questions, keeps the experience read-only and local, and asks you to guess the answer before you see it — so the habit of understanding a change is practiced, not just described.

Greta.sh is an AI app builder and vibe coding platform that turns a described idea into a live, production-ready full-stack website or web app. You type what you want to make, Greta.sh builds it on screen, and it can be deployed with one click. Alongside the builder, Greta.sh offers Self-Growing Websites: sites that find what customers search for, write pages that answer those searches, and get you showing up on Google, ChatGPT, Perplexity and everywhere else. The pages it produces are content you review and approve. The platform is positioned as one place to design, build, launch, manage and grow a product, from first idea to live product. The starting point for the product is a question every builder recognises: you built a website or an app, so why is nobody visiting? Getting found on Google and on AI tools such as ChatGPT and Perplexity takes months of SEO and AEO work. Greta.sh frames its Self-Growing Websites offering as the fix for that gap. Instead of treating search visibility as a project you complete once, the site is designed to keep growing itself: Greta.sh finds what customers search for, writes pages that answer them, and works to get the site showing up on Google, ChatGPT and elsewhere. The Product Hunt listing summarises the pitch simply: don't just build a website anymore, build a customer magnet with Greta.sh. At the centre of the platform is a four-step build flow. Step one is to describe your idea: you type what you want to make, like you are talking to a friend, and the content stresses that no special skills are needed — your idea is enough. Step two is to see it built, as the idea starts turning into something visible on screen. Step three is to bring your own tools, connecting Claude, Gemini, Slack, GitHub and more so that Greta.sh brings it all together. Step four is to deploy with one click, where everything from your domain to search ranking to safety checks just works, with the promise that you can build today and launch today. The Full Stack feature supports this: you can build your full app, website and tools without writing a single line of code, and the platform distinguishes between a Self-Growing Website, a Web App and full stack, with a "Which one do I need?" prompt to help you choose. The Self-Growing Websites capability is the growth side of the platform. Rather than leaving search visibility to manual keyword research and page writing, Greta.sh finds what customers search for, writes pages that answer those queries, and keeps adding pages that you approve. Because the content is subject to your review and approval, you stay in control of what goes live while the system handles discovery and drafting. The stated aim is presence in both traditional Google search and AI search tools, which is where the SEO and AEO combination comes in: the pages are built to answer the questions customers actually ask, so the site can appear in search results and in the answers AI assistants give. The Greta.sh MCP is the control layer for people who prefer to work inside their existing AI assistant. With the MCP, you can run the platform from Claude, ChatGPT or Cursor: you ask it to build, grow, publish or report, and the work is carried out through the connected assistant. The MCP feature is described as connecting your tools, systems and data so everything works together smoothly. In practice this means the build-and-grow workflow does not require switching into a separate dashboard each time — a builder can stay in the assistant they already use and issue instructions in natural language, whether that is to add pages, publish approved content or produce a report. Beyond building and growing, Greta.sh bundles the supporting pieces a live site needs. Domain lets you launch your brand online with your own custom domain in minutes, and the deploy step folds domain setup together with search ranking and safety checks. Asset Manager keeps files, images, videos and content in one clean, organised place, so the media that supports your pages is managed rather than scattered. Templates give you a head start: the platform advertises a choice of 100+ templates, where you find one you like, click it, and Greta.sh does the rest — the library includes published examples such as an e-commerce frontend, an analytics template, a content dashboard, a marketing template and agency or portfolio-style sites. Support scales with your plan: community support on Free, email support from Starter, and priority support on Pro and Business. The overall approach is to keep the whole lifecycle in one platform rather than stitching together a builder, a host, a domain registrar and a content team. You begin with a prompt, watch the build appear, attach the tools you already use, and deploy — then the same platform keeps working on growth by finding search demand, drafting answering pages and waiting for your approval before publishing. The MCP extends the same control surface into Claude, ChatGPT and Cursor, so the build, grow, publish and report actions are available wherever you already work. The stated benefits follow from that loop. You can go from idea to live product quickly, because deployment handles domain, search ranking and safety checks and the platform promises you can build today and launch today. You do not need to write code, since the full-stack capability covers apps, websites and tools. You do not need to run a separate SEO and AEO programme from scratch, because the site is designed to find what customers search for and write pages that answer them on your approval. And you get a single place to design, build, launch, manage and grow, rather than a set of disconnected tools. Concrete scenarios described in the content include building a full-stack web app or a website from a plain-language description; launching a brand on a custom domain; and running a site that keeps adding approved pages aimed at what customers search for. The MCP scenario is explicit: running the platform from Claude, ChatGPT or Cursor and simply asking it to build, grow, publish or report. The template library suggests additional starting points, with published examples including an e-commerce frontend, an analytics template, a content dashboard, a marketing template, and agency and portfolio-style sites. The platform is presented for people with an idea rather than a development team — the build flow explicitly says no special skills are needed, just your idea. The connector wall shows the tools the product works with: GitHub, OpenAI, Slack, AWS SES, Resend and Netlify, plus Claude, Gemini, MongoDB, Google Analytics, Stripe and xAI. Pricing detail in the content is limited to support tiers: a Free plan with community support, a Starter plan with email support, and Pro and Business plans with priority support. The Product Hunt listing categorises the product under Marketing and SEO. Greta.sh combines an AI app builder with a growth engine: describe your idea, get a production-ready full-stack site or app, deploy it with one click, and let the platform find what customers search for and draft the pages that answer them for your approval. It is aimed at getting a new site found on Google and in AI tools like ChatGPT and Perplexity without months of manual SEO and AEO work, and at keeping build, growth and publishing under one roof.

Ghostlaunch is a seven-day validation service for startup ideas that have not been built yet. A founder describes the idea in a few sentences, and Ghostlaunch asks a handful of questions to get the details right. From there the service builds a landing page written and designed to convert, complete with the founder's pricing, fast on any phone, and then puts real people in front of it. Every button on that page opens a 'coming soon' note that asks for an email. Ghostlaunch finds people on Reddit, X and LinkedIn who are already asking for a fix to the problem the idea solves, reaches out to each one about what they asked, and tracks every visit, scroll, click and email for seven days. On day 7 the test closes and a full report arrives: who came, how far they read, which plan they reached for, whether to build it, and every lead, ready to contact. The product sums the division of labour up as 'five minutes of yours, seven days of ours.' Ghostlaunch exists because, in its own argument, the usual ways to validate an idea share the same flaw: nobody has anything at stake. Asking AI means the model has read everything about your market but has never wanted to buy anything. Asking friends means they will tell you what you want to hear, because saying yes costs them nothing. Surveys produce hypothetical answers to hypothetical questions, and 'I would pay for that' is free to say. Feedback swaps are coloured by the other person's own launch. Market research tells you the market exists, not that anyone wants yours; Ghostlaunch calls it homework, not proof. Building first is expensive too. Ghostlaunch's own tally runs to 300 hours for the MVP, 40 hours designing it and writing the page, $600 for domain, hosting and tools, 20 hours of launch posts, DMs and cold emails, $500 of ads for first visitors, and 30 hours refreshing the dashboard for signups, plus endless second-guessing: 390+ hours and $1,100 to find out nobody wanted it. The only test Ghostlaunch treats as valid is putting a real page in front of people who are already looking for a fix, and watching what they actually do. The first half of the service is the page itself. Ghostlaunch writes and designs a landing page made to convert, includes the founder's pricing, and keeps it fast on any phone. Every button opens a short 'coming soon' note that asks for an email and sometimes one follow-up question, so nobody is charged and nothing is sold. The page is editable before the test starts: a founder can change any text or rewrite a section with one click. Ghostlaunch publishes examples of what these pages look like, built from the idea underneath each one, for products that do not exist yet: Paylark, a tool for freelance designers that automatically chases late invoices with polite, escalating reminders; Migalo, an app for neighbourhood bakeries that alerts locals over WhatsApp when fresh bread comes out and lets them reserve before it sells out; Nibblo, an app for parents of picky eaters that suggests tiny, realistic next foods from a photo; Strainly, a Slack bot for remote engineering managers that spots quiet overload; and Slotpup, a booking tool for independent dog groomers that fills last-minute cancellations from a waitlist. A gallery of 44 more examples is linked from the site. The second half is distribution and measurement. Ghostlaunch does the outreach itself: it finds people on Reddit, X and LinkedIn already asking about the problem the idea solves, keeps only the ones who clearly want a fix, and reaches out to each one about what they asked. Founders do not post, DM or ask anyone. Ghostlaunch addresses the obvious objection directly, stating that it only reaches people who are already asking about the problem, one conversation at a time, with no mass posting and no bought lists. That is what makes the traffic meaningful rather than a vanity number: the visitors arriving at the page are people who had already described the problem in public before the page existed. Measurement is continuous and public to the founder through a live dashboard. The site shows the feed filling up during a test on day 3 of 7: a visitor from a Reddit DM reached Pricing, clicked 'Get Team' on the Team plan, opened the waitlist and joined it; a visitor from a Reddit comment reached the FAQ and opened a specific question; a visitor from X reached the footer and scrolled 100%; a visitor from LinkedIn reached How it works. Every visit, scroll, click and email is tracked in that dashboard whenever the founder looks. At the end of the seven days, Ghostlaunch closes the test and writes the report: the verdict, the numbers behind it, what visitors told us, and what to change if you run it again. A published sample report for an idea called Shiftswap returned a 'Promising' verdict with 140 visitors, 8 emails, 6% of visitors leaving an email and 34% of button clicks going to a paid plan, plus a recommendation to build the smallest version, starting with the restaurants that left an email. If almost nobody shows up, the verdict says 'not enough data yet' below 100 visitors, together with how many more visitors it would take; Ghostlaunch states it never stretches a small sample into a conclusion. The overall method is deliberately linear, and Ghostlaunch frames it as sending the ghost first: watch the idea go out before the code does. The founder describes the idea, Ghostlaunch builds the page, Ghostlaunch finds the high-intent leads, Ghostlaunch measures everything, and on day 7 it delivers the verdict. The pitch throughout is to pay for answers rather than for code you will throw away, and the closing line is 'Don't build it yet. Ghostlaunch it.' After day 7 the page and the data stay in the founder's dashboard rather than disappearing, so the test can be revisited, relaunched or rewritten later on a subscription plan. Because the whole cycle is short and the output is a report rather than a product, the benefits are stated in terms of decisions rather than features. A founder gets a verdict grounded in what visitors did, not in what they said, along with the underlying numbers: how many came, how far they read, and which plan they reached for. The emails collected are the founder's to keep, stored with the plan each person picked and what they told Ghostlaunch, visible in the dashboard as they arrive, included in the day-7 report and exportable to CSV at any time. The report also records what visitors said and what to change on a second run, so a negative result still leaves something actionable. And the cost of being wrong drops from the estimated 390+ hours and $1,100 of building first to seven days and the price of a test. Concrete workflows follow directly from the published examples. A founder with a Slack bot idea for remote engineering managers can see whether managers click through to a paid plan before writing any bot. A maker with a booking tool for dog groomers, a reminder tool for freelance designers, a feeding app for parents of picky eaters or a WhatsApp reservation app for bakeries can put the pitch in front of people already complaining about those problems. Founders who are unsure what to build can start from Ghostlaunch's idea library, which it built by reading 1,452 posts where people asked for help and turning what they keep asking for into 50 ideas, each with the posts behind it, grouped into AI business ideas, app ideas, online business ideas, SaaS ideas and small business ideas. Founders working through a notebook of ideas can queue several tests rather than running one. Ghostlaunch is aimed at founders who have an idea but have not built it, including those testing in batches. Pricing reflects that: One test costs $49 once and includes the page built and hosted, 7 days of outreach to high-intent leads, every email collected yours to keep, the live dashboard and the day-7 report. Founder is $99 per month and is marked as recommended for a notebook full of ideas, with 3 tests a month, the ability to rewrite any section and relaunch any page, and everything in One test. Studio is $249 per month for testing in batches, with 10 tests a month, everything in Founder, and priority for its tests. Separately, the team notes that Ghostlaunch itself is for ideas you have not built yet, and points founders with a live product to ReplyHey, which finds people on Reddit, X and LinkedIn asking for what you sell, writes the reply and posts it from your account every day. In short, Ghostlaunch replaces encouragement with evidence. One described idea becomes a converting page, targeted outreach to people already asking for a fix, seven days of click-level measurement and a written verdict with every lead attached, so the decision to build rests on what strangers did rather than on what friends politely said.

Clayzo is an animation engine built for coding agents. Rather than building motion design by hand in a separate animation tool, you describe the motion you want and an agent such as Claude Code, Codex or Cursor writes it in code, looks at the result, and iterates. The finished animation can be shipped as a video or played live on your website. Clayzo presents itself as animation built for Claude Code, Codex and Cursor, and its showcase spans campaigns, 3D, product UI and interactive pieces, from a ten-second feature launch post to a cursor-driven halftone portrait. That range makes the product relevant to anyone who already works with a coding agent and wants motion graphics to come out of that same workflow instead of a separate pipeline. The idea behind Clayzo is a shift in how motion design gets made. Coding agents like Claude Code, Codex and Cursor already work inside a code workflow, and Clayzo gives them an animation engine to work with, so motion becomes something the agent produces rather than something assembled by hand frame by frame elsewhere. The site states the loop plainly: you describe the motion, your agent writes it, looks at the result, and iterates. The output is either a video you ship or an animation that plays live on your site, so one piece of work can serve a launch video and an interactive page. Everything you make is yours to reuse and remix, and Clayzo says work is never locked in: you can easily change anything or spin up as many variations as you like. The product is backed by Afore Capital and is free. Clayzo is designed to work with coding agents rather than replace them. The site names Claude Code, Codex and Cursor specifically, and its own FAQ asks which agents it works with and which models work best, making agent compatibility a first-class part of the product. In practice this means the animation is authored in code by the agent you already use, inside the environment you already work in, so there is no separate animation editor to learn. You describe the motion in plain terms, the agent writes it, and Clayzo provides the engine the agent targets. For teams already directing agents at other development tasks, motion graphics become one more thing the same agent can be asked to produce, keeping animation inside the existing coding workflow instead of splitting it off into a different tool and a different set of skills. A defining part of the workflow is the review loop. Clayzo's pitch is that your agent writes the animation and then reviews every frame, iterating until the motion matches what you described. That feedback cycle is what makes an agent-authored animation practical: instead of accepting whatever code comes out first, the agent can inspect the rendered result and refine it. When the animation is right, you choose the output. You can ship it as a video, exporting it as motion graphics for use wherever a video file is needed, or you can play it live on your site as a running animation. The same underlying work therefore covers both a rendered clip and an in-browser experience, which is useful for launch posts, ads, logo stings and product explainers that often need to exist in more than one format. Clayzo also covers live and interactive animation on the web. Beyond video, animations can play live on your site, and the showcase includes interactive pieces whose motion responds to the visitor: a halftone portrait rendered as a screen with a lens that follows the cursor and reveals the photograph underneath, a field of split discs that turn to face the cursor and flare hot where it passes, with a click sending a ripple through them, and a premium card that tilts toward the cursor with a highlight sliding across the metal and a shadow that follows. Other examples include a settings toggle, an agent thinking state, a payment confirmation and a piece that follows your cursor, showing that interaction and micro-motion are part of what the engine is built to produce. Clayzo's player is presented as lightweight. The site includes a direct comparison of WebGL players and states that Clayzo is 1.0× smaller than Rive, showing the Clayzo WebGL player against the Rive WebGL player. A smaller player matters for live animations because the runtime is what every visitor loads when the animation plays on your site, so keeping it small supports the goal of playing motion live rather than only embedding pre-rendered video. Getting started is similarly lightweight: the site shows a single command, npx clayzo@latest init, which is how you initialize Clayzo in a project. From there the work happens through your coding agent, which writes the animation in code and iterates against the result, with Clayzo supplying the engine and the WebGL player that ultimately runs it. Overall, Clayzo's approach is to treat animation as code written and refined by an agent. You describe the motion; the agent writes it, looks at the result, and iterates; the output ships as a video or plays live on a site. Because the work is code rather than a locked file inside a proprietary editor, everything you make is yours to reuse and remix, and the site emphasizes that it is never locked in: you can easily change anything or spin up as many variations as you like. The showcase illustrates the breadth of that approach across campaigns, 3D, product UI and interactive categories, covering launch posts, ads, logo stings, 3D shorts, explainers, product demos and cursor-driven web interactions, all produced through the same agent-driven loop. The benefits follow from that loop. Because the agent reviews every frame and iterates, you can refine motion by describing what you want rather than by manually adjusting it. Because the result can be exported as video or played live, you avoid rebuilding an animation for each destination. Because the player is WebGL and described as smaller than Rive's, live playback stays lean. Because everything is yours to reuse and remix and is never locked in, animations can be changed, extended and turned into multiple variations without starting over. And because Clayzo works with the coding agents teams already use, motion graphics fit into the existing workflow. The product is free, which removes cost as a barrier to trying the agent-driven approach on a real project. The showcase lays out concrete use cases. Campaign work includes a launch post for a Stack feature, where loose shapes snap into an auto-layout grid and teammates resize and duplicate them live. Advertising includes a punchy square ad for three new flavours, with each can flooding in on its own colour and a stamp that keeps turning. Branding includes logo stings: four quarter tiles swinging in and locking into a mark for Quarto, and a single white stroke on red drawing a crescent and writing a wordmark for lune. 3D work includes a wooden marionette short and a 3D explainer of Intel's earnings with a chip riding a rollercoaster. Product UI work includes a running app product shot where a tap opens into detail with the route replaying and splits rolling in, an AI spreadsheet where a Funding stage column fills in and grows into a chart, and an issue tracker where a new issue lands on the board and is dragged to In Progress. Interactive work includes the halftone lens, pointer field and tilt card. Clayzo is aimed at people who work with coding agents such as Claude Code, Codex and Cursor and want to produce motion graphics, product animations and interactive web animations through them. Its showcase categories, campaigns, 3D, product UI and interactive, indicate the kinds of work it is used for, from marketing launch posts and ads to product demos and cursor-responsive web experiences. The site notes that Clayzo is backed by Afore Capital and states that it is free, with a getting-started step of running npx clayzo@latest init. Its FAQ addresses questions about what Clayzo is, how to get started, which models work best, whether you can use your own images, fonts or components, which agents it works with, whether it is really free, where you can play Clayzo animations, and how it differs from Rive or Lottie. Clayzo's core value proposition is simple: animation, built for coding agents. You describe the motion, your agent writes it and reviews every frame, and you ship the result as a video or play it live on your site through a WebGL player the site describes as smaller than Rive's. Everything you make is yours to reuse and remix, nothing is locked in, and variations are easy to spin up, all through agents you already use and at no cost.

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AI Manga Translator is an online tool at aimangatranslate.org that translates the text inside manga, manhwa and manhua page images and returns a translated page image. It detects speech bubbles and captions, erases the original lettering, and places the translation back onto the artwork, so readers follow the dialogue next to the panels instead of copying lines into a separate translator. It is built for people reading raw Japanese manga, Korean manhwa or Chinese comics who want the scene kept in view while they read. The alternative is taking a screenshot, copying a line, and then finding your place on the page again — work that only tells you what happens next after several manual steps. Copying dialogue line by line also means cropping every speech bubble by hand, and reading translations in a separate window makes it easy to lose track of who said what. Repeating the same steps across a chapter compounds the friction. Short replies are especially awkward, because the speaker, expression and previous panel may explain more than the words alone, and that context disappears once the text is pulled away from the artwork. The core single-page translator accepts a whole PNG, JPG, JPEG or WebP page up to 15 MB. You choose the reading language and upload the complete page rather than cropping individual bubbles; the returned image replaces the source lettering and aims to keep the panel art and layout. You can review the text and artwork before downloading. The website tools offer 133 reading languages, including English, Simplified Chinese, Japanese, Korean, Spanish, French, German and Portuguese, with the full list in the language selector. A local preview of a chosen file is available before you sign in, and the homepage recommends starting with a page that has ordinary speech bubbles and enough dialogue to judge whether the result is useful. The batch translator handles up to 20 separate page images at 15 MB each. You add the pages together, pick one target language once, and start the queue; you do not set up every page separately. Pages process in queue order, and you can open a finished translation while later pages are still processing, so you can start reading before the batch finishes. If a page fails, it shows its own error message and can be retried without losing the pages already completed — there is no need to restart the chapter. Completed pages download as separate images, and the browser may ask for permission for multiple downloads. For chapters that already exist as an archive, the CBZ translator takes one CBZ or ZIP of up to 100 MB containing up to 50 supported images, with a 15 MB per-image and 200 MB extracted-total limit. It sorts numbered filenames naturally, and once all selected pages finish, paid users can export an ordered CBZ or ZIP. Original archive metadata is not retained. A separate manga URL translator lets you paste links and import up to 20 pages without saving files first, which suits image URLs and plain webpages. PDF, CBR/RAR and EPUB are not supported directly, so documents and archives need to be organised into page files before upload. The Chrome extension covers manga you are already reading on a webpage. It works in Chrome and compatible Chromium desktop browsers: you install from the Chrome Web Store listing, sign in to the website with your account, choose a language, and translate accessible images already loaded on the page using your website account credits. Installation is free, translation uses credits and requires an internet connection. The extension offers 25 reading languages, compared with 133 in the website tools. Because it works on images already loaded on the page, nothing has to be downloaded and re-uploaded first. The Manga Cleaner is a related utility that does not translate. It accepts up to 20 images at 15 MB each and removes either bubble text or all detected text, exporting PNG or WebP files or a ZIP. That is useful when you intend to reletter a page yourself. Across these tools there is no built-in text editor for correcting or re-lettering translations after they are generated, no editable glossary, no OCR correction screen and no human proofreading, so corrections happen in your own editing tool. Every workflow uses the same page translation service. An account and sufficient credits are required to start a translation; each page costs 1 credit, and credits for failed pages are refunded automatically. Trial credits and free-account limits depend on the current account offer, and there is no unlimited free plan. Downloading translated images, cleaned images or archives requires a purchased one-time credit pack, starting at USD 4.99. There is no subscription, and purchased credits do not expire. Source images and translated results are stored for your signed-in account history, so returning to completed work does not depend on an unfinished local queue — and refreshing a running queue is discouraged. The tool returns a translated image to review and download; it does not promise that every word or piece of artwork will be unchanged. AI translation can misread names, miss context, or struggle with vertical text and stylized sound effects, and fine details or text over busy backgrounds may change. Clear images help, but no page is guaranteed to be error-free, and terminology is not guaranteed to stay consistent across a chapter. The comparison images on the homepage are illustrative examples of the interface, not live translation results, and the site recommends checking a page you know first and comparing important lines, names and tone against the original. Typical scenarios follow the sources the site lists. You have reached the latest chapter of a raw manga and the dialogue is still in Japanese, so you translate the page to follow what happens next. You have a whole chapter as image files or a CBZ or ZIP and want the pages back in order. You read on manga websites in Chrome and want pages translated without downloading them first. You have a link to an image URL or a plain webpage and want to import it directly. Or you need the text removed from a page so you can reletter it yourself. The site frames the product as a good fit if you want to read a raw manga, manhwa or manhua page with the translation placed on the artwork; if you have a chapter as images or a CBZ or ZIP and want the pages back in order; or if you read on manga websites in Chrome and want to translate pages without downloading them. It suggests looking elsewhere if you need direct PDF, CBR/RAR or EPUB import, a text editor for correcting or re-lettering translations after generation, offline processing, or unlimited free use without an account. In short, AI Manga Translator keeps the page and the dialogue together: you upload a page or a queue of pages, choose a reading language, and receive a translated image with the translation lettered back into the bubbles, priced per page with failures refunded and one-time packs that never expire. It is a reading aid rather than an edition-ready localisation pipeline, and the site is explicit that results should be reviewed against the original.

magpie is a visual library for everything you save online. It pulls your saves from Instagram, X, TikTok, Reddit, Pinterest, Cosmos, Are.na, GitHub, YouTube, Dribbble, Behance and your browser's bookmarks into a single place, then uses AI to tag them so you can find them by what's in them, or by colour. Its stated purpose is simple and direct: everything you've saved, in one visual library, so that the references you collect while browsing can actually be found again later instead of disappearing into separate apps and feeds. The problem magpie is built around is one most people who collect visual references know well. Saves end up scattered across many platforms, each with its own bookmarking gesture and its own search that rarely works on images. What you saved to Instagram lives in Instagram, what you pinned lives in Pinterest, what you bookmarked in your browser is buried in a bookmarks list, and none of those places can answer a question like "that photo of somewhere by the sea." On top of that, posts get deleted, accounts go private and content vanishes, which means the references people rely on for work can quietly disappear. magpie addresses both halves of that problem: it consolidates the sources and it keeps the save even when the post is gone. At the centre of the product is the unified library. The site describes saving from Instagram, X, TikTok, Reddit, Pinterest, Cosmos, Are.na, GitHub and your browser's bookmarks, with Chrome, Safari and Firefox represented alongside those sources, and the Product Hunt listing summarises it as saving from Instagram, X, TikTok, Pinterest, YouTube, Reddit, Dribbble, Behance, Are.na and the web. Rather than visiting each network to remember what you liked there, the library presents everything as a single visual collection. In the sample library shown on the site, that collection holds 265 saves consisting largely of design references: identity work, packaging, posters, typography specimens, photography and motion pieces, each labelled with the domain it came from. AI tagging is what makes that single collection usable. Every save is tagged by AI so that you can find saves by what's in them, rather than by whatever caption or title the original post happened to have. The site demonstrates this with a search for somewhere by the sea, which surfaces seaside imagery, and with a view of the saves that are mostly one of its blues, showing how colour functions as a first-class way to filter a library. Colour search is described as a way to find saves by colour, and it is presented next to meaning-based search as a paired way of navigating a large visual collection. A second search layer is the bar that sits over the library, labelled "Find, or ask for references." It can be reached with ⌘K, and the site notes that you can paste a link anywhere to add something new. Beyond plain lookup, the bar accepts a request in natural language: the demo types in references for a calm hardware launch. In response, magpie picks six saves from the library, gives a reason for each pick, and makes a project of them. That turns a passive archive into something you can interrogate, where the library proposes material from what you have already saved and explains why each piece fits the brief you typed. Browsing the library itself is supported by several views. The interface shown on the site includes Rows, Cells, Grid and Canvas as ways of arranging the same set of saves, with a control for zooming the grid in and out. Individual saves can be opened: the demo shows a video save opening and playing inside the library, and an Instagram collection of posters and prints being viewed as a group. Because saves are kept even when the original post is gone, the copy in the library remains available after the source disappears, which is the core archival promise of the product. The overall approach combines capture, automatic organisation and retrieval. Capture happens by pasting a link anywhere or by bringing in saves from your connected sources, including your browser's bookmarks. Organisation happens automatically through AI tagging, so nothing depends on you filing things correctly at the moment you save them. Retrieval then happens through three routes that the content demonstrates: searching by meaning, searching by colour, and asking for references in plain language and receiving a curated project with reasons attached. The result is a library that behaves like a visual memory rather than a folder tree. The stated benefits follow from those mechanics. You get one place for everything you save instead of many; you can find an image by describing it or by its colour rather than trying to recall its caption; and your saves survive deletion of the original post. For people whose work depends on accumulated visual references, the demo's calm hardware launch request illustrates the practical payoff: instead of scrolling back through months of saves, you type the brief and receive six relevant pieces with an explanation of why each one is there, already grouped into a project you can work from. Concrete use cases visible in the content include searching a library for somewhere by the sea; opening and playing a saved video directly in the library; filtering to the saves that are mostly a particular blue; browsing an Instagram collection of posters and prints as a set; and asking for references for a calm hardware launch, where magpie assembles six annotated saves into a project. The sample library itself is full of design material such as brand identities, packaging, type specimens, campaign films and photography, which reflects the kind of reference gathering the product is demonstrated with. magpie is available as a Mac download and through the web, where you can start for free. The Product Hunt listing files it under Chrome Extensions, Design Tools and Productivity, and the site's source list spans social and visual platforms alongside browser bookmarks. Its natural audience is people who collect and reuse visual references, particularly designers working with identity, packaging, typography, photography and motion, who need their saved inspiration to be searchable, colour-aware and permanent. The takeaway is straightforward: magpie turns scattered saves into one visual library that is tagged by AI, searchable by meaning and colour, answerable in plain language, and kept even when the post is gone.

ernd calls itself the credit card for the AI stack, and it is built for US companies that pay for AI. The product is a business credit card whose rewards are weighted toward AI vendor spend: card spend at AI vendors earns 4% back, while everything else earns 1.5%, and the balance is paid off once a month. On top of rewards, ernd negotiates partnerships with AI providers so that cardholders get better pricing and capacity. The company is backed by Y Combinator and is issuing cards to US companies that request them, with requests handled on a first-come, first-served basis. The context ernd sets out is simple: companies now carry a monthly AI bill, and that spend is spread across vendors. ernd's stated model is that it negotiates the contracts once, and every cardholder gets them. Instead of each company negotiating on its own with each AI provider, ernd negotiates with each provider for all the companies on the card and adds more perks as deals get signed. The card is positioned as a way to get something back from spend a company is already making, while also pooling the terms ernd negotiates with each provider. The rewards structure is stated directly on the site. Card spend at every AI vendor earns 4% back, and the FAQ confirms this includes OpenAI and Anthropic. Payments to Superserve, Conifer and AgentPhone each earn 5% back. Everything else earns 1.5%. The FAQ adds that the balance is paid off once a month. ernd repeats that final terms come with the card, so the published rates should be read as the current description of the offer rather than a locked contract. For a company routing its AI bills through the card, the gap between a general-purpose business card and a 4% rate at AI vendors is the core of the proposition. Beyond the earn rates, ernd lists negotiated perks from specific providers, and these are presented as part of the card rather than as separate purchases. Firecrawl offers 2x usage limits. Superserve, Conifer and AgentPhone each offer 5% back. EigenPal offers 5% back on OpenParser and DOCX Editor. Experiential Labs offers 5.5% back. Osto offers a free security stack for the first 100 cardholders. According to the FAQ, these perks come from ernd negotiating with each provider for all the companies on the card, and more are added as deals get signed. The perk list doubles as a signal of which AI vendors ernd is currently working with. Rewards are issued as points, and the site describes two ways to use them. The first is cheaper work travel: ernd says that because it buys airline points in bulk, it can pass the savings on to cardholders for customer visits, offsites and more. The second is cash back, which the site calls the simple option. ernd notes that a point is worth more at partners like Superserve, so redeeming there is described as the better deal for companies that already use those providers. The FAQ summarises the same position: points are redeemable as cash back and go further on travel ernd books and with recommended providers. Final terms come with the card. What makes the model distinctive is where the value comes from. The rewards are described as coming from ernd, not from the AI providers: the FAQ states that OpenAI and Anthropic do not sponsor or endorse ernd, that ernd is an independent company, and that companies pay OpenAI and Anthropic with the ernd card the same way they would with any business card. The second source of value is negotiation: ernd signs partnerships with AI providers and extends the resulting pricing and capacity to every cardholder. The site also lists priority GPU and inference capacity as coming soon, presented as another negotiated benefit rather than a shipped feature. ernd puts a number on the payoff with an on-site estimate. At a monthly AI bill of $10,000, the page estimates the card pays back roughly $4,800 a year. The FAQ adds that requesting a card is free and there is no annual fee, with final terms coming with the card. Taken together, the stated benefits are 4% back on AI spend at every AI vendor, 5% back at named partners, 1.5% back on everything else, access to negotiated provider perks, and a choice between travel savings and cash back when redeeming. The estimate is presented as an illustration tied to the monthly bill entered on the page. The concrete scenarios the site describes are paying AI vendors, redeeming points on work travel such as customer visits and offsites, and claiming partner perks. A company paying OpenAI and Anthropic monthly would route those payments through the card, as it would with any business card, and the 4% rate applies at every AI vendor. If it also uses Superserve, Conifer, AgentPhone, Firecrawl, EigenPal, Experiential Labs or Osto, the listed perks — extra usage limits, additional cash back, or a free security stack for the first 100 cardholders — attach to that spend. Travel booked through ernd is the alternative redemption path for teams that would rather convert points into cheaper flights. The card is for US companies. A company admin signs in with a work email or a Google work account and sends the request from the ernd app, which the site says takes a few minutes. The request asks for the company's legal name, type, state, address, phone, EIN, revenue and cash estimates, expected card spend, and the name, title and email of each owner of 25% or more. Submitting a request does not guarantee a card; ernd issues on a first-come, first-served basis and emails the admin about next steps, and a request can be withdrawn from the app at any time. The site also offers a 15-minute booking with Connor. In short, ernd is a credit card aimed at companies whose AI bills have become a real budget line. It combines 4% back at every AI vendor, 5% back at named partners, 1.5% on everything else, negotiated provider perks, and points that can be taken as cash back or stretched further on work travel, with no annual fee and a request process open to any US company.

CutCat is a free Chrome extension that mutes or skips mature content, nudity and gore while you watch Netflix. It works from scenes that other viewers have already marked, so the film keeps playing and you simply do not sit through that part. The extension runs inside the Netflix player you already use in Chrome on a computer, and it is not affiliated with Netflix. Its promise is deliberately narrow: keep watching, skip the scene. CutCat is built for people who want to stay with a series or a film but would rather not sit through nudity, gore, strong language, kissing or credits, and who want to decide for themselves which of those categories count. The problem it addresses is a familiar one for anyone watching a title that mixes a story they want with material they do not. A marked scene arrives, and the viewer is left to either sit through it or reach for the remote and guess where it ends. CutCat removes that guesswork by relying on viewers who got there first: the exact stretch of a film or episode has already been identified by someone else, and that mark is what the extension acts on. Because the marks are shared across everyone using CutCat, the effort of finding and timing a scene only has to be made once. The catalogue is presented openly on the site, so a viewer can see what is covered before starting a title, and it grows as viewers mark more. Skipping is the first of the two core behaviours. When playback reaches a marked stretch, CutCat jumps the whole stretch rather than blurring, dimming or fast-forwarding it. The story continues either side of the gap, and the part in between is never shown. This is the behaviour used for the categories the site lists as skipped: mature content, nudity, violence and gore, kissing and credits. Because the jump covers the entire marked stretch rather than a guessed interval, the viewer does not have to scrub back to check whether the scene is really over. Muting is the second behaviour, and it is handled differently on purpose. When a word or a line is marked, only that line is silenced, and the sound comes straight back afterwards. The site describes the mute as measured to the frame, so the scene around the line is not lost. That distinction matters for strong language: a viewer who does not mind the scene itself but would rather not hear the profanity can keep the picture, the performances and the surrounding dialogue intact instead of losing whole minutes of a conversation. Skipping and muting therefore serve two different viewing preferences within the same extension. Control over categories is the third piece. Every category can be switched off, and the settings stay in your browser. The site lists the categories as mature content, nudity, violence and gore, strong language and kissing, each with its own switch, and describes the outcome in plain terms: switch whole categories on or off, and mute strong language and skip nothing else if that is what you want. This means the extension does not impose a single definition of acceptable content. A viewer can mute profanity only, skip nudity and mature content, or leave gore in place, without having to configure anything complicated or explain their choice to anyone. Overall, the approach is shared marking plus local playback control. CutCat does not analyse the video itself, and the site is explicit that nothing is downloaded or altered. When playback reaches a marked stretch it jumps past it, or the sound drops for a marked line and comes straight back. On a film or episode that nobody has marked, it plays as normal, because CutCat only acts on scenes viewers have marked. The coverage list on the site is the practical companion to that rule: it currently reports roughly 1,500 scenes marked across roughly 150 titles, on 2,500 films and episodes, and the list grows as viewers mark more. The outcomes for a viewer follow directly from those mechanics. A title that would otherwise be abandoned can be watched through, because the parts a viewer objects to are removed without touching the rest of the film. Nothing is re-encoded and nothing is edited permanently, so the viewing experience either side of a mark stays as it was. Privacy is stated just as plainly: CutCat never records what you watch, and your category settings stay in your browser, with the privacy policy listing exactly what is and is not collected. It is free and has no ads, so there is no subscription to manage and no advertising layer inside the player. Concrete use cases come straight from the coverage the site publishes. Someone working through Breaking Bad, with 115 scenes marked, or Dark, with 107, can watch with marked stretches removed. A viewer who wants Bridgerton (58 scenes marked) or Outlander (74) without the mature content can skip those stretches while keeping the drama. A viewer who cares about language but not about violence can leave the skip categories off and mute strong language only. Anyone unsure about a title can hover or drag through the coverage list first, then put the extension on and forget it is there. Because credits are one of the listed categories, it also covers viewers who simply want the credits skipped. The target user is someone who watches Netflix in Chrome on a computer and wants a filter they control, rather than a rating or a general-purpose parental control. Product Hunt lists the product under Chrome Extensions, Parenting and Streaming Services, which reflects that mix of everyday viewers and parents choosing what plays in front of others. The restrictions are stated without hedging: CutCat is a Chrome extension, so it runs in Chrome on a computer and cannot be installed on a phone, a tablet or a smart TV, and it works on Netflix only, not on Prime Video, Hotstar or other services. Pricing is free with no ads, and any category can be switched off at any time. The published catalogue shows where the coverage is deepest. Breaking Bad leads with 115 scenes marked, followed by Dark on 107, Outlander on 74, Bridgerton on 58, Vikings on 54, Spartacus on 52, Shameless on 45, Castlevania on 44, You on 42, Squid Game on 37 and 13 Reasons Why on 34. Further down the list are Cyberpunk: Edgerunners on 27, The Platform and Bodies on 26 each, UNABOMBER on 25, Troy: Fall of a City and The Witcher on 24, Apex on 22, Captain Fall on 20, Ozark on 19, Colors of Evil: Red and Unfamiliar on 18, The Tudors on 17, Something Very Bad Is Going to Happen and Stranger Things on 16, and American Psycho, Unlocked, The Beast in Me, Black Mirror and Peaky Blinders on 15 each. The list is a snapshot rather than a fixed set. CutCat is best understood as a small, single-purpose tool that does one job in the Netflix player and then stays out of the way. It skips or mutes marked scenes, lets the viewer choose which categories apply, keeps settings in the browser, records nothing about what is watched, and costs nothing. The value proposition is the sentence the site opens with: keep watching, skip the scene. Put it on, then forget it is there.

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Knucklebop is a Mac app that turns tapping your MacBook’s palm rest or the desk beside it into shortcuts. The product’s core idea is that “a tap is a button” and the number of taps determines which button you pressed. It supports two to six light taps beside the trackpad or on the desk next to the MacBook, and the timing does not matter—only the count. Each app or website can have its own taps, while everything else uses “anywhere” taps. The app then makes your Mac perform the chosen action and shows a small pill to confirm what happened. The content frames Knucklebop around giving the palm rest a job. Instead of reserving quick controls for keyboard shortcuts or on-screen buttons, users can tap a surface that is already under their hands. The app checks which app is active and applies the appropriate rule, so a tap pattern can mean one thing in a meeting app and another in a music app. Website rules beat app rules, and app rules beat “anywhere” rules, with one pattern allowed per tap count. This makes the same physical gesture useful across different contexts without requiring the user to remember a different global shortcut for every situation. Users tap two to six times on the palm rest or the desk next to the MacBook. Knucklebop uses the MacBook’s motion sensor, described as an accelerometer and gyroscope in the base, to recognize the tiny, sharp jolt made by a tap. It does not use the microphone to hear taps. The default window for keeping taps together is under about 0.7 seconds apart, and the content says this can be made longer. Before use, the app asks the user to calibrate by tapping a few times on each palm rest and the desk so it learns how hard they tap, with taps animating on screen during calibration. Knucklebop includes ready-made actions across several groups. For meetings, it can mute in Zoom, Teams, Slack huddles, and Google Meet using each app’s own shortcut so the app’s button stays in sync, or it can mute the mic everywhere with an on-screen “Mic muted” pill. For music, taps can play or pause, go to the next or previous track, and adjust volume up, down, or mute. For windows and desktops, actions include Mission Control, next or previous desktop, switch to last app, show desktop, Spotlight, lock screen, dark mode, and screen saver. For editing and tabs, the listed actions include copy, paste, undo, screenshot, emoji picker, full screen, and new, close, next, and previous tab. A “boss mode” can hide every app and pause media in one move, optionally locking the screen too. When five buttons are not enough, four taps can open the tap menu, a list of as many actions as the user likes. Each extra tap moves down the list; when the user stops tapping, the highlighted action runs. Arrow keys and Return also work, and Esc cancels. The menu comes with switch app, desktops, play/pause, screenshot, and confetti, and items can be added, removed, and reordered under Rules. For power users, Knucklebop can run any keyboard shortcut, run a Shortcuts automation, or execute AppleScript or a shell command. These options let a tap count point at existing workflows rather than only at a fixed set of built-in commands. Every app gets its own taps. Ready-made rules switch on for apps the user already has installed, and the user can change them, add new ones, or turn any of them off. The content lists Zoom, Teams, and Slack rules for muting in the app, which press the app’s own mute shortcut so its button changes too. Google Meet has a mute-in-Meet rule that works in Chrome, Safari, Arc, Edge, or Brave, and a Meet tab gets the Meet rule rather than the global one. Spotify and Music have play/pause and next-track rules. VS Code and Xcode have toggle terminal and build rules. Anywhere else can use mute mic, tap menu, and confetti. The hierarchy is explicit: website rules beat app rules, and app rules beat anywhere rules. Knucklebop is built to not misfire. A tap has a sharp signature, while typing, trackpad clicks, picking up the laptop, and a train ride do not. Every pattern requires at least two taps in quick succession, so a dropped pen or a single bump does nothing. The app ignores taps during and just after keyboard or trackpad use, and the gyroscope tells a tap from a lift or a tilt; detection pauses in a moving car until things settle. Test mode shows what would fire and why without running anything. Nothing runs while the screen is locked, and the same rule cannot fire twice from one burst. The app can be switched off from the menu bar or paused for 15 minutes or an hour. With the lid closed, the Mac sleeps or the laptop is not where the hands are, and taps on the top edge of the lid are not supported. Fun mode is one switch and goes quiet by itself whenever the mic or camera is on. Tap sounds come in four packs: Woodblock, Bubbles, 8-bit, and Drum kit. The drum kit turns the palm rest into an instrument: soft taps hit the hi-hat, harder ones hit the snare and kick, and users can record a 10-second loop and export it while tap patterns take a break. Every tap sends a ripple across the bottom of the screen, five taps throws confetti, and other taps can play a rimshot, a round of applause, or have Bop wave hello. A weekly stats card shows taps, mutes, and hardest tap, and there are two secret tap patterns the content does not disclose. Setup is described in four short steps. The user downloads and opens the app—notarized by Apple—and it lives in the menu bar. They calibrate by tapping each palm rest and the desk, then try the first rule, double-tap to mute. Permissions are requested only when needed: Accessibility for shortcuts and meeting-app mute, Automation for website rules, and none for the system-wide mic mute. Privacy is by design: sensor data is processed on the Mac and never leaves it, and the app has no analytics, no telemetry, and no account. Its only network calls check the licence and look for updates. Website rules read the current tab’s domain only when a tap pattern finishes and never store it. The app does not use the microphone to hear taps. Knucklebop is a paid app with a free 7-day trial that includes every feature. There is no account and no subscription. A one-time purchase is $12.99 for one Mac or $19.99 for three Macs, for work and home laptops. Every feature is included, the licence key arrives by email, the licence can be moved to a new Mac at any time, and there is a 14-day refund. Prices are in US dollars and local taxes may be added at checkout; payments are by Dodo Payments. After the trial ends, patterns and rules are kept but actions stop until a licence key is entered; nothing is deleted. The app is not on the Mac App Store because macOS has no public API for the sensor, so it uses an undocumented system interface, is signed with an Apple Developer ID, and is notarized by Apple. Concrete use cases follow directly from the rules: mute in Zoom, Teams, Slack huddles, or Google Meet with a tap; control Spotify or Music; toggle the terminal in VS Code or build in Xcode; open the tap menu for screenshot, desktop switching, or confetti; run a Shortcuts automation, AppleScript, or shell command; use boss mode to hide apps and pause media; or use fun mode for tap sounds, drum loops, ripples, and confetti. For MacBook users who want fast, app-aware controls under their hands, Knucklebop’s primary value is turning tap count into a context-sensitive button while guarding against accidental triggers and keeping sensor data local.

telemetry.dev is an LLM observability platform for AI applications and agents, built on OpenTelemetry. It shows each run of an AI agent as one trace, where each model call and tool call is a span carrying its duration, tokens, cost, and error. From that trace, developers follow model calls, tool steps, and retrievals together, find failures, inspect latency, and track token usage and cost. The product is aimed at teams building and operating AI agents and LLM-backed applications who need to see what actually happens inside a run instead of inferring it from scattered logs. An agent run is rarely a single request. The live demo on the site shows a support-agent trace containing thirteen spans: loading a customer profile, loading a session, routing intent, retrieving policy context, generating embeddings with text-embedding-3-small, calling gpt-4.1-mini, searching a help center, running a second retrieval, calling the model again, running a guardrail output step, and saving the session. When something breaks in that chain, the failure can sit in any step, and the slowest step is often not the model call itself. telemetry.dev frames the problem plainly: find the slow step, inspect the failed call, pinpoint a tool timeout, inspect a model response, and see where your budget goes. It also aims to tell you about errors, cost spikes, agent loops, and model or tool errors before your users do. The core of the product is trace detail. Each trace lists its spans with the duration of every step, so a run is readable end to end. In the example trace, a track_shipment span takes 2,418ms and accounts for 49% of the run, while the two chat steps take 845ms and 1.16s. Selecting a span opens its summary and attributes, including fields such as gen_ai.operation.name set to "execute_tool", gen_ai.tool.name set to "track_shipment", and gen_ai.tool.call.id. Those attributes identify precisely which tool executed inside the run, which is what makes it possible to separate a model problem from a tool timeout. Finding the right trace is handled through search and filtering. Traces can be searched by name, or filtered by status, environment, framework, or session. The production view lists runs such as support-agent, classify-ticket, and summarize-session with their durations, and a selected trace reports how many spans it contains and what it cost. A separate overview keeps cost and reliability in view by comparing spend, latency, and errors by model, provider, and environment. In the sample dashboard, LLM cost for the last seven days totals $2.98 across gpt-4.1-mini, claude-sonnet-4.5, gemini-2.5-flash, and text-embedding-3-small, alongside 812 requests, a p95 latency of 1.8s, and 2.6M tokens. For Model Context Protocol workloads, telemetry.dev reports spans, failures, and p95 duration by method and tool, measured from both the client and the server. The example MCP analytics table lists create_refund with 2,330 spans, a 2.4s p95, and 18.0% failed; get_invoice with 5,912 spans, 640ms, and 1.1% failed; check_inventory with 4,208 spans, 310ms, and 0.2% failed; and cancel_order with 1,077 spans, 880ms, and 0.6% failed. Email alerts complement the dashboards, covering cost, budgets, agent loops, and model or tool errors, with states such as tool errors alerting, monthly budget all clear, agent loop all clear, and missing prices needing more data. Privacy controls determine what gets stored. Prompt capture is on by default and can be turned off for an environment before storage. The settings screen lists production, staging, development, and preview with whether stored prompts and responses were captured, and in the example production shows prompts and responses are not stored. Redaction patterns, both built-in and project-specific, are applied, but the product states that pattern matching cannot catch all sensitive data, that capture controls only cover recognized prompt, response, and tool-payload fields, and that removing sensitive content in your instrumentation before export is the way to prevent transmission. Separately, prompts and evals use the same traces: a prompt can be changed without a deploy, then run on a dataset and its scores compared with production. Overall the product works by ingesting OpenTelemetry data. You create a project API key, configure a telemetry.dev integration or an OTLP/HTTP exporter, run an instrumented request, and open its trace in your project. Cost is calculated at ingest from reported token usage and a model-pricing catalog, with cache-read and cache-write tokens using separate rates when available, reasoning tokens not charged separately, and an explicit span cost taking precedence; without matching model pricing or token usage, computed cost is unavailable. Trace detail depends on what your instrumentation emits, and each integration guide describes its coverage. Ingestion is metered in ingestion units, where each normalized span, OTLP log record, or metric data point uses one unit. The benefits follow from that structure. Instead of stitching together provider dashboards and application logs, a team gets one view of a run with durations per step, so a tool timeout or a slow model call is visible in context. Cost is attributed by model, provider, and environment, which makes the source of spend changes observable. Failure rates and p95 latency for MCP tools show which tool calls are unreliable. Alerts on budgets, agent loops, and errors surface incidents before users report them, and per-environment prompt capture settings give a way to keep recognized prompt and response fields out of storage. Typical scenarios map directly to the interface. A developer debugging a failing support agent opens the trace, scans spans for the longest duration, and inspects the selected tool span and its attributes. An engineer investigating errors in production filters traces by status and environment to isolate affected sessions. A team watching spend compares the cost breakdown across models and providers over the last seven days and drills into the traces contributing to it. An MCP-based integration checks which tools have the highest failure rate or p95 duration. An on-call engineer relies on email alerts for cost spikes, budget thresholds, agent loops, and tool errors. A team preparing a prompt change tests it on a dataset and compares scores against production on the same traces. Development teams connect through a first-party TypeScript SDK or Python SDK, or through any exporter that sends OTLP/HTTP. Model provider integrations cover OpenAI, Anthropic, Claude SDK, Claude Platform, Google Gen AI, Amazon Bedrock, OpenRouter, and LiteLLM. Framework integrations cover Vercel AI SDK, TanStack AI, LangChain and LangGraph, CrewAI, Pydantic AI, Eve, and Model Context Protocol. Coding agents can be traced as well, including Claude Code, Cursor, opencode, OpenClaw, Hermes Agent, Oh My Pi, and Pi, and the platform exposes a read-only MCP server so an AI agent can investigate errors, latency, and cost through telemetry.dev itself. The Free plan costs $0 with no credit card, includes 30,000 ingestion units per month, 14-day retention, one project, and two seats. A demo call is offered for teams that want tracing, cost tracking, and privacy controls walked through for their own frameworks and providers. In short, telemetry.dev turns an AI agent run into a single inspectable trace of model calls, tool steps, and retrievals with duration, tokens, cost, and errors attached to each span, and layers search, cost and reliability overviews, MCP analytics, alerts, and prompt capture controls on top of OpenTelemetry data you already emit.

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Norah Studio is a creative studio for exploring, transforming and reimagining your music on Mac, iPad and iPhone. It separates any song into vocals, drums and instrumental on the device in your hands, then lets you loop it by bars, change its tempo and key, add effects, take the stems with you, follow chords, build harmonies, turn melodies into instruments and export audio or MIDI. It is aimed at anyone who wants to take a song apart — to learn a part, build a backing track or pull a drum loop — and it works as both a practice room and a remix desk. Recorded music usually arrives as one finished mix. The vocal, the drums and the band are already blended together, which is exactly what you want for listening and exactly what gets in the way when you want to learn a part, play along in a different key, sing over the record's own band or sample a drum groove. Norah Studio's answer is to split the song into its parts first: vocals, drums and instrumental, each in its own lane, ready to be soloed, looped, reshaped and exported. Because streaming services lock their files to their own apps, the app works with audio files you already have that the system decodes — MP3, M4A and AAC, WAV, AIFF, FLAC, CAF and more, up to ten minutes — rather than with tracks from streaming catalogues. The workflow starts with Open, split, play. You open a song and three tracks come out. It starts playing from the first second while the rest is still being separated, so there is no queue and nothing to wait for. The app reports 1.1 s to first sound on a Mac and 1.5 s to first sound on an iPhone 16, measured in October 2026 with a song of 2 min 18 s, and 0 bytes uploaded, ever. There is no account to create and no upload step: the models that take the song apart travel inside the app and run on your own chip. The chosen lane can be soloed on its own, so you can hear just the vocal, just the drums or just the instrumental. Once the song is apart, the practice tools take over. Loop by bars gives you a bar grid and named sections, so you can loop exactly one section or any number of bars instead of guessing at a selection by ear. Tempo and key let you slow a song down without the pitch dropping, or move it to your range without changing its speed. Chords are read above the vocal, one per beat group, together with the key and the tempo, so you can see the harmonic shape of the passage you are working on. The app labels tempo, key and chord results as estimates — good enough to loop by bars and play along. Each lane has its own processing chain. Norah Studio offers 24 effects and 4 visualizers, an EQ on every lane, and the effects run in the order you choose. MIDI Learn maps any control to a knob on your controller, so the parameters you reach for most can sit under your hands rather than on the screen. Melody Synth takes the vocal melody and plays it with 19 instruments, from synth leads to a grand piano, turning a sung line into an instrumental part. Harmonize adds a choir that follows the chords, with six voicings — Duet, Trio, Pad, Choir, Angels and Double. Export closes the loop. You can take the three stems with their effects, and the melody as MIDI, so the work you did inside the app can move into whatever comes next. The Library keeps recent songs and reopens them with everything as you left it, so a session can be picked up later rather than rebuilt from scratch. Reopening a song you already took apart is always free. Norah Studio is the same engine, native on each device. On a Mac it is the whole desk, with a keyboard and a mouse; on an iPad it is the whole desk under your fingers; on an iPhone it is the desk folded upright for one thumb. Your songs never travel between them: each device separates on its own, offline. There is no server and no analytics, and the App Store privacy label reads Data Not Collected. Because the models are inside the app, the first song works in airplane mode, and nothing leaves your device. The app is also candid about its limits: no separation is perfect, and on a dense mix a little of the voice can stay in the instrumental; tempo, key and chords are estimates and are labelled as such. The practical benefit is that a finished recording becomes material you can work with. A song you own can become a slower version in the same key for practice, a version without the singer for karaoke, a drum loop lifted from a track you like, a backing track built from the record's own band, or a vocal melody handed to a piano. Because everything happens on the device, the work is private by default and available without a connection — on a plane, in a practice room, wherever the song and the device are. Because every feature is in the free version, the whole studio can be tried on five songs before deciding about Pro. The app suggests concrete places to start. Remove vocals takes the singer out of any song you own, leaving the record's own band. Slow down a song lets you practise at your speed in the same key, with looping to repeat the passage you are learning. Karaoke gives you the record's own band in your key. Beyond those, the separated lanes make it possible to learn a part against the lane that carries it, pull a drum loop for reuse, or turn a vocal melody into an instrument line and export it as MIDI. Norah Studio runs on a Mac with Apple silicon on macOS 15 or later, an iPad on iPadOS 26 or later, or an iPhone on iOS 26 or later. The free version includes every feature and five songs; Pro adds unlimited songs and nothing else, with Pro Monthly at €7.99 per month (cancel any time) and Pro Lifetime at €69.99 once (never renews). Prices are shown in your country's currency on the App Store, and Pro bought on one device also covers your other devices signed in with the same Apple Account. In short, Norah Studio turns any song you own into parts you can play with — separated, looped, retuned, processed and exported entirely on your own device, with no account, no upload and no connection required.

Nocto is a native macOS app that turns the black notch at the top of a MacBook display into a small, interactive control center — the product itself calls it a Dynamic Island for the MacBook's notch. Rather than staying empty, the notch grows into a panel when there is something to show: the song that is playing, the next meeting on the calendar, files parked on a shelf, items recently copied to the clipboard, or battery status for the Mac and connected AirPods. It shrinks back when it is not needed. Nocto is aimed at MacBook owners running macOS 14 Sonoma or later who want everyday information and quick actions within reach without opening another app or managing another window. It is an independent Mac app and is not made by Apple. The starting point is the notch itself. On a MacBook the notch is a strip of black space that, for most people, does nothing at all — it occupies part of the screen and gives nothing back. Nocto treats that space as an opportunity: a place where the small, frequent things that interrupt a working day can live briefly instead of demanding a window. A single day involves checking what a song is called and skipping it, remembering a call that starts in ten minutes, holding onto a file that needs to be dragged somewhere else, reusing something copied a moment ago, and glancing at battery levels. Each of those normally means switching context, and each is a place where Nocto puts the answer next to the notch instead. The music mode is built for whatever is playing. Nocto supports Apple Music, Spotify, YouTube, browser-based players and podcasts — anything macOS reports as Now Playing appears in the notch, browser tabs included. Pointing at the notch reveals artwork, playback controls and a scrubber, so the current track and its progress are visible without switching to the player. Gestures sit on top of that: swiping sideways over the music changes songs, next on a right swipe and previous on a left swipe, while scrolling over the progress bar seeks through the track. The point is that hearing a song you want to skip no longer requires hunting for the app that is playing it; the control is already at the top of the screen, in the space the pointer passes anyway. The meetings mode is where Nocto is most opinionated. Today's meetings appear in their calendar colours, so work and personal events look the way they do in Calendar. Ten minutes before a call begins, a countdown appears next to the notch. When the meeting actually starts, the notch opens on its own and offers a Join button. Nocto finds the call link inside the event and recognises Zoom, Google Meet, Microsoft Teams, Webex and FaceTime, so joining does not require opening the calendar, finding the event and looking for the right link in the notes. The calendar is read on the Mac and is never uploaded. For anyone who joins several calls a day, the countdown works as an early warning and the Join button as the single action that matters at the moment the call begins. The shelf gives files a place to wait. Files, images, links and text can be dragged onto the notch to park them there, then retrieved after switching to another app by dragging them back out. Dragging an item onto AirDrop sends it straight to an iPhone or another device. It behaves like a temporary holding area on top of the screen: the file does not need a folder, it does not need a window kept open, and it does not need to be found again through Finder. Dragging out works anywhere, into any app, so the shelf can be used to move an image into a document, a file into an email, or a link into a message without saving anything first. Clipboard history turns what was copied into something visible. Copy a hex value and Nocto shows the colour; copy a screenshot and it shows the picture; copy a link and it shows the site rather than the tracking. Items can be filtered by text, links, colours and images, clicked to copy again, or dragged directly into any app. Links are cleaned before they are pasted: tracking parameters such as utm, fbclid and si are removed. Privacy is handled explicitly — history is kept in memory only and is cleared when the app quits, copies that password managers mark as private are skipped, and clipboard history is off until it is turned on. Battery information appears once and then disappears. Plugging in shows how long until the battery is full; connecting AirPods shows the level of the left bud, the right bud and the case. On the Mac, Nocto covers charging status, time to full and low battery. The design choice here is deliberate: the notch shows the information for a moment and then gets out of the way, rather than leaving a persistent indicator on screen. The information arrives when it is relevant — on connection or on power — and does not compete with anything afterwards. Beyond the five main modes, Nocto collects a set of smaller behaviours. Clean links remove utm, fbclid and si tracking before pasting. Drag out anywhere means files, text, links and images can be pulled from the notch into any app. The open notch can be shaped — width and corner radius are adjustable to match the screen. Nocto hides in full-screen apps, has no dock icon and opens no windows to manage, so it does not add to the window clutter it is meant to relieve. macOS 14 Sonoma or later is required; MacBooks with a notch get the full effect, while other displays get a small virtual notch at the top of the screen. Nocto is driven by the trackpad and a few consistent gestures. Resting the pointer on the notch makes it open, so a peek requires no click. A swipe down pins the notch open while work continues; a swipe up dismisses it or clears a notice; sideways swipes over the music change songs. Everything else follows the same idea of small, reversible interactions. The app is native and described as light — about 4 MB — and it is signed and notarized by Apple. Data stays local: music, calendar, clipboard and files are read on the Mac and remain there. There is no account, no analytics and no tracking, and Nocto only goes online to check the license and look for updates. Permissions are requested only when they are used: calendar for meetings, Bluetooth for AirPods, and permission to read Music or Spotify. The outcome Nocto is selling is fewer interruptions, not more features. Music control, the next meeting, a parked file, the last thing copied and the battery level are all things a person checks many times a day; putting them at the notch means checking them does not require leaving what is being worked on. The countdown and the Join button mean a call is harder to miss. The shelf means a file can be held without a desktop full of windows. Clipboard history means something copied can be found again instead of re-copied. And because the notch hides in full-screen apps and has no dock icon, all of this arrives without adding another thing to manage through the day. Concrete workflows follow from those behaviours. During focused work, a swipe on the notch skips a track without opening Music or Spotify. Ten minutes before a standup, the countdown appears, and when the call starts one click joins it in Zoom, Meet, Teams, Webex or FaceTime. A screenshot copied for a design review can be found again in clipboard history, or dragged straight into a document. A file pulled from Finder can be parked on the shelf while switching to another app, then dragged out — or dropped on AirDrop to reach an iPhone. A hex code copied from a design tool shows its colour before it is pasted. And on plugging in a charger or connecting AirPods, the levels appear and then disappear. Nocto is aimed at people who spend the day on a MacBook — particularly those who join several meetings and switch between apps constantly — and who would rather have small pieces of information at the edge of the screen than another window. It requires macOS 14 Sonoma or later. Pricing is a one-time purchase: $9.99 at launch, down from $14.99, and not a subscription. A license covers everything on the page on up to three Macs plus all 1.x updates. There is a 7-day free trial with no card and no account, a 14-day refund with no questions asked, and secure checkout through Lemon Squeezy with the license key arriving by email. A license can be moved between Macs by deactivating it in Settings → License. After the trial, music in the notch keeps working for free; meetings, the shelf, AirDrop, the clipboard and battery updates need a license. Nocto's proposition is narrow and concrete: the notch is already there, and it can do something. By putting music, meetings, files, clipboard and battery into that black space — with a countdown before calls, a one-click Join, a shelf with AirDrop, a clipboard that shows colours and clean links, and information that appears once and leaves — it turns an unused part of the screen into a genuinely useful one. One payment, local data, no account, and music free forever.

XWiki MCP Server is a free, open-source, self-hostable extension that connects an XWiki knowledge platform to the AI agent of your choice. XWiki itself is a versatile knowledge platform for teams of all sizes, used to create wikis, documentation spaces, intranets, procedures and knowledge bases, where knowledge is organized in real time, collaborators work together, information is found faster and access is managed. The MCP Server adds a connection layer so that AI agents such as Claude Code, Codex, OpenCode, Pi, GitHub Copilot and MS Copilot — or anything else that speaks MCP — can work directly with that accumulated knowledge. Its stated purpose is to give your AI your wiki's knowledge while keeping the control: the extension is optional, installed by you and configured by you, rather than being a vendor-side feature that operates on your documentation without your say. The extension arrives at a specific moment for XWiki. XWiki has been around for over 20 years, which the team notes is plenty of time for organizations to build up a lot of knowledge in their wikis: documentation, internal procedures, and custom applications full of structured data. As AI agents become ingrained in daily workflows, the makers started thinking of ways to make all that internal knowledge available to them. The positioning is blunt: when knowledge grows, other tools start to break, and XWiki scales. A related problem raised in the launch discussion is that knowledge systems can eventually become write-only, and connecting them to an agent of choice may help surface what is already stored. The other half of the problem is control. The makers point out that Confluence announced this year that it will train AI on user documentation, something they completely disagree with, and that is why the XWiki MCP server keeps control in the hands of users, with the freedom to host as needed. Once an agent is connected, the first capability is reading. You can use the extension to query your knowledge base to get answers, so an agent can find out about an old project decision rather than a person digging through pages manually. Because the agent navigates the wiki with the same permissions as your user account, what it can see mirrors what you can see. Write, delete and page creation tools are destructive and are disabled by default, which the makers describe as the default starting point so no accidental oopsies happen; they can be turned on by ticking a box on the configuration page. That means a team can begin with read access, observe what the agent does with its wiki, and only later decide to allow changes. When write operations are enabled, the scope goes well beyond reading. The extension lets agents create and edit pages, update attached documents, and even build a small internal app. Since XWiki supports structured data, agents can work with objects and classes, not just read and write pages — the makers call this combination of XWiki and AI especially good for developers. Working with structured data means an agent is not limited to free text: it can operate on the data models that custom applications inside the wiki are built on. The launch description lists three things you can already do with the extension: query your knowledge base to get answers, create and edit pages, and work with structured data directly in XWiki. Control is enforced at several levels. The MCP endpoint is an OAuth protected resource: it advertises the XWiki OIDC Provider through the standard RFC 9728 metadata and a WWW-Authenticate header on a 401 response. Clients such as Claude Code or Claude Desktop discover it, run the normal OAuth authorization code flow against the OIDC Provider, and send a Bearer token; XWiki validates the token and the agent acts as that user, with that user's rights. The XWiki OIDC Provider extension must be installed for this. On the content side, XWiki has several deconflicting mechanisms: when collaborative editing is disabled, pages are locked while someone edits them; when collaborative editing is on, page saves are propagated to all users; if conflicts do appear, you choose whether to merge the changes or keep one or the other. Unlimited version history lets you revert a page to a previous version, and reverting restores the whole page — as a workaround for partial reverts you can preview any version and copy-paste changes into the latest version. Scripting capabilities, on top of being open source, make it possible to define and use your own deconflicting strategies. Overall, the product is an MCP server: an extension that speaks the Model Context Protocol, the interface through which AI agents call tools on an external system. Anything that speaks MCP can be connected, and the makers name Claude Code, Codex, OpenCode, Pi, GitHub Copilot and MS Copilot as examples. Installing it is a choice rather than a given: you decide if and when to install it, which model to connect through it, and where everything runs. You can self-host everything — XWiki, the MCP server and the AI model — if that is what you need. Because XWiki and the MCP server are fully open source, anything missing from the MCP layer is probably already doable through the REST API, which agents are described as quite good at using; if something is missing from the API, the open-source project can be extended or the changes contributed upstream. There is also a repository of skills and knowledge for agents (xwiki/xwiki-dev-llm on GitHub), an installation guide, source code on GitHub (xwiki-contrib/ai-llm) and feedback tracked on JIRA. The stated benefits follow from that design. Teams get an agent that can find out about an old project decision, update pages and attached documents, or build a small internal app, without exporting their knowledge to a third party. Discoverability and efficiency improve with the help of agents while the platform remains human-focused, which the makers describe as the focus: not turning the wiki into an agent-only system, but making what people wrote easier to reach. Documentation owners keep control rather than handing it to a vendor, and administrators keep the ability to run everything on their own infrastructure. Starting with read access gives a team room to see what the agent does with its wiki before enabling writes, and unlimited version history plus page-level conflict handling provide a route back if an agent edit goes wrong. Concrete scenarios named in the launch material include asking an agent about an old project decision recorded in the wiki, having it update pages, having it update attached documents, and having it build a small internal app. More broadly, XWiki is used to create wikis, documentation spaces, intranets, procedures and knowledge bases, and the MCP Server exposes that material to an agent of choice, so an organization's documentation, internal procedures and custom applications full of structured data become reachable by that agent. The makers also frame this as a fit for agentic workflows: one commenter noted they were using Confluence org-wide and that it was not coping with agentic workflows. The extension can be explored for use cases the team has not yet thought of, and the makers explicitly invite experimentation. The extension targets teams and organizations already using XWiki, and by extension any team that treats documentation as a long-lived asset — the wiki, intranet, procedure and knowledge base use cases XWiki serves. Named integrations are the AI agents themselves: OpenCode, Pi, Codex, Claude Code, GitHub Copilot and MS Copilot, plus anything else that speaks MCP. Two XWiki extensions are relevant: the XWiki OIDC Provider extension, required for the OAuth flow, and the MCP Server extension itself, installed and enabled as needed. The product is free and open source, listed with free options on Product Hunt and launched under Productivity, Open Source and Artificial Intelligence tags; the launch reached 175 upvotes and 38 comments with a #4 of the day badge for October 30th, 2026, while XWiki itself is rated 4.8 from 6 reviews. The takeaway is straightforward: XWiki MCP Server gives AI agents access to a wiki's knowledge — reading, writing and working with structured data — while every decision about installation, connection, hosting and permissions stays with the organization that owns the documentation. It is free, open source, optional and self-hostable, with destructive operations off by default and the agent bound to the rights of the connecting user. For teams whose knowledge has grown beyond what older tools can carry, that combination of reach and control is the point.

SpeakON Agent is a voice-first AI agent presented by SpeakON. Its stated purpose is to turn natural speech into real work: the user speaks naturally, the Agent understands the request, finds the right context, and prepares Actions for the user to review and confirm. The page describes it as part of a voice-first AI communicator experience, shown alongside the SpeakON device and an iPhone app download. It is designed for people who want to move from spoken intent to structured tasks and coordinated actions across connected tools rather than starting every workflow with typing. The core promise is to say what you need, review what the Agent prepares, and move it forward after approval. The page frames the product around the idea that the next workflow does not have to start with another keystroke. The problem context is repeated typing and fragmented work: users have ideas, meeting Notes, and requests that need to become tasks, messages, calendar events, reminders, and updates across different apps. SpeakON Agent is presented as a way to turn natural speech into structured work while keeping the user in control as the Agent prepares the next steps. It can start with the current Note and use relevant details from saved Notes and connected apps, so the spoken request is not handled as isolated text but as work that can be prepared across the tools a user already uses. A central capability is handling one request that contains multiple Actions. The page says Agent understands the people, timing, context, and different Actions inside one natural request. In the illustrated flow, a spoken request asks for a one-hour product discussion tomorrow at 10:30 a.m. in Conference Room 203, a Slack notification to #productteam, an email to Daniel from Marketing with an invite, and a reminder to review the product brief. Agent identifies four tasks, finds the right context, prepares the calendar event, Slack announcement, Gmail draft, and reminder, and then waits for review. This shows how a single natural request can become a coordinated set of prepared actions rather than separate manual steps. Agent starts with the user's current Note and turns it into a structured Task. It can also use relevant details from saved Notes and connected apps. The page includes an example of finding related context in saved Notes, such as asking Agent to find customer feedback Notes from the past month and summarize the biggest issues. This context-finding step matters because the Agent is locating calendar availability, a room, a contact, and relevant Notes before preparing the task. The page describes this as finding context: calendar availability, the room, Daniel's contact, and the relevant Notes. That context is then used to prepare the event, announcement, invitation, and reminder that the user can review. Connectors bring in context from the apps a user connects. The page lists Gmail, Google Drive, Calendar, Slack, Reminders, Contacts, Notion, Evernote, and Obsidian, and says more integrations are to come. These connectors allow the Agent to prepare actions that touch tools people already use for communication, scheduling, storage, and note-taking. In the illustrated workflow, Calendar provides availability, Slack receives the #productteam announcement, Gmail holds the email draft to Daniel, and Reminders stores the personal reminder. The Connectors section is explicitly about bringing context from connected apps into the Agent's work, so the Agent can prepare tasks with details that come from those sources. You decide what happens next. The page says Agent moves things forward and checks with the user when it needs approval. The user can review the details and make changes along the way. Messages stay unsent until approved, and only the actions the user approved are completed. In the example, an email to Daniel is ready for review, and the page shows a Voice edit option before Confirm. The Agent waits for approval before sending the email. This approval step is central to the product's workflow because it keeps the user in control of what is sent and what is completed, even when multiple Actions have already been prepared. The overall methodology is described as a six-step flow: Request, Understand, Find Context, Prepare Task, Review, and Complete. The user speaks naturally and gives Agent the full request. Agent identifies the people, timing, and every action. It finds calendar availability, the room, Daniel's contact, and the relevant Notes. It prepares the event, Slack announcement, invitation, and reminder. The user reviews the email to Daniel before Agent sends it. After the user confirms, Agent completes the approved actions. This Request-to-Complete flow is the product's described approach: natural speech goes in, structured context is gathered, Actions are prepared, and the user approves before completion. The stated outcomes are that approved actions are completed: the meeting is scheduled, the team is notified, Daniel is invited, and the reminder is set. The page emphasizes that only approved actions are completed and that messages stay unsent until approved. For users, this means the Agent can prepare multiple parts of a workflow while the user retains final review. The page also presents the product as a way to stop typing and let spoken ideas move forward, with creator quotes about less typing and getting things done. The benefits are tied to voice-first input, context from Notes and connected apps, prepared Actions, and user approval rather than unapproved sending. The page shows several concrete use cases. Agent In Action includes moving a launch forward by scheduling a launch check-in, inviting Maya, posting the agenda in #launch, and setting a reminder to review the rollout checklist. It includes keeping a shipment moving by finding the latest Rotterdam shipment update, summarizing the delay, drafting a customer email, notifying #operations, and setting a follow-up reminder. It includes following up after a property showing by emailing Olivia the property details, scheduling a second viewing for Saturday at 2 p.m., and reminding the user to send disclosure documents. It includes preparing a renewal response by finding the latest renewal proposal from Marcus, comparing it with a previous Note, summarizing what changed, and preparing a reply. The product discussion example and the customer feedback Notes summary example add further workflows. The page frames SpeakON Agent for iPhone users and people who want to turn speech into work without typing every step. It is presented alongside the SpeakON device and an iPhone app download, and press coverage describes a MagSafe AI communicator and a voice-first interface. The Connectors list includes Gmail, Google Drive, Calendar, Slack, Reminders, Contacts, Notion, Evernote, and Obsidian, with more integrations to come. The page does not list detailed Agent pricing or plan tiers; it does show a Halloween Sale with 15% OFF SpeakON using code HW15, and it mentions AI Agent credits as a Creator Challenge reward. Those are the only pricing-related details present in the content. In summary, SpeakON Agent's primary value proposition is turning natural speech into real work with an agent that understands Notes, connects tools, and prepares Actions for review and confirmation. It combines voice input, Notes-based context, connectors, multi-action preparation, and an approval step so users can move from spoken intent to completed, approved work. The page presents this as a voice-first workflow where the user says what they need, reviews what Agent prepares, and decides what happens next.

ilse is a visual feedback layer for AI code agents. It puts a toolbar on top of the React app you already have running in your browser, letting you click an element, write a note, or drag, resize, and tweak things directly on the live interface. Those instructions then reach the coding agent you already use — Claude Code, Codex, Cursor CLI, Gemini CLI, or any agent that speaks MCP — already pointing at the right spot in your code. ilse is built for developers and designers whose app already exists and who want to refine its interface without leaving it, on their own machine, using their project's own tokens and components. The problem ilse addresses is captured in the maker's own story. He had been a designer for over nine years and found himself spending more time in code than in Figma. He would ask Claude Code to fix a button, describe in words what he saw on screen, and the agent would edit the wrong file. As he puts it, the agent writes well, but it does not see what he sees — it was missing a designer's eye. So ilse was built so he could point at the real app, let ilse gather the context, and have the agent change the code. The site draws an explicit line between ilse and generators such as Lovable, Claude Design, and Replit: those are described as great for starting from scratch in their own environment, while ilse is for when the code already exists and lives on your machine. The core of the product is Design Mode. You click an element or select some text and write what should change. The note reaches your agent already pointing at the right spot in the code, so there is no loose "crooked button" description to interpret. That is the point-and-say workflow: the instruction is anchored to a specific element in a specific file rather than to a vague verbal description of a screen. The panel extends that same idea with gestures. Move, resize, edit text, and choose whether a change applies only to this instance or to all of them. There is also a Draw control and Screens, which is marked as new. Drag lets you reorder elements, and you can tweak spacing and colors using your project's own tokens. Simple tweaks land instantly with no AI involved, spending few or zero tokens, while anything that goes beyond a class mapping is handed to your agent to change in the code. A component library is listed as a Pro capability. Before calling your agent, ilse does the search for it. What the agent receives includes the element's file and line with only the snippet that matters, the component that wrote the JSX and where it is used, your project's design tokens, your note plus the panel tweaks and gestures you made, what a test found (the journey step, the screen, and the element), and any pasted references you optionally add. This context packaging is what replaces the agent guessing at which file to edit. Test Mode, available in Pro, is the second major capability. You describe what a person does — the site's example is "buy the lamp" — and ilse walks the flow in the real app on its own, then shows where it got stuck and reports what breaks. Pro is described as test mode with accessibility, errors, layout, the memory, and journeys, plus the Library's hands: dragging components onto the page, their copies, and taking your design system to other tools. A Review Mode is listed as coming soon: reviewers would leave notes on your product's own link without needing an account, and those notes would reach both you and the agent so the loop starts again. The overall workflow is deliberately lightweight. You run npx ilse-design@latest in your project folder; Next.js, Vite, CRA, Remix, and Astro are detected on its own; the app opens at the usual address with the bar on top. There is no extension, no app, and no account. ilse runs on your machine as a CLI and an MCP server, and the agent you already pay for is the one that changes the code. The loop is refine, test, and repeat. Privacy is a structural part of the design rather than an add-on. Everything runs on your computer and ilse has no server to send your code to. The code, the context, and the history stay local. ilse never touches a key — your agent is the one calling the AI. And the bar only exists on the dev server, so the final production build ships nothing. ilse describes itself as an agentic WYSIWYG rather than a faster generator, and it does not generate anything from scratch. The site's comparison table frames the difference: generators start from a blank prompt, while ilse starts from the app that already runs; generators produce code generated anew, while ilse edits your repo in place; generators apply whatever design system the tool picks, while ilse uses your tokens and components; and generators use the platform's AI on its credits, while ilse uses the agent you already use. A testimonial on the page from Huxley Dias of Punk Metrics states that if you develop locally, this is excellent for refining interfaces, and calls it way better than Claude Design, Replit, and Lovable. The benefits follow directly from that approach. Because simple tweaks map to a class and land instantly, common adjustments cost few or zero tokens. Because the note arrives with the file, the line, and the component context attached, the agent no longer edits the wrong file. Because your own tokens and components are part of the context, refinements stay inside your design system instead of introducing a parallel one. And because everything runs locally against your dev server, your code and history never leave your machine. Concrete use cases described on the site include clicking any element in a demo shop interface to change it, reordering items by dragging, resizing elements, editing text in place, tweaking spacing and colors against your tokens, and pasting optional references into the note. Test Mode covers journeys such as buying a lamp, where ilse walks the flow and identifies the step, screen, and element where it got stuck. Design Mode covers the everyday case of pointing at a crooked pixel and telling the agent what to do about it. ilse works with any React project that has a dev server. The listed frameworks are Next.js, Vite, Remix, Astro, and React, with CRA mentioned as auto-detected. Styling and component systems listed are Tailwind, shadcn/ui, Radix, and MUI, and the project's design tokens and components go into the context. Supported agents include Claude Code, Codex, Cursor CLI, Gemini CLI, Cline, Windsurf, GitHub Copilot, Goose, and any agent with MCP. Pricing is free for the core tool under the PolyForm Shield license, which allows free use including at work as long as you do not turn it into a competing product. A founder pre-sale offers Pro at US$49 instead of US$79 as a one-time payment, limited to the first 10 founders, with Pro shipping by November 30, 2026, a refund if it does not, renewals locked at the founder price, a roadmap vote, a direct channel with early builds, and a 30-minute 1:1 setup. The Pro purchase includes 12 months of updates counted from Pro's launch, after which the version you have stays yours. In short, ilse's value proposition is precision: you show the agent what you mean by pointing at the real running app, ilse gathers the code context so the instruction lands in the right place, and your existing agent makes the change in your repo with your tokens — all locally, with nothing shipped to production.

Kestrix is a CI/CD control plane that orchestrates pipelines across multiple repositories using declarative HCL configuration. Rather than scattering CI logic across dozens of separate pipeline files, teams define repositories, tasks, and their relationships in one place. Kestrix resolves those definitions into a DAG, or directed acyclic graph, and executes tasks in dependency order. It is aimed at engineering teams whose delivery spans several repositories, whether a monorepo or a multi-repo estate, and who need builds to run conditionally based on what actually changed. The project describes itself as a repo-agnostic CI/CD orchestrator with HCL-based DAG pipeline definitions that supports OR/AND/NOT trigger logic for cross-repo conditional builds. The problem Kestrix addresses is configuration sprawl. When CI logic lives in dozens of `.gitlab-ci.yml` files, keeping trigger conditions consistent across repositories becomes difficult, and it is hard to see which services depend on which. The README states the intent directly: instead of scattering CI logic across dozens of `.gitlab-ci.yml` files, you define everything in one place with clear trigger conditions. A related problem is wasted compute, since pipelines that run unconditionally rebuild things that never changed. Kestrix's answer is that a task runs only when the repositories its trigger names have changed, so cross-repo conditional builds are expressed declaratively instead of being hand-coded in each repository's pipeline file. Pipelines are written in HCL, typically in a `pipeline.tf` file inside a pipeline directory. Two concepts carry most of the weight. A `ci_repo` maps a repository name to a path (for a monorepo) or to a Git URL, so the same definition style works whether the code sits beside the pipeline or in a separate remote repository. A `ci_task` defines what to run, when to run it, and what it depends on. The README shows a `ci_task "deploy_staging"` block whose trigger names two repositories joined with `||`, whose repo is `infra`, and whose script runs `terraform apply -auto-approve`, illustrating that a task is essentially a trigger expression plus a target repository plus a command. Trigger expressions are the heart of the product. Kestrix supports references such as `repo.auth_service.changed`, negation such as `!repo.docs.changed`, and the `||` and `&&` operators, which together provide OR/AND/NOT logic for cross-repo conditional builds. The engine resolves these expressions into a DAG, so dependencies determine execution order and a task fires only when the repositories its trigger names have actually changed. The CLI exposes this directly: `kestrix resolve` resolves triggers against a supplied list of changed repositories, for example `--changed auth_service,billing_service`, so you can inspect what a given change set would set off before running anything. `kestrix validate` checks a pipeline definition, and `kestrix variables` prints resolved variables for a repository and environment, which helps where pipeline definitions use variable interpolation. Operations are visible in a web UI. Pipeline runs appear in a global runs list, with a new-run view that combines a project selector with the DAG, and projects have list and detail pages with URL routing. Task logs stream live into a React Flow UI, so the graph itself becomes the monitoring surface. Runs are persisted in PostgreSQL, and a single task can be re-run from the run detail view rather than restarting the entire pipeline. Execution can be delegated to remote runners, which the project describes as making only outbound HTTPS requests, meaning workers poll rather than accepting inbound connections, which suits networks that will not expose an inbound port to the internet. The server side ties these pieces together. `kestrix serve` listens on port 8080 by default and refuses to start without PostgreSQL, a credential master key, and a JWT signing key. Kestrix serves no request until an administrator account exists: the UI opens directly on a "Create the first administrator" form when it detects a fresh instance, and a scripted deployment can create the same account from the CLI with `kestrix user create --role admin`. The API is protected by bearer tokens obtained from the login route, with `/api/health` as the one open route. Project registration is not a metadata write, because the server validates `repo_url` by cloning it at `repo_ref` and parsing the pipeline directory, so the remote must be reachable from the server; a private repository needs a credential created first and referenced as `credential_ref`. An unreachable URL answers `400` with the Git error and the project is not created. Benefits follow from that design. Because triggers are declarative, a change to an authentication service or a billing service can cascade to the infrastructure tasks that depend on it without anyone maintaining per-repository rules by hand. Because tasks are skipped when nothing relevant changed, work and compute are reserved for real changes. Because webhook verification fails closed, a delivery is accepted only if it verifies against the matched project's own `webhook_secret` or the global `KESTRIX_WEBHOOK_SECRET`, and with neither configured every delivery is rejected with `401` and nothing is recorded, so the control plane does not act on unverified input. Central definitions also give teams one place to read, review, and audit pipeline behaviour instead of reconstructing it from many files. Concrete scenarios appear throughout the documentation. A team registers a monorepo project by supplying `repo_url`, `repo_ref`, `pipeline_dir`, and `pipeline_file`, then reads it back to see the sources it now listens to. A developer runs a pipeline locally in dependency order with `kestrix run --changed kestrix ci/pipeline.tf`, or resolves triggers for a change set with `kestrix resolve`. The example task deploys staging with Terraform when the auth or billing service changes. Webhooks come from the Git host, for example GitLab under Settings then Webhooks, pointing at `/webhook` with the project's secret as the token, and a webhook must be registered on every source repository: a project listens to its own repository and to each `ci_repo` in its pipeline that declares a `url`, and a push to one of those secondary repositories triggers the project only if that repository also posts to `/webhook`. Kestrix is built for developers and platform teams running multi-repository or monorepo CI. The repository shows the stack: Go, with the badge stating Go 1.25, for the server and CLI; HCL for pipeline definitions; PostgreSQL for persistence; React Flow for the DAG UI; and Docker for distribution, with released images pulled from the GitLab Container Registry and a production deployment that defaults to Docker Compose with PostgreSQL, TLS, backups, and upgrades. A Terraform and OpenTofu provider, listed at version v0.1.0, lets teams manage projects and configuration as code. Integrations described in the content include GitLab dispatch and webhook verification through the `--dispatch`, `--gitlab-token`, `--secret`, and `--method gitlab` flags, Git webhooks generally, repositories addressed by a `repo_url` and `repo_ref`, and a GitHub push mirror used for releases and distribution. On licensing, the project is licensed under PolyForm Noncommercial 1.0.0, with a separate `LICENSE-COMMERCIAL.md` covering the paid commercial license described in `PRICING.md`. Kestrix's value proposition is centralization plus conditionality: one HCL definition per pipeline, resolved into a DAG, with tasks that run only when the repositories named in their triggers change. Combined with a live DAG interface, outbound-only remote runners, and a CLI that resolves and validates definitions without a server, it gives teams an inspectable control plane instead of a sprawl of pipeline files.

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