Stack Overflow

Your AI pilot works perfectly, but the minute it hits real-world data, hallucinations start and trust collapses. The truth is, most enterprise AI pilots never make it to production, and it's because you're missing an important layer in your AI tech stack—one that brings your company's important context to your AI tools. Stack Internal is the trusted knowledge layer for enterprise AI, turning raw data into human-validated context your models can rely on. Learn more about Stack Internal: https://lnkd.in/eakWmHAW

🎙️ We sit down with James Gosling, creator of Java at Sun Microsystems, to talk about the early networking friction that drove the need for interconnectedness, the stringent reliability requirements of consumer electronics companies that inspired the Java virtual machine, and the evolution of garbage collectors that allow Java to handle large-scale concurrent memory management. https://lnkd.in/e_vQfu9w

83% of respondents from this year's Developer Survey report using at least one AI tool, with 66% using AI coding assistants or coding agents. Meanwhile, over a quarter of developers are using AI agents or automated workflows. If you're using agents and autonomous workflows, developer or otherwise, we want to hear from you! Share a bit about how you're working with agents and get a chance to participate in our paid research interviews on agentic workflows. https://lnkd.in/ec8WqVSV

🎙️ Live from LDX3 by LeadDev, we chat with Nivia Henry, an engineering leader most recently at Netflix, about the reality of the supposed SaaS apocalypse, the complex calculus of the modern build-versus-buy decision in the era of AI, and the critical importance of understanding system architectures to manage long-term infrastructure costs. https://lnkd.in/ec_ESJgh

According to our 2026 Developer Survey, 55% of devs think that AI performs well or very well at complex tasks—although 39% think it's only good, not great. We want to know what kind of complex tasks you're handing off to your AI tools, especially in autonomous agent workflows. If you're using agents to get work done—especially at a smaller company—we want to hear from you. We're recruiting for paid research interviews across engineering, product, support, and more. https://lnkd.in/ec8WqVSV

You need verified knowledge to build. You don't need to be wasting your time searching for it. This month on Stack Internal, we're introducing everything from Scopes—a new way to deliver focused context to your team and AI agents with permission-governed spaces—to a Github connector to new moderator tools. And that's just the tip of the iceberg. Explore the newest features on Stack Internal, the trusted knowledge layer for your AI tech stack: https://lnkd.in/e4Vd4Pi9

Finding important context shouldn't be a scavenger hunt through old chat threads and scattered repos. With the October release for Stack Internal, we're bringing you Scopes, a new way to create permission-governed spaces so teams and AI agents get focused knowledge without unauthorized exposure. Plus we're launching an out-of-the-box GitHub connector, service accounts, and new expert validation tools, so your teams and AI tools can stop searching and start building. Learn more about Stack Internal: https://lnkd.in/eztkXrPg

According to our 2026 Developer Survey, 63% of developers using AI agents are letting them run every single day. But what does that work actually look like? We want to know—and we're recruiting for paid interviews to learn more. If you're using agents to get real work done, whether that's in engineering, product, support, operations, or marketing, we want to hear about it. Sound like you? Tell us a little about what you're doing for a chance to participate in our paid research interviews: https://lnkd.in/ec8WqVSV

🎙️ At OpenAI Dev Day 2026, host Ryan Donovan sat down on the Stack Overflow Podcast with OpenAI's Andrew Ambrosino for a conversation on OpenAI's strategy for merging ChatGPT and Codex into a unified experience, the advantages of localized computer access in their successful desktop application, and the technical hurdles of constructing ChatGPT Spaces to support real-time multi-agent collaboration. Listen to the episode: https://lnkd.in/eNiaas2V

Building an AI context layer in-house seems like a piece of cake for a capable engineering team. But six months later, maintaining it has become your platform team’s full-time job. Keeping the pipeline trustworthy, organized, and compliant actually costs more than you think. Eira May dives into the true cost of an in-house knowledge system and what you should keep in mind when you're deciding between build vs. buy. https://lnkd.in/e7GshpSN