my learning of all the things
This is my public notebook. I write down what I learn while building software, debugging things, reading documentation, and changing my mind about how something works.
browse all entries, browse by day, or start with a topic below.
topics
recent entries
- PythonOctober 3, 2026 — Python Codewars Fundamentals
Worked references for filtering, counters, return values, indentation, thresholds, linear formulas, and integer boundaries, with a small problem-solving checklist.
- Learning LogLearning Log — October 3, 2026
Python Codewars practice: reading requirements, list filtering, return values, loops, Boolean counting, conditionals, linear relationships, and century boundaries.
- Learning LogLearning Log — October 2, 2026
Predict-before-reveal learning, disciplined debugging, smaller practice habits, conservative local-AI product design, and runtime configuration versus actual behavior.
- AI SystemsOctober 1, 2026 — Persistent Local AI Social Simulations
Architecture lessons from Driftroom: scheduler ownership, grounded state, memory provenance, novelty guards, versioned prompts, and reproducible local-model experiments.
- Learning LogLearning Log — October 1, 2026
Persistent local-agent simulation, grounding and repetition controls, prompt versioning, local-model experiments, Ollama model customization, and practical debugging lessons.
- CareerSeptember 30, 2026 — From Self-Taught Builder to Hireable Engineer
A research-informed roadmap for turning practical self-taught development experience into stronger fundamentals, production depth, interview readiness, and clear hiring evidence.
- Learning LogLearning Log — September 30, 2026
Editorial-agent architecture, human-in-the-loop writing systems, API boundary validation, spec-drift benchmarking, notebook information architecture, career strategy, and practical learning infrastructure.
- Learning LogLearning Log — September 29, 2026
Empirical AI-code research, controlled model benchmarking, realistic local AI architecture, evidence-bound decisions, technical-writing positioning, and community judgment.
- AI SystemsSeptember 29, 2026 — Evidence-Bound Decision Systems
Designing AI systems where recommendations stay attached to evidence, assumptions, permissions, tests, and invalidation conditions.
- AI SystemsSeptember 29, 2026 — Small Local AI Systems: Capability Through Composition
A realistic architecture for useful offline AI on constrained hardware without pretending a tiny model is a frontier model.
- Software EngineeringSeptember 29, 2026 — Empirical Software Research: Provenance Before Conclusions
How to study AI-assisted code without confusing disclosure, code quality, runtime behavior, and causation.
- Infrastructure & DeploymentSeptember 27, 2026 — Docker & Kubernetes: Containers, Orchestration & When They Matter
A practical mental model for containers, Docker, Kubernetes, pods, deployments, services, ingress, and when orchestration becomes useful.
