Thoughts on AI agents, automation, and building systems that work
Agents made building almost free, which exposed what you were actually buying all along — not the software, but the maintenance, the de-risking, and your attention. A build-vs-buy calculus rewritten around the one resource that doesn't parallelize.
Read more →Reviewing used to mean reading the implementation. Increasingly I verify what the model produced, not the path it took — and what 'verify the result' means changes wildly depending on whether the result is a number or a behavior.
Read more →I build systems whose business logic I couldn't recite if you woke me up at 3am. The model holds it for me — and that's fine. The hard question isn't how much you delegate, it's whether you can still tell when the answer is wrong.
Read more →Two philosophies of the client demo — mock-first spectacle vs functional proof-of-concept — and why, with AI, the prettier one hides exactly what matters: latency and non-determinism.
Read more →Generative UI lets an agent render interactive widgets — forms, charts, signatures — inside the chat instead of plain text. AG-UI is the protocol standardising it, and I've already been building it by hand.
Read more →Before you write a judge prompt, you owe three decisions: the context, the unit, and the dimension. Here is the framework, applied to evaluating my production DevOps agent.
Read more →How I built an AI agent that investigates infrastructure issues autonomously — with read-only guardrails, output compaction, and infrastructure knowledge baked into the system prompt.
Read more →How a simple question about vitamin D synthesis turned into a Claude artifact, then a full PWA with D3.js visualizations, push notifications, and 6 languages.
Read more →What we write is becoming markdown skills, what users see is becoming generated pixels, and the middle keeps getting thinner. A look at where software is heading in 2026.
Read more →AI can now build almost anything. The hard part is no longer the code — it's managing your own cognitive limits as you orchestrate multiple agents in parallel.
Read more →Why most AI-driven tests should be reproducible scripts, not CLI exploration — and how to use each approach effectively based on real deployment automation experience.
Read more →Lessons from writing a 16-chapter essay with two AI coding tools — why prose is harder than code for AI, and what actually worked.
Read more →How I used Claude Code to build a recording pipeline, edge-tts for voiceover, ffmpeg for assembly, and Gemini to QA the result — without touching a video editor.
Read more →How I built an RLM prototype that processes 71 arXiv papers (~1M tokens) without injecting them into the prompt, using out-of-core document analysis with Azure OpenAI tool calling.
Read more →How to orchestrate multiple AI agents working in parallel using Claude Code's new Agent Teams feature within a tmux session.
Read more →Analyzing the legal and ethical implications of 'Co-Authored-By: Claude' appearing in commits. Tool or author? The future of AI-generated code ownership.
Read more →TypeScript provides essential guardrails for AI coding agents but creates friction with type errors. The solution isn't changing languages—it's changing workflows with specialized sub-agents.
Read more →How I built a Claude Code skill that standardizes and automates the creation of bilingual blog articles, ensuring consistency across EN/ES content.
Read more →A workflow for running multiple Claude Code agents simultaneously on different features, using git worktree to manage branches and automating PR reviews.
Read more →How Azure's content filter blocks legitimate browser automation tools based on word choice, not actual risk—and simple workarounds that expose the flaw.
Read more →How I built a comprehensive MCP server with 58 tools to manage Bitbucket repositories, pull requests, and pipelines through natural language.
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