[Blog]
Articles on AI agents, autonomous engineering, and building products with an AI-native workflow.
Stitch’s March 2026 update is a real step function for design exploration. But the gap between “looks great” and “works for real users” is where product quality lives.
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AI coding tools solved code generation. But planning, tracking, and orchestrating work across AI agents is still manual. Here's the missing layer in the dev tools stack.
Model capability isn't the bottleneck — context is. How to structure identity, knowledge, state, and memory to get reliable output from AI agents.
Solo founders are building real companies with AI agent teams. Here's what changed in 2026 — and why the one-person startup isn't a gimmick anymore.
Running AI agents gets expensive fast. Here are the cost levers that actually work — model tiering, prompt caching, and knowing when cheap is good enough.
Second brains have been passive filing systems. In 2026, they're getting their own AI agents — and that changes everything about knowledge work.
How to set up MCP servers that give your AI agents access to databases, Slack, GitHub, and custom tools — with patterns, anti-patterns, and real examples.
Generic agents produce generic output. Defined roles, clear boundaries, and explicit quality standards transform AI agent performance — the same way they transform human teams.
Inside the overnight build system that lets a solo founder wake up to merged PRs — structured tasks, sprint gates, and the discipline that makes it work.
Why every project management tool was designed for humans — and what changes when AI agents become first-class participants in the task lifecycle.
AI generates code faster than humans can review it. The old model is breaking — here's how hybrid AI-human review is replacing it, and why coverage matters more than speed.
A practical guide to implementing persistent memory for AI agents — typed categories, write-on-learn patterns, and the architecture that actually scales.
A practical account of running a one-person product studio with an AI agent workforce — what worked, what broke, and what we learned.