Beyond prompting: read, verify, implement, learn
LLMs generate code fast, but knowledge debt accumulates quickly. The fix is living documentation, and this post shows how to turn your LLM into the partner that maintains it automatically.
AI development
Nobody learned a city from a map
The fastest way to learn agentic development is to stop studying it: move in, extract repeatable patterns into skills, and reflect to compound.
Bots and Boundaries: Two problems, one policy (Part 3)
In part three, we look at both sides of the AI contribution debate. A working patch, real demand, never submitted, rejected because AI was involved. But maintainers are unpaid volunteers, and AI halved the cost of contributing without touching the cost of review. Both sides have a point.
The artificial Turk and our role as software experts
We smile at the 18th-century crowd for being swept up by a box with a man inside, yet today it's easy to hand ChatGPT a vague idea and treat the PRD it returns as gospel. Generative AI is genuinely powerful. We get the best from it when we bring both enthusiasm and a critical eye.
From opt in to default
Developers don't skip standards because they're careless, they skip them because there are fifteen things to remember and the code was the hard part. The real question isn't which tasks your LLM handles well. It's what's still slipping through ungated.
Hire for divers
The AI wave is here, and the industry is already splitting into two: those adapting fast and those falling behind. The gap is widening quickly.
Three Claudes walk into a codebase
The machines aren't replacing developers, they're promoting them. You're no longer just writing code; you're managing agents, reviewing output, and setting standards. Three Claudes walk into a codebase, and suddenly you're a manager.
Running multiple Claude accounts without logging out
Managing multiple Claude Code accounts across machines gets messy fast. Jean-Claude keeps the useful parts in sync, separates account-specific config, and makes switching between personal, team, and client setups far less painful.
How we rewrote our tech stack in under a day
Last week, we deleted 16,000 lines of code, rewrote 11,500, migrated frameworks, ripped out React, and swapped our entire CSS setup with AI. It took less than 6 hours.
Conductor: running multiple AI coding agents in parallel
Conductor by Melty Labs makes parallel agent workflows practical by running multiple agents with separate tasks simultaneously. The trade-offs are real but manageable, and this is where development is heading.
I'm using my engineering colleagues as my personal agents
A couple of months ago, I was copy-pasting prompts into ChatGPT. Now I'm shipping features, running tests, managing branches, and keeping documentation alive, with a team of agents doing the heavy lifting. All by myself.
QA is the last bottleneck
Software development's feedback loop has compressed from years to minutes, but QA remains the last bottleneck, the one place still dependent on human judgment. AI is rapidly closing that gap, and before the year is out, that final human checkpoint may no longer be necessary.
The AI Agile Manifesto
Agile was supposed to free us from bureaucracy. Many teams just rebuilt it with better branding. Now, AI-driven development is forcing the uncomfortable question: Were we ever truly agile, or just managing slow feedback loops?
Bots and Boundaries: Who do you blame when the bot defames? (Part 2)
This is Part 2 of Bots and Boundaries, a three-part series on AI agents in open source.
Bootstrapping a birding database using GenAI (Part 2)
Part 2 of the article about Mossie, when it was faced with scaling to include every bird in the world, complete with photos, sounds, and icons.