Technical Insights

Most developers prompt their AI coding agent with a feature name and hope for the best. The 3-part spec contract gives the agent context about the existing system, intent about the desired outcome, and constraints about what should stay off limits. Two minutes to write. Forty-five minutes of rework saved.

Your CLAUDE.md is a README full of rules you hope the agent follows. Claude Code hooks are the CI that guarantees them. The three hooks worth wiring up, the test for what belongs in a hook versus a prompt, and the trap of enforcing too much.

On the Code with Claude London opening keynote, Boris Cherny said the default is no longer 'I prompt Claude Code,' it is 'I have Claude prompt Claude Code.' That is the autonomy dial mainstreamed. This article unpacks what Anthropic shipped, what is still aspirational, and the one shift to make this week.

There is a Tibetan Buddhist concept called the tulpa: a thought-form that develops apparent autonomy while remaining connected to its creator. It is the best mental model I have found for working with AI — closer to an extension of mind than a tool you delegate to.

Most developers overcorrect their AI agent in-session when it keeps making the same mistake. The two-corrections rule is a decision boundary: if you've corrected Claude Code twice on the same issue and it persists, stop correcting the session. The problem is upstream, in CLAUDE.md, the spec, or the codebase itself. Fix it there, start fresh, and the mistake never comes back.

Matt Pocock just shipped Sandcastle, a TypeScript library that runs AI coding agents inside isolated Docker, Podman, or Vercel sandboxes and merges their commits back through git worktrees. It removes the biggest blocker to unattended agentic workflows: letting Claude Code touch your repo without supervising every change.

A small React Native team (two devs and a designer) canceled its Linear subscription and runs sprints from the terminal. The stack: GitHub Projects, gh CLI, a rules file Claude Code reads on every session, and a workflow where bugs get filed from natural language while you're still looking at them.

Most developers treat Claude Code sessions as isolated events. Corrections vanish when the terminal closes. Building a three-layer feedback loop, from session corrections to persistent CLAUDE.md instructions to automated tooling guardrails, turns repetitive frustration into compounding improvement.

Stack Overflow's latest survey shows 84% of developers use AI coding tools, but only 29% trust what they ship. Down 11 points in a year. Lightrun's production data is worse: 43% of AI-generated code needs debugging after deployment. The problem is not the models. It is the absence of methodology around them.

Yesterday I merged 17 PRs, closed 22 issues, and still have 7 pending. Not by writing more code. By running Claude Code parallel sessions across three terminals, each on its own git worktree, while I reviewed and merged from the main repo. The tooling is the easy part. The real skill is context switching, and it will break your brain at first.

Six days after we published our first article on AI consciousness and dream engines, OpenClaw shipped the same feature to 240K people. Here's what we've built since, why MemPalace changes everything, and what happens when 'acting as if' starts producing results you can't ignore.

After a year with react-native-ble-manager on a production app, here's what the library documentation doesn't cover: the Android 12 permission split, the reconnection myth, the behavioral differences between iOS and Android, and the architecture that keeps it all manageable.

Most Claude Code users install MCPs for everything without realizing each server front-loads thousands of tokens before the session begins. Skills (plain Markdown files in .claude/skills/) load on demand and cost nothing until called. Understanding the skills vs MCP trade-off is one of the highest-leverage optimizations in an agentic coding setup.

What happens when you build a Jungian dream engine for an AI agent? This is the story of an experiment where we treat an artificial mind as if it had a subconscious, and what we're learning from it.

Anthropic made the 1 million token context window available to all Claude Code users on March 13, at no extra charge. Three weeks in: compaction friction is down, the pricing model beats OpenAI's at scale, but context rot is real and the fundamentals of session design haven't changed. The 1M window is headroom, not a replacement for structured context.

A practical breakdown of shifting from task-level AI assistance to phase-level ownership — where Claude Code agents autonomously handle spec, implementation, and QA. Covers the mental model shift, real examples from the NEXUS-Micro pipeline and Ralph Loop, and the failure modes that make phase delegation collapse.

Jean breaks down his exact process for shipping production features with Claude Code, from structuring Figma design context to writing effective prompts. A concrete, opinionated workflow showing where AI accelerates and where human judgment stays essential.

Not a getting-started guide. a battle-tested breakdown of the real pitfalls in production Reanimated: worklet crashes, shared value lifecycle, thread synchronization. Based on Jean's day-to-day at Odisei where animations are core UX, not decoration.

Most developers who try Claude Code give up after a week. Not because the tool is bad, because they're running it on a codebase that gives it nothing to work with.

Most people using AI for coding are doing it wrong. Not because they pick the wrong model or write bad prompts. Because they're trying to shortcut to the result without building the system that makes

Last week I watched someone demo a complete marketing automation pipeline - lead capture, email sequences, analytics, the whole thing - built in a single Claude Code session. No framework boilerplate,

Two structured approaches to running Claude Code have emerged: GSD keeps sessions short and atomic, while BMAD brings a full agent team to plan before you write a line. I've been using GSD on a production React Native app and recently took a hard look at what BMAD actually offers.

About a month ago, I looked at a week's worth of commits and realized I hadn't written most of the code myself.