The exact prompts and CI workflows my team runs on every PR. Copy-paste, MIT licensed.
Every PR on my team gets reviewed by Claude before a human sees it. Here's the exact prompt and CI setup.
AI code review isn't about finding bugs—static analysis does that better. It's about pedagogical review: explaining trade-offs, questioning assumptions, and raising standards without slowing the team down.
We give Claude the full PR diff plus the codebase context. We ask it to review for readability, maintainability, and whether the change leaves the codebase in a better state. We ignore style—linters handle that.
The review runs as a GitHub check. It doesn't block merges; it educates. Engineers read the comment, decide if it's valid, and move forward. After a month, code quality visibly improves.
A walkthrough of the state machine, audio pipeline, and fallback design I use for Chasyr.
When RAG actually beats fine-tuning, when it doesn't, and how to tell which one you need.
The boilerplate I clone for every new SaaS bet. Auth, billing, RLS, AI hooks pre-wired.
How I restructured a 7-person team around AI tooling. Velocity numbers, cultural pitfalls, what worked.
A guest lecture at COMSATS on how mid-career engineers can move into architecture roles.
Panel at AusFinTech 2026 - the legal, technical, and ethical scaffolding for AI that talks to customers.
A handful of titles - short list, opinionated commentary, no affiliate nonsense.
My exact dev stack - IDE, terminal, AI agents, productivity hacks. Updated quarterly.
Papers, blog posts, and talks I send every engineer I mentor on getting up to speed with AI.
Remote-friendly companies, visa sponsors, OSS scholarships - things I wish I had a decade ago.