# ai-coding

AI can handle 80-90% of frontend work, but the remaining edges — depth, sensitivity to new platform features, and knowing when the stable default is not the best answer — are becoming the real moat. Fundamentals are not obsolete. They are compounding assets.

As CTOs aggressively push AI coding, software engineers split into two classes: the lazy and the craftsmen. The lazy throw code up, never read it, never test it, never care. The craftsmen carry the whole review burden, watch quality collapse, and eventually become lazy too.

Code-writing models are 'English to code' interpreters — writing code is no longer the bottleneck; reviewing and merging it safely is. Treat low-risk code as a black box and verify empirically; save line-by-line review for what can hurt you. Claude Code's creator Boris Cherny agrees.

Zed founder Nathan Sobo argues the real source of software is the ongoing conversation with your agents, not the tidy commits you slice it into. Git can't hold that flow, so Zed built DeltaDB: every operation becomes a delta with a stable identity, keeping the conversation glued to the code.

Garry Tan argues the real breakthrough in AI coding is not speed. It's turning tests, docs, and evals into a forward-only quality ratchet, so every change locks in what the team learned and makes the codebase harder to silently degrade.

SemiAnalysis argues leaked Claude Code source shows TypeScript becoming a language AI produces, consumes, and evolves. The post reads 600,000 leaked lines through memory architecture, KAIROS, security holes, and static types.

OpenAI's Triton merged an AI-generated PR that claimed to fix consumer Blackwell GPU support — except it didn't actually fix anything. NVIDIA's PyTorch tech lead personally called it out as pure slop. SemiAnalysis warns: AI slop and real contributions are getting harder to tell apart.