Vibe coding, autonomous coding agents, and software engineering with AI.
14 posts
I dug into SDK type definitions and system prompts for both tools. The 29 vs 7 gap isn't about feature count. It's about two fundamentally different answers to the same question: how should an AI coding agent interact with your system?
Someone benchmarked an LLM-written Rust reimplementation of SQLite. The gap between code that looks right and code that is right turned out to be five orders of magnitude.
I reverse-engineered how Codex handles context overflow compared to Claude Code. The answer involves AES encryption, session handover patterns, and KV cache tricks.
Anthropic's Claude Code team rebuilt their tools three times. Fewer tools made the AI perform better. Here are four hard-won design principles.
I couldn't sleep after a conversation about shipping more work publicly, so I built frouter at 3am. It pings free AI models in real-time and wires them into your coding tools with one keystroke.
When agents push 3,000 commits a day, humans can't review them all. Here's how to build a machine-verified pipeline that catches what people can't.
New benchmark data shows AGENTS.md and CLAUDE.md context files actually hurt coding agent performance. Sometimes laziness is the best engineering decision.
Three companies updated their coding agents at the same time. The directions overlap. The real battleground isn't models; it's how fast they absorb developer workflows.
Thomas Wolf's five predictions for how AI will fundamentally reshape software architecture, from the end of dependency culture to AI-native programming languages.
OpenAI's Codex team built a 1M-line codebase using only AI agents. Here are the five harness engineering principles they discovered along the way.
While the market warns of GPU overcapacity, OpenAI declares it needs even more compute. The real winner won't be whoever has the most power - it'll be whoever closes the gap between AI capability and actual user experience.
How a Claude Code plugin named after Ralph Wiggum is redefining autonomous coding through iterative loops, memory architecture, and stop hooks.
Peter Steinberger, who built GitHub's fastest-starred project, shares 10 hard-won principles for working with AI coding agents.
In 2026, the grammar of startups is changing. The founder's role is shifting from writing code to orchestrating AI - and taste is the new technical depth.