31 AI Coding Agent Terms You Should Know, Sorted Into Five Pillars
I classified every term I kept encountering while using Claude Code and Codex daily. Five groups emerged, and they map the entire system these tools run on.
Simple thoughts on building, designing, and shipping
I classified every term I kept encountering while using Claude Code and Codex daily. Five groups emerged, and they map the entire system these tools run on.
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.
Blog version of a 2026-03-12 LinkedIn note — same claims, cleaned structure for reading.
Four projects shipped in the last two months show what happens when AI agents handle not just coding but earning, orchestrating, and running entire companies.
After a year of agent-assisted development, I found that structured spec files fixed the inconsistency problem better than any prompt technique.
I reverse-engineered how Codex handles context overflow compared to Claude Code. The answer involves AES encryption, session handover patterns, and KV cache tricks.
Shopify CEO Tobias built QMD, an open-source search engine. Connect it to Claude Code and every session gets persistent memory.
Anthropic's Claude Code team rebuilt their tools three times. Fewer tools made the AI perform better. Here are four hard-won design principles.
Your AI isn't getting dumber. Your main session is overloaded. Sub-agents keep it lean and accurate for over an hour.
Every connection matters to me.
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