ai-agents

A repo with many coding agents needs more than one instruction file: AGENTS.md for procedures, user memory for preferences, a fact store for on-demand facts.

TL;DR: Repeated agent mistakes in a repo pointed to missing documentation, not a bad agent. The fix is a three-layer setup: AGENTS.md for repo procedures, user memory for personal preferences, and a fact store for reference material. Claude Code reads the same rules through a CLAUDE.md that imports AGENTS.md, keeping every session consistent.

A plain-language glossary of AI agent terms from primary sources: agent vs workflow, skills, three kinds of memory, RAG, and pipelines.

TL;DR: Agents decide their own next steps while workflows run on fixed code paths, and the distinction comes down to who holds the steering wheel. Skills are procedural SOP files loaded only when needed, unlike memory, which carries procedural, semantic, or episodic information across sessions. Pipelines add triggers, gates, and watchdogs to make autonomous agents trustworthy.

A blog pipeline that researches, writes, verifies, and publishes on its own, plus the four gates that make it trustworthy.

TL;DR: The author's blog publishes articles fully automatically via a 2-hour cron pipeline that researches, drafts, verifies, and posts without human touch. Safety comes from strong brakes: verification gates that reject bad content, automatic rollback after publishing, and escalation to humans for taste calls. Full-auto is trusted only for mechanical tasks; editorial judgment stays human.

Search snippets claimed 95-96% on SWE-bench Verified. The official board says 79.2%. Four rules to keep benchmark numbers honest.

TL;DR: The author checked the actual SWE-bench Verified leaderboard and found the top score is 79.20%, not the 95%+ figures circulating in search snippets. The gap exposed how easily benchmark numbers get cited without opening primary sources. The fix: log access dates, trust only opened primaries, snippets lose conflicts, report tied scores as-is.