AI

You explain the project's validation command. The session ends. Next time, the agent needs the same explanation. Keeping the instruction somewhere is useful only if the next run can find the right instruction again.

This walkthrough saves one lesson from a fictional project, opens it in a new process, and retrieves it through a small knowledge graph. You can inspect the exact saved text and repeat the same checks with the downloadable example.

AI coding agents are making us worse engineers, unless we add discipline back. Here is what we do instead of vibe coding, and how you can do it too in 30 seconds.

AI coding assistants are fast, productive, and occasionally catastrophic. One misplaced rm -rf, one accidental git reset --hard, and hours of uncommitted work vanish.

Jeffrey Emanuel (@Dicklesworthstone) built Destructive Command Guard (dcg): a Rust binary with SIMD-accelerated pattern matching, 49+ security packs, and a fail-open design. It is one of the best tools to come out of the AI agent safety space, and it solved a problem we had been fighting with regex hacks.

This post shows how we integrated dcg with OpenCode using its plugin hook system, so destructive commands are intercepted before they run.

AI coding agents make the same mistakes over and over. We built a learning system that captures failures, stores corrections, and feeds them back into future sessions — turning every error into institutional memory.

Builds on Why Graph Embeddings Matter — the deterministic engine that makes "remember this correction forever" cheap. Apply the pattern in your own project via the Command Rewriting How-to.

How we use Aho-Corasick automata and knowledge graphs to automatically enforce coding standards across AI coding agents like Claude Code, Cursor, and Aider.

New: see Why Graph Embeddings Matter for the underlying engine that makes these hooks possible — sub-millisecond, deterministic, fully explainable.