Posts

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.

Most "personal AI" tools split your context across silos: one search box for email, another for notes, a third for your chat history. Terraphim treats every source as a haystack on the same role, so a single query crosses all of them. This post shows how to wire up the two most common personal sources -- email via JMAP and notes in an Obsidian vault -- under a new Personal Assistant role.

Terraphim's System Operator role is the demo we point people at when they want to see a real Logseq knowledge graph drive search. 1,347 Logseq pages, 52 of them carrying explicit synonyms:: lines, covering Model-Based Systems Engineering vocabulary -- requirements, architecture, verification, validation, life cycle concepts. This post walks the demo end-to-end and shows the piece people miss: the KG is doing real work, not just re-ranking text matches.

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.