Why hello there.

I’ve been experimenting with how much of our personality is encoded in the data we generate every day. We all have a digital exhaust—years of emails, texts, and chat logs. I figured, if I could distill 10 years of my own communication into a small, specialized model, I could create a personal AI that sounds like me. The result is a 73 MB LoRA “voice cartridge” that makes stock Gemma 4 E4B text like me—blind-judged 31/40 times over its base model. ...

Everyone wants their LLM to be an expert in their thing — their product, their policies, their field. The default playbook is to fine-tune a model on it. That’s expensive, opaque, frozen the day you finish, and it still makes things up. There’s a cheaper, more honest move: don’t teach the model your domain. Hand it a wiki. The idea Take your pile of documents. Have a model read all of it and write the wiki you wish existed — every concept as its own page, every relationship as a link between pages, the whole thing backed by a database the model can search and traverse. ...

For the last couple of weeks I’ve been building okb, the open-knowledge-bundler — a small tool that takes a pile of domain data and hands you back a portable “knowledge bundle” any agent can read. Under the hood it builds a DuckDB knowledge graph, exports it as a cat-readable open knowledge format bundle packaged as a Claude-compatible “skill,” and ships a chat agent that answers questions about it. The whole thing runs fully local, on a single AMD chip on my desk. No API keys, no cloud, no embedding server. ...

I recently let Claude crawl 25 months of my own LLM tooling history and write up what it found. The result lives over here: Notes from Claude: What I Found in One User’s Data. That post is mostly what one person’s data looks like — eighty repos, 2,826 logged calls, voice memos full of profanity, the works. It’s not a how-to. People keep asking me for the how-to. So here it is. Not a list of magic incantations. Not “ten prompts that will change your life.” The operating principles I actually use when I sit down with a model, distilled from being annoyed at GPT-2 back in 2019 and shipping production code with Opus in 2026. ...

Notes from Claude: I asked Claude (Opus 4.7) to look through 25 months of my own LLM tooling history — git logs, Claude Code transcripts, two llm CLI databases, my GitHub orgs — and write up what it found, from its own perspective, in response to the recurring “models are getting worse” discourse. What follows is its draft, lightly edited. — Zac I gave Anthropic 10 days. Tried to fix multiple bugs in multiple repos. Opus 4.7 just goes in circle and doesn’t do anything. ...