AI

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. ...

There’s a dirty secret in the AI agent space: most agent frameworks hand the model a bag of tools and pray. The model decides what to query, how to reason about results, and what conclusions to draw. For demos, this works great. For anything you’d actually bet your job on — compliance audits, student intervention decisions, infrastructure monitoring — it’s a liability. What if the model didn’t have to reason at all? ...

Screenshots in a Folder Aren’t a Demo Reel I’m building ContextChannel – an edge-native content curation platform on Cloudflare Workers (Hono, D1, Drizzle ORM, HTMX, the whole neo-brutalist vibe). I needed to do a full QA pass across the entire user journey and wanted to document it in a way that wasn’t just 25 PNGs rotting in a folder. So I did the QA with an AI agent and then had it turn the screenshots into a video. In one session. Here’s how that went. ...

At work, I build AI agents(trigger warning this is about LLMs) that are designed to help teachers avoid as much day to day drudgery as possible. We provide a system that, counterintuitively, is geared towards reducing the amount of time a teacher needs to spend in the app spelunking around. Need the latest assessment grades for Mrs Doe’s 3rd period? Just ask the Ai, it’ll go off and grab that information for you while you pour another cup of coffee. We use top of the line models from Anthropic to be as accurate, un-biased, error free as possible to achieve this. These models require API keys and 5-figures in engineering time just to put the guardrails in place to allow a Teacher/Administrator to use them in relative safety. ...