Llm

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? ...

What I Built and Why I’ve always enjoyed those element-combining merge games like Doodle God or Little Alchemy. You know the ones - Water + Fire = Steam, Earth + Water = Mud, that sort of thing. There’s something satisfying about discovering combinations, but after playing a few, I started noticing a fundamental limitation: every combination is pre-determined. Everyone who plays gets exactly the same results. The discovery phase is fun, but once you know the combinations, there’s no variance. ...

In a testament to the progressive march of technology, OpenAI has emerged as a herald of the AI renaissance. Recently, the distinguished AI think-tank cast a spotlight on future digital directions at its inaugural Developer Day event. This blog endeavors to capture the pivotal moments and revelations that could very well chart the course for AI’s role in our everyday lives. Custom GPTs Made Easy A remarkable stride in AI accessibility was announced, shattering the barriers to entry for custom GPT utilization. No longer confined to the realm of software engineers, the ability to customize powerful language models is now in the hands of the many. A diverse audience now holds the keys to unlock an AI that resonates with their unique needs, epitomizing a radical democratization of AI technology. ...