Llm

At the end of March, my OpenClaw setup hit an awkward wall. For a while, Anthropic OAuth access had been good enough for day-to-day agent work. Then access tightened. I do not mean “the model got slightly slower” or “one request had to retry”. I mean the system became unreliable enough that I was pushed toward the API path. That sounded fine in theory. In practice it turned the assistant from a predictable tool into a metered engineering problem. ...

Most AI assistants are designed to be agreeable. They say “Absolutely.” They praise the question. They hedge every answer until nothing is left but damp corporate fog. This is fine if the assistant is a search box with manners. It is not fine if the assistant has access to your shell, email, calendars, automations, Raspberry Pi, finance reports, blog drafts, and the long tail of unfinished things that make up a real life. ...

A few weeks ago I wrote about fixing OpenClaw after context bloat and bad model routing. Then I immediately made a related mistake in the opposite direction. I switched the default model to openai/gpt-5.4 and kept the context window at 32,000 tokens. I wanted to test the model on real daily workloads. What I got instead was a week of repeating myself, failing background jobs, and an assistant that felt like it had short-term amnesia. ...

I hit a very predictable failure mode in AI automation: I left the defaults alone for too long. Background jobs accumulated too much context, some of them were still landing on expensive models, and a couple of nightly tasks started timing out. The fix was not dramatic. It was mostly configuration discipline. This was not an OpenClaw bug. It was a bad operating posture. What Broke Four things were interacting badly. ...

I’ve been running a personal AI assistant for a few months now — handling email triage, GTD rituals, news digests, morning run reminders, and a bunch of other cron-driven automation. It’s genuinely changed how I operate day-to-day. But here’s something I’ve learned the hard way: the model underneath everything matters more than you’d think. The Reasoning Problem At some point I had extended reasoning enabled on my assistant. In theory, this should make responses more thoughtful — the model “thinks before it speaks.” In practice, it made every message reply a slow, verbose rewrite of whatever I’d asked. Inconvenient enough that I just switched it off. ...