Mastodawn

Wrote up about my personal journey from AI skeptic to someone who finds a lot of value in it daily. My goal is to share a more measured approach to finding value in AI rather than the typical overly dramatic, hyped bait out there. https://mitchellh.com/writing/my-ai-adoption-journey

My AI Adoption Journey

Mitchell Hashimoto

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Bogdan Buduroiu Feb 6

@mitchellh I thought letting agents roam wild would work eventually and I could shepherd them as I would a junior sending me PRs for review. This, unfortunately, didn't work for me.

I ended up using smaller, "dumber" models that I could theoretically self-host (GLM-4.7, Qwen-3-Coder-Next) and working in a tighter loop with them (I design the interface, they fill it in).

Primeagen's 99 tool was a big big saviour here

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/c64/youth.sid Feb 7

@budududuroiu @mitchellh I am on a similar journey. Now focussing on how to define a good agent. It is working out to create simple, focussed agents that can do one task well. It’s a bit counterintuitive but it has to do with. 1. Choosing the right model and temperature 2. Limiting context by having a system prompt focussed on the one task. 3. Limiting the tools it can use and the information it can access. And keep tweaking that. Often I do that by letting the agent modify its own system prompt

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/c64/youth.sid

@budududuroiu @mitchellh So instead of feeding the agent as much information and as many instructions and tooling as possible, I’m now at creating as little distractions for the agent to do his one job well. I keep in mind that LLMs do not learn, have a hard time focussing and cannot judge their own accuracy. We have to help them.