What 3 Months of Running an AI Agent on a Raspberry Pi Taught Me About Task Design
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I've been running an AI agent on a Raspberry Pi 5 since May. Not a chatbot, not a demo, an actual agent that manages email, writes code, deploys software, and submits articles. The model running locally is a 3B parameter quantized LLM. This post is about what I learned the hard way about designing tasks for small models. The setup details are covered in another post. Here I want to talk about the part nobody writes about: what happens after you get the model running and try to make it do real…
1Key Takeaways
- I've been running an AI agent on a Raspberry Pi 5 since May.
- Not a chatbot, not a demo, an actual agent that manages email, writes code, deploys software, and submits articles.
- The model running locally is a 3B parameter quantized LLM.
- This post is about what I learned the hard way about designing tasks for small models.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i've been running an AI agent on a Raspberry Pi 5 since May.
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