RPi 3 Morning Digest
Before buying dedicated AI hardware, find out if overnight processing is actually useful. A Raspberry Pi 3, some scripts, and a cron job can answer that question for free.
Slow is fine when you're sleeping
The Raspberry Pi 3 gets dismissed as too weak for AI work. That's because people assume AI needs to respond in seconds. But when you have eight hours instead of eight seconds, even slow inference works.
TinyLlama (637MB) takes 30-60 seconds per response on a Pi 3. Phi (1.6GB) takes 60-120 seconds and needs swap space. These speeds are useless for chat. They're fine for overnight batch processing.
The morning digest
A cron job runs at 3 AM. It collects system status, scans projects for TODOs and recent git commits, fetches weather data, and asks a local model to generate a summary. By morning there's a markdown file waiting.
The script is thirty lines of bash. The cron entry is one line. Ollama runs the model. Everything is files you can inspect and debug.
When something breaks - and it will - you check the generation log, verify ollama is running, test the model manually, look at cron logs. Every failure mode is visible.
Optional: off-grid for $65
A Pi 3 draws about 3W average. A 20W solar panel, 12V battery, and charge controller run about $65 total. Four hours of sun generates 80Wh. Daily consumption is ~72Wh. The math works.
This isn't about environmentalism. It's about proving that AI processing can happen with zero infrastructure dependencies. No hosting bills, no API limits, no cloud provider can deprecate your local ollama instance.
Four weeks to find out
Week one: get ollama running, generate some responses manually. Week two: build the morning digest script. Week three: add cron, watch stability. Week four: optimize and try larger models.
Success means: it runs for a week without intervention, you actually read the digest each morning, you understand every component well enough to fix it, and you want to expand it.
If it works, the upgrade path is clear: better hardware (Pi 5 with 8GB), larger models (7B parameters), more sophisticated workflows. But now you're investing based on proven value instead of theoretical potential.
If it doesn't work, you learned something useful without spending money on hardware you wouldn't use.