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Discussion on: Demystifying AI Agents with Turtle & Gemma

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motedb profile image
mote

The Turtle agent loop you described maps pretty cleanly to what I've been wrestling with on a drone project. Perception→Reasoning→Action works until the agent restarts and loses everything it learned in the last 10 minutes.

I ended up embedding a local DB directly on the device (moteDB, Rust crate) so the agent's observations survive reboots. Not a cache layer — actual persistent state that lives next to the model. The tricky part was deciding what to persist vs recompute. Too much state and latency creeps up. Too little and you're back to square one after power cycles.

What's your thinking on state persistence in the Turtle loop? Is the assumption that every cycle starts fresh, or did you bake in any memory across invocations?

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bebechien profile image
bebechien Google AI

Good question. I haven't thought about it deeply yet, but to keep things simple, I'll probably just use a text file for the current status (e.g. current pen color, position info). I'm also going to try using screenshots to leverage multimodal capability. Sometimes an image gives you way more context than a bunch of text.