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Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI (No Priors Podcast). Andrej Karpathy is always worth listening to because he has the time to experiment and tinker with the latest developments in a way that most people working at companies don't. He effectively lives a few months in the future compared to the rest of us.

Two things stuck with me from this conversation. First, Karpathy frames Claws (from OpenClaw) as another layer of the AI stack: LLMs → Agents → Claws. I have never actually set up a Claw yet, but the persistent memory architecture and how "your Claw" gets to know you over time are things I want to experiment with, as this is directly relevant to what we're working on at Ren as the product becomes more agentic.

Second, his work on AutoResearch. We've discussed the concept internally at Ren multiple times over the past few months, but never found the time to actually try it. We have a concrete problem that would lend itself well to this approach: building a more efficient multi-label classifier. We currently use a relatively heavy model for it, we have abundant training data, and the objective is clear (maximize precision/recall/F1 for a given latency budget). We could just let an AutoResearch system loose on this task. What I'm missing is knowing how to set up a sandbox that's safe enough but has sufficient permissions for the agent to carry out the research on its own. The meta task would then be similar to Claws: build a system in a few markdown files that defines how the agent approaches and documents its research.