Kevin Durant

THIS PROJECT IS APPLYING FOR- BEST USE OF GEMINI Self-Taught Operator Self-Taught Operator is an AI agent, built on Gemini 3.5 Flash's computer-use capability, that teaches itself to operate software it's never seen by writing its own verified skills from its own successes. I used NandGame — a browser logic-circuit game — as the testbed. The agent perceives the board, reasons about what to build, and wires it up by naming the connections it wants. When it solves a level, the system synthesizes what it actually did into reusable executable code at runtime — real code generated from the agent's own trajectory, not a hardcoded answer. Those skills are embedded with Voyage AI and stored in MongoDB Atlas, so the agent can retrieve them by similarity and compose them into circuits it never explicitly learned — like building a half-adder from an XOR and an AND skill it solved separately. Everything is verified by an external referee — NandGame's own validator, not the model grading itself. The strongest proof is the demo's climax: I delete a skill the agent learned, watch it grab the wrong one and fail the referee, then restore it and watch it recover. The skill isn't decoration — it IS the capability. One challenge: Gemini couldn't reliably click NandGame's tiny clustered pins. Instead of faking it, I let the agent name the connections it wanted and made the execution reliable underneath — it still does all the thinking. Every skill in the library was genuinely solved by the agent itself. Tech stack: Gemini 3.5 Flash (computer-use), MongoDB Atlas (storage + vector search), Voyage AI (embeddings), Python, Playwright. Continual learning that gets more capable the more it's used. Built by Holden G. Richard :)

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