Go has a fast, unambiguous outer loop (win rate vs. KataGo) and contains subtasks that overlap with LLMs and robotics — distributed systems, hyperparameter search, hypothesis generation. It's a cheap, verifiable testbed for automated AI research.
Go has a fast, unambiguous outer loop (win rate vs. KataGo) and contains subtasks that overlap with LLMs and robotics — distributed systems, hyperparameter search, hypothesis generation. It's a cheap, verifiable testbed for automated AI research.
Where this was said
At 2:19:40 · chapter starts 2:16:16
Eric reports from his AlphaGo project: LLMs excel at hyperparameter tuning and executing experiments, but fail at lateral thinking — choosing the right question and recognizing dead-end research tracks. [1] — Eric Jang "LLMs can autonomously run experiments, tune hyperparameters, and optimize code. What they can't do: decide which question to investigate ne…" 2:14:00
Distillation from soft targets provides far more bits per sample than one-hot labels because the entropy of a soft distribution is vastly higher.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
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