AlphaGo Lee (the original paper) used two separate networks for policy and value; all subsequent AlphaGo papers merged them into one network with two heads.
AlphaGo Lee (the original paper) used two separate networks for policy and value; all subsequent AlphaGo papers merged them into one network with two heads.
Where this was said
At 1:31:03 · chapter starts 1:25:38
Eric explains neural fictitious self-play — training best-response policies against fixed opponents and distilling them — as an MCTS substitute for games without tractable tree search.
When you can't build a search tree — like in StarCraft — you fix your opponent and use model-free RL to find the best response. That best response becomes a better label for your current policy. Same idea as MCTS, different implementation.
Current LLM RL treats the entire token sequence as a single action (T=1), avoiding compounding variance but losing per-token credit assignment.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
Tool-focused apps like PuffCount are poor candidates for ad monetization because users don't stay in-session long enough.
A hard paywall is a screen that blocks all app features unless the user pays or starts a free trial — it cannot be dismissed.
Mobile apps are primarily monetized through either ads (best for games) or in-app purchases/subscriptions (best for tools).
According to the episode, YouTube outperforms every other social platform for building trust and driving SaaS conversions.
Vasco stated that the majority of his app's user base came directly from his YouTube channel.
SEO Bot features a 'Boost My Domain Rating' button that routes users directly to Listing Bot, an example of in-product cross-selling.
The founder's entire product portfolio is AI-related, making it easier to package products attractively for directories.
The founder attached their SaaS demo to the trending debate about whether AI coding is actually good enough to build a full SaaS product.
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