AskPG, a real-time WebGPU Paul Graham avatar for startup advice

Hacker News

How this works is that I extracted knowledge from PGs publicly available essays. Instead of using RAG I used a state map approach, if you are keen about the extraction method I can share more in the comments. TLDR: it’s a PG avatar trained on his essays and the avatar renders on webgpu, give it a shot and ask it about the hardest problem you are facing in your startup right now. If you like the avatars answer you can share your conversation here!

Visit product View on Hacker NewsJun 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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