Ask anything from Paul Graham powered by GPT-3

Hacker News

As a programmer and entrepreneur, Paul Graham's essays have inspired many in the tech community. I grew up reading his essays frequently. Previously, I would use Google and perform a site search with keywords to find Paul Graham's thoughts on a topic. With the launch of GPT-3 and vector search capabilities, I have created an app that makes it easier to find specific insights and perspectives from Paul Graham's essays. The app indexes and categorizes each essay, providing a streamlined way for users to access and understand Graham's thoughts on programming, startups, and innovation. Do try it out and let me know your feedback in the comments.

Visit product View on Hacker NewsFeb 2, 2023

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

91%91% 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 HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

53%53% 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 · Strong signals: users · Missing: plus, platform, intuitive

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

TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal

35%35% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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