WhyBot, making GPT-4 question itself

Hacker News

Hi HN — we’re John and Vish! We built WhyBot, a tool to help you deeply explore a question or topic. You ask a question, and WhyBot responds by building an ever-expanding knowledge graph. It does this by recursively generating answers and follow-up questions. You can change its persona to change the flavor of the generations (try toddler mode!). We originally built this for the AngelList Agent Hackathon ( https://twitter.com/AqeelMeetsWorld/status/16502799744050421... ) and got a lot of interest from folks asking to play around with it. So we thought it’d be fun to brush it up and release it as a web app! It’s a work in progress and we plan on adding more features, such as saving, sharing, focusing on one branch and potentially executing code. We hope you enjoy playing around with it and would love to hear any of your feedback or thoughts.

Visit product View on Hacker NewsMay 14, 2023

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Actual performance

77points

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Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

65%65% 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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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

15%15% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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