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An X-like interface for LLM chat

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An X-like interface for LLM chat

Hi, this is Lapin - an X-like interface for LLM chat. X has a really great interface for going down rabbit holes and opening up different threads for discussion. So I thought "why not apply that for interacting with LLMs"? Conversations with LLMs don't have to be so linear! See a demo at https://x.com/ajeon66/status/1799987480705605915 , or try it yourself at https://lapin.hajeon.xyz/ All comments / concerns / feedback are welcome - thank you!

Visit productView on Hacker NewsJun 11, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
62%62% 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.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
57%57% 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
48%48% predicted probability of success on TrustMRR, 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
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
27%27% predicted probability of success on Indie Hackers, 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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