Us
User Embeddings – Personal Navigation for LLMs
Hacker NewsUser Embeddings – Personal Navigation for LLMs
User Embeddings lets you build user-intent AI agents, hyper-personalized semantic search, and bring up-to-date information to GenAI applications in a personalized way. Docs: https://firstbatch.gitbook.io/firstbatch-sdk/ If you are a YC company , you can get User Embeddings free for a year by signing up here: https://www.firstbatch.xyz/subscribe
Share cardActual performance
5points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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