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Client Side anti-RAG solution
Hacker NewsClient Side anti-RAG solution
Client Side Only Example, chat with this page. Because we don't use API keys we don't have any real need for a server. view the page source, it's _all_ there! The program doesn't take history into account & yet is has surprising coherence. View Source and start on line 258.
Share cardActual performance
7points
2comments
Made the leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting30%30% 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, revenue14%14% 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, cryptocurrency6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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