I
I Open-Sourced the Future of Search
Hacker NewsI Open-Sourced the Future of Search
Hey HN I am a bit researcher, I love searching and scraping the web. But I find that most research tools like Perplexity, search engines and Deep Research features in LLMs miss the personalization component of the search I get a lot of answers that do not match my goals . That's why I decided to write this manifesto and draft a simple AI search Assistant to represent my ideal of the Future of Search.
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Product HuntOn track for Day 1 leaderboard · Strong signals: perplexity, open · Missing: mac, agents, macos89%89% 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: ide, io · Missing: https docs, excited, just released64%64% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting62%62% 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, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, answers · Missing: mobile apps, ios, entrepreneurs33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto13%13% predicted probability of success on BetaList, 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.
Incorrect prediction on native model
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