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An interface for doing research fast with an LLM
Hacker NewsAn interface for doing research fast with an LLM
I built ProRead as a new interface for researching anything with an LLM. Instead of chat, you explore topics through an interactive map of ideas. The interface changes the level of detail like when zooming in and out on Google Maps. Click to go deeper, expand branches, and always stay grounded in sources. You still have chat for questions, and can switch to listening seamlessly. Curious if this feels useful!
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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new · 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.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal48%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: ide, io · Missing: https docs, excited, just released38%38% 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, podcasting31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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