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Deep search engine for science

Hacker News

Deep search engine for science

Undermind is a deep, systematic search engine to discover incredibly complex topics in scientific papers. We optimized everything for accuracy and comprehensiveness. That’s the bottom line that matters to scientists, doctors, etc. (not compute cost/time required). How it works: You type in what you’re looking for, in all its complexity - whatever you need to solve your problem. Then, we find every paper on the topic in ~3-6 minutes, and give you a detailed and comprehensive report. It works by mimicking a human’s research process, first carefully and systematically searching 200M+ papers, then classifying the best results, and then adapting based on relevant papers it uncovers. It continues until it finds every paper. We’re making it free to individuals for now, and would love people to try it and give feedback and suggestions.

Visit product View on Hacker NewsMar 26, 2024

Actual performance

3points

1comments

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

78%78% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% 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

30%30% predicted probability of success on TrustMRR, 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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