AI Search Engine for Academic Research

Hacker News

Get answers to research questions with citations to academic literature. Some ways Epsilon is different from other AI research tools - Has inline citations to peer reviewed literature - Can process up to 25 papers for a single query - Has deep links to the relevant passage in the source paper - Let's you save papers and run queries only across your library - Let's you run multiple questions across multiple topics in one view It's free to use and doesn't require sign-up to test it out. Would love feedback from the HN community!

Visit product View on Hacker NewsSep 13, 2023

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

75%75% 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 released

55%55% 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 · Strong signals: answers, way · Missing: mobile apps, ios, personal

50%50% 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, podcasting

31%31% 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, intuitive

26%26% 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, revenue

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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