Alternate reader with similarity search for Hacker News

Hacker News

Hi everyone, We built this for ourselves, and as practice for vector databases, LLMs, software as a service, etc... Its becoming my own frontend to hacker news. Hope you find it useful. 1st post 10 days ago, didn't get much traction because it required login with email and password: https://news.ycombinator.com/item?id=41404856 Removed the need for passwords and email address to try it out. (ignore the instructions on the about page for paid and free trials for now). Now you just need to pick any username, similar to karpathy's: https://arxiv-sanity-lite.com/profile . There is a rate limit by ip address for now. So, take 2 ! features: 1. find similar users, discussions, comments 2. similarity and dissimilarity search for comments 3. Browse user comments from oldest first 4. Flattened comments in reverse chronological order on stories 5. see user karma next to comment Open to feature requests.

Visit product View on Hacker NewsSep 10, 2024

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Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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

80%80% predicted probability of success on Indie Hackers, 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: hacker news, io · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

31%31% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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