Bruno – helping you discover your curiosities

Hacker News

I started working on Bruno because I wanted to give myself a consistent stream of essays and podcasts that would send me down new rabbit holes and make me lose track of time (a good feeling). The internet is tailor-made for exactly this, but parsing the signal from the noise is too difficult, especially in the age of generative AI. Bruno uses a “give-to-get” model to aggregate high-quality thinking and then predicts which ideas that are anomalous to a user's stated interests they'd find most interesting. Each week, users can submit links to three things they find interesting, and by doing so, they get access to recommendations for the next 4 weeks. The recommendation system will try to predict ideas you’d find interesting, but you didn’t know you hadn’t yet discovered.

Visit product View on Hacker NewsAug 11, 2023

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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.

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

67%67% 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

44%44% predicted probability of success on AppSumo, 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 released

40%40% 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: ios, users · Missing: mobile apps, personal, entrepreneurs

35%35% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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