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A directory of open source alternatives to proprietary software

Hacker News

A directory of open source alternatives to proprietary software

Hi, Piotr here I was collecting nice open-source companies for quite some time now. Mostly to take some inspiration and learn from their code. Last week, I though it would be fun to learn this Astro thing everyone's talking about. So I thought building a directory website out of this collection was pretty good idea. After 2 days of building, OpenAlternative was born. It's a community driven list of open source alternatives to proprietary software and applications. Enjoy and thank you for your support!

Visit product View on Hacker NewsMar 8, 2024

Actual performance

156points

52comments

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

70%70% 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: open source, ide, io · Missing: https docs, excited, just released

64%64% 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 HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

15%15% 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.

Correct prediction on native model

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