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Non.io, a Reddit-like platform Ive been working on for the last 4 years

Hacker News

Non.io, a Reddit-like platform Ive been working on for the last 4 years

Heya HN, I've been working on a reddit-like platform as my primary side project for the last few years. Doing a (very) soft launch today, mainly because I want to use it to encourage discussion of alternatives. How non.io works: 1. Free to browse, paid to interact. 2. Minimum subscription is $2 (though you can choose more). I take $1 to run the servers, everything left gets split evenly between everything you upvote that month. It's a simple model, but I hope it's a better one than the freemium model we've been relying on for the last few years. Fundamentally I feel like any ad-supported network doesn't have alignment between the needs of the users and the needs of the platform, which is what drove me to make this. Because this is a soft launch, if you do subscribe I'd encourage you not to pay for the time being. I'm still testing the distribution algorithm for returning funds - you won't get overcharged or anything, but I just want to guarantee your funds are properly distributed at the end of the month. I've opened up free accounts to post and interact in the meantime. If you want to try a test account, use this login: login: hackernews pw: helloworld Edit: Loginless browsing here: https://non.io/#all If you want to browse the code or the api: https://api.non.io https://github.com/jjcm/nonio

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Actual performance

1,943points
590comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
92%92% 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
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
72%72% 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: month, users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
18%18% 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
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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