CleverCrow, Get paid to work your backlog

Hacker News

Howdy all, My first post [1] on CleverCrow generated some great conversation and a few things stood out: less AI, more money for open source maintainers please! So in the intervening 84 days, that's what I've done. I've entirely removed all AI and pivoted to a model where backers put money on issues they want fixed and the whole pool goes towards paying the maintainer of the project (minus Stripe fees and a 5% platform fee). Along the way I ditched javascript [2] which tightened up the security model! Sponsorship is still the best way to go, but for those that want to see specific issues fixed and keep the money and control in the hands of the maintainer, CleverCrow.io is there. [1] - https://news.ycombinator.com/item?id=48621645 [2] - Technically 40 lines for copy to clipboard and 90 for some Stripe glue to avoid an unrestricted key in the web tier

Visit product View on Hacker NewsSep 14, 2026

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

75%75% 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: model, stripe, new · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

19%19% 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

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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