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OpenPledge – Encourage Companies to Fund Their Open Source Dependencies

Hacker News

OpenPledge – Encourage Companies to Fund Their Open Source Dependencies

I discovered that many corporations don't support open source packages because they can't see a direct return on investment. And honestly... who could blame them? They donate a certain amount to a person they don't know, with no guarantee it will help in any way. With OpenPledge, we want to change that. We understand that small donations won't move the needle, since an engineer's salary is usually pretty decent. However, if a corporation that heavily depends on open source repositories (and most of them do) is presented with a new way of supporting that actually shows results, we believe we can reduce the number of abandoned repositories and, therefore, reduce the number of vulnerabilities in enterprise products. With substantial donations by corporations, which can be distributed among the issues of the repositories they use (this would be done by developers in these corporations), rewards would be paid to the actual contributors who solve those issues, and corporations would actually see results. Hop on our wait list to be one of the early adopters in what I believe could be a revolution in open source community :) !

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
80%80% 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: new, open · Missing: mac, agents, macos
76%76% 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
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
47%47% 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
27%27% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward, 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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