StackAid – Fund all your open-source dependencies

Hacker News

We strongly believe working on open source software should be a viable source of income for many more developers. Unfortunately, the following barriers limit the extent of open source funding: - Only a small fraction of open source projects are funded, and most money goes to a few notable projects. - Each project has to market is self to get significant funding. - Large corporate donations provide the bulk of the funding, making it unreliable and unattainable for many. - Finding and supporting each of your dependencies is a cumbersome task. Which ones, how much, and on which platforms? So we built StackAid, a service that automatically discovers and funds your direct and indirect (second order) open source dependencies with a monthly subscription. StackAid is early and has a unique allocation model, so we're working with supporters and open source projects to validate the experience further. We're matching subscriptions up to $100/month during the beta.

Visit product View on Hacker NewsSep 14, 2022

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

75%75% 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, open · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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, monthly · Missing: mobile apps, ios, personal

36%36% 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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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