opub, donated compute for open-source

Hacker News

Hey HN, I hacked together an open source project not too long ago that became somewhat popular on GitHub. It was a fun way to reconnect with programming after spending more time on the management side of the house. It also showed me firsthand how much open source has changed. The volume of generated issues and pull requests took a toll. Some of it was helpful. A lot of it was not. Either way, maintainers now have to review, triage, and respond to a growing amount of "AI"-generated work, on top of everything they were already doing for free. One practical response is to use these tools on the maintainer side too: filtering issues, doing first-pass PR review, helping with security patches, writing tests, or handling some of the repetitive work around a project. That is not everyone’s cup of tea, and I respect that. But used carefully, it can help. The problem is cost. Open source maintainers should not have to personally pay for the compute needed to keep up with the generated workload around their projects. This emerging pressure is why I built open public (opub). opub lets donors fund donated compute for open source projects. Maintainers can create dollar-limited compute keys and use them with coding agents and tools across 30+ models. Token usage and spend are linked back to the project, alongside donations, so the project’s compute balance is visible in the open. Registration is open, and I’d love feedback from the HN community. If you maintain an open source project with 100+ GitHub stars, the first 20 to register can claim $50 in starter donated compute from us.

Visit product View on Hacker NewsMay 21, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

85%85% 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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

47%47% 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: personal, way · Missing: mobile apps, ios, entrepreneurs

31%31% 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

24%24% 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 · 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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