SpongeCake – open-source SDK for OpenAI computer use agents

Hacker News

Hey HN! Wanted to quickly put this together after seeing OpenAI launched their new computer use agent We were excited to get our hands on it, but quickly realized there was still quite a bit of set-up required to actually spin up a VM and have the model do things. So we wanted to put together an easy way to deploy these OpenAI computer use VMs in an SDK format and open source it Hopefully this tooling is helpful to other folks building AI agents! Here’s a link to the repo ( https://github.com/aditya-nadkarni/spongecake ) - please try it out and give us a star. If you have any feedback, add it as a comment to this post! Or if you simply just love spongecake, show support for the delicious treat

Visit product View on Hacker NewsMar 20, 2025

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

98%98% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% 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: way · Missing: mobile apps, ios, personal

40%40% 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

30%30% 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

18%18% 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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