GoPOSIX – a Go-native POSIX userland, ~97% BusyBox-compatible

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A few things kind of aligned over the last month. I'd been wanting to try pi.dev ( https://pi.dev ), DeepSeek has a very aggressive 75% discount on their v4-pro model until end of May, and I had this old itch from my LFS days to "do my own thing" with POSIX utilities. So I set up to do an experimental project with strict architectural rules, pointed an LLM at it, and fed it the BusyBox test suite as ground truth. The result: 77 POSIX utilities in pure Go, single static binary (~10MB), and it passes 548 of 552 BusyBox tests. My Honest take: this project is ~90% wiring the AI to do the heavy lifting, ~10% steering it in the right direction. Took about 3 weeks of "prompts" and plans and all that. "Harness engineering" works when you have a solid test suite to validate against, I know, it is an obvious statement, but I have hard evidence now to prove it. The test suite is the actual hero here, I cannot stress this enough. Without BusyBox's brutally thorough test suite, this project is just random hallucinated code. Also, I'm not trying to replace BusyBox. It's an experiment. I'm happy with how it went, cheers! Repo: github.com/ramayac/goposix (https://github.com/ramayac/goposix)

Visit product View on Hacker NewsMay 20, 2026

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

72%72% 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, user, single · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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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