I made a WASI orchestration prototype

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I've really come to hate containers and orchestrators over the years, so I decided to prototype out my ideal replacement: a WASI orchestrator with TOML. What I ended up with turned out to be a lot like Spin, but with some key differences and additions. - one-off 'job' support. - QEMU and native binary support. (QEMU is probably broken right now, but would not be hard to round-out support for it) - mesh networking. adding new nodes involves setting the owner key and pointing it to a bootstrap peer - Built-in UI. Every node on the mesh can serve a management interface - Run now, codify later. Making config changes can be done in the UI and push to TOML when you're happy with it. So in addition to hard isolation and resource quotas with WASI and QEMU, you can also use the system like a distributed computing mesh and submit jobs to it. I've been using it to make linux builds of itself on my linux machine automatically. The README goes into a lot more depth. I hope you feel inspired by this idea, and I really hope a real Rust dev will want to redesign it to be production ready.

Visit product View on Hacker NewsAug 25, 2025

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

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.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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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.

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