Sayiir – A simple durable workflow engine (Rust core, Python/Node.js)

Hacker News

Temporal, Airflow, Step Functions are great — but sometimes you just need "run these steps, resume where it stopped if something crashes," without the infrastructure and learning curve that comes with a full platform, and whole mental model to internalize So I built something simpler and maybe i over simplified things.. It sticks with philosophy of "workflows as code", so no dsl , no no-code, or visual designer. just a developer friendly lib.. Sayiir is a library you pip/npm/cargo install. Define steps as normal async functions, it checkpoints each output. On failure, completed steps are skipped, execution picks up where it left off. No replay, no determinism rules, no separate server to deploy, no activities — just your good old existing async functions with durable execution underneath. Rust core, Python/Node.js bindings. MIT licensed Docs: https://docs.sayiir.dev Playground: https://docs.sayiir.dev/playground/

Visit product View on Hacker NewsFeb 26, 2026

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, 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

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

Incorrect prediction on native model

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