Oomol – A local-first, code-first workflow automation engine

Hacker News

Hi HN, We’ve been working on [OOMOL]( https://oomol.com ) for the past few years – a workflow automation platform built for developers. Unlike Zapier or n8n, OOMOL runs locally in a containerized environment, supports full Python and Node.js (with third-party libraries), and gives you the same flexibility as writing real code. It’s like VS Code meets workflow automation. Key points: - Local-first (no cloud lock-in) - Python and Node.js support (you can mix both!) - Pip/npm libraries supported out of the box - Fully visual with programmable nodes - Share & reuse flows as containers or subflows We’re still early, and would love feedback from the HN community. Happy to answer anything!

Visit product View on Hacker NewsJun 30, 2025

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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: platform · Missing: plus, intuitive, reviews

50%50% 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

38%38% 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

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.

Incorrect prediction on native model

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