I built an open source job scheduler to help script everything

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As a software engineer, my reflex has always been to script everything I can. Scripting is easy, but everything around it is tricky: scripts have to run somewhere, they need access to credentials, they need to be monitored. None of it is simple. No-code automation platforms such as Zapier or IFTTT are too restrictive for technical tasks; CI/CD platforms make it easy to script tasks related to a repository, but are limited when trying to do anything else. I wanted the low level control you have when writing scripts while retaining the comfort of a platform managing execution for me. So I built a program for that. Eventline schedules, runs and monitors any script. Jobs can be executed on various runtimes (currently locally, with SSH, in Docker or in Kubernetes), manually or in reaction to various events. You then get observability (script output, history, execution stats…), full control (with the web UI, HTTP API or command line tool) and the ability to share scripts with others. Eventline 1.0 is out. I use it myself, so does a friend and a first client. The roadmap is packed with features which will make Eventline even more useful: global registry for value injection, sub-jobs, scratchpad to share data between jobs, artifacts, ACL (and SSO for Eventline Pro), a lot more connectors, identities and events… I quitted my job in 2020 and created a company, Exograd, hoping to become financially independent. This has been quite a ride, both exhilarating and scary: I made lots of mistakes and learned more in 22 months than in my entire career. I would like to bring enough revenue to be able to live from it, and then work on expanding Eventline and developing new technical products. I currently offer a support contract, a proprietary extension (Eventline Pro) for enterprise use, and cloud hosting to use Eventline without having to host it. Happy to answer any question!

Visit product View on Hacker NewsSep 6, 2022

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

95%95% 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: dock, new, tasks · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

53%53% 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, host · Missing: plus, intuitive, reviews

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

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

14%14% 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.

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