Runnable – Build user-facing internal workflows without writing UI code

Hacker News

Would love to share a new open-source project I've been working on - Runnable. Runnable is a framework to build user-facing admin-like workflows with ease, while keeping operations internal. No need to setup REST or GraphQL for internal applications. There are other similar tools out there that have comparable features, but Runnable is fully open-source and is much easier to set up (due to its simplicity). For example, it does not require a DB or even it's own server. There is an option to use Runnable as a library or as a standalone web-socket server. Site here: https://getrunnable.com/ You can demo Runnable here https://demo.getrunnable.com/admin/login and login with any Google creds. Open to feedback and feature requests.

Visit product View on Hacker NewsApr 28, 2023

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

93%93% 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: io · Missing: https docs, excited, just released

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% 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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