Rulebricks: A visual rule engine

Hacker News

A friend and I spent a month throwing together a visual rule engine product– wanted to share it with HN today. I've been building automation tooling for a few years at prefix.app and one of the messier things both to support and to teach users was around encoding logic in their automations– most folks get a hold of the basic concepts quite easily, but every (visual) automation tool out there seems to have their own way of actually pulling it all together. For small decisions those work great! But for bigger decisions and more complex logic we don’t think it makes much sense to be embedding logic in these platforms. A surprising amount of resources can get spent trying to work around seemingly arbitrary constraints in UI’s, entire processes usually need to be reconfigured/redeployed when a single decision point changes, and it’s pretty difficult to surface data around what are sometimes pretty important choices that get made from inside automation tools. Generally speaking, both for automation tool providers and automation users logic seems like a pain to support– and we think these folks should probably focus on managing actions and states over implementing graph UI’s and logical operators, so we built Rulebricks– inviting you all to check out our product and would love any feedback.

Visit product View on Hacker NewsDec 16, 2022

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

84%84% 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.

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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

41%41% 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

21%21% 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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