Effect UI – PoC of a reactive UI framework built with Effect

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Hi HN, I'm Mathieu and two days ago I ran into a YouTube video about Effect ("a powerful TypeScript framework that provides a fully-fledged functional effect system with a rich standard library"). This made me so exited that I wanted to build something with it and this is the result! Effect UI - A reactive UI framework completely build with Effect. Reactive properties are SubscriptionRefs which make fine-grained reactivity possible (like in SolidJS, no re-renders like in React). Components are just Effects, which makes dependency injection of themes, clients and stores possible by using Contexts. It is just a proof of concept, but I think it's pretty cool! Let me know what you think or how I can improve it, since I'm definitely not yet an Effect expert. You can see a basic demo on my Twitter/X profile: https://x.com/m9tdev/status/1938148717158076811

Visit product View on Hacker NewsJun 26, 2025

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

82%82% 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: context, activity, using · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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.

Incorrect prediction on native model

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