Q.js – Smaller than React/Vue, yet more powerful (40KB gzipped)

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Q.js is a lightweight JS framework that I recently distilled from our in-house Qbix platform that I’ve been building since 2011. It powers many of our social apps, which have all the features of Facebook, LinkedIn, X, etc. We’re not a big company like Google or Meta, so we never released it publicly. Now I’d like to, and thought it would be a good idea to post it on HN and gather some feedback. Q.minimal.js was designed to be dropped into any website. It lazy-loads all your components only as they are needed and appear on the screen. The minimal file is meant as a starting point for developers, and if you later want more features from the Qbix platform, you can simply swap it out for the larger Q.js file instead. Here are some advantages of Q.minimal.js compared to React, Angular, Vue, or whatever you might be using now: 40KB gzipped, smaller than React (without ReactDOM), smaller than Vue runtime, far smaller than Angular No build step, just drop it in; works with plain .html <template> files or with JS/Handlebars templates Components & tools, like React components or Vue directives, but attachable as behaviors to any DOM element Faster rendering with requestAnimationFrame and .rendering(), no giant virtual DOM reconciliation Built-in power: batching, caching, lazyloading, routing, slot-based page activation, all included in core Universal dev model: designers can use pure HTML, developers can use JS, both work interchangeably Incremental: drop it into an existing site without rewriting or compiling anything If you have a free hour, give it a try! Play around with it, and let me know what you think. It's 100% free and open source under MIT license and I'm looking to polish up any rough edges before letting developers know about it.

Visit product View on Hacker NewsAug 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, apps · 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.

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

77%77% 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

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

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

31%31% 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

16%16% 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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