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Million Lint – ESLint for Performance

Hacker News

Million Lint – ESLint for Performance

Hey HN! Founder of Million – We’re building a tool to that helps fix slow React code. Here is a quick demo: https://youtu.be/k-5jWgpRqlQ Fixing web performance issues is hard. Every developer knows this experience: we insert console.log everywhere, catch some promising leads, but nothing happens before "time runs out." Eventually, the slow/buggy code never gets fixed, problems pile up on a backlog, and our end users are hurt. We started Million to fix this. A VSCode extension that identifies slow code and suggests fixes (like ESLint, for performance!) The website is here: https://million.dev/blog/lint I realized this was a problem when I tried to write an optimizing compiler for React in high school (src: https://github.com/aidenybai/million). It garnered a lot of interest (14K+ stars) and usage, but it didn't solve all user problems. Traditionally, devtools either hinge on full static analysis OR runtime profiling. We found success in a mixture of the two with dynamic analysis. During compilation, we inject instrumentation where it's necessary. Here is an example: function App({ start }) { Million.capture({ start }); // inject const [count, setCount] = Million.capture(useState)(start); // inject useEffect( () => { console.log("double: ", count * 2); }, Million.capture([count]), // inject ); return Million.capture( // inject <Button onClick={() => setCount(count + 1)}>{count}</Button>, ); } From there, the runtime collects this info and feeds it back into VSCode. This is a great experience! Instead of switching around windows and trying to interpret flamegraphs, you can just see it inline with your code. We are still in the very early days of experimentation! Million Lints focuses on solving unnecessary re-renders right now, and will move on to handling slow-downs arising from the React ecosystem: state managers, animations, bundle sizes, waterfalls, etc. Our eventual goal is to create a toolchain which keeps your whole web infrastructure fast, automatically - frontend to backend. In the next few weeks, we're planning to open source (MIT) the Million Lint compiler and the VSCode extension. To earn a living, we will charge a subscription model for customized linting. We believe this aligns our incentives with yours: we only make money when we make your app faster. We'd love to know your thoughts – happy to answer :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
91%91% 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: model, user, code · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, 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
51%51% 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: users · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money · 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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