Streamix – A modern reactive stream library for JavaScript/TS

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Hi HN, I built Streamix as a fresh take on reactive streams for JavaScript and TypeScript. It’s inspired by RxJS, but designed to be lighter, simpler, and more ergonomic — especially for developers who just want clean async data pipelines without having to learn a full reactive framework. Some features: Fully pull-based (async iterable in, async iterable out) Composable operators like map, filter, mergeMap, audit, etc. A coroutine operator that offloads heavy processing to Web Workers Zero dependencies, tree-shakable, TypeScript-first Small and fast — ideal for frontends, workers, and reactive logic I created Streamix because I often wanted RxJS-like power without RxJS-level complexity. Most stream libraries are either too limited or too heavy. Streamix aims for a sweet spot: expressive, composable, and easy to debug. You can see code examples and usage in the GitHub repo: https://github.com/actioncrew/streamix I’d love feedback from the HN community — whether it’s feature ideas, API critiques, or just thoughts on the reactive model. Thanks!

Visit product View on Hacker NewsMay 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, code · Missing: mac, agents, macos

74%74% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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 · Missing: plus, platform, intuitive

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

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% 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.

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