Chakra – An open source WASM runtime in Rust

Hacker News

Hey folks I'm building *[Chakra]( https://github.com/anistark/chakra )* — a lightweight WebAssembly runtime and dev tool that runs `.wasm` files in your browser with live logging, reload, and module introspection. ```sh chakra myfile.wasm ``` What it does: * Launches a local Chakra server * Runs the `.wasm` file in-browser * Streams logs to console and browser * Includes `verify` and `inspect` modes for detailed module analysis of your wasm binary. Built for: * Rust (via `wasm32-unknown-unknown`) * TinyGo * C/C++ (via Emscripten) * Python (via py2wasm) * AssemblyScript It's in very early phase and highly experimental. Building Chakra as an open source project the open and would love feedback, bug reports, and contributions. You can read more about the motivation and background here: [ https://blog.anirudha.dev/chakra ]( https://blog.anirudha.dev/chakra ) GitHub: [ https://github.com/anistark/chakra ]( https://github.com/anistark/chakra ) Let me know what works, what’s missing, and what you’d want in your ideal WASM workflow! Give us a shout-out or star the repo on github if you like the idea.

Visit product View on Hacker NewsMay 25, 2025

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

82%82% 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.

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% 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

17%17% 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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