TinyWasm – A tiny WebAssembly Runtime written in Rust

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Show HN: TinyWasm - A tiny WebAssembly Runtime written in Rust Hi HN! I'm excited to share TinyWasm ( https://github.com/explodingcamera/tinywasm ), a WebAssembly Interpreter I developed for my final university project. The main goal of this project was to deepen my understanding of WebAssembly and interpreter design. TinyWasm successfully passes all the official WebAssembly 1.0 tests and also includes features from future proposals such as like bulk memory operations. Initially more of a research project, TinyWasm focuses on simplicity and portability. It has minimal third-party dependencies, a small codebase, and is compatible with no_std environments. It's now available as a standalone library ( https://crates.io/crates/tinywasm ), and I hope it can be useful for embedding into other projects and hacking on, especially because it's designed to be easy to understand and modify, while still being decently performant. I'm looking forward to feedback, hope you find it useful!

Visit product View on Hacker NewsMar 7, 2024

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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

57%57% 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

44%44% 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

39%39% 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

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.

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