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Wrote a tiny WebAssembly (wat2wasm) compiler in Go

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Wrote a tiny WebAssembly (wat2wasm) compiler in Go

As a personal project I wrote a really tiny Wat 2 Wasm compiler in Go. Mainly for demonstrative and educational purposes. It was tough: I didn't know anything about WebAssembly internals and I'm a newbie with Go... so I tried to document it as much as I could for anyone that would like to approach the quest in the future! It misses a lot of features (that will be gradually implemented). Any feedback is welcomed!! Demo: https://luna-demo.vercel.app

Visit productView on Hacker NewsNov 1, 2022
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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, tiny · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, education · Missing: mobile apps, ios, entrepreneurs
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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
23%23% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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