A tiny WASM compiler and runtime for demonstration purposes

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Hello, I'm building a WebAssembly compiler and documenting the process in the meantime. This aims to be a tool for demonstration and education purpose. It doesn't aim to do fancy stuff nor to replace existing ones. I'm building this to learn and to improve and hopefully it can be useful for others as well :) From last time I've presented the idea, I've implemented more basic features on Luna and I've built a custom and tiny runtime for Luna! (documenting it of course). Any feedback is really appreciated :) Repo: https://github.com/thomscoder/luna

Visit product View on Hacker NewsNov 13, 2022

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

70%70% 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

60%60% predicted probability of success on Indie Hackers, 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.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · 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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