I created a language called AntiLang – breaking all the conventions

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AntiLang is an interpreted programming language written in Go. The basic idea of this is to keep all the logical parts of a language same, but reverse the structure of it. The idea for this project came when I was having a long midnight conversation with my friend and thought of writing such a weird language. The initial draft was far worse than the current implementation; we thought of reversing the brackets and the language would be read from bottom to top. I'm happy that I dropped that idea Technical details: As the interpreter is written in Golang, I compiled it to WASM, and the whole interpreter is running in the browser. For the editor, I'm using Monaco, the same library that powers the text editor in VSCode. I learnt how to build it while reading "Write an Interpreter in Go" by Thorsten Ball. The project is opensourced - https://github.com/SirusCodes/AntiLang - do give it a star if you like the project.

Visit product View on Hacker NewsFeb 23, 2025

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

74%74% 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: using, code, open · Missing: mac, agents, macos

68%68% 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: created · Missing: supports, reddit linkedin, podcasting

66%66% 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

45%45% 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

29%29% 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

19%19% 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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