LYML – a configuration format with the expressed goal of being readable

Hacker News

Hey, this is my brain-fart from about a year ago which was kindly dragged back into my brain through a groupchat. I now want to move this forward and would like some feedback. Originally, this was conceived while looking for a config language for a different project. I wanted something "Open-BSD-y", type safe, and avoid the ini-hellhole that has one spec for every implementation. Thus, LYML (/ˈlʏ.məl/) was born. And soon forgotten again. (I blame the ADHD and my dog). What I'm primarily looking for here are: — feedback — peer-review (read: let others find bugs I don't see) — people who may want to collaborate Oh, right. No Norway problem here because strings have to be quoted..

Visit product View on Hacker NewsJan 31, 2024

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

80%80% 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.

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: adhd, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, collaborate · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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