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Dogma: a metalanguage for describing data formats in documentation

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Dogma: a metalanguage for describing data formats in documentation

Dogma: A human-friendly metalanguage for describing data formats (text or binary) in documentation. Dogma was born out of frustration trying to describe data formats (in particular, binary) using existing BNF and other metalanguages. What I wanted was something that makes it clear to a HUMAN reader how a data format is structured and interpreted. After lots of input from friends and colleagues (as well as testing it out writing numerous examples of existing formats https://github.com/kstenerud/dogma/tree/master/v1/examples ), I feel that it's almost ready for a 1.0 release, but I'd like to get some last minute critiques just to make sure :) It's not going to solve 100% of use cases, but if it can solve 80-90%, it has met its goal. Please have a look and let me know what you think! https://github.com/kstenerud/dogma/blob/master/v1/dogma_v1.m...

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
61%61% 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
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
54%54% 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: friendly · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% 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
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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