TYON, a Simple JSON Alternative

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TYON (typed object notation) is my attempt to address the main frustrations I have with JSON. 1. Key repetition - you can specify the key names once at the start of a list or map, instead of on each object separately. 2. Escaping strings - everything in a string is literal except for " which is escaped as "". This makes writing regexes and multiline text easier without introducing a bunch of different string styles or excess escaping, which I am not a fan of. 3. While not nearly as important as the first two to me, I did reduce symbol clutter when possible. Files are implicitly maps, keys don't require quotes in most cases, and you don't need commas between items. 4. Decoupling syntax and data formats - TYON has recommended formats for interoperability, but none of these are part of the core spec, so tools are equally welcome to just treat everything as a string. The linked repo contains the spec and a cli app that provides formatting, validation, and conversion to JSON.

Visit product View on Hacker NewsApr 14, 2023

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

45%45% 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 · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, 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 · Missing: web3, chat, crypto

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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