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Another JSON Alternative

Hacker News

Another JSON Alternative

I realize that there are 1001 alternatives to JSON encoding, that are leaner, faster, or just completely different, but I still have my own that I'd like to share. LOEN stands for Lean Object Encoding Notation and aims to reduce the character count compared to JSON, especially in the case of arrays of objects; JSON is crazy verbose in this regard, and so using an alternative when transmitting lists or database results, is pretty much a must-do. Other than being lean, the MVP of LOEN is that it is backwards compatible with JSON. JSON is valid LOEN but not the other way around. This is useful in that it allows for a LOEN interpreter to be used for both LOEN and JSON requests. If by magic LOEN were to become commonplace, it would allow for legacy apps using JSON to continue to function. Anyway not expecting anything but happy to here what you think. Here's an article I wrote about it: https://www.offthebricks.com/articles/lean-object-encoding-a... Here's a link to the GitHub repo: https://github.com/offthebricks/LOEN/tree/master

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
59%59% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, coding · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 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 · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · 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 · Strong signals: arr · Missing: mrr, revenue, profit
13%13% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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