Hh

Hh.ht – a lightweight JSON-extended scripting language

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Hh.ht – a lightweight JSON-extended scripting language

Hi all, I've built a small scripting language called hh.ht. It extends JSON with an embedded C-like code block that can directly manipulate the JSON object. The format contains a JSON part and a code part in `{ ... }`. Features: - JSON + C-like code in one file - Weak typing and minimal syntax - `this` refers to the JSON section - JSON iteration in defined=>/reverse=</ascending=+/descending=- order - ljson objects can be executed as functions - Lightweight interpreter (186 KB) - Reference-counting GC - Full C FFI (loadlib + dynamic .so) - Bytecode save/load (savebc/loadbc) - Depends only on libc · Runs on any Linux since glibc 2.34 File size: 190632 bytes (exactly, -O2, x86_64 Linux) Repo: https://github.com/chunpulee/hh.ht I’d love feedback or criticism. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
63%63% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% 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
36%36% 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
35%35% 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
20%20% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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