Show HN: Jacquard, a programming language for AI-written, human-reviewed code

📝 Discussion Summary (Click to expand)

Skepticism about LLM‑generated language design

“Given how poorly LLMs do with writing prompts for LLMs, I'm not sure I'd trust their judgement in designing a language for LLMs.” — wren6991
Concern over the massive resource consumption of AI experiments

“I love how people create so many new things with AI, but to think how much tokens, and in turn money we all have collectively burned for these side projects is crazy.” — sajithdilshan
Playful commentary on ambitious AI language concepts

“Esperanto for Clankers” — erelong

🚀 Project Ideas

A declarative language + runtime that lets AI developers define "worlds" (real network, recorded traffic, mock environments) and explicitly grant filesystem/network permissions per world.
Core value: Write once, run the same agent code in any simulated or production environment with deterministic sandboxing.

Key	Value
Target Audience	AI engineers, LLM application developers, security-focused devs
Core Feature	World definition language and sandbox API with explicit permission grants
Tech Stack	Rust for runtime, WebAssembly sandbox, JSON schema for world configs
Difficulty	Medium
Monetization	Revenue-ready: SaaS subscription $19/mo per team

Directly addresses HN complaints about “runtime requires explicit permission to touch the filesystem, network, etc” and “run one program against many worlds”.
Sparks discussion on safe AI experimentation and cheaper testing alternatives.

A UI-driven testing harness that lets developers run a prompt against multiple pre‑recorded “worlds” (network traces, file system snapshots, mock services) and compare outputs.
Core value: Guarantees prompt behavior is stable across environments without manual mock server setup.

Key	Value
Target Audience	Prompt engineers, QA teams, AI startups
Core Feature	Multi‑world execution with result diffing and regression alerts
Tech Stack	TypeScript/React frontend, Node.js backend, Docker containers for world containers
Difficulty	Low
Monetization	Hobby

Answers the HN question “How is the “world” model different from plain dependency injection?” with concrete UI and diffing.
Generates conversation about prompt reliability testing and reproducible experiments.

A lightweight analytics dashboard that aggregates token consumption across multiple AI projects, flags cost overruns, and suggests cheaper model or prompt alternatives.
Core value: Gives users visibility and control over the “collective token burn” they’re concerned about.

Key	Value
Target Audience	AI project founders, freelancers, researchers
Core Feature	Token usage logging, budget alerts, cost‑optimization suggestions
Tech Stack	Python backend, PostgreSQL, Grafana frontend, serverless deployment
Difficulty	Low
Monetization	Hobby