lee-to

lee-to / hlv

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Specs first. Code second. Proof always. You define the what. LLMs generate the how. hlv validates the proof.

hlv.cutcode.dev ai aiworkflow sdd spec-driven-development vsdd
30
2
100% credibility
Found Mar 11, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

HLV is a command-line tool for managing and validating AI-generated code against human specifications through structured contracts, milestones, tasks, and quality gates.

How It Works

1
🔍 Discover HLV

You hear about a helpful companion that keeps AI-built projects perfectly on track with your ideas.

2
📥 Get it running

Download and launch the tool in seconds—no hassle, just works on your computer.

3
🚀 Start your project

Create a new space for your idea with one simple command, setting up everything you need.

4
💡 Describe what you want

Write down your requirements and goals in plain files, like a clear blueprint for success.

5
🤖 AI builds it right

Watch as smart helpers generate code and plans that exactly match your blueprint—exciting and precise!

6
✅ Check it all lines up

Run a quick scan to confirm every piece of code traces back to your requirements perfectly.

7
Spot any gaps?
✨
All good—celebrate!

Everything matches; your project is ready to shine.

🔄
Tweak and recheck

Make a small fix, then scan again to confirm perfection.

🎉 Ship with confidence

Your AI-powered project is fully validated and ready to launch—reliable from day one!

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Star Growth

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AI-Generated Review

What is hlv?

hlv is a Rust CLI for specs-first development: humans define requirements via contracts, glossaries, and constraints in YAML/MD, LLMs generate all code, and hlv runs 30+ machine validations across traceability, gates, and markers like @hlv in source. It solves LLM hallucinations by enforcing proof from intent to compiled binaries, with commands like hlv check, dashboard TUI, and MCP server for AI tools. Similar to asyncapi specs github or github specs ai, but project-wide.

Why is it gaining traction?

Stands out with phase-aware checks that downgrade expected issues (e.g., missing code markers pre-implementation), plus visuals for plans and traces—no more chasing drifts manually. MCP exposes 12 resources/27 tools for agents like Claude, beating ad-hoc prompts or self-verifying LLMs. Flat llm/ structure optimizes for regeneration, fitting github specs kit workflows.

Who should use this?

Backend teams in Rust/Go/TypeScript building services, needing formal contracts beyond cocoapods specs github or kiro specs github. Devs using object first specs or iphone se specs first generation rigor for AI codegen, especially with hlv ergebnisse-style gates before shipping.

Verdict

Early at 17 stars and 1.0% credibility (v0.0.3, active dev), but strong README, tests, and curl install make it trial-ready for specs-first AI experiments—hlv init a milestone and validate. Skip if not using LLMs heavily.

(198 words)

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