I am launching Nadhi Audit today on Product Hunt to solve the problem of cloud security scanners that require uploading your private source code to external servers while only returning noisy alert lists that leave all the repair work to you. I built a tool that delivers complete airgapped privacy on Apple Silicon Metal GPUs paired with closed loop code repair that compiles, verifies syntax, and creates GitHub pull requests automatically. Every patch is verified by local compiler syntax gates and local evaluation judges before touching your git tree, and the app includes a native Model Context Protocol server that plugs directly into Claude Code and Cursor. To celebrate the launch, I am giving away free one year community licenses with zero signup and zero credit card friction, so you can grab your key with one click on the site and test it out.

I have been thinking about a major irony in developer tools today where developers are copying and pasting their codebase, environment variables, and database connection strings into third party cloud models just to check their code for privacy compliance and security vulnerabilities. Uploading confidential source code to check for privacy compliance is itself a data disclosure, which violates basic data protection rules for healthcare, fintech, and defense teams. I spent the past few months building a fine tuned four billion parameter model that runs fully offline on Apple Silicon Metal GPUs, scanning files at twenty milliseconds per file and repairing vulnerabilities with zero bytes leaving the laptop. How does your team currently handle code security when using artificial intelligence tools, and do you require airgapped on device solutions for proprietary repositories?