Hardware.Info (Jinjinov/Hardware.Info) is an open-source AI project on GitHub. Repository summary: Battery, BIOS, CPU - processor, storage drive, keyboard, RAM - memory, monitor, motherboard, mouse, NIC - network adapter, printer, sound card - audio card, graphics card - video card. Hardware.Info is a .NET Standard 2.0 library and uses WMI on Windows, /dev, /proc, /sys on Linux and sysctl, system_profiler on macOS. Its focus includes retrieval-augmented generation, developer-centric engineering workflows, video generation and processing, speech and audio processing. It is suitable for extension, integration, and iterative delivery in real workflows.
License
MIT
Stars
686
Features
- Core capability: Battery, BIOS, CPU - processor, storage drive, keyboard, RAM - memory, monitor, motherboard, mouse, NIC - network adapter, printer, sound card - audio card, graphics card - video card. Hardware.Info is a .NET Standard 2.0 library and uses WMI on Windows, /dev, /proc, /sys on Linux and sysctl, system_profiler on macOS.
- Supports vector retrieval and retrieval-augmented reasoning
- Built for code generation, debugging, or engineering integration
- Covers video generation, editing, or avatar pipelines
- Supports speech recognition, synthesis, or audio processing
- Repository: Jinjinov/Hardware.Info
Use Cases
- Builds enterprise knowledge Q&A and document retrieval systems
- Supports AI engineering build-and-iterate workflows for dev teams
- Used for marketing videos, training content, and media production
- Used for meeting transcription, voice assistants, and audio production
- Build internal AI workflow prototypes with Hardware.Info
- Validate Hardware.Info in production-like engineering scenarios
FAQ
Teams should first define integration boundaries and call patterns, then map repository capabilities into concrete interfaces, parameters, and access rules. GitHub repository: https://github.com/Jinjinov/Hardware.Info. Community traction is around 686 stars. License: MIT.
It usually works as an execution component or capability layer, with common deployment fits such as: Builds enterprise knowledge Q&A and document retrieval systems, Supports AI engineering build-and-iterate workflows for dev teams, Used for marketing videos, training content, and media production.
