// summary
Thunderbolt is an open-source, cross-platform AI client designed for on-premise deployment and data ownership. It supports a wide range of frontier, local, and on-premise models across desktop and mobile environments. The project is currently under active development with a focus on enterprise readiness and security.
// technical analysis
Thunderbolt is an open-source, cross-platform AI client designed to provide users with full control over their models and data, effectively eliminating vendor lock-in. By supporting both local and on-prem deployments, it addresses the critical enterprise need for secure, private AI infrastructure. The project prioritizes flexibility by allowing integration with various model providers, though it currently requires users to manage their own inference endpoints and backend authentication.
// key highlights
// use cases
// getting started
To begin using Thunderbolt, developers should consult the deployment documentation to set up the backend using Docker Compose or Kubernetes. Users must provide their own model providers, such as Ollama or OpenAI-compatible APIs, and configure them within the application settings. For those interested in contributing or local testing, the development guide provides instructions for setting up the environment and running the project locally.