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Browser AI assistant for reading, analyzing, and generating text within web pages

A desktop work log tool that captures thoughts and decisions while maintaining a knowledge graph.

Road maintenance robot that automatically detects and seals asphalt cracks

PDF information processor that extracts content to generate summaries, tables of contents, and answer targeted questions.

An academic writing assistant platform supporting reading, writing, citation, and multilingual content generation.

Audio and video transcription tool that converts content into summaries, mind maps, and key Q&A.

Lightning AI is an integrated advanced platform that simplifies the entire AI development lifecycle, from model building and training to deployment. Built on PyTorch Lightning, it provides researchers and engineers with a unified environment, freeing them from the complexities of underlying infrastructure. The platform seamlessly integrates mainstream machine learning tools, eliminating differences between local and cloud environments, while delivering powerful on-demand computing resources. Lightning AI significantly enhances productivity, enabling users to focus on innovation rather than infrastructure management.

Zerve is a professional development environment designed specifically for data science workflows, distinct from traditional software development IDEs. The platform combines Agent capabilities with comprehensive IDE controls, enabling data scientists to generate code, orchestrate computing resources, and build complete workflows through natural language prompts, while maintaining full oversight of data previews, code editing, and compute configurations. With contextual awareness of data and code throughout the development process, Zerve enables seamless iteration from experimentation to production scale, supporting instant scaling from single runs to thousands of parallel experiments, along with built-in reproducibility and artifact tracking.

PyTorch is a popular machine learning library developed by Meta AI, widely used in fields such as computer vision and natural language processing. Its dynamic computation graph enables flexible model experimentation and rapid prototyping. PyTorch combines NumPy-like tensor computation with powerful GPU acceleration, along with a tape-based automatic differentiation system for efficient gradient computation. Its rich ecosystem includes libraries for vision, text, and audio, supporting both research and production deployment.

Zed is an advanced code editor completely redesigned in Rust, harnessing the power of multi-core CPUs and GPU acceleration to significantly enhance speed and efficiency. It features built-in AI capabilities, integrating large language models such as Anthropic Claude 3.5 Sonnet directly into the development workflow to enable code generation, transformation, and analysis. Zed supports real-time collaboration, allowing teams to chat with low latency, share screens, and edit code together. With its open-source nature and plugin extensibility, Zed is adaptable to a wide range of programming tasks and environments.

Vagon is a cloud computer platform designed for creatives, engineers, and professional users, delivering powerful computing performance on any device. The platform supports NVIDIA RTX GPUs, scalable CPUs and memory, and ultra-high-definition streaming up to 4K/60fps. Users can instantly create, customize, and upgrade their cloud PCs while maintaining continuous data and file synchronization. Vagon enables seamless file sync, application streaming, and low-latency access worldwide, providing professional-grade computing power without hardware limitations—available anytime, anywhere.

Pieces is an AI-powered productivity platform designed to enhance developer efficiency through personalized workflow assistance. It integrates code snippet management, context memory, and an AI-powered programming assistant to deliver a seamless experience across desktop and IDE environments. Built on PiecesOS, the platform supports local AI processing to ensure data privacy, while also enabling cloud synchronization. Pieces works with multiple large language models (LLMs), empowering developers to generate, debug, and refactor code with a context-aware AI assistant—minimizing context switching and improving collaboration efficiency.