I am thrilled to share the release of the Penpot Docker Extension, a tool designed to streamline the deployment and management of a complete self-hosted Penpot instance directly within Docker Desktop.
Quantization = compressing a model by lowering the precision of numbers, making it smaller, faster, and cheaper to run, often with only a small drop in accuracy.
From Claude Desktop to Cursor: A complete breakdown of which AI chat interfaces support MCP—and which ones are worth your time. Because in 2025, your AI assistant should do more than just talk.
No monitor? No problem. Learn how a simple USB Type-C charging cable and serial console access saved a student demo at our Docker meetup.
What if your AI chatbot could configure itself based on what customers ask, without developers editing config files? That's Dynamic MCP.
Want to add vision capabilities to your applications without sending data to external APIs? Docker Model Runner makes it straightforward to run multimodal AI models locally, giving you complete control over your data while using the familiar OpenAI-compatible API format.
Docker
This guide walks you through connecting models from the Docker AI Model Catalog to MCP servers, enabling your applications to leverage both local inference and external capabilities in a secure, reproducible Docker Compose environment.
Stop wasting hours setting up MCP servers. The Docker MCP Catalog provides 270+ enterprise-grade, containerized Model Context Protocol servers that install in seconds—no dependency hell, no environment conflicts, no cross-platform issues.
How we built, trained, and deployed a dental X-ray analysis system achieving 99.5% mAP50 accuracy using YOLOv8, Docker containers, and iterative model improvement using NVIDIA Jetson AGX Thor
If you're building robots, you're going to want to hear about this.
In this world of influencers, life is not easy. The pressure to stay visible, relevant, and knowledgeable is constant.
Docker Model Runner uses llama.cpp's KV cache for automatic token caching, eliminating redundant prompt processing in local LLM deployments. Discover how this built-in optimization works.