MCP App StoreLe meilleur de l'écosystème IA et MCP, sélectionné chaque jour.
Sources
A minimal PyTorch implementation for training Vision Language Models (VLMs). This repository provides a streamlined way for developers to experiment with VLM training without complex dependencies.
Microsoft and Hugging Face are deepening their integration with Azure AI Foundry. This collaboration aims to streamline the deployment of open-source models for developers on the Azure cloud.
Hugging Face outlines the effort to standardize model definitions within the Transformers library. This move aims to improve interoperability and simplify how new architectures are integrated into the ecosystem.
Anthropic launched integrations allowing Claude to connect directly to external tools and services — an early milestone in Claude’s MCP-powered ecosystem. Users can connect Claude to apps, databases, and APIs so it can take actions and retrieve live context rather than working only from conversation input. This laid the groundwork for the MCP integration ecosystem.
Tutorial on integrating Gradio with the Model Context Protocol (MCP) to build interactive servers. Provides a practical path for developers to expose ML models via the MCP ecosystem.
A technical deep dive into the Qwen-3 chat template, highlighting critical lessons in prompt structuring and model alignment. Essential reading for developers optimizing open-source model performance for chat interfaces.
Llama Guard 4 is now available on the Hugging Face Hub, providing enhanced safety filtering for LLM applications. This release strengthens the ecosystem for deploying secure, compliant, and moderated AI agents.
Intel introduces AutoRound, a high-precision quantization tool for LLMs and VLMs that minimizes accuracy loss. It enables efficient model deployment across hardware by optimizing weights without sacrificing performance.
A concise demonstration of building a functional AI agent using the Model Context Protocol (MCP) in just 50 lines of code. It showcases the simplicity and extensibility of MCP for developing lightweight agentic tools.
Technical deep-dive into fine-tuning olmOCR to improve the faithfulness and accuracy of OCR engines. Essential reading for developers working on document processing and high-precision text extraction from images.
Anthropic's official best practices guide for Claude Code — covering how to structure tasks, write effective CLAUDE.md files, use tools efficiently, manage context, and get reliable results on complex coding tasks. The canonical reference for getting the most out of Claude Code in production. Should be required reading before any serious Claude Code deployment.
An analysis of LLM performance focusing on prefill and decode phases during concurrent requests. Essential for developers optimizing throughput and latency in high-traffic AI applications.
A deep dive into Gradio's capabilities for creating ML model interfaces. Highlights its role in rapid prototyping and sharing AI tools within the developer community.
Cohere is now available through Hugging Face Inference Providers, expanding access to their models for developers. This integration simplifies deployment and scaling for those using HF's infrastructure.
Hugging Face introduces HELMET, a new framework for the holistic evaluation of long-context language models. It provides critical benchmarks for understanding how models actually handle massive context windows in practice.
Introduction of Visual Salamandra, a multimodal model focusing on enhanced visual understanding and reasoning. This development pushes the boundaries of how AI interprets complex visual data alongside text.
Hugging Face welcomes Llama 4 Maverick and Scout, expanding the availability of high-performance open-weights models. This release provides developers with new options for fine-tuning and deploying advanced LLMs.
Hugging Face details its approach to managing secrets at scale for massive AI infrastructure. A practical look at ensuring security without sacrificing developer velocity in high-compute environments.
Text Generation Inference (TGI) now supports Intel Gaudi accelerators. This provides an alternative to NVIDIA GPUs for high-performance LLM serving and inference.
The latest update on the Open R1 project, focusing on the replication of DeepSeek-R1's reasoning capabilities. It provides insights into the training process and current performance of open-source reasoning models.