MCP App StoreLe meilleur de l'écosystème IA et MCP, sélectionné chaque jour.
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Hugging Face and NVIDIA have launched Training Cluster as a Service, simplifying the deployment of massive compute resources for model training. This collaboration lowers the barrier for developers to scale their training infrastructure efficiently.
ScreenSuite is a new comprehensive evaluation framework specifically designed for GUI agents. It provides a robust set of benchmarks to measure how effectively AI agents can navigate and interact with graphical user interfaces.
A technical deep-dive into implementing KV Caching within nanoVLM. This guide provides a foundational look at optimizing inference efficiency for small language models.
HCompany introduces Holo1, a new family of Vision-Language-Action Models (VLMs) designed for GUI automation. These models power the Surfer-H agent, improving the ability of AI to interact with and navigate complex graphical user interfaces.
Hugging Face releases SmolVLA, an efficient Vision-Language-Action model trained using LeRobot community data. This model aims to democratize robotic control by providing a lightweight, open-source VLA for embodied AI tasks.
Hugging Face introduces co-located vLLM in TRL to maximize GPU efficiency during reinforcement learning. This optimization allows for better resource utilization when training agents with complex feedback loops.
A new approach to CodeAgents that emphasizes structured action execution for better reliability. By combining code generation with formal structures, agents can more accurately perform complex tool-use tasks.
Liger GRPO is now integrated with TRL, enabling more efficient Group Relative Policy Optimization for LLM training. This allows developers to scale reinforcement learning from human feedback with significantly reduced memory overhead.
A practical guide demonstrating how to build a functional AI agent using the Model Context Protocol (MCP) in just 70 lines of Python. This highlights the accessibility and efficiency of the MCP ecosystem for developer tooling.
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.
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.
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.