MCP App StoreLe meilleur de l'écosystème IA et MCP, sélectionné chaque jour.
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OpenMed demonstrates that training mRNA language models across 25 species can be done at extremely low cost (~$165), making cross-species biological language models accessible to smaller labs. This reduces the barrier to experimenting with genomics-focused LMs and may accelerate reproducible research and tool development in computational biology.
Hugging Face released TRL v1.0 — a major milestone for their post-training library covering RLHF, DPO, PPO, and other alignment techniques. The v1.0 release signals API stability and production readiness for teams fine-tuning and aligning open-source models. The go-to library for post-training just became officially stable.
Hugging Face published a guide on running OpenClaw agents locally with open-weight models — covering model selection, setup, and the tradeoffs between hosted and self-hosted agent deployments. Relevant for developers wanting to reduce API costs or run agents in air-gapped environments using open-source models.
ServiceNow AI introduced EVA — an evaluation framework specifically designed for voice agents, covering turn-taking, interruption handling, latency perception, and task completion in speech-to-speech interactions. Fills a gap in agent evaluation tooling where most existing evals focus on text. Useful for any team building production voice agents.
Hugging Face's spring 2026 snapshot of the open-source AI ecosystem — covering model downloads, trending architectures, the growth of multimodal and agent-capable models, and shifts in what the community is building. A useful benchmark for understanding where open-source AI stands relative to proprietary frontier models.
H company released Holotron-12B — a 12B parameter model optimised for high-throughput computer use, balancing strong automation performance with the speed needed for real-time desktop and browser control. Positioned as a cost-effective option for teams that need computer use at scale without paying frontier model prices.
Hugging Face launched Storage Buckets — a new Hub feature for storing arbitrary files (datasets, checkpoints, outputs) alongside models and datasets in a unified workspace. Removes the need to use separate S3/GCS buckets for ML artefacts, keeping everything in the HF ecosystem. A practical platform improvement for teams with large training pipelines.
A comparative analysis of 16 open-source reinforcement learning libraries for LLM training — covering throughput, async rollout support, scalability, and ease of use. Cuts through the rapidly crowded RL-for-LLMs space with concrete benchmarks and recommendations for different training scales. Useful reference for teams choosing a post-training RL stack.
GGML and llama.cpp — the foundational libraries behind local LLM inference — are joining Hugging Face to ensure their long-term development and maintenance. This brings the most widely-used local inference stack under HF's open-source umbrella, with commitments to continued compatibility and community governance. A significant consolidation in the local AI ecosystem.
IBM Research and UC Berkeley released IT-Bench and MAST — a benchmark suite and failure taxonomy for enterprise AI agents in real IT environments. The research identifies the root causes of enterprise agent failures across categories like tool misuse, context loss, and ambiguity handling. Practical reference for teams debugging why their production agents underperform in enterprise settings.
Hugging Face demonstrates using Codex and Claude agent skills to generate custom CUDA kernels — making GPU-level optimisation accessible to developers without deep CUDA expertise. The post shows how agent skills can encode specialist knowledge (GPU programming patterns) and apply it on demand. A compelling use case for agent skills beyond typical software engineering tasks.
OpenEnv is a new evaluation framework that tests tool-using agents in real-world execution environments rather than sandboxed simulations — catching failure modes that controlled benchmarks miss. The post covers the framework design, evaluation methodology, and results across several agent models. Useful for any team building agents that interact with real APIs or filesystems.
Transformers.js v4 is now available on NPM, bringing Hugging Face's in-browser and Node.js inference library to a major new release with improved model support, better performance, and a cleaner API. Developers can now run transformer models directly in JavaScript without a Python backend. A big upgrade for web developers building AI-powered browser applications.
Hugging Face launched Community Evals — an open framework for community-run model evaluations that anyone can contribute to, challenging the opacity of existing leaderboards. The goal is to build a more trustworthy, diverse set of model benchmarks driven by real use cases rather than optimised for leaderboard gaming. Important infrastructure for the open-source AI evaluation ecosystem.
Hugging Face reflects on one year since DeepSeek disrupted the AI landscape, examining how open-source AI has evolved in response — from the proliferation of capable open-weight models to new training approaches and the emerging AI+ ecosystem. The third in a series, focusing on where the global open-source AI movement goes next.
LinkedIn's AI team shares a practical retrospective on training GPT-OSS with agentic reinforcement learning — covering the infrastructure challenges, reward design decisions, and lessons learned from scaling RL training to production-grade agent models. One of the more candid engineering accounts of what it actually takes to do agentic RL at scale.
Hugging Face marks one year since DeepSeek-R1 shocked the AI world by matching frontier performance at a fraction of the cost, fundamentally changing assumptions about what open-source models can achieve. The post reflects on how that moment catalysed a wave of open-weight model development and reshaped the competitive landscape between open and closed AI.
Hugging Face launched Open Responses — an open-source implementation of the Responses API spec, letting developers use the same agent-friendly API pattern against any open-weight model instead of being locked to OpenAI's hosted version. Compatible with OpenAI's SDK, making migration straightforward. Important infrastructure for the open-source agent ecosystem.
ServiceNow AI presents AprielGuard, a new safety guardrail designed to improve adversarial robustness in LLM systems. The framework focuses on preventing malicious prompts and ensuring safe model outputs in production environments.
IBM Research introduces CUGA on Hugging Face, a framework for creating and sharing configurable AI agents. This initiative aims to democratize agent development by providing a standardized way to define and deploy agentic behaviors.