TL;DR: Daggr is a new, open-source Python library for building AI workflows that connect Gradio apps, ML models, and custom functions. It automatically generates a visual canvas where you can inspect intermediate outputs, rerun individual steps, and manage state for complex pipelines, all in a few lines of Python code! Table of Contents Background Getting Started Sharing Your Workflows End-to-End Example with Different Nodes Next Steps    

Welcome back! This is the second part of our series on training efficient text-to-image models from scratch. In the first post of this series, we introduced our goal: training a competitive text-to-image foundation model entirely from scratch, in the open, and at scale. We focused primarily on architectural choices and motivated the core design decisions behind our model PRX. We also released an early, small (1.2B parameters) version of the model as a preview of what we are building (go […]

This is the third and final blog in a three-part series on China’s open source community’s historical advancements since January 2025’s “DeepSeek Moment.” The first blog on strategic changes and open artifact growth is available here,    

Two months since releasing our first batch of Holo2 models, H Company is back with our largest UI localization model yet: Holo2-235B-A22B Preview. This model achieves a new State-of-the-Art (SOTA) record of 78.5% on Screenspot-Pro and 79.0% on OSWorld G. Available on Hugging Face, Holo2-235B-A22B Preview is a research release focused on UI element localization. Agentic Localization High-resolution 4K interfaces are challenging for localization models. Small UI elements can be difficult to pinpoint on a large display. With agentic localization, […]

TL;DR: Benchmark datasets on Hugging Face can now host leaderboards. Models store their own eval scores. Everything links together. The community can submit results via PR. Verified badges prove that the results can be reproduced. Evaluation is broken Let’s be real about where we are with evals in 2026. MMLU is saturated above 91%. GSM8K hit 94%+. HumanEval is conquered. Yet some models that ace benchmarks still can’t reliably browse the web, write    

SyGra 2.0.0 introduces Studio, an interactive environment that turns synthetic data generation into a transparent, visual craft. Instead of juggling YAML files and terminals, you compose flows directly on the canvas, preview datasets before committing, tune prompts with inline variable hints, and watch executions stream live—all from a single pane. Under the hood it’s the same platform, so everything you do visually generates the corresponding SyGra compatible graph config and task executor scripts. What Studio lets you do    

We’re excited to announce that Transformers.js v4 (preview) is now available on NPM! After nearly a year of development (we started in March 2025 🤯), we’re finally ready for you to test it out. Previously, users had to    

tl;dr: We built an agent skill that teaches coding agents how to write production CUDA kernels. Then we pointed Claude and Codex at two real targets: a diffusers pipeline and a transformers model. The agents produced working kernels for both, with correct PyTorch bindings and benchmarks, end to end. Writing CUDA kernels is hard. Writing CUDA kernels that correctly integrate with transformers and diffusers is harder. There are architecture-specific memory access patterns, vectorization strategies, warp shuffle reductions, and a dozen […]

Gradio 6 quietly shipped a very powerful feature: gr.HTML now supports custom templates, scoped CSS, and JavaScript interactivity. Which means you can build pretty much any web component — and Claude (or any other frontier LLM) can generate the whole thing in one shot: frontend, backend, and state management, all in a single Python file. We tested this by building different types of apps. Each one is a single Python file, no build step, deployable to Hugging Face Spaces in […]

Ayhan Sebin Saurabh Jha Rohan Arora Daby Sow Mert Cemri Melissa Pan Ion Stoica ITBench HF Space ITBench HF Dataset MAST HF Dataset ITBench Github MAST Github IBM Research and UC Berkeley collaborated to study how agentic LLM systems break in real-world IT automation, for tasks involving incident triage, logs/metrics queries, and Kubernetes actions in long-horizon tool loops. Benchmarks typically reduce performance to a single number, telling you whether an agent failed but never why. To solve this black-box   […]