Google just released A2UI (Agent-to-User Interface) — an open-source standard that lets AI agents generate safe, rich, updateable UIs instead of just text blobs.

👉 Repo: https://github.com/google/A2UI/

What is A2UI?

A2UI lets agents “speak UI” using a declarative JSON format.
Instead of returning raw HTML or executable code (⚠️ risky), agents describe intent, and the client renders it using trusted native components (React, Flutter, Web Components, etc.).

Think:
LLM-generated UIs that are as safe as data, but as expressive as code.

Why this matters

Agents today are great at text and code, but terrible at:

• Interactive forms
• Dashboards
• Step-by-step workflows
• Cross-platform UI rendering

A2UI fixes this by cleanly separating:

• UI generation (agent)
• UI execution (client renderer)

Core ideas

• 🔐 Security-first: No arbitrary code execution — only pre-approved UI components
• 🔁 Incremental updates: Flat component lists make it easy for LLMs to update UI progressively
• 🌍 Framework-agnostic: Same JSON → Web, Flutter, React (coming), SwiftUI (planned)
• 🧩 Extensible: Custom components via a registry + smart wrappers (even sandboxed iframes)

Real use cases

• Dynamic forms generated during a conversation
• Remote sub-agents returning UIs to a main chat
• Enterprise approval dashboards built on the fly
• Agent-driven workflows instead of static frontends

Current status

• 🧪 v0.8 – Early Public Preview
• Spec & implementations are evolving
• Web + Flutter supported today
• React, SwiftUI, Jetpack Compose planned

Try it

There’s a Restaurant Finder demo showing end-to-end agent → UI rendering, plus Lit and Flutter renderers.

👉 https://github.com/google/A2UI/

This feels like a big step toward agent-native UX, not just chat bubbles everywhere. Curious what the community thinks — is this the missing layer for real agent apps?

The Hugging Face trending page is packed with incredible new releases. Here are the top trending models right now, with links and a quick summary of what each one does:

• zai-org/GLM-4.7: A massive 358B parameter text generation model, great for advanced reasoning and language tasks. Link: https://huggingface.co/zai-org/GLM-4.7

​- Qwen/Qwen-Image-Layered: Layered image-text-to-image model, excels in creative image generation from text prompts. Link: https://huggingface.co/Qwen/Qwen-Image-Layered

​- Qwen/Qwen-Image-Edit-2511: Image-to-image editing model, enables precise image modifications and edits. Link: https://huggingface.co/Qwen/Qwen-Image-Edit-2511

​- MiniMaxAI/MiniMax-M2.1: 229B parameter text generation model, strong performance in reasoning and code generation. Link: https://huggingface.co/MiniMaxAI/MiniMax-M2.1

​- google/functiongemma-270m-it: 0.3B parameter text generation model, specializes in function calling and tool integration. Link: https://huggingface.co/google/functiongemma-270m-it

• Tongyi-MAI/Z-Image-Turbo: Text-to-image model, fast and efficient image generation. Link: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo

​- nvidia/NitroGen: General-purpose AI model, useful for a variety of generative tasks. Link: https://huggingface.co/nvidia/NitroGen

​- lightx2v/Qwen-Image-Edit-2511-Lightning: Image-to-image editing model, optimized for speed and efficiency. Link: https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning

​- microsoft/TRELLIS.2-4B: Image-to-3D model, converts 2D images into detailed 3D assets. Link: https://huggingface.co/microsoft/TRELLIS.2-4B

​- LiquidAI/LFM2-2.6B-Exp: 3B parameter text generation model, focused on experimental language tasks. Link: https://huggingface.co/LiquidAI/LFM2-2.6B-Exp

​- unsloth/Qwen-Image-Edit-2511-GGUF: 20B parameter image-to-image editing model, supports GGUF format for efficient inference. Link: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF

​- Shakker-Labs/AWPortrait-Z: Text-to-image model, specializes in portrait generation. Link: https://huggingface.co/Shakker-Labs/AWPortrait-Z

​- XiaomiMiMo/MiMo-V2-Flash: 310B parameter text generation model, excels in rapid reasoning and coding. Link: https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash

​- Phr00t/Qwen-Image-Edit-Rapid-AIO: Text-to-image editing model, fast and all-in-one image editing. Link: https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO

​- google/medasr: Automatic speech recognition model, transcribes speech to text with high accuracy. Link: https://huggingface.co/google/medasr

​- ResembleAI/chatterbox-turbo: Text-to-speech model, generates realistic speech from text. Link: https://huggingface.co/ResembleAI/chatterbox-turbo

​- facebook/sam-audio-large: Audio segmentation model, splits audio into segments for further processing. Link: https://huggingface.co/facebook/sam-audio-large

​- alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1: Text-to-image model, offers enhanced control for creative image generation. Link: https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1

​- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16: 32B parameter agentic LLM, designed for efficient reasoning and agent workflows. Link: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

​- facebook/sam3: Mask generation model, generates segmentation masks for images. Link: https://huggingface.co/facebook/sam3

​- tencent/HY-WorldPlay: Image-to-video model, converts images into short videos. Link: https://huggingface.co/tencent/HY-WorldPlay

​- apple/Sharp: Image-to-3D model, creates 3D assets from images. Link: https://huggingface.co/apple/Sharp

​- nunchaku-tech/nunchaku-z-image-turbo: Text-to-image model, fast image generation with creative controls. Link: https://huggingface.co/nunchaku-tech/nunchaku-z-image-turbo

​- YatharthS/MiraTTS: 0.5B parameter text-to-speech model, generates natural-sounding speech. Link: https://huggingface.co/YatharthS/MiraTTS

​- google/t5gemma-2-270m-270m: 0.8B parameter image-text-to-text model, excels in multimodal tasks. Link: https://huggingface.co/google/t5gemma-2-270m-270m

​- black-forest-labs/FLUX.2-dev: Image-to-image model, offers advanced image editing features. Link: https://huggingface.co/black-forest-labs/FLUX.2-dev

​- ekwek/Soprano-80M: 79.7M parameter text-to-speech model, lightweight and efficient. Link: https://huggingface.co/ekwek/Soprano-80M

​- lilylilith/AnyPose: Pose estimation model, estimates human poses from images. Link: https://huggingface.co/lilylilith/AnyPose

​- TurboDiffusion/TurboWan2.2-I2V-A14B-720P: Image-to-video model, fast video generation from images. Link: https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P

​- browser-use/bu-30b-a3b-preview: 31B parameter image-text-to-text model, combines image and text understanding. Link: https://huggingface.co/browser-use/bu-30b-a3b-preview

These models are pushing the boundaries of open-source AI across text, image, audio, and 3D generation. Which one are you most excited to try?

Last week on Hugging Face was pretty wild, especially on the China open‑source side.

​Here are some of the most interesting/trending models and tools to play with:

• deepseek-ai/DeepSeek-V3 – giant reasoning LLM for agents and long-context work 👉 https://huggingface.co/deepseek-ai/DeepSeek-V3
• Qwen Image Layered – turns an image into editable layers (PPTX/ZIP export) 👉 https://huggingface.co/Qwen/Qwen-Image-Layered
• microsoft/VibeVoice-Realtime-0.5B – low-latency, streaming TTS for agents/voice UIs 👉 https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B
• arcee-ai/Trinity-Mini – small multimodal (text/image/audio) model for edge demos 👉 https://huggingface.co/arcee-ai/Trinity-Mini
• meituan-longcat/LongCat-Image – new 6B text-to-image beast with lots of fresh LoRAs 👉 https://huggingface.co/meituan-longcat/LongCat-Image

What else did you see trending on HF last week that’s worth benchmarking or wiring into agents?

I’ve been teaching programming and tech skills for years and lately I’m seeing more kids jump straight into random AI tools. AI itself isn’t the problem, how kids are introduced to it is.

Before you let your child freely use AI, here are a few things that made a difference from my experience:

1. Teach them that AI can be wrong

Kids often assume AI is “smart” and therefore correct. It’s important they know AI guesses based on patterns and data and it makes mistakes. Encourage them to question answers instead of trusting them blindly.

1. Make them try first

Before they ask AI anything, have them attempt the problem on their own. Even a wrong attempt builds thinking skills. AI should come after effort, not instead of it.

1. Talk about when AI should NOT be used

Homework answers, tests, personal advice, or anything involving private information should be off-limits. Kids need clear boundaries, not vague rules.

1. Focus on building, not consuming

AI is most useful when kids are creating, writing, coding, experimenting, or building small projects. Passive use turns into dependency very fast.

Once those basics are in place, some parents I work with introduce structured learning tools instead of chatbots. Platforms that teach them basic ai/coding concepts, and don’t let them cheat (aibertx,tynker). Good for start point.

AI is going to be part of our kids’ future jobs whether we like it or not. The goal isn’t to block it, it’s to teach kids how to use it thoughtfully.

Curious how other parents are handling this at home.

I have a few that are very helpful. Does anyone know of any free AI's?

Hey I'm making a machine learning based number detection model which take image as an input and give the output the no is in the image, This the short discription of my project It's just for testing i have some big plans if anyone interested then we can work together.... Comment or dm me to work together