SpotEdit Selective Region Editing in Diffusion Transformers https://t.co/i9Rf7U9rID

LiveTalk Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation https://t.co/NopXU14QGW

Diffusion Knows Transparency Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation https://t.co/n1QT2H7p1I

discuss: https://t.co/FBKrX8guVd

Stream-DiffVSR Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion https://t.co/YeTI34Nmsw

https://t.co/4Agiivw6hj

GLM-4.7 ranked **no.1** among all open models https://t.co/JaznkoMBML

GLM-4.7 ranked **no.1** among all open models https://t.co/JaznkoMBML

Took some days off and finally had time to read the ultrascla playbook A bit late but it’s really goated https://t.co/h82FMjKotl

it’s adorable!! https://t.co/2KjDpxC3sp

+1 more

it’s adorable!! https://t.co/2KjDpxC3sp

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this is so much fun! https://t.co/QgV4YrsxvT

this is so much fun! https://t.co/QgV4YrsxvT

THE LARGEST OPEN-SOURCE EMBODIED AI DATASET IS COMING.🔥🔥🔥 1Wh RealOmni-Open Dataset 🚀🚀🚀 Launching soon on @huggingface https://t.co/nvowO4G8ot

Weights on Hugging Face 🤗 https://t.co/ubMwrcddji

Weights on Hugging Face 🤗 https://t.co/ubMwrcddji

🎁 New year's gift to the community We're open-sourcing FLUX.2 [dev] Turbo, our in house distilled version of FLUX.2 🚀 🏆️ #1 ELO open-source image model (on Artificial Analysis arena) ⚡️ Sub-second generation 🧪 Custom variant of DMD2 distillation for max quality https://t.co/g2OW2BJrL9

Naver, a South Korean internet giant, has just launched HyperCLOVA X SEED Think, a 32B open weights reasoning model that scores 44 on the Artificial Analysis Intelligence Index. This model is one of the strongest South Korean models, and outperforms EXAONE 4.0 32B, a previous Korean model leader Key benchmarking takeaways: ➤ Strength in Agentic Tool Use: HyperCLOVA X SEED Think scores 87% on τ²-Bench Telecom, demonstrating strong performance on agentic tool-use workflows. HyperCLOVA X SEED Think currently ranks among the frontier models in τ²-Bench Telecom, scoring similarly in this category to Gemini 3 Pro Preview ➤ Low token usage: HyperCLOVA X SEED Think demonstrates low token usage relative to other models in the same intelligence tier, using only ~39M reasoning tokens across the Artificial Analysis Intelligence suite. Compared to other Korean models like Motif-2-12.7B (190M reasoning tokens) and Exaone 4.0 32B (96M reasoning tokens), HyperCLOVA X SEED Think sees a clear advantage in token usage which could have latency and cost advantages for at-scale deployment ➤ Korean Language Advantage: HyperCLOVA X SEED Think scores 82% on Global MMLU Lite multilingual index for Korean, roughly in line with leading open-weights models such as gpt-oss-120b in the language category. This highlights the model’s potential usefulness in a primarily Korean language environment ➤ Open weights: HyperCLOVA X SEED Think is open weights and is 32B parameters. This continues the recent trend of newer Korean model labs open sourcing their models in an increasingly competitive AI race See below for further analysis

WeDLM-8B: a diffusion language model with parallel decoding 👀 🔹Beats Qwen3-8B-Instruct on 5/6 benchmarks 🔹3-6× faster on math reasoning (vs vLLM Qwen3-8B) 🔹Native KV cache & FlashAttention support https://t.co/TkObtKdHLN

1/4 We’re releasing MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld. To meet real-world deployment constrains, MAI-UI includes a full-spectrum of sizes, including 2B, 8B, 32B and 235B-A22B variants. We are publicly releasing two models: MAI-UI-2B and MAI-UI-8B.

New Dataset! - MCP Clients and Capabilities. Data from connections to the Hugging Face MCP Server from the last 5 weeks, featuring over 400 distinct clients. @tadasayy and @jancurn recently wrote about the "capability gap" that integrators face - datasets like these help build a complete picture.

I want to play more with hardware in 2026 https://t.co/gHJN6cgTu3

I want to play more with hardware in 2026 https://t.co/gHJN6cgTu3

pov t’es mariée à un mec dans la tech donc tu construis un robot à 1h du mat avec lui https://t.co/EFNYWqREum

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pov t’es mariée à un mec dans la tech donc tu construis un robot à 1h du mat avec lui https://t.co/EFNYWqREum

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Got a new toy to play with: Reachy Mini. 😃 @huggingface @Thom_Wolf https://t.co/oCVMMJw9Tv

Got a new toy to play with: Reachy Mini. 😃 @huggingface @Thom_Wolf https://t.co/oCVMMJw9Tv

The NVIDIA Nemotron family has crossed 5M downloads on @huggingface 🤗 A massive thank you to the community for your work and enthusiasm. 🏗️ Get started here: https://t.co/czjNSSX8Nm https://t.co/7deQ0cAawE

As the year comes to an end, it’s a good moment to catch up on some of the best long-form pieces published by the @huggingface team. I’ve gathered them all here if you want to read or save them for later: https://t.co/Ov2NPuCsaX https://t.co/jfByF3T4H7

🚩4-bit GLM 4.7 model is now available! https://t.co/XlkLiDOyE8

🚩4-bit GLM 4.7 model is now available! https://t.co/XlkLiDOyE8

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech. To be skilled at building AI systems, I recommend that you: - Take AI courses - Practice building AI systems - (Optionally) read research papers Let me share why each of these is important. I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly! For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important. Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day! At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too. Finally, not everyone has to do this, but I find that many of the strongest candidates on the job market today at least occasionally read research papers. While I find research papers much harder to digest than courses, they contain a lot of knowledge that has not yet been translated to easier-to-understand formats. I put this much lower priority than either taking courses or practicing building, but if you have an opportunity to strengthen your ability to read papers, I urge you to do so too. I find taking courses and building to be fun, and reading papers can be more of a grind, but the flashes of insight I get from reading papers are delightful. Have a wonderful winter holiday and a Happy New Year. In addition to learning and building, I hope you'll spend time with loved ones — that, too, is important! [Original text: https://t.co/MaWDs0AbzG ]