Ash DCosta

Connecting UX and Workflow with AI Models #AI #huggingface #stablediffusion #mistral #llm #whisper youtu.be/wkaQZnSgBvk?fe…

@vllm_project Cool. Does this version work well with Codex? Codex cli was giving me errors connecting when using vllm to host Qwen 3.6 27B model with the responses api compatibility issues.

vLLM v0.20.0 is here! 752 commits from 320 contributors (123 new). 🎉 Highlights: DeepSeek V4, Hunyuan v3 preview support, CUDA 13 / PyTorch 2.11 / Transformers v5 baseline, FA4 as default MLA prefill, TurboQuant 2-bit KV (4× capacity), vLLM IR foundation. Thread 👇

We built a 1TB local AI cluster using 8x NVIDIA GB10 systems... with a twist youtu.be/uYepcMoqvKQ

Xiaomi MiMo-V2.5 is now officially open-sourced！ MIT License, supporting commercial deployment, continued training, and fine-tuning - no additional authorization required. Two models, both supporting a 1M-token context window : • MiMo-V2.5-Pro: built for complex agent and coding tasks, ranking No.1 among open-source models on GDPVal-AA and ClawEval • MiMo-V2.5: a native omni-modal model with strong agent capabilities A model's value isn't measured by rankings alone — it's measured by the problems it solves. Let's build with MiMo now! 🤗 Weights: huggingface.co/collections/Xi… 📄 Blog: #blog" target="_blank" rel="nofollow noopener">mimo.xiaomi.com/index#blog

THE FIRST MAN IN HISTORY TO BREAK 2 HOURS IN A MARATHON!!!🤯🤯🤯 Sabastian Sawe 🇰🇪 has just shattered the World Record at the London Marathon, running 1:59:30!!! He makes history as the first man to officially break 2 hours in the marathon. Yomif Kejelcha 🇪🇹 in his debut ran 1:59:41 to become 2nd fastest alltime, while Jacob Kiplimo 🇺🇬 finished in 2:00:28. All under the previous World Record.

The reason people are panicking about AI taking jobs is lot of people focus on task based work items instead of their Job Purpose. Kudos to Jensen making the distinction and not spreading fear

🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length. 🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models. 🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice. Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today! 📄 Tech Report: huggingface.co/deepseek-ai/De… 🤗 Open Weights: huggingface.co/collections/de… 1/n

🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power! Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇 What's new: 🧠 Outstanding agentic coding — surpasses Qwen3.5-397B-A17B across all major coding benchmarks 💡 Strong reasoning across text & multimodal tasks 🔄 Supports thinking & non-thinking modes ✅ Apache 2.0 — fully open, fully yours Smaller model. Bigger results. Community's favorite. ❤️ We can't wait to see what you build with Qwen3.6-27B! 👀 🔗👇 Blog: qwen.ai/blog?id=qwen3.… Qwen Studio: chat.qwen.ai/?models=qwen3.… Github: github.com/QwenLM/Qwen3.6 Hugging Face: huggingface.co/Qwen/Qwen3.6-2… huggingface.co/Qwen/Qwen3.6-2… ModelScope: modelscope.cn/models/Qwen/Qw… modelscope.cn/models/Qwen/Qw…

Introducing Nucleus-Image: the first sparse Mixture-of-Experts diffusion model 17B parameters. Only 2B active. 10x more parameter-efficient than leading diffusion models. Toe-to-toe with GPT Image 1, Imagen 4, and Qwen-Image: from pure pre-training alone. No DPO. No RL. No preference tuning. Day 0 support in 🤗 Hugging Face diffusers. Fully open-source under Apache 2.0. Weights, training code, and dataset recipe - we're not holding anything back <3

@BambulabGlobal Hoping for a smaller version or cheaper of the H2C with multi-nozzle support in the same print.

For years, you may have asked: “why does 3D printing have to be so hard?” We asked the same question. That’s why we created Bambu Lab X2D - a printer designed to make creation effortless, precise, and reliable. With dual-nozzle extrusion, intelligent thermal management, and multi-color printing with minimal waste, X2D doesn’t just print, it empowers your ideas! But our printers are only the beginning. They're part of the Bambu Experience, a full ecosystem connecting hardware, software, materials, and community. That helps to make advanced 3D printing simpler and more accessible. 👉 Read the full story and discover how Bambu Lab X2D can unlock new possibilities for your projects: blog.bambulab.com/xcellence-made…

@RyanLeeMiniMax This feels like a good balance. We don't want companies to stop spending money on training, if there is NO ROI in releasing open weight models.

I just updated our license. For personal use, you’re free to run the software on your own servers for coding, building applications, agents, tools, or integrations, as well as for research, experimentation, and other personal projects. Don’t worry, bro — go ahead and use it freely!🤗 huggingface.co/MiniMaxAI/Mini…

the doomer narrative from ai labs will go down as a big fumble i don't know what the plan was by pitching that everyone will lose their job did they really expect people to root for them

@NousResearch @Su Are there plans to extend the API, so that someone can build a full fledged client instead of using the terminal client. ie. Get access to entire set of features...

@Su It’s your lucky week :)

Hermes from @NousResearch just needs a dashboard and it will win against openclaw easily. Just everything about it is better

👏👏👏

Distance makes the heart grow fonder…it didn’t take 219,669 miles to remind me how much love I have for Ellie and Katey. They have been incredible supporters during this journey, and they now fully understand why it was important for me to go explore into the unknown.

I really do have a problem with AI companies confusing intelligence and consciousness for marketing reasons. All LLMs are next token generators and depend on their training data to appear intelligent.

Claude Mythos is Delusional

Introducing the Manim skill for Hermes Agent. Manim is an engine for creating precise programmatic animations for mathematical and technical explainers, made famous by the @3blue1brown channel.

There are no words.

llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though - local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: - We don't need frontier intelligence to automate searches and sending emails - We don't need trillion parameter models to be able to summarize articles or technical documents - We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!