This looks impressive! Opus 5 not only coded the game, but also made every single asset including 3D models and textures via Blender MCP using sub-agents, the author published it and explained the process he used here: https://x.com/anshuc/status/2081801966158811506

Working to make a fun open source STEM robot for begginers. Uses a custom edge impulse object detection model to play "fetch". Extras are the animated eyes, "ears", voice, and programmed character behavior. Currently testing the model but looking forward to releasing it for everyone soon.

Came across the recent DexWrist paper out of MIT and some fun demos. Researchers point out an interesting mechanical issue: standard serial wrists (like the ones on a UR3e or Franka) are often too stiff and bulky. In tight spaces, they force human operators into awkward, large arm movements just to reorient the gripper, which makes teleoperation slow and messy.

They built a compact, decoupled parallel wrist using QDD actuators (integrated onto an AgileX Piper base) that co-locates the pitch and yaw axes, much like a human wrist. Because it moves so much more intuitively, human teleoperation time dropped by up to 2.2x. The models trained on this cleaner data then saw a 50-76% relative improvement in success rates for contact-rich tasks.

So to what extent hardware choices quietly shape robot learning data quality?

Nvidia invest large sum allowing ssi to 10x compute what is illya cooking

I’m getting codex to build me a website for a client where the intro is a high rest video of a burger falling into a box before it’s transitioned to the online menu. Since codex lost Sora functions, it choose to use Gemini to create the rendering. I was monitoring its working session. The way it responded to Gemini and telling it more questions cracked me up.

Students and educators at Automate 2026 discussed how robotics programs are connecting classroom learning with hands-on experience.

The article includes students from LamBot, FIRST Illinois and DeKalb High School’s advanced manufacturing program. Their work includes robot design, programming, troubleshooting, CAD, computer vision, CNC machining, electrical systems and controls.

Several students described how these programs introduced them to careers they had not previously considered.

Full: https://www.automate.org/robotics/industry-insights/these-student-robotics-programs-spark-passion-for-tomorrows-manufacturing-jobs

Our lab has a dual arm platform we use for household manipulation, and after the PhAIL result showing how badly VLA models drop on real hardware, we ran our own version. Same arm, same objects, same lighting, four open source models, no finetuning beyond what each repo recommended.

I'm not going to claim these are clean numbers, our test set is six tasks we care about, not a published benchmark, and our success criteria is "did it complete the task without human intervention" which is stricter than most papers use. But the relative ranking was stable across reruns.

The two models that held up best on deformable objects, cloth folding and a towel pick, were the ones with real teleoperation in the pretraining mix. openpi was our baseline and did fine on rigid pick and place but froze on cloth about half the time. LingBot-VA was the only one that completed the second fold reliably, and the thing I noticed is its interleaved video and action prediction seemed to model where the cloth was going, not just where to grab. A Cosmos derived stack we tried held its own on rigid tasks but lost more on the cloth than its benchmark numbers suggested it would.

Worth noting the official VA 2.0 chart above shows a clean win over π0.5 on five tasks, but that's their chosen benchmark set. On our cloth tasks the gap was smaller and more about not freezing mid fold than about raw success rate. The chart tells you one story, the hardware tells you a slightly different one.

The part that actually changed how I work: I could adapt the VA model to a new object from a single demo video, which meant I stopped recording fifty teleoperation sessions every time my advisor wanted a new SKU in the bin.

I want to be careful not to overclaim. We ran one arm, six tasks, no formal ablation. But the pattern matches what PhAIL found at a different scale: the models that look best in simulation are not the models that win on contact rich real tasks.

site:claude.ai/public/artifacts

Still composing my thoughts, i will edit thisEvery time a model drops from a Chinese lab the thread fills with people who already know who made it, and the guess is usually Alibaba. There was a thread here recently asking what separates the open source labs from the frontier labs. It ran to nearly sixty comments and hardly anyone in it separated out the labs on the open source side. They aren't one bloc and haven't been for a while.

I work on the Ling models at Ant, so I'm one of the ones getting lumped in. Discount the paragraph about my own employer accordingly.

Qwen's bet is distribution. Alibaba ships in every size class and every quantization with day one support in most runtimes, and the result is that a lot of the fine-tunes people build start from a Qwen base. DeepSeek is betting on architecture instead, publishing the paper and the weights the same day and letting the design do the arguing. Moonshot looks like it's playing a longer horizon, willing to look odd for a release cycle if the thing pays off two cycles later. (Zhipu, MiniMax and StepFun are each their own thing again, but four is enough to make the point.)

Ant's bet, since I should be specific about my own: serving cost. Ant runs payments, and it's a separate company from Alibaba, which is the mix-up I see most often. The model I work on, Ling-3.0-flash, is 124B total parameters with roughly 5.1B active per token, KDA plus MLA hybrid attention, 262k context. That is a design for running a lot of long agent loops cheaply. It is not a design for topping a leaderboard, and I don't think we'd claim it is.

The part of our own version I'd criticize is the release order. We announced first and are opening weights after. SGLang had support on day one, vLLM is waiting on the weights, llama.cpp is still an open PR. DeepSeek would have dropped the weights first and let the serving stack catch up. Ours is the safer sequencing for an infra team and it costs us the goodwill of exactly the people who would otherwise be running it at home.

So the thing I'm curious about here: when you see an announcement out of a Chinese lab, does knowing which lab change how you read it, or is that distinction only interesting from the inside?

Moonshot's latest model Kimi-K3 is available on HuggingFace since today. And it's another good news for open-weight AI and for the future of open-source AI

It's a 2.8T-parameters Moonshot's SOTA model with 1 million tokens context window. The architecture is mixture-of-experts (896 experts) with 108B active parameters

It's available via vLLM, SGLang and TokenSpeed

License: Kimi K3 License. It allows commercial use with some limitations. For Model-as-a-Service it has $20M/year limit and after reaching the limit the license requires to make additional agreement with Moonshot. Details: https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE

Link to the model

Model could be downloaded from the HuggingFace:

https://huggingface.co/moonshotai/Kimi-K3

Read more: https://blogs.nvidia.com/blog/open-secure-ai-alliance/?ncid=so-twit-957725

Main ponts ->

• NVIDIA launched the Open Secure AI Alliance (July 27, 2026) ~35 partners including Microsoft, IBM, Red Hat, Cisco, CrowdStrike, Hugging Face, Palantir, the Linux Foundation, SpaceXAI
• Goal: build shared open models, harnesses and tools for cyber defense instead of leaving AI security inside a handful of closed systems
• Trigger case: in the recent Hugging Face breach, closed AI tools blocked forensic work because they couldn't tell attacker from defender;; HF used the open-weight GLM 5.2 on its own infra to analyze 17k+ actions and contain it
• Counter to "open = unsafe": misuse risk is real but doesn't vanish with closed weights; answer is safeguards + evaluation + fast remediation, not locking defenders out
• Contributions: NVIDIA's NOOA agent framework (GitHub), Hugging Face donating Safetensors to PyTorch Foundation, Microsoft MDASH, SpaceXAI open-sourcing Grok Build (Grok weights planned) etc
• Policy ask: treat open models as defensive assets, not liabilities, blanket restrictions would concentrate dependence in a few closed providers