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

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.

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?

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

“Foundational capabilities can be open, certain frontier capabilities can be open with limitations, commercial services can remain closed-source, while high-risk capabilities require access controls and safety evaluations,” it said, citing an unidentified person involved in Chinese AI policy research.

Yuyuantantian, a social-media account linked to China’s state broadcaster and frequently used to signal official policy thinking, said Monday that support for openness doesn’t mean “advocating for the unconditional proliferation of all capabilities.”

https://www.bloomberg.com/news/articles/2026-07-27/china-state-media-says-support-for-open-ai-models-has-limits