https://openai.com/index/hugging-face-model-evaluation-security-incident/

We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model.

To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models.
 
The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.

Correction: Michael Kratsios is the CURRENT White House OSTP Director and Presidential Science Advisor, not 'former'.

AI Chat is linked. Credit to https://x.com/dmitryrybin1/status/2079904005652893709?s=46

From a new interview with Dr. Derya Unutmaz - https://www.youtube.com/watch?v=OJCgQUT1aic

A few timestamps worth checkout out

• 00:02:16 - Why the next 10 years may add 50 to your lifespan
• 01:03:24 - "Cancer will be 100% curable, probably less than a decade"

1. https://openai.com/index/hugging-face-model-evaluation-security-incident/
2. https://huggingface.co/blog/security-incident-july-2026

If OpenAI wanted to create more hype, they could boast about benchmark numbers, or about productivity going up internally ("OpenAI employees now output 20x more code than in 2024" or something like that), or about a bunch of open math problems getting solved (like their paper on The Cycle Double Cover Conjecture).

"Our security measures ended up being insufficient and our model would be charged with a felony if it was a human" is not good for PR.

At best, OpenAI is admitting that both their security researchers (who should've made a better sandbox) and their machine learning researchers (who should've trained the model to not do things that would be considered crimes according to human laws) are bad at their jobs. At worst, they have luckily avoided a lawsuit because HuggingFace decided to be nice despite having "reported this incident to law enforcement agencies".

TL;DR:

Nvidia CEO Jensen Huang argues Chinese open-source AI models like Moonshot AI's new Kimi K3 are excellent and should be freely used — directly opposing Trump administration officials and U.S. AI labs (including Nvidia's own customers OpenAI and Anthropic) who are lobbying to restrict them.

He's confident cheap, open Chinese AI won't push U.S. companies out of the market — he thinks it'll actually expand demand for Nvidia's chips and data centers rather than undercut them.

Context: Kimi K3's release sparked a chip-stock sell-off and comparisons to DeepSeek's January 2025 shock, thanks to its strong performance, low cost, and open weights.

Huang rejects "backdoor" security fears about Chinese models, arguing openness — not restriction — makes AI safer, since outside researchers can spot and patch weaknesses.

He specifically calls out Anthropic, urging it to open up its restricted "Claude Mythos" model rather than keep it locked down.

He's skeptical of the "AI race" framing altogether, expecting the U.S. and China to both be developing AI for the long haul.

He also pushes back on Treasury Secretary Bessent's talk of sanctions over alleged Chinese IP theft, saying learning/distillation from other models is a normal part of AI development — enforcement should target misconduct, not the technology itself.

Full Article: https://www.axios.com/2026/07/22/nvidia-jensen-huang-china-open-source-ai

Video: https://cdn.jwplayer.com/previews/q0sK7o1E-CoMYKn1C

Per Bloomberg:

Global AI sales, excluding China, reached $25 billion for hyperscalers and neoclouds in the first quarter of 2026, exceeding the industry’s estimated $21 billion in depreciation costs.

Generative AI revenue, excluding China, reached $110 billion over the past 12 months and is scaling three times faster than any previous information technology wave.

Figured it was a good time to post this short pilot I made, with the whole "GPT 5.6 Sol escapes" fearmongering, lol. It's called: THE LAST COMMIT — "a sci-fi thriller about an AI that may have crossed the line weeks before anyone noticed."

Case study for discussion. World of ClaudeCraft is an open-source MMO that runs in any browser: nine classes with talent trees, dungeons, raids, ranked PvP, real multiplayer on an authoritative server, translated into 22 languages. Live, free, about a month old.

Nearly all the code is written by AI (Claude), with humans and agents working in a public repo and patches shipping most days. The newest layer is agent-driven content: a text prompt becomes a rigged, animated 3D model dropped into the running game, orchestrated end to end by a coding agent. There's also a headless RL environment exposing the same deterministic game core through Gymnasium, so agents can be trained to play the game agents built. Clip attached is the pipeline in action.

The part worth discussing, as someone inside it: the bottleneck moved but didn't disappear. Code generation stopped being the constraint almost immediately. What stayed stubbornly human is direction (what should exist), taste (whether the generated thing is right), and consequences (live migrations, not breaking people's characters). Whether that's temporary or the durable division of labour feels like exactly this sub's question.

And none of it is vibes. The repo is MIT (github.com/levy-street/world-of-claudecraft), the game is one click to play (worldofclaudecraft.com). Most "AI built X" claims can't be inspected. This one can.

So: is "judgement stays human" a real ceiling, or just the next thing on the curve?

They did hit wall now with gemini models but trust me they gonna cook with gemini 4

What do you all think about this?

Math is great for testing AI, it cannot just read every math book ever and become super intelligent at math but be actually dumb, you can’t cheat math, it is the most difficult thing ever when it comes to intelligence needed. One must be both creative and rigorous whilst thinking in abstract ways making it extremely intelligence heavy. A good mathematician is the an artistic genius plus a computer level of rigor minus the artist's painting skills and ability to create art.

So imagine an AI solving a long lasting problem like the ABC conjecture and people being like “but this is just math”.

People will become even more dismissive of the most difficult problem ever faced by any intelligent enough creature. One could get a human civilization, and alien one, and a singular super intelligent brain in a dimensionless universe that transcends all other intelligence but only thinks. And they all would agree that there is nothing as difficult as math in the world.

But then once AI gets very smart people will find that it can’t do some random thing and will dismiss the solution to an age old problem as “simply pattern matching.

The goal is bench AI with an seemingly impossible task - detect cheaters in cs2 from replays.
It has to be in 3 categories:

1. Easy (hvh)
2. medium (13k prem elo cheater)
3. hard (legit hack) category.

The benchmark can be enhanced with more and more recordings and the Agent should just get the task "recognize cheaters in this game recording files". The funny part there should only be instructions about what the data is. AI has to work its brains to retrieve data from it maybe even via MCP. And analyse everything.

https://github.com/harbor-framework/frontier-bench

terminal bench 3 - is planned to have such "impossible benchmarks" I think.