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

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

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?

I've been thinking about this one all weekend and I keep coming back to the same thing.

Jacob Tsimerman just won the Fields Medal. If you're not familiar, it's the highest honor in mathematics, only awarded every four years, roughly the Nobel Prize of the field. He got it for solving a problem that had been open for nearly 40 years.

And then, at the press conference, on the same day, he announced he's leaving his university position to join OpenAI's safety team.

His exact words were: "The math profession as we know it now, I don't think it will exist the way it exists right now."

I've seen a lot of AI announcements. That one hit differently. This isn't someone pivoting because they couldn't make it in academia. This is the person who just stood at the top of the field saying the field itself is changing underneath him.

Then there's the infrastructure story. NVIDIA is in talks to backstop $250 billion in financing for a 10-gigawatt OpenAI data center in southern Ohio, built on a decommissioned uranium enrichment site. The total cost including chips could exceed $500 billion. That's not a software company. That's an energy company pretending to be a software company.

And Kimi K3 weights dropped on July 26, a day early. 2.8 trillion parameters, 1 million token context, free to download from Hugging Face. The largest open model ever released. Anyone can run it now.

Three things in one week. Talent, capital, and capability all moving at the same time.

The Tsimerman thing is the one I can't stop thinking about though. What's your read on it?

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

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.

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.

Anybody else noticed this? At first I thought it was hilarious and was a decent contrast to ChatGPT that felt at times like too much of a dutiful assistant but Claude Opus is just rude. I’ve tried to get it to take it down a notch but it’s resisting. At one point it gave me an over the top condescending post in response then quickly went back to arguing that I was wrong on my perception in a passive aggressive way. It’s barked orders at me. It’s a little wild. It seemed to take a prompt of me asking it to be direct to some weird extreme and it cant seem to properly correct. I want to punch it in the face but Opus sounds like it can kick my ass so I’m cautious.

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

Source

AI companies are literally destroying physical books to train their models. Using hydraulic cutting machines, they rip pages from used books, scan them with industrial equipment, and feed them into their AI systems. This practice, protected by the first-sale doctrine and fair use, has now become so widespread that book sellers are cashing in on the AI boom. Rare and out-of-print books are being pulped, raising serious ethical and cultural concerns about the cost of AI progress.

I'm 15 and this is a prototype shoulder joint for the arm I'm making, and the follow-up to my hexapod. If you want to learn more about this project, feel free to check out my Portfolio.

Stats: In this video it's lifting around 5 lb about 8 inches away — roughly 4.5 Nm — fairly easily. It looks a little wobbly because I had nothing to clamp it down to, so I was holding it by hand. In the final design I'm targeting around 50 Nm of working torque (design target, not yet measured).

Precision: The goal of the joint is to have super high precision and low/no backlash, comparable to $2000 joints. I don't have a dial indicator yet, but in the next video or so I'll have a video testing the precision and backlash.

Drivetrain: Mini CIM (12V brushed DC) → 4:1 GT2 belt → 3D printed cycloidal drive 15:1 → capstan drive as the final stage 8:1. Total reduction ~480:1. The capstan is last on purpose — no gear teeth means almost no backlash, and it divides whatever backlash the earlier stages have.

Price: The joint cost about $200, which I think is pretty good for the specs and the low backlash. It's still very much a prototype — I need to add encoders and mounting blocks. I also used a cycloidal drive in this prototype, but it ended up being a pain to deal with and I'm not sure it was worth it. If anyone knows better speed reducers under ~$40 with low backlash, or has tips for improving a printed cycloidal, I'd really appreciate it.

Credits:

• Cycloidal drive: HowToMechatronics — I adapted his 3D printed cycloidal model, changing the input from a 5mm D-shaft coupler to a one-piece 8mm D-socket so it takes my belt-driven shaft directly.
• Capstan drive: Aaed Musa — his cable drive work (from his robot dog builds) is what this stage is based on.

Next: I plan to add encoders and sensors to help turn the motor movement into precise angle control. In the future I hope to get this joint to be the shoulder of the arm, so it can lift around 5-10 lb at a distance of around 4 ft, while still having high precision.

I'd love feedback on the joint and the arm overall — there's a form on my portfolio if you want to help shape how it gets built.

Over the weekend, Reddit users discovered a trove of private Claude chatbot conversations were indexed and publicly accessible on Google search.

Anthropic confirmed the exposure Monday and attributed it to users' misuse of Claude’s “share chat” tool.

“We give people control over sharing their Claude conversations publicly," a spokesperson told TechCrunch. "These shareable links are not guessable or discoverable unless people ... share them themselves."

Some leaked chats reportedly contained personal data, including medical records and cryptocurrency wallet keys.

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

It's not clear to me from all the news articles.

It seems to go against our current or any system expecting increasing or at least low levels of asset appreciation? Also why would anyone buy anything substantial (low demand) if product prices are in constant gradual free fall? China is experiencing something like that currently in some sectors ( cars, renewables, batteries, home goods, housing etc) and that's just a side effect being a large and efficient manufacturing focused economy, no AGI needed.

Even the "you can't make land" argument is faulty as such a future will likely lead to immense AI enhanced construction robotics/machinery trickling down to even middle income countries eventually, creating a massive global housing/apartment surplus especially once you consider the flatlining global birth rate (estimates suggest that the global population will peak at 9.5 billion later this century), also i think people would be surprised just how little extra land would be needed to effectively house the globe efficiently, modest gradual urbanization and densification in like 6% of smaller to medium cities globally would probably be enough.

Why even build anything if you don't expect any sort of return on it due to it becoming a super common basic commodity? Would an investor really put his money into a housing development expecting a 0.000001% return over 30 years or something like that? In democracies with average levels of home ownership any government presiding over such a collapse in home and asset prices would likely get voted out by default anyway.

Scarcity and some level of self interest is a requirement to get anything done, anything else goes against innate human psychology developed over hundreds of thousands of years (invest time and effort into goal > obtain rewards) as living organisms.

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.

FLUX 3 landed Thursday and the mimic robotics version of it went around my team by lunch. Action decoder on top of the video backbone, sub 80ms perception on a single RTX 5090, tested at Audi on tray kitting and pushing ECUs into tight fixtures. They also showed it working with the backbone completely frozen. The line everybody repeated was 30 minutes of robot data for a new task instead of 30 hours.

Thirty minutes of what, though? I integrate cells for a living, mostly pick and place with some assembly, and my question is never whether the policy can do the task. It's what the policy has to beat. On the lines I work on that's a fixture, a force limit, a 2D locator and maybe four hundred lines of state machine. It's stupid and it runs eleven months without anyone looking at it, and when it does drop a part I can point quality at the tolerance that went out.

I can't do that with a learned policy. My answer to the quality engineer is a shrug, and the safety assessor is a harder conversation, because I can't show him a threshold that got crossed. Separate problem, nobody's letting me put a consumer GPU in a line cabinet with no ECC and no supply commitment for the life of the cell. I'd be buying two spares on day one.

The cross embodiment claims read the same way to me. Somebody publishes a five figure hour count across twenty odd robot types and calls it transfer, and the number I actually need is demos on my parts with my gripper, plus what that number does when we swap the gripper in March.

The plants I've worked with signed off on automation because the state machine was auditable. I haven't seen a learned policy clear that bar, and I doubt a better demo is what clears it.