isusmelj

I was also quite surprised to see that. Considering the effort Aleph Alpha put into to their new model, it seems like the Qwen team needs access to vast amounts of german data o.0 I'm really glad for these efforts for open models from within Europe.

Any AI safety experts here? I'm wondering if this claim here really holds: > Anthropic invests significantly in making Claude safe, helpful, and harmless. We conduct rigorous pre-release testing, implement multiple safety layers, and continuously monitor our models' behavior. When Outputs are used to train new models without our oversight, additional risks emerge. Safety controls may be lost – models trained on Claude's Outputs won't have our safety measures, potentially leading to harmful or dangerous AI systems.

From my understanding distillation pretty much copies behaviour. If someone intends to distill from a Model, it can't extract unsafe behaviour, but would learn the same safety mechanisms, no?

I'm waiting for an agent evaluated on a vending benchmark to start hacking into banks and wiring more money to its account so it can do better business.

I think this is the most satisfying video I’ve seen of a robot doing laundry. Not sure if it’s the camera angle, the 3× speed, or the music.

Not sure if it’s just me, but with Anthropic, every new feature has metered usage and “unlimited spending” (aka no limit) enabled by default for our team org. So if I activate something (Claude Code, Claude Tag) and don’t actively go to the usage page to set a spending limit, there is no limit. I’m not surprised Anthropic makes a lot of money. Most people in a typical org probably don’t even know how to check usage. Now, using Claude via Slack will just escalate that even further. And the only models I can use are Opus 4.7 and 4.8 for Claude Tag. If someone knows how to change the defaults, please let me know. With OpenAI, it’s the opposite. Everything is part of the plan, and by default there is no extra spending.

No note about the specific GPU they use. One might speculate. B200? H200? H100?

Is it just me, or does it feel like everyone now uses AI to write any kind of blog?

These parts here somehow trigger me:

- Enter TorchTPU. As an engineering team, our mandate was to build a stack that leads with usability, portability, and excellent performance.

- Engineering the TorchTPU Stack: The Technical Reality

- Eager First: Flexibility Without Compromise

- The breakthrough, however, is our fused eager mode.

- The Road Ahead: 2026 and Beyond

I have mixed feelings about this. On one hand, we all seem to be using the same tools and converging to the same style. On the other hand, if we all use the same models with the same system prompts, we might lose a lot of creativity and diversity in online content.

Yes, Marble (from World Labs) feels like it's generating Gaussian Splats or similar. I guess it's more compatible and easier to use for 3d asset generation and reusing in other software. Very exciting times ahead!

Is it just me or is the page barely readable? Lots of text is light grey on white background. I might have "dark" mode on on Chrome + MacOS.

I think we are just very close to the peak of a typical Gartner hype cycle around LLMs. They are useful but overhyped. There will be more posts about fuckups that happen because people run things on autopilot and cannot keep up with reviewing AI generated code.

Do not get me wrong. I use AI all day to speed things up. But I believe that there is only a small group, maybe 5 percent or less, that actually knows how to use AI properly (I'd count myself not yet in that 5%), which I see as potentially dangerous. The other issue I see is inexperienced software engineers writing software. Although I see this as a great value add and productivity boost for prototyping, I am afraid of the “I do not know much about coding but can also make PRs to our codebase” mentality.

For those of you that run things on autopilot, how do you keep code quality under control? And how do you handle refactoring? I am really curious, because one option now is also to just YOLO your LLMs to write code based on the maturity of the product. You can refactor an app or parts of it pretty fast again with LLMs. While tech debt accumulates faster, we also have the opportunity to rebuild faster.

Are there any benchmarks? I didn’t find any. It would be the first model update without proof that it’s better.

Very proud as a Swiss that Soumith has a .ch domain!

I can only agree with your experience in Europe. I do not get how they do that, but Tesla Superchargers are more reliable. The occupancy information works better, they are easier to use, and they almost always offer a more competitive price. I often see other chargers that are 50 to 100 percent more expensive and only very rarely see offers that are within 10 to 50 percent.

What strikes me is that this difference can make EVs more expensive per kilometer if you only compare energy cost with fuel cost.

Here is the math with numbers. Tesla chargers in Switzerland and Germany are usually at most CHF 0.50 or EUR 0.60 per kilowatt hour at the more expensive locations, along highways for example. They offer fast charging of 150 kW or more. Alternative providers often start at around CHF 0.75 for 50 kW or CHF 1.00 for more than 250 kW fast charging. If your electric car consumes 20 kWh (Model 3 is at around 15 I think) per 100 km you end up with costs of CHF 10.00, CHF 15.00, or CHF 20.00 per 100 km at CHF 0.50, CHF 0.75, or CHF 1.00 per kilowatt hour. If you drive a petrol car that uses 8 l per 100 km and the cost per liter is CHF 1.70 you pay CHF 13.60 per 100 km.