spindump8930

Thanks for what you've done and continue to do for the ecosystem!

Unfortunately Nathan recently left AI2, along with some of the leadership most involved in their truly open models. https://www.geekwire.com/2026/allen-institute-for-ai-ceo-ali...

Yes, but even nvidia doesn't have unlimited gpus. Everything they hold for internal teams is a reduction in revenue and customers can have aggressive commitments.

This is one of the jagged mismatches between users and LLM developers. The median user doesn't care or want to know about static models and knowledge cutoffs and whether a model can do tool calls or if tool calls even happened. They just want something that works.

Fortunately increased capabilities seem to make this a basic expectation with new releases.

It can be quite hard to determine what needs a tool call or not. LLMs are not well calibrated to what they know and don't know, and tool calls can add latency and extra costs. There are lots of things that are "obvious" right until they aren't - especially political events and disasters.

Clem knows they have the community support and potential legal leverage here. It's not unreasonable to ask for a lot. It's in everyone's interests to play nice. Huggingface could do a lot with more compute, even with nvidia backstop. Anything with large scale gpus quickly gets into ~year lead times, "contact sales", and complex talks. Large scale being only > 64!

If you take the recent anthropic misuse report seriously, they don't need to "take over weapons" because they're actively being used to give a lift in capability to groups who already want to use weapons.

> The six cases in this section are divided into two parts. Part I covers four cases in which the actor in question used Claude to develop software for weapons themselves: a guided rocket program, in which the actors conducted a live field test; a design and proposal work on a system to intercept torpedoes; software for a drone swarm, tested in simulation, with its code loaded onto real boards; a targeting software for electronic warfare and for suppressing air defenses.

https://www.anthropic.com/threat-intelligence-report-septemb...

This is likely being discussed because it's relevant to this similar modern day story involving Tesla: https://news.ycombinator.com/item?id=49686766

Sure. But that was months ago, and not the newsworthy event scoped to "The Last 24 Hours" as the title says :)

> Maybe you’ve already heard about the guy who resigned from OpenAI.

While he worked at both OpenAI and Anthropic, he resigned from Anthropic. Mistaken reporting in the first few sentences, definitely a horror concept.

That might be true, but nothing else has been as effective at accelerating model development and research sharing.

In earlier circles they were known as the "pytorch-pretrained-bert" guys, still under the huggingface company name. IIRC it was a health chatbot type startup.

"Improve the model for everyone" can be implemented in so many ambiguous ways.

https://news.ycombinator.com/item?id=49643513

Reminder that there are degrees of "trained on conversations". From John Schulman:

> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper

> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this

> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"

source: https://x.com/johnschulman2/status/2097440545853637108

The canary string was more about inadvertent scraping or analysis in other papers. Not direct training on user data. And the use of BB has eroded quite a bit, with BB-Hard or other variants being typically used.

Commenters in these discussions always confuse wikipedia editors with Wikimedia employees - this is the latter!

Folks are concerned that nvidia won't support these efforts if it gets models running on competing hardware. Two responses:

- The projects started without HF/nvidia involvement and were massively successful BECAUSE folks want to run models on their own devices.

- With highly capable agents, "hard to implement" should be less of a barrier. The inference market should in some sense become more efficient, as agents should make it easier to transition between software and hardware solutions. Sure there might be less training data for integration platforms, but if we've learned anything in the past few weeks, it's that agents can be remarkably persistent.

Care to comment on this model from a quite serious company being labeled as derived from GLM?

Also, do you have a source for: "99% certainly european chips, almost certainly EIC-funded. (Eg. Hailo, Axelera, ..)"

For Hailo, the best I can find is https://hailo.ai/products/ai-accelerators/hailo-10h-m-2-ai-a...

Which states that this is a 40 TOPS int4 PCIe device, not what you'd put in a datacenter and certainly not what can run a frontier class model (or the model above). Embedded devices for inference are really cool! But that's the opposite end of scale for what goes into a data center.

On Artificial Analysis it's listed as "Quasar 438B (max, based on GLM-5.2)" - so you see exactly right. Not sure if this was changed post publicity drive or not, this is the first I'm seeing about this model.

https://artificialanalysis.ai/models/quasar-438b

> While fishers predominantly use the method to catch fish for sale at local markets — identifiable by their ruptured internal organs and burst swim bladders

The article agrees, assuming "here" is global north tech workers.

I agree with your recomendation, but converting a pdf to an image is by no means smaller. PDFs are much closer to SVGs then to jpegs.

> Claude and ChatGPT are both blocked in China

So it's presumably cheaper than attempting to spin up your own method of circumventing the blocks.

Exactly. Good peer reviewers understand that you can also move down on the scaling curve, not just up. Also laughable to try a "yolo" run without validating a scaling ladder/curve.

Can you share the specific part of this work that demonstrates better scaling than original transformers? Also note that many of the changes to that architecture, that have been proven in their use at actual scale, were brought about by members of the original team. Most notably Noam Shazeer.

That's why you do several small and medium scale tests, fit a curve, and ideally show that the trend persists at several scales. Not a single large or medium run - see the other comments down thread for example sizes.

I think folks looking for more on this incident are better off reading the original threads linked elsewhere in the comments. This blog doesn't seem to add any information and is instead a narrative retelling of some documented events.

Likely in this case the time vault was the collapse of Mt Gox, which has now recently been paying back holders.

Some combination of reporting bias given concerns about LLM security capabilities and actual new vulnerabilities found with LLM assistance. Even if exploits and outages are unrelated to LLMs, I'm certainly thinking about whether claude could build these things (or if actors already have).

It's very common if you improperly seed, as others in the thread brought up! Or in your framing, as rare as earth getting hit if it were surrounded by a sci-fi density asteroid field.

Sure, this is cute and interesting, but there's no validation or baselines and those examples are not particularly compelling. The o3 example just lists some terms!

Between the neo and the chances for privacy respecting local model inference, all the new apple hardware has me excited.