Also every time I see Miyazaki concept sketches, I’m blown away by how beautiful his watercolors are.

You can make an ultra high quality fake check and deposit it and yet it will still bounce because it turns out the quality is irrelevant if it isn't real.

It also didn't win. By definition a disqualified entry is not a winning entry. That is what the entire article is about, if you read it! The winning entry is here: https://www.nikonsmallworld.com/galleries/2026-small-world-i...

Here’s an easy to understand analogy; if you deposit a fake check for a million dollars, your account will briefly show a million dollars, but then the check bounces, and your balance goes back to what it was before because you were in fact never a millionaire.

https://www.nature.com/articles/s41593-026-02433-7

The peer reviewed publication is linked to from the press release. The press release's headline does somewhat overhype up the actual finding though. The actual finding is that different parts of the brain arise from different neural ectoderm progenitors, which explains why it’s so hard to get a forebrain neuron to behave like a hindbrain neuron and vice versa in the lab. The paper then postulates that this difference in progenitors resembles being two different organs evolutionarily, but this is not really the most important part of the paper.

BTW I suspect that this finding comes as completely unsurprising to biologists and the medical field generally. I'm not in either field, but even in high school biology we learned that the brain stem is behaviorally and structurally radically different from the rest of the brain, to the point where it seems almost obvious in hindsight that it's a different cell line and potentially has a different evolutionary history from the rest of the brain.

[1] https://www.scottspizzatours.com/blog/worlds-largest-deliver...

[1] https://en.wikipedia.org/wiki/James_Baxter_the_Horse

Also why separate out OS X and macOS at all when it appears that all Windows versions are lumped together and all Linux versions and distros are lumped together?

All of this seems very suspect.

So, as an example, if you use Codex through Bedrock, that’s a totally separate instance of Codex from anything you would be interfacing with if you directly used OpenAI’s API; if you use Codex via Bedrock, OpenAI never sees your data or prompts because they stay sandboxed in an ephemeral Bedrock instance. For many large enterprise deployments this hard boundary is a big big deal.

Over the past year, Claude being available via Bedrock and ChatGPT/Codex not being available via Bedrock has been a huge competitive advantage for Anthropic in the enterprise space.

1. Large companies tend to already have an existing relationship with AWS, which makes things way easier to go through vs. setting up a new vendor relationship 2. Large companies tend to have strong internal requirements about making sure that internal data stays under company control. With AWS Bedrock, you can be a lot more confident that what you're feeding into the models is not going to end up in someone's training set somewhere. For where I work, this requirement is a dealbreaker for going directly through OpenAI's API instead of going through AWS Bedrock.

1. As mentioned in the post above, the Dolphin emulator famously implements the entire Gamecube/Wii GPU pipeline in a single gigantic ubershader, and this is useful because it avoids shader compilation stalls [1].

2. Blender's Cycles renderer implements its shading graph eval system as a bytecode VM in a GPU kernel [2]. IIRC early versions of Vray GPU did something similar. There are better ways of course, but a VM gets you surprisingly far as a general approach.

3. Finally, a lot of ML frameworks (Tensorflow, PyTorch, etc) by default use the GPU relatively suboptimally (especially without kernel fusion and such). Tensor frameworks can extract a lot more perf out of GPUs using a VM-in-a-giant-kernel approach [3].

If you think abstractly about how a GPU SM actually works (using CUDA terminology here), all threads in a warp must execute in lockstep and the cost of execution divergence across threads in a warp is that you effectively run serially, losing the parallel advantage of the SM. This penalty gets magnified enormously if you are doing memory reads after wherever the execution divergence happens, since you now have multiple slow memory stalls in serial instead of one big memory read at once for all threads. If you're clever about implementing a bytecode VM, you can load as much state as you need upfront into shared memory, and then if your bytecode VM is just looping through executing a bunch of opcodes in a huge switch statement, then at least as far as the SM is concerned, there's no execution divergence! All threads look like they're doing the same thing at the same time; even if within the VM what is happening a lot is just no-ops, at the SM level you're not dealing with serialized memory stalls and serial scheduling and such.

Is it the _best_ most optimal approach imaginable? Almost certainly not! But can it be a _surprisingly good_ and possibly even reasonable approach for some problem domains and specific constraints? Yeah absolutely!

[1] https://dolphin-emu.org/blog/2017/07/30/ubershaders/ [2] https://www.youtube.com/watch?v=etGMk9wYwNs&t=1882s [3] https://hazyresearch.stanford.edu/blog/2025-09-28-tp-llama-m...

I actually don't know the answer to that; my understanding is that LLMs by nature of what they are can't understand concepts that are independent of the existing language they are trained on, but I don't have enough in-depth nitty-gritty knowledge of like, core LLM implementation details and architecture and stuff to know if that understanding is correct or not.

It's possible LLMs can handle this after all! But at least so far we only have existence proofs of humans doing this, not LLMs yet, and I don't think it's easy to be certain how far away LLMs are from doing this. I should distinguish between LLMS and AI more generally here; I'm skeptical LLMs can do this, I think some other kind of more complete AI almost certainly can.

I supposed you could just, I dunno, randomly combine words into every conceivable sentence possible and treat each new sentence as a theory to somehow test and brute force your way through the infinite possible theories you could come up with. But at that point you're closer to the whole infinite random monkeys producing Shakespeare thing than you are to any useful conclusion about intelligence.

Imagine every bit of human knowledge as a discrete point within some large high dimensional space of knowledge. You can draw a big convex hull around every single point of human knowledge in a space. A LLM, being trained within this convex hull, can interpolate between any set of existing discrete points in this hull to arrive at a point which is new, but still inside of the hull. Then there are points completely outside of the hull; whether or not LLMs can reach these is IMO up for debate.

Reaching new points inside of the hull is still really useful! Many new discoveries and proofs are these new points inside of the hull; arguable _most_ useful new discoveries and proofs are these. They're things that we may not have found before, but you can arrive at by using what we already have as starting points. Many math proofs and Nobel Prize winning discoveries are these types of points. Many haven't been found yet simply because nobody has put the time or effort towards finding them; LLMs can potentially speed this up a lot.

Then there are the points completely outside of hull, which cannot be reached by extrapolation/interpolation from existing points and require genuine novel leaps. I think some candidate examples for these types of points are like, making the leap from Newtonian physics to general relativity. Demis Hassabis had a whole point about training an AI with a physics knowledge cutoff date before 1915, then showing it the orbit of Mercury and seeing if it can independently arrive at general relativity as an evaluation of whether or not something is AGI. I have my doubts that existing LLMs can make this type of leap. It’s also true that most _humans_ can’t make these leaps either; we call Einstein a genius because he alone made the leap to general relativity. But at least while most humans can’t make this type of leap, we have existence proofs that every once in a while one can; this remains to be seen with AI.

At the time the consensus narrative was that SpaceX no longer needed Colossus 1 for Grok and that was why it could be leased to Anthropic while Colossus 2 would handle Grok training and inference. Does Anthropic also leasing Colossus 2 change this?