Dang it. My app is broken.

I love the idea. I couldn't get it to work; I navigated to (0,0) but didn't see a cursor; kept changing colors and trying to place blocks but saw nothing. Eventually my Ghostty crashed.

So the result here is that you fed a few thousand segments of old books into Pangram and zero of them were classified as AI. Seems impressive?

Me too. maybe Pangram solves this?

Why did Qwen stop producing open models? They've gone from building the best open models ~1 year ago to producing like the 10th-best closed models. I don't understand this pivot at all.

Edit: I saw online they do in fact plan to release this openly at some point – x.com/Alibaba_Qwen/status/2078759124914098291

They've announced that they're releasing the weights for a 2.4T model soon:

https://xcancel.com/Alibaba_Qwen/status/2078759124914098291

It's not a pivot, giving away the weights was a marketing strategy that they don't need to keep up with.

Lo and behold, a nice arithmetic coding implementation that wasn't written by an LLM! A sight for sore eyes – a treat, even. Looks like it was written by someone else though.

Check it out: https://github.com/samyak112/pym-particles/blob/main/arithme...

Ohh yeah , I took it from Project Nayuki as mentioned in the file as well, i tried to pip install it but there were some issues so just took the file and kept the copy right as it is.

Its not an issue is it? I am not sure.

Haha. Unfortunately is my regular voice, since long before I started using Codex. You can check through some of my old writing. It definitely could've gotten worse though. Not sure if I'm training on Codex, or Codex is training on me...

This is certainly part of it! My point was that focusing on problems proposed by others is one very specific and pretty short-term mode of thinking. Good researchers improve benchmark scores. Great researchers think about what problem they're solving.

Incredible concept and a very well-crafted site. I scored very low, but then very high with my legal name. It seems DeepSeek knows a lot of arxiv papers (or at least, about the authors).

There’s nothing to read.

Model A: A_1, …, A_n Model B: B_1, …, B_n

C_i = A_i * p + B_i * (1 - p)

In other words, it’s just a linear combination of the other models’ weights, per position.

It's been a while since I looked at neural networks in detail. Do all the large models have a close enough architecture that this makes sense? Do they have the same number of layers and width? I had thought that each model it's own "secret sauce" of normal and special layers (convolution, max-pooling, something-something) stacked together. Genuinely curious.