> GPT‑5.6 Terra and GPT‑5.6 Luna outperform Fable 5 at around one-sixteenth the cost
If that's the case, we're definitely going to have some Mythos level open weights models by the end of the year. Maybe even at a size that can run on a ~40k locally hosted server.
I just did this on one .claude directory and >20% of the answers there included some variation of "real", "actual", "exact", "honest", "genuine", "valid", "true". ~15% in that directory contain some variation of "real", "genuine" or "honest". This is excluding thinking tokens, sub-agent output, etc.
Definitely not just a classifier layered on top, although there is one of those as well. Pretty sure it's different post-training / finetune run and the model weights are different between them. Which is bound to have effects on the model output even in general use, although I'd imagine they have pretty stringent evals to make sure it doesn't regress too much in things they don't want to restrict.
> Both conditions used GitHub Copilot (Claude Sonnet 4.5 or Haiku 4.5, depending on study) running in VS Code within isolated Docker containers. The only difference was Mouse tool availability. (https://hic-ai.com/papers/mouse-paper-v13.pdf)
Haiku/Sonnet 4.5 on GitHub Copilot is not a valid comparison whatsoever.
You need to benchmark against Claude Code running Opus. I mean, being revolutionary is a big claim to fame.
Doesn't make sense to fixate on LLMs and not the actual Transformer/attention foundation. The Transformer/attention architecture is the breakthrough, not LLMs. Especially the RLHF chat paradigm is 100% a byproduct. Which is easy to see when you look at how ChatGPT originally came about.
DeepMind has already has had real impact on science with the same foundational architecture as LLMs, for protein folding. They won a Nobel prize for it.
Tangential, but I'm pretty sad about EU having absolutely nothing in the actual SotA LLM market. Especially given the recent events of US completely restricting the actual SotA models.
Has this been just pure lack of funding and infra?
Dario's been openly talking how worried he is about China and labs getting synthetic training data off their models, for years. Most recently in relation to "Mythos level" capabilities.
Not really distillation, just synthetic training data.
The issue is that using Claude Code is an easy compromise for most to make, when you get to use the models 10x cheaper than through API pricing with a custom harness.
I actually thought about that while writing the original comment as well. For Emma, Forever Ago is one of my all time favorite albums, good example of raw emotion with no need for any bells or whistles.
The big thing there is, that he already was a professional musician and completely inside a creative scene before leaving for the cabin. (DeYarmond Edison was the band he was in before Bon Iver.)
But yes, things were going way sideways for him, liver issues with mono, so he went to process whatever was going on and had been going on in complete isolation. (Although for the next album, he actually set up a whole "creative commune", a new band around Bon Iver instead of it being just himself, and so on. And I think you can hear the colors he wanted back in the music from it directly.)
A lot of examples of artists going into bouts of isolation, but almost always coming into it from an intense experience. So, the two don't have to be day to day intertwined, although for Techno specifically it's usually the case.
I don't believe this is how great music usually comes about, not even Techno. It's missing the other essential piece. Being influenced by and completely immersed in a niche of other brilliant people. (The most extreme example of this would be the 90's Detroit-Berlin connection.)
Paired with an obsessive work ethic in the studio.
If it's only obsession in the studio, things come out dry, uninspired. If there's no surge of energy running through your bones when making the music, why would anyone else feel anything? Mixing and the music sounding "professional" is completely secondary. Even detrimental a lot of the time, to be honest.
Applies to many other things than music as well. I don't any great technology comes out and about without that loop, either.
Deep Research has been using the Orchestrator -> Subagents -> Synthesizer loop since the beginning. It's just strange that they'd put a loop benchmark next to actual model benchmarks.
Maybe it's a tune of the base model that works especially well with the subagent loop?
OpenAI also announced two days ago that they're starting to make Cerebras style chips themselves [0], will be interesting to see how fast SotA model inference will be by the end of the year.
If that's the case, we're definitely going to have some Mythos level open weights models by the end of the year. Maybe even at a size that can run on a ~40k locally hosted server.