yaoke259

It could be interesting to experiment with more complex representations in the memory/file system, i.e. vectors. Seems like the full advantage of this architecture is yet to be explored. Blending neurons with deterministic compute to fit on one GPU is a fascination concept, never considered it before!

Given that a lot of the bottleneck in speed is human trial, I wonder if overall speedup will be limited anyway?

Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.

> Instead, labor and computing power are being diverted toward a singular “frontier-model effort”

They are just changing their strategy to invest in a single model? It's not like their AI department is in shambles...

First class hardware support makes this really exciting and different. On device processing is cheaper and faster, critical for real time applications of AI. Really excited for the rise of local, smart intelligence!

Whilst general self-reporting of confidence will be hard to achieve (we can't even do it as humans), I'm sceptical of the notion that we can't train models to give some confidence scores accurately. I think over the past year that with the correct RL, models can showcase some pretty insane capabilities. If reporting the correct confidence was a RL training target say for predicting weather events or a game outcome that is supported by real RL environment or simulated data, then I suspect LLMs will develop a strong intuition for confidence similar to how the recent JEV and decision models are trained.

Awesome write up and intro by a fellow Sydney-sider, kept me hooked! As someone who's not a maths wizard, the technical details flew by my head but the prose allowing me to empathise deeply with those in the field was very enlightening. I never realised how big of a deal the maths dump was and how much it has shaken up the field. I guess we are all just trying to figure out this whole "intelligence on tap" thing together.

what's the advantage of a tool like this compared to existing mature tools like infisical?

> we can't tell the future for the next year- how could we possibly predict what works well 20 years from now?

well said, absolutely agree

I often think of this question. Probably things that are enduring. How to be a good person, how do people work, how does society work, how to learn, some hobbies and life skills.

I've been building voice agents for the past year, and honestly the word error rate and how good they sound are "good enough" across countless providers. The real gaps are in latency, handling interruptions, interrupting the speaker if needed (this is surprisingly useful in most contexts) and adapting to the emotion/tone of the context. Would love to see a benchmark or tests that measure these effectively and test the claims of Voxtral 3! These existing benchmark results look very impressive.

XD

in terms of raw token throughput and serving users it is, but in terms of R&D and long term lab capabilities, training is still the goose that lays the golden egg