d3m0t3p

They are currently targeting more material science, such as quantum chemistry for drugs discovery or new material discovery. They can’t and do not aim to do prime factorization on those.

I agree that they should be doing a press release and a separate technical deep dive.

Here they mix both and don’t get the public. It has always been IBM problem to correctly target the product to the audience.

We see it here again

When everyone push on friday, and you have 400 CICD pipeline triggers spawning that many nodes. How do you know if this is unexpected ?

Their cloud compute might be on demande, someone starts training a model and 50 machines are spawned. Knowning when something is unexpected is hard

They are using Qwen, so this is decoder only.

This is exactly the same with providers from the USA.

It was 15 years ago. Whole management got replaced, they are quite ambitious. Let's see if how this works out now.

I don't think so, faster trains are overtaking slower trains. There is simply not enough space between the station to overtake without having an acceleration that would damage the trains or the tracks. For example in western switzerland the maximal train speed for the fastest trains are ~130 Km/h while the same train can go up to 200 in some swiss-german part, only due to more congestion on the western part. Trains cannot be bigger, some of them are already too big for the smaller train station and in case of rerouting / unexpected stop this causes issue. You cannot make them higher too.

You could get ride of the smaller train , only allowing big city to survive or decrease the commodity traffic or increase the rail network or increase the train station (more tracks allowing to overtake there, and have bigger trains) There is no easy solution otherwise it would have been done.

Interesting to see clearml but not its bigger counterpart mlflow

I think this is due to their AI insight, they run locally a model and it start to burn the whole computer.

It is really fun that the navbar has unaligned elements. (Docs is lower)

Yea but the goal it not to bloat the context space. Here you "waste" context by providing non usefull information. What they did instead is put an index of the documentation into the context, then the LLM can fetch the documentation. This is the same idea that skills but it apparently works better without the agentic part of the skills. Furthermore instead of having a nice index pointing to the doc, They compressed it.

Same, Firefox iOS

The model is fined tuned for chat behavior. So the style might be due to - Fine tuning - More Stylised text in the corpus, english evolved a lot in the last century.

Is that really the only thing you managed to remember ?

Because the ML ecosystem is more mature on the NVidia side. Software-wise the cuda platform is more advanced. It will be hard for AMD to catch up. It is good to see competition tho.