you can set the "temperature" which is a lever on how stochastic the prediction is. If you are doing your own inference this is clear and easy. If you are consuming tokens this is outsourced.
if "the ai" was delivering value you'd think it would be lowering both cost to develop these dev tools as well as the cost to support them. In that light the move is odd unless they don't know how to use AI to lower those costs or it cant.
maybe now isn't the time but at some point it needs to be better understood which models are best for which types of programming or styles or languages. These models are not all the same for every language. The harness is also a factor. Python seems to be somewhat an exception today but that may not last. Another question might be: is there a pattern where you prototype in language X and implement in language Y. The models seem to be very good at porting code. I've used this pattern with python -> c++ SDL to squeeze out performance after I had a working gui. Has anyone measured this in terms of speed (wall clock) and in terms of token efficiency?
my experience with elixir is that if you tell the LLM "let it crash" it is productive. If you don't tell it this it hides errors and writes crappy tests. So, maybe it is go and not concurrency programming.
I think GB10 is a bit of a counter point. There are tons of features that are not implemented for GB10 which was released 8/2025 It isn't all roses on the cuda side.
you can buy dual coaxial shaft steppers ( X40 ) for car instrument panels open them and remove the hard stops. A very small magnet and 2 hall sensors gets you end stops.
This is a bit of a simplification of how enterprise sales works. A few extra dimensions are sometimes required. For "decision makers" their own personal view of reality may not in fact be accurate. You sometimes must vet that what they tell you (and may in fact believe) actually reflects the actual power dynamics of a big enterprise. There are extra dimensions to consider; The first is: are they in a cost center or are they generating revenue. The second is: are you trying to get new technology adopted or is this considered "standardized". The third is: who might perceive your product as a risk, or a threat to their power? These data points inform your sales strategy both in who you sell to, who you avoid, and when you might choose to engage a particular persona, when to go small and when to go big.
