With hardware you get extra safety risks of fires and shocks, so let's see

We've had 4 teams on this model and sure it helped in some things (mostly data analysis and scripts) but generating code and doing architecture is utter crap 90℅ of the time. So much so that we've even had juniors noticing some design patterns that, and I quote: "this is one the examples of bad code we were taught in sw design classes'. The worst is how non deterministic they are. The same prompt from different people yields vastly different results

SW engineering is mostly x + y + z Where x=planning, y=writting code and z=reviewing, then rinse and repeat. Llm spedup y but made everything else take so much longer that equation result is much worse. Now reviewing is utter torture and during planning we're dicussing how to mitigate the pitfalls of LLM (like over engineering, too many abstractions) that we spend fewer time on the planning of the engineering itself and more on cuddling this brain addled tool

What an utter piece of BS. AI goons really like to smell their own crap

I'm currently looking and I'm considering cutting my salary up to 50℅ to work for a company with a very interesting product that doesnt push AI and let us instead decide where to use it.

I'd rather lower my quality of life than put up with this bs and being forced to use a tool that I disagree so much on an ethical and moral pov. Let alone letting managera decide which tools I have to use on my engineering work

Also tokenmaxxing was never an intentional and smart strategy employed by companies like you say. It was a mix of fear of missing out, signaling to investors they were in on the hype and recouping investmenets in data centers

Besides many of the companies on the list are suspext numero uno for the state of open source

This sent me through a rabbit hole of uring, kernel development and C. I've been a rust and c++ dev for quite a few years now, but there's such a simplicity and even artistic feel to small(ish) C programs.