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I'll be looking into ways of running a local LLM for this purpose (code assistance in VS Code). I'm already really impressed with various quite large models running on my 32 GB Mac Studio M2 Max via Ollama. It feels like having a locally running chatgpt.
Earlier quoted context omitted.
I'm continually surprised by the amount of negativity that accompanies these sort of statements. The direction of travel is very clear - LLM based systems will be writing more and more code at all companies. I don't think this is a bad thing - if this can be accompanied by an increase in software quality, which is possible. Right now its very hit and miss and everyone has examples of LLMs producing buggy or ridiculou…
> if this can be accompanied by an increase in software quality That’s a huge “if”, and by your own admission not what’s happening now. > other LLM agents reviewing code, feeding in compile errors, letting other LLM agents interact with the produced code, etc. What a stupid future. Machines which make errors being “corrected” by machines which make errors in a death spiral. An unbelievable waste of figurative and lit…
Humans are machines which make errors. Somehow, we got to the moon. The suggestion that errors just mindlessly compound and that there is no way around it, is what's stupid.
which puts it in line with previous code-generation technologies i would imagine. I wonder which of these increased productivity the most?
- Assembly Language
- early Compilers
- databases
- graphics frameworks
- ui frameworks (windows)
- web apps
- code generators (rails scaffolding)
- genAI
Like, isn't this announcement a terrible indictment of how inexperienced their engineers are, or how trivial the problems they solve are, or both?
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
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> For teams you can measure meaningful outcomes and improve team metrics. How? Which metrics?
That is what we pay managers -to figure out- for. They should find out which and how by knowing the team, familiarity with domain knowledge, understanding company dynamics, understanding customer, understanding market dynamics.
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…