'If you can't build a TODO list app using only punchcards, then you can't do your job...'
Obviously our ambitions expand due to better tools. I now commit to and deliver much more work than before LLMs, and — before then — ditto for frontend frameworks, generation 4 languages etc.
There are projects I now start without thinking twice that I never would have considered a few years ago.
That's what productivity looks like, and it makes you more valuable, and your job more secure (up until the ASI kills us all...).
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Trying two things and giving up. It's like opening a REPL for a new language, typing some common commands you're familiar with, getting some syntax errors, then giving up.
You need how to learn to use your tools to get the best out of them!
Start by thinking about what you'd need to tell a new Junior human dev you'd never met before about the task if you could only send a single email to spec it out. There are shortcuts, but that's a good starting place.
In this case, I'd specifically suggest:
1. Write a CLAUDE.md listing the toolchains you want to work with, giving context for your projects, and listing the specific build, test etc. commands you work with on your system (including any helpful scripts/aliases you use). Start simple; you can have claude add to it as you find new things that you need to tell it or that it spends time working out (so that you don't need to do that every time).
2. In your initial command, include a pointer to an example project using similar tech in a directory that claude can read
3. Ask it to come up with a plan and ask for your approval before starting
> The critical factor is the dataset, not the specific hard-coded bells and whistles that constrain the curve's shape
I have almost the opposite take. We've had a lot of datasets for ages, but all the progress in the last decade has come from advances how curves are architected and fit to the dataset (including applying more computing power).
Maybe there's some theoretical sense in which older models could have solved newer problems just as well if only we applied 1000000x the computing power, so the new models are 'just' an optimisation, but that's like dismissing the importance of complexity analysis in algorithm design, and thus insisting that bogosort and quicksort are equivalent.
When you start layering in normalisation techniques to minimise overfitting, and especially once you start thinking about more agentic architectures (eg. Deep Q Learning, some of the search space design going into OpenAI's o1), then I don't think the just-an-optimisation perspective can hold much water at all - more computing power simply couldn't solve those problems with older architectures.
Obviously our ambitions expand due to better tools. I now commit to and deliver much more work than before LLMs, and — before then — ditto for frontend frameworks, generation 4 languages etc.
There are projects I now start without thinking twice that I never would have considered a few years ago.
That's what productivity looks like, and it makes you more valuable, and your job more secure (up until the ASI kills us all...).