Here is how I think about it: Learning to program is learning a new way of thinking. When you learned to do mental arithmetic the point was not that you would necessarily do mental arithmetic at all times in the future. Programming is the last step when solving a problem with a computer, learning to program teaches you how to solve problems more generally.
I recommend reading a book like https://mitp-content-server.mit.edu/books/content/sectbyfn/b..., going through it will hopefully as enjoyable as it was for me when I read it in high school. There are many kinds of programming which are not super enjoyable (to me), so I gladly leave those to AI, but based on personal observation, my experience programming lets me be much more effective at using AI to solve problems than a fresh MIT / Oxford grad with less programming experience.
Finally it depends on your interests: If your interests are computers and X, than combining both to solve problems you find interesting can make using AI worthwhile, because then programming isn't the main point.
This is an old idea that there is great value in learning Lisp, or other "unusual" languages, because they force you to think differently about problems.
An amusing implication here is that the more "useless" a language is economically, the more valuable it's likely to be to learn it (for the effect it has on your mind).
It’s amusing that you boil their comment down to “you should learn Lisp” based on a book recommendation that is about anything other than learning Lisp.
When I learnt programming I had a big dusty Perl book, I didn't even have a computer. A mix of library books and using my savings to print out c++ tutorials etc was the thing. Then came forums, then came youtube (matrix soundtrack and notepad), then stackoverflow etc.
If I had access to even the weakest offline model now as I did back then where it can save me hours of trial and error, docs sifting and getting my questions closed I'd be a different man.
I love using Claude to one shot interactive tutorials, so far I've done voxels, shadow mapping, sdfs for font rendering, a weird dialect of asm and much more. I see it as the perfect assistant to mentor someone nowadays, if you have actual passion for the field it's hard to go wrong.
I learned programming in QBasic in MS-DOS, you could just start the IDE and the documentation was filled with cool examples. Super easy to run any program. I made music / weird drawings etc.
The big thing is that learning to program is really about improving your ability to formalize your ideas by methodically breaking them down into a decomposed set of deterministic logical statements.
I think this cross‑pollinates into many domains, because in a sense what you’re doing is learning how to take something you know and express it rigorously in a mathematical form / structural syntax.
Jujutsu has changed how I work with git. Switching tasks is just "jj edit <change>" or "JJ new <change>". The only thing it can't do properly is git worktrees (it doesn't replicate the .git dir to the worktrees, breaking tooling that relies on git) but there is a (old) issue relating to it. Not sure on the priority, though.
they were definitely totalitarian, slightly different mix of ideology. Fascist is a fairly good description here, it describes close collaboration of government with corporations to advance national goals. US had somewhat fascist tendencies for a long time now.
I don’t get that, the use of these books was instrumental and necessary for the success of the training run. The expected value of these training runs is high as the build out of 100 billion+ infrastructure demonstrates, so the book publishers should at a minimum be paid a licensing fee, a small fraction of every inference run revenue or whatever they decide. The fact that authors and publishers didn’t get any say under what conditions their intellectual property can be used is pretty outrageous.
The conclusion was they suffered no legal harm, in that their interests such as their continued publishing of books was not affected by LLMs; no one is using AI to compete with publishers, if anything "authors" might very well use those same publishers to get their generated books on shelves.
So pretty much the same as the Authors Guild, Inc. v. Google, Inc. case ruling it as fair use as a transformative work. I mean if indexing the worlds books is transformative then a neural net run on them certainly is a transformative work and fair use.
In fact 5g and all previous standards have a provision for lawful intercept. So your domestic intelligence service and police can always turn it into a listening device.
Curious how this relates to what lean4 is doing, I guess in lean's case some of the data structures are special cased (Array) and there is no easy way to implement such data structures yourself
At some point we will be so tired of distinguishing between AI generated content and human content that we will stop using the Internet and it will be left to bots.
I recommend reading a book like https://mitp-content-server.mit.edu/books/content/sectbyfn/b..., going through it will hopefully as enjoyable as it was for me when I read it in high school. There are many kinds of programming which are not super enjoyable (to me), so I gladly leave those to AI, but based on personal observation, my experience programming lets me be much more effective at using AI to solve problems than a fresh MIT / Oxford grad with less programming experience.
Finally it depends on your interests: If your interests are computers and X, than combining both to solve problems you find interesting can make using AI worthwhile, because then programming isn't the main point.
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