As technology advanced, humans grew accustomed to relying on the machines.
aio
honestly the only important difference between them is that emacs's default keybindings can and will give you a repetitive stress injury (ask me how i know...)
Apparently MIT is teaching a vibe coding class:
How will this year’s class differ from last year’s? There will be some major changes this year:
- Units down from 18 to 15, to reflect reduced load
- Grading that emphasizes mastery over volume
- More emphasis on design creativity (and less on ethics)
- Not just permission but encouragement to use LLMs
- A framework for exploiting LLMs in code generation
i might try writing such a post!
When people compile compilers do they actually specialize a compiler to itself (as in definition 3 in the paper) as one of the steps? That's super interesting if so, I had no idea. My only knowledge of bootstrapping compilers is simple sequences of compilers that work on increasing fragments of the language, culminating with the final optimizing compiler being able to compile itself (just once).
I've been using Anki, it works great but requires you to supply the discipline and willingness to learn yourself, which might not be possible for kids.
Writing "My Immortal" in 2006 when nothing quite like it had ever been written before, is a (possibly unintentional) stroke of genius. Writing "My Immortal" after it's already been written is worthless.
are we really clutching our pearls because someone named themselves after a demon
ok but what does this mean for Batman vs Lex Luthor
I did yes :)
As we know, the critical age for a boy genius is somewhere from 11 (Harry Potter) to 15 (Paul Atreides), so the gene-enhanced baby ought to have a fair shot after a few months or so.
I think this theorem is worthless for practical purposes. They essentially define the "AI vs learning" problem in such general terms that I'm not clear on whether it's well-defined. In any case it is not a serious CS paper. I also really don't believe that NP-hardness is the right tool to measure the difficulty of machine learning problems.