Em Adespoton

There’s also the fact that

1. It’s only really good at this if you want it to generate Python, PowerShell, bash, or C++ code. Try any other language and it quickly assumes you’re using outdated and often incompatible libraries or doesn’t really understand how the language functions.
2. at the end of it all, neither you nor the AI has learned anything new; you’ll have to put in the exact same amount of work the next time. If you do it yourself, then over time that 10% advantage goes away.

Now, these things could both change over time, but humans are much more efficient to train than current state of the art probability sieves we call GenAI.

I remember cliques and a lack of online monoculture on Usenet and IRC before the World Wide Web even existed; the web exploded things even further, as did the privatization of DNS and takeover of funding by VCs and ad conglomerates. All that had happened by 1998.

When was this?

Asking as someone who’s been on the Internet since 1989.

For decades, many computer scientists have presumed that for practical purposes, the outputs of good hash functions are generally indistinguishable from genuine randomness — an assumption they call the random oracle model.

Er, no. The falsity of this is taught in virtually all first year CS courses.

Computer programmers and other IT workers? Sure… but hash functions have never been considered a substitute fore pure randomness.

That’s why we have a random generator in each computer based on thermal variance, I/O input, and other actually random features. And even then, we have to be careful not to hash the randomness out of the source data.