There’s a famous Usenet story about a programmer (Mel) who refused higher level abstractions.
It was the late 1950s, and even in that era, Mel was…well today we’d call him a boomer.
Mel only wrote in raw hexadecimal. He didn’t approve of compilers, and refused to use optimizing assemblers.
"You never know where it's going to put things”, he said.
Everyone else in the company was moving on to FORTRAN, and they didn’t understand why Mel was so stubborn about using new tools. He *loved* self-modifying code.
“If a program can’t rewrite its own code”, he asked, “what good is it?”
Mel eventually left the company, and other engineers were tasked with understanding what was left.
Mel’s hand-optimized routines always beat the assemblers; but some of it looked absolutely bizarre.
One engineer took ~2 weeks to understand why there were loops with no exit condition…yet the program worked fine.
I won’t spoil all the details, you should really read it, it’s short. But it’s a fantastic piece on “what defines a real programmer?”…which is becoming increasingly relevant in this vibe-coded era.
I strive to understand computers as deeply as Mel! If we aren’t careful, we’re going to lose the “Mels” of this world to time.
That’s part of why I go so deep in my youtube videos. I hope that younger viewers are genuinely fascinated by the inner workings of our machines, instead of handing everything off to higher abstractions.
@JonathanRoss321 In an age of agentic development pouring oceans of unreviewed code every day, the promise of saying no has already become the value proposal. I want bloat removers, not slop zombies.
@AlexRozinov@ylecun Ditto. A modern Transformer in Lisp clearly has a mathematical beauty lacking from the noisier, imperative Python-based code.
https://t.co/mtYETallHG
Unfortunately Lisp remains a niche in ML nowadays.
@Mayhem4Markets LLMs are hitting their limits. MOE, KV caches, quantization and compactions can only go so far, and parameters don't scale well.
What Anthropic needs is an architectural breakthrough, not more parameters.
@DmytroKrasun I'm working on a functional ML framework and have to ship it with a context file, because LLMs constantly try to write models "the PyTorch way".