That is fighting the last war on the early days of 5 GLs, and taken care by agentic runtimes.

This isn't futurology, besides a few projects that already came up on Phoronix and around here, with Assembly generation, there are enough SIGPLAN papers on ML driven compilers already.

Like inform7 but irreproducible and shitter on every iteration.

Like JIT and PGO machine learning optimizer passes more likely.

ML driven compilers are different than LLM assembly generation, and former is more sound than the latter one. I believe we both have enough experience to understand that.

However, starting with LLMs directly generating machine code and moving to ML directed optimization passes, you're moving the whole stadium with a crawler-transporter, not only the goalposts.

Helping compiler in a well defined way, and working on IR or driving it repeatedly to optimize the output is one thing, generating machine code from a training set is other. Let's not mix them.

Yeah, well as mentioned, that is where it is going.

"Compiler 2.0: Building the Next Generation of Compilers with Machine Learning"

https://eecs.berkeley.edu/research/colloquium/compiler-2-0-b...

There are other researchers if you interest yourself on the matter.

Then the whole iPaaS agentic coding environments like Workato and Boomi, that even if they might generate 3GL as intermediate step, you don't get to see it.

Thanks for the link. I have also found a small trove as a warm-up. If anyone interested, here it is:

https://github.com/zwang4/awesome-machine-learning-in-compil...

Using small, purpose built ML "things" to make things better makes sense. We use them everywhere. I never argued against it, as well.