Wish I had the time and skill to actually contribute to the LSP, if you have ever used Nim it's a seriously underrated language.

For me, the act of sitting down and writing the code is what actually leads to true understanding of the logic, in a similar way to how the only way to understand a mathematical proof is to go trough it. Sure, I'm not doing anything useful by showing that the root of 2 is irrational, but by doing that I gain insights that are otherwise impossible to transfer between two minds.

I believe that coding was one of the few things (among, for example, writing math proofs, or that weird process of crafting something with your hands where the object you are building becomes intimately evident) that get our brains to a higher level of abstraction than normal mammal "survival" thinking. And it makes me very sad to see it thrown out of the window in the name of a productivity that may not even be real.

If it wasn't obvious by now, the big capital doesn't really care about open source, hobby coding or small companies.

But try to do something novel and... they become nearly useless. Not like anything particularly difficult, just something that's so niche it's never been done before. It will most likely hallucinate some methods and call it a day.

As a personal anecdote, I was doing some LTSpice simulations and tried to get Claude Sonnet to write a plot expression to convert reactance to apparent capacitance in an AC sweep. It hallucinated pretty much the entire thing, and got the equation wrong (assumed the source was unit intensity, while LTSpice models AC circuits with unit voltage. This surely is on the internet, but apparently has never been written alongside the need to convert an impedance to capacitance!).

But just take a look at how LLMs struggle to handle dynamical, complex systems such as the "vending machine" paper published some time ago. Those kind of tasks, which we humans tend to think of as "less intelligent" than say, converting human language to a C++ implementation, seem to have some kind of higher (or at least, different) complexity than the embedding mapping done by LLMs. Maybe that's what we typically refer to as creativity? And if so, modern LLMs certainly struggle with that!

Quite sci-fi that we have created a "mind" so alien we struggle to even agree on the word to define what it's doing :)

I like to think that the fundamental difference between AlphaEvolve and your typical genetic / optimization algorithms is the ability to work with the context of its goal in an abstract manner instead of just the derivatives of the cost function against the inputs, thus being able to tackle problems with mind-boggling dimensionality.