LLMs are famously bad at producing rust code. I'm not sure how much of it is the lesser amount of Rust code in the training data, or just the fact that Rust has a very large number of pitfalls, and a large standard library with many edge cases and things you'd imaging should exist but don't for a variety of reasons. Rust also has a much wider variety in the way things could be structured, compared to something like go where there is often only one way of doing a particular thing.

People who avoid ORMs endup writing their own worse ORM*. ORMs are perfect if you know how and when to uses them. They encapsulate a lot of the mind numbing work that comes with raw sql such as writing inserts for a 50 column database.

This seems superficial and doesn't really get to the heart of the question. To me it's not so much about bits and parameters but a more interesting fundamental question of whether pure language itself is enough to encompass and encode higher level thinking.

Empirically we observe that an LLM trained purely to predict a next token can do things like solve complex logic puzzles that it has never seen before. Skeptics claim that actually the network has seen at least analogous puzzles before and all it is doing is translating between them. However the novelty of what can be solved is very surprising.

Intuitively it makes sense that at some level, that intelligence itself becomes a compression algorithm. For example, you can learn separately how to solve every puzzle ever presented to mankind, but that would take a lot of space. At some point it's more efficient to just learn "intelligence" itself and then apply that to the problem of predicting the next token. Once you do that you can stop trying to store an infinite database of parallel heuristics and just focus the parameter space on learning "common heuristics" that apply broadly across the problem space, and then apply that to every problem.

The question is, at what parameter count and volume of training data does the situation flip to favoring "learning intelligence" rather than storing redundant domain specialised heuristics? And is it really happening? I would have thought just looking at the activation patterns could tell you a lot, because if common activations happen for entirely different problem spaces then you can argue that the network has to be learning common abstractions. If not, maybe it's just doing really large scale redundant storage of heurstics.

I think some people need to step back and ponder what's the purpose of economics? Why do we even study it? What are we hoping to achieve in analyzing it?

I can only think it began with the philosophy for making better decisions, solving problems, and improving society - particularly in the allocation of its resources.

As so, I think a huge part of the problem with economics, including most economists, is that people equate value for wealth. Value certainly is subjective, I mean, do you value having $80k, or a nice car? How about a pool, or a boat? Do you value your time less than the cost of a flight? All very subjective. But I think it's flawed to conflate value for wealth. Wealth is something real, the things you are evaluating in trade. Time is wealth. Money is wealth. Oil is wealth. Land is wealth. Why don't we simply measure these things, and how they're allocated? IMO, it seems more practical and more noble of pursuit to figuring out if someone actually needs food in society, rather than what people with plenty of resources ascribe their values to.

Frameworks that do “everything” are not a good idea. I’m from the Java ecosystem. Spring is the “batteries included” framework there. If you have migrated any real world application to a new major version once, you’ve learned forever that “all in one” frameworks are bad. Please don’t do it!

Instead, use scaffolding tools, that give you a head start on creating a new project, using smaller, specialized libs.

Also, don’t use an ORM. Just write that SQL. Your future self will be thankful.