[ssalazar]

> very fancy search engines

This is a common misunderstanding of LLMs. The major, qualitative difference is that LLMs represent their knowledge in a latent space that is composable and can be interpolated. For a significant class of programming problems this is industry changing.

E.g. "solve problem X for which there is copious training data, subject to constraints Y for which there is also copious training data" can actually solve a lot of engineering problems for combinations of X and Y that never previously existed, and instead would take many hours of assembling code from a patchwork of tutorials and StackOverflow posts.

This leaves the unknown issues that require deeper reasoning to established software engineers, but so much of the technology industry is using well known stacks to implement CRUD and moving bytes from A to B for different business needs. This is what LLMs basically turbocharge.

[ActorNightly]

Right, so search engines, just more efficient.

But given a sufficiently hard task for which the data is not in the training set in explicit format, its pretty easy to see how LLMs can't reason.

[ssalazar]

Lmao no, what Ive described is a reasonably competent junior engineer.

[ActorNightly]

To be a competent engineer in 2010s, all you really had to do was understand fundamental and be good enough at google searching to find out what the problem is, either for stack overflow posts, github code examples, or documentation.

Now, you still have to be competent enough to formulate the right questions, but the LLMs do all the other stuff for you including copy and paste.

So yes, just a more efficient search engine.

[chasd00]

Right, so search engines, just more efficient.