71–80 of 140 posts
Over like five years we've been promised a revolution that has yet to appear and is still lighting billions of dollars on fire. Don't bet on it materialising tomorrow.
If you need comforting, go read Merleau-Ponty and Heidegger, perhaps condensed down as Hubert Dreyfus.
RAG could largely be replaced with tool use to a search engine. You could keep some of the approach around indexing/embeddings/semantic search, but it just becomes another tool call to a separate system.
How would you feel about becoming an expert in something that is so in flux and might disappear? That might help give you your answer.
That said, there's a lot of comparatively low hanging fruit in LLM adjacent areas atm.
My 2 centes: 1. Learn basic NNs at a simple level, build from scratch (no frameworks) a feed forward neural network with back propagation to train against MNIST or something as simple. Understand every part of it. Just use your favorite programming language. 2. Learn (without having to implement with the code, or to understand the finer parts of the implementations) how the NN architectures work and why they work. Wh…
> Learn how to use AI effectively for coding. This is absolutely non-trivial, and a lot of good programmers are terrible LLMs users (and end believing LLMs are not useful for coding). I've been asking this on every AI coding thread. Are there good youtube videos of ppl using AI on complex codebases. I see tons of build tic-tac-to in 5 minutes type videos but not on bigger established codebases.
My 2 centes: 1. Learn basic NNs at a simple level, build from scratch (no frameworks) a feed forward neural network with back propagation to train against MNIST or something as simple. Understand every part of it. Just use your favorite programming language. 2. Learn (without having to implement with the code, or to understand the finer parts of the implementations) how the NN architectures work and why they work. Wh…
Any suggestion on where to start with point 1? (Also a SWE).
Consider this (possibly very bad) take: RAG could largely be replaced with tool use to a search engine. You could keep some of the approach around indexing/embeddings/semantic search, but it just becomes another tool call to a separate system. How would you feel about becoming an expert in something that is so in flux and might disappear? That might help give you your answer. That said, there's a lot of comparatively…
Isn't that true for almost every subject within computers though, except more generalized concepts like design/architecture, problem solving and more abstract skills? Say you learn whatever popular "Compile-to-JS" language (probably TypeScript today) or Kubernetes, there is always a risk it'll fade in popularity until not many people use it.
I'm not saying it's a problem, as said by someone who favors a language people constantly claim is "dying" or "disappearing" (Clojure), but more that this isn't exclusive to the LLM/ML space, it just seems to happen slightly faster in that ecosystem.
So instead, embrace change, go with what feels right and learn whatever seems interesting to you, some things stick around, others don't (like Coffeescript), hopefully you'll learn something even if it doesn't stick around.
Earlier quoted context omitted.
Going into trades sounds nice on paper but the salaries there were mostly always low because you need only a handful of those to saturate market needs. Its not IT where you can create value from thin air and thus grow the market and increase need for even more professionals. As soon as a tiny percent goes into trades (bet tons of new people already doing this) the market will be oversaturated in a few years when they…
Yes, in the worst case scenario we wind up with basically nobody having jobs. I mean, when humanoid robots get sufficient dexterity, they can even come for the skilled trades folks as well, as far as that goes. Look, I don't know if any of this is actually going to to come to pass or not. But it seems at least a little bit less like pure sci-fi now than it did a decade or two ago. Anyway, if we play along with the th…
Call me optimistic or whatever, but isn't that the best case scenario? If having a full-time job is basically just for the 0.1% or whatever, then we must have figured out a different way of distributing goods and solving peoples needs, that doesn't involve "trading time for money" (a job), and that sounds like it can be a good thing, not "worst case scenario".
My 2 centes: 1. Learn basic NNs at a simple level, build from scratch (no frameworks) a feed forward neural network with back propagation to train against MNIST or something as simple. Understand every part of it. Just use your favorite programming language. 2. Learn (without having to implement with the code, or to understand the finer parts of the implementations) how the NN architectures work and why they work. Wh…
Depends on what you want to do. But my 2 cents are that like all new technology, LLMs will become a commodity. Which means that everybody uses them but few people are able to develop them from scratch. It's not different from other things like databases, GPU drivers, 3D engines for games, etc. That all involves a lot of hardcore computer science and math. But lots of people use these things without being hindered by…