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Ask HN: Go deep into AI/LLMs or just use them as tools?

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Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#111

Post by zwnow »

yubblegum wrote: post #106
zwnow wrote: post #86

Earlier quoted context omitted.

Every programmer really is just mediocre. There is no perfect software yet. Hence people who built it are mediocre.

Like any skillset, programming skills likely form a distribution pattern. There are exceptional programmers out there, I've worked with a few. "Every programmer really is just mediocre" merely indicates you have only worked with mediocre colleagues and are one yourself. > There is no perfect software yet. "Software" you refer to is actually 'software product', not merely 'code'. So the reality is that even with excep…

I even consider well respected devs mediocre. Obviously there is a distribution, like with everything. But even the best of the best produce garbage

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#112

Post by teleforce »

Nobody is expecting you to be able to derive and write automatic differentiation (AD) library from scratch but it's always good to know the fundamentals [1].

Andriy Burkov has written excellent trilogy books series on AI/LLMs namely "The Hundred-Page Machine Learning Book" and "Machine Learning Engineering" and the latest "The Hundred-Page Language Models Book" [2],[3],[4].

Having said that, the capability of providing useful AI/LLMs solutions for intuitive and interactive learning environment, training portal, standards documentation exploration, business and industry rules and regulations checking, etc based on the open-source local-first data repository with AI/LLMs are probably the killer application that're truly useful for end users, for examples here [5],[6].

[1] Automatic differentiation:

https://en.wikipedia.org/wiki/Automatic_differentiation

[2] The Hundred-Page Machine Learning Book:

https://www.themlbook.com/

[3] Machine Learning Engineering:

https://www.mlebook.com/wiki/doku.php

[4] The Hundred-page Language Models Book

https://www.thelmbook.com/

[5] Local-first software: You own your data, in spite of the cloud:

https://www.inkandswitch.com/essay/local-first/

[6] AI-driven chat system designed to support students in the Introduction to Computing course (ECE 120) at UIUC, offering assistance with course content, homework, or troubleshooting common problems. It serves as an educational aid integrated into the course’s learning environment:

https://www.uiuc.chat/ece120/chat

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#113

Post by gpjt »

antirez wrote: post #21

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…

This, 100%. A full-stack engineer will likely have at least a solid understanding of the HTTP protocol, HTTPS, WebSockets, the interface layer between the frontend server and their chosen Web webdev stack, and so on. Then a more general understanding of networking protocols, TCP vs UDP, DNS, routing, etc. In general, you need to have a solid understanding of the layer below where you're working, some understanding of the layer below that, and so on, less and less detail needed for each layer down.

(That's not to say that you shouldn't bother with learning more -- more knowledge is always good -- or that the OP specifically only knows that. It's more a sensible minimum.)

My own "curriculum" for that has been Jeremy Howard's Fast AI course and Sebastian Raschka's book "build an LLM from scratch". Still working through it, but once I'm done I think I'll be solid on your point 2 above. My guess is that I'll want to learn more, but that's out of interest more than because I think its necessary.

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#114

Post by nnadams »

roncesvalles wrote: post #91
jillesvangurp wrote: post #34

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…

>It's not different from other things like databases, GPU drivers, 3D engines for games, etc. Not quite the same. E.g. databases are a part of the system itself. It's actually pretty helpful for a SWE to understand them reasonably deeply, especially when they're so leaky as an abstraction (arguably, even the more nuanced characteristics of your database of choice will influence the design of your whole application).…

We have a service at work which categorizes internal documents and logs, then triggers some automation depending on the category. It processes maybe 100 per day. Previously we only used some combination of metadata, regex, and NLP to categorize. Now a call to a LLM is part of that service. We save a lot of manual time where we used to have to resolve unknown documents. The LLM can help fill out missing data, too. It's all stored as annotations so it's clear who/what edited the data.

Granted this is a pretty simple task and a low stakes scenario, but I don't think we should limit ourselves to assuming AI will always only be dev tooling.

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#115

Post by fxtentacle »

jillesvangurp wrote: post #34

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…

LLMs already are a commodity. Google has already kicked off the competitive price wars. Plus I’ve already seen some local companies just buy a beefy GPU server and deploy an open LLM model. While OpenAI is still trying to push quality, their competitors have already positioned themselves to offer the lowest possible prices. And since Nvidia has no easy path for scaling up compute anymore, I also wouldn’t bet on much larger LLMs anytime soon.

That means, if you learn more about the internals of LLMs, your market angle is going to be artisanal customised models. Fashion is commoditised, but people still pay for a custom tailored suit. In the same way companies will continue to pay for finetunes optimised for their business usecase.

If you decide to focus more on the application of LLMs, you should really invest into high-level architectural skills. Good “code completion” models can already do what an outsourced 10 bucks per hour developer used to do. Your job in the future is going to be to decide the structure of which fuse and against the towel and or which type of state is being stored and managed. But the actual coding of the UI forms and the glue code to synchronise from an SQL query to the client state, that part is probably going to be fully outsourced to LLMs.

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#116

Post by lelanthran »

xiphias2 wrote: post #6

It's your choice, but it's definitely not ,,just another tool''. Most of my LLMs made lots of mistakes, but Codex with $200 subscription changed my workflow totally, and now I'm having 40 pull requests/day merged. Treat LLMs as interns, increase your test coverage with them to the point that they can't ruin your codebase and get really good at reviewing code and splitting tasks up to smaller digestible ones, and prom…

I'm sceptical of 40 PRs per day.

In an 8 hour workday you are merging one new PR every 12 minutes?

I'm very sceptical that anyone can review a significant chunk of code that fast, unless these are all one and two liners that pass review on the first go.

In this best case scenario, where no review results in reworking the PR, and you can review and merge every 12 minutes, without any breaks of any sort, why is your review even required?

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#118

Post by antirez »

losvedir wrote: post #108
antirez wrote: post #21

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…

As someone who I both respect a lot and know is really knowledgeable about the latest with AI and LLMs: can you clarify one thing for me? Are all these points based on preparing for a future where LLMs are even better? Or do you think they're good enough now that they will transform the way software is built and software engineers work, with just better tooling? I've tried to keep up with them somewhat, and dabble wi…

I think that LLMs already changed the way we code, mostly, but I believe that agentic coding (vibe coding) is right now able to produce only bad results, and that the better approach is to use LLMs only to augment the programmer work (however it should be noted that I'm all for vibe coding for people that can't code, or that can't find the right motivation. I just believe that the excellence in the field is human+LLM). So failing to learn LLMs right now is yet not catastrophic, but creates a disadvantage because certain things become more explorable / faster with the help of 200 yet-not-so-smart PHDs in all the human disciplines. However other than that, there is the fact that this is the biggest technology emerging to date, so I can't find a good reason for not learning it.

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#119

Post by becquerel »

apwell23 wrote: post #70
antirez wrote: post #21

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.

IIRC the guy who makes Aider (Paul Gauthier) has some videos along these lines, of him working on Aider while using Aider (how meta).

Re: Ask HN: Go deep into AI/LLMs or just use them as tools?

#120

Post by scrozart »

Lots of good answers here about part of your question: is it hype or not? In that it likely isn't going away, and is becoming a valid force multiplier, it's not.

However, this question is better answered by asking yourself what you're interested in. Do you _want_ a deeper understanding of AI/ML? If so, jump in. If you're not genuinely interested it'll be an interminable slog, and you'll revert to doing whatever you actually want to do eventually.

Nothing wrong with continuing to develop web/full stack apps while leveraging the new tools; that's also quite interesting.

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