Ask HN: Go deep into AI/LLMs or just use them as tools?
59 comments
·May 24, 2025jillesvangurp
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 such skills.
It probably helps a little to understand some of the internals and math. Just to get a feel for what the limitations are.
But your job as a software engineer is probably to stick things together and bang on them until they work. I sometimes describe what I do as being a glorified plumber. It requires skills but surprisingly few skills related to math and algorithms. That stuff comes in library form mostly.
So, get good at using LLMs and integrating what they do into agentic systems. Figure out APIs, limitations, and learn about different use cases. Because we'll all be doing a lot of work related to that in the next few years.
eric-burel
Hi, I am working in making the term "llm developer" more popular in France and train people to this new job. We will need a bunch of them in the months/years to come to implement advanced AI systems after companies manage to properly pick and set up their AI platforms. Currently people would tend to involve data scientists into this job, but data scientists are often less versed into the software engineering aspect, eg when they work more on notebooks than web apps. The job is akin to being a web developer, so a "normal" developer but specialized in a certain field. Knowing the internals of LLMs is a big bonus, but you can start your journey with treating them as black box tools and still craft relevant solutions. You'll need to learn about running systems with databases (vector, graph, relational and nosql are all useful) and plugging multiple services together (docker, kubernetes, cloud hosting).
elAhmo
I don't really understand why would you even consider going deep if this is not something you have experience with or strong interest. Sure, it is good to know how some of the things work under the good, but you can be perfectly capable developer by reading docs and knowing how to use tools - without needing to know how to write them or how they do complex things under the hood. Take databases as example, network stack, etc.
Just because a field is popular, doesn't mean you should switch to going deep into it. But that doesn't mean you shouldn't use it - it costs a few dollars to try it out and see whether it fits your workflow. If it does, this is great and you can be more productive and focus on stuff that you can solve and LLM can't, but if it doesn't, that is fine too.
antirez
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. What is an encoder-decoder? Why the first part produces an embedding? How a transformer works? What are the logits in the output of an LLM, and how sampling works? Why is attention of quadratic? What is Reinforcement Learning, Resnets, how do they work? Basically: you need a solid qualitative understanding of all that.
3. Learn the higher level layer, both from the POV of the open source models, so how to interface to llama.cpp / ollama / ..., how to set the context window, what is quantization and how it will affect performances/quality of output, and also, how to use popular provider APIs like DeepSeek, OpenAI, Anthropic, ... and what model is good for what.
4. Learn prompt engineering techniques that influence the qualtily of the output when using LLMs programmatically (as a bag of algorithms). This takes patience and practice.
5. 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).
6. Don't get trapped into the idea that the news of the day (RAG, MCP, ...) is what you should spend all your energy. This is just some useful technology surrounded by a lot of hype of all the people that want to get rich with AI and understand they can't compete with the LLMs themselves. So they pump the part that can be kinda "productized". Never forget that the product is the neural network itself, for the most part.
manmal
My problem with 5. is that there are many unknowns, especially when it comes to agents. They have wildly different system prompts that are optimized on a daily basis. I’ve noticed that Gemini 2.5 Pro seems way dumber when used in the Copilot agent, vs me just running all the required context through OpenRouter in Continue.dev. The former doesn’t produce usable iOS tests, while the latter was almost perfect. On the surface, it looks like those should be doing the same thing; but internally, it seems that they are not. And I guess that means I should just use Continue, but they broke something and my workflow doesn’t work anymore.
And people keep saying you need to make a plan first, and then let the agent implement it. Well I did, and had a few markdown files that described the task well. But Copilot‘s Agent didn’t manage to write this Swift code in a way that actually works - everything was subtly off and wrong, and untangling would have taken longer than rewriting it.
Is Copilot just bad, and I need to use Claude Code and/or Cursor?
antirez
I never ever use agents for coding. Just the web interface of Gemini, Claude, ..., you are perfectly right that agentic coding just creates a layer of indetermination and chaos.
prohobo
Agreed with most of this except the last point. You are never going to make a foundational model, although you may contribute to one. Those foundational models are the product, yes, but if I could use an analogy: foundational models are like the state of the art 3D renderers in games. You still need to build the game. Some 3D renderers are used/licensed for many games.
Even the basic chat UI is a structure built around a foundational model; the model itself has no capability to maintain a chat thread. The model takes context and outputs a response, every time.
For more complex processes, you need to carefully curate what context to give the model and when. There are many applications where you can say "oh, chatgpt can analyze your business data and tell you how to optimize different processes", but good luck actually doing that. That requires complex prompts and sequences of LLM calls (or other ML models), mixed with well-defined tools that enable the AI to return a useful result.
This forms the basis of AI engineering - which is different from developing AI models - and this is what most software engineers will be doing in the next 5-10 years. This isn't some kind of hype that will die down as soon as the money gets spent, a la crypto. People will create agents that automate many processes, even within software development itself. This kind of utility is a no-brainer for anyone running a business, and hits deeply in consumer markets as well. Much of what OpenAI is currently working on is building agents around their own models to break into consumer markets.
I recommend anyone interested in this to read this book: https://www.amazon.com/AI-Engineering-Building-Applications-...
antirez
I agree that instrumenting the model is useful in many contexts, but I don't believe it is something so unique to value Cursor such valuation, or all the attention RAG, memory, MCP get. If people say LLMs are going to be commodities (we will see) imagine the layer about RAG, tool usage, memory...
The progresses we are seeing in agents are 99% due to new LLMs being semantically more powerful.
loveparade
I come from a more traditional (PhD) ML/DL background. I wouldn't recommend getting into (1) because the field is incredibly saturated. We have hundreds of new, mostly low quality, papers each day. If you want to get into AI/ML on a more fundamental level now is probably the worst time in terms of competition. There are probably 100x more people in this field than there are jobs, and most of them have a stronger background than you if you are just starting out.
risyachka
If there indeed were 100x people more than jobs the salaries would tank. And this is not the case at all with AI/ML salaries being much higher than regular devs
drdunce
Really?
sMarsIntruder
Looks like OP’s curiosity isn’t just about deep diving LLMs —he’s probably itching to dig into adjacent topics like RAG, AI pipelines, and all the other adjacent LLM rabbit holes.
So in that case I don’t see why not?
drdunce
I just wanted to second the previous comment, and this is even for adjacent fields. Also a PhD AI/ML grad, and so many of us are out of work at the moment that we'll happily settle for prompt engineering roles, let alone RAG etc., just to maintain appearances on CVs/eligibilty for possible future roles.
xg15
Kinda surprised of that, actually. Sure, I get that research interest in any if the "traditional" ML methods (SVMs, markov models, decision trees, that kind of stuff) is probably essentially dead right now(*), but I had thought interest in neural networks and "understanding" what LLMs do internally to be ballooning.
(* I could imagine that even those "ancient" techniques might some day make a comeback. They're far inferior to LLMs in terms of expressive power, but they also require literally orders of magnitude less memory and computation power. So when the hype dies down, interest in solutions that don't require millions in hardware cost or making your entire business dependent on what Sam Altman and Donald Trump had for breakfast might have a resurgence. Also, interestingly enough, LLMs could even help in this area: Most of those old techniques require an abundance of labeled training data, something that hard to achieve in practice. However, LLMs are great at either labeling existing data or generating new synthetic data that those systems could train on.)
NitpickLawyer
> Is this another bubble that might burst
I see this a lot, but I think it's irrelevant. Even if this is a bubble, and even if (when?) it bursts, the underlying tech is not going anywhere. Just like the last dotcom bubble gave us FAANG+, so will this give us the next letters. Sure, agentsdotcom or flowsdotcom or ragdotcom might fail (likely IMO), but the stack is here to stay, and it's only gonna get better, cheaper, more integrated.
What is becoming increasingly clear, IMO, is that you have to spend some time with this. Prompting an LLM is like the old google-fu. You need to gain experience with it, to make the most out of it. Same with coding stacks. There are plenty of ways to use what's available now, as "tools". Play around, see what they can do for you now, see where it might lead. You don't need to buy into the hype, and some skepticism is warranted, but you shouldn't ignore the entire field either.
ednite
I think about this a lot. If you're early in your career, it must feel like you're staring at a technological fork in the road, with AI standing there ominously, waving both paths like it's the final boss in a RPG game.
Between your two options, I’d lean toward continuing to build what you’re good at and using AI as a powerful tool, unless you genuinely feel pulled toward the internals and research side.
I’ve been lucky to build a fun career in IT, where the biggest threats used to be Y2K, the dot-com bubble, and predictions that mobile phones would kill off PCs. (Spoiler: PCs are still here, and so am I.)
The real question is: what are you passionate enough about to dive into with energy and persistence? That’s what will make the learning worth it. Everything else is noise in my opinion.
If I had to start over today, I'd definitely be in the same uncertain position, but I know I'd still just pick a direction and adapt to the challenges that come with it. That’s the nature of the field.
Definitely learn the fundamentals of how these AI tools work (like understanding how AI tools process context or what transformers actually do). But don’t feel like you need to dive head-first into gradient descent to be part of the future. Focus on building real-world solutions, where AI is a tool, not the objective. And if a cheese grater gets the job done, don’t get bogged down reverse-engineering its rotational torque curves. Just grate the cheese and keep cooking.
That’s my 2 cents, shredded, not sliced.
mindcrime
3) go back to school and study something that isn't done entirely on a computer and requires human physical presence (for now). Learning plumbing, electrical wiring, welding, etc. are options. Even if you don't make that your primary path, it never hurts to have a fallback plan JUST IN CASE some of the buzz around AI-fueled job displacement turns out to be valid.
Or, if you believe there may be some merit to "AI is coming for your job" meme, but really don't want to do blue collar / skilled trades work, at least go in with the mindset of "the people who build, operate, and maintain the AI systems will probably stay employed at least a little bit longer than the people don't". And then figure out how to apply that to deciding between one or both of your (1) and (2) options. There may also be some white collar jobs that will be safe longer due to regulatory reasons or whatever. Maybe get your physician's assistant license or something?
And yes, I'm maybe playing "Devil's Advocate" here a little bit. But I will say that I don't consider the idea of a future where AI has meaningful impact on employment for tech professionals to be entirely out of the question, especially as we extend the timeline. Whatever you think of today's AI, consider that it's as bad right now as it will ever be. And ask what it will be like in 1 year. Or 3 years. Or 7 years. Or 10 years. And then try to work out what position you want to be in at those points in the timeline.
risyachka
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 finish apprenticeships.
After that it will be harder to find a job than in IT with AI around the corner.
mindcrime
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 thought experiment of asking "what happens to our society when a very large swathe of the human population is no longer needed to exchange their labor for wages?" it really leads one to wonder what kind of economic system(s) we'll have and if we'll find a way to avoid a straight up dystopian hellscape.
joshdavham
I’d recommend you simply follow your curiosity and not take this choice too seriously. If you’re simply doing this for career purposes, then the honest answer is that absolutely no one knows where these fields will go in the next couple years so I wouldn’t take anyone’s advice too seriously.
But as for my 2 cents, knowing machine learning has been valuable to me, but not anywhere near as valuable as knowing software dev. Machine learning problems are much more rare and often don’t have a high return on investment.
janalsncm
As an MLE I get a decent amount of LinkedIn messages. I think I got on someone’s list or something. I would bucket the companies into two groups:
1) Established companies (meta/google/uber) with lots of data and who want MLEs to make 0.1% improvements because each of those is worth millions.
2) Startups mostly proxying OpenAI calls.
The first group is definitely not hype. Their core business relies on ML and they don’t need hype for that to be true.
For the second group, it depends on the business model. The fact that you can make an API call doesn’t mean anything. What matters is solving a customer problem.
I also (selfishly) believe a lot of the second group will hire folks to train faster and more personalized models once their business models are proven.
mdp2021
Building AIs has always been there - it's a (fuzzy, continuous to its complement) way to engineer things. Now we have a boom over the development of some technologies (some next-layer NN implementations).
If you are considering whether the future will boost the demand to build AIs (i.e. for clients), we could say: probably so, given regained awareness. It may not be about LLMs - and it should not, at this stage (it can hit reputation - they can hardly be made reliable).
Follow the Classical Artificial Intelligence course, MIT 6.034, from Prof. Patrick Winston - as a first step.
y42
Depends on your goals. :)
If you're good at what you're doing right now and you enjoy it — why change? Some might argue that AI will eventually take your job, but I strongly doubt that.
If you're looking for something new because you are bored, go for it. I tried to wrap my head around the basics of LLMs and how they work under the hood. It’s not that complicated — I managed to understand it, wrote about it, shared it with others, and felt ready to go further in that direction. But the field moves fast. While I grasped the fundamentals, keeping up took a lot of effort. And as a self-taught “expert,” I’d never quite match an experienced data scientist.
So here I am — extensively using AI. It helps me work faster and has broadened my field of operation.
I'm a software engineer with a solid full-stack background and web development. With all the noise around LLMs and AI, I’m undecided between two paths:
1. Invest time in learning the internals of AI/LLMs, maybe even switching fields and working on them
2. Continue focusing on what I’m good at, like building polished web apps and treat AI as just another tool in my toolbox
I’m mostly trying to cut through the hype. Is this another bubble that might burst or consolidate into fewer jobs long-term? Or is it a shift that’s worth betting a pivot on?
Curious how others are approaching this—especially folks who’ve made a similar decision recently.