Ask HN: What are the most useful, concrete AI skills to independently learn?
Specifically, what are the most useful, concrete skills to learn as an individual to take economic advantage of the advances in LLMs, agents and associated new technological capabilities?
What is accessible to learn just from the internet and experimentation, without a degree program, assuming a preexisting moderately technical background? The clearer and more specific (eg. “Learn how-to x with y”) the better.
Thank you!
57 comments
[ 9.0 ms ] story [ 109 ms ] threadIf you want to secure your career, make friends with powerful people, learn how to kiss ass. Or do good deeds and pray for reincarnation to be a child of a rich family.
The point of my comment is that it’s basically over for skilled technical workers, besides maybe a tiny minority of companies that both respect deep knowledge AND can continue to defend their revenue. Companies don’t want skilled workers, they never have, they’re costly and a risk. They want unskilled workers that they can replace at a moments notice. AI is their dream come true.
Even the ones with niche skills are at risk, as soon as word gets out there’ll be a swarm of hungry, recently unemployed people coming for their jobs.
The writing is on the wall, this is the entire objective of this AI craze.
Since you do have a moderate technical background you can start studying PhD material right now from your desk which is widely accessible all over the web, however the time you need to spend on it is not very different. I think everyday for ten years is a good time span to invest for any one subject unless you have several fields of interest (you named three) multiply that by a few years of just skimming through the material for each so you have a mediocre understanding to solve "learn how to x with y" for x and y. Hope this helps.
.... until one day it all comes crashing down - well almost.
short scene: https://future-secured.com/41754
To successfully steer AI, you need essentially the same stuff you'd need if you were to steer humans in a management role. Meaning communication, introspection, remote introspection, emotional intelligence and all that kind of stuff.
Reading when someone is bullshitting you. Reading when someone is insecure and overcompensates that. Reading the actual problems people struggle with and not the ones they claim they do nor their suggested solutions.
Verbalizing what you need someone to do in a way that actually enables them to do it. Knowing what you want to do to a level where that is possible
Also my leadership skills on the emotional side are really going to waste with AI. It doesn't need motivation, it doesn't need convincing, it isn't insecure in a way that can be dealt with other than symptomatically etc.
The leadership skills that apply are communicating requirements and structuring tasks, but these are only a subset of leadership, and they are also not sufficient on their own.
Communication skills and bullshit radar are key.
And that you can probabaly do the same way you get to Carnegie Hall: Practice, practice, pracTtice.
The thing about AI is AI should know everything so you don't need to learn anything, except how to use AI effectively to accomplish business goals.
Imagine the 2000s, should you study internet tech like Cisco's routers? Not a bad idea, but who made money? Cisco? Not so much relatively speaking. It was Google and Amazon who learned how to use the new web-tech to do something people and companies needed.
To the question in the body of the post, take a look at Jeremy Utley's 11ish minute YouTube video "How Stanford teaches AI". It's a couple years old but he figured out the correct attitude and communicated it way before anybody else. Watch it in full without distractions and immediately apply the lesson to the various questions in the OP post.
I presume you don't want to drop thousands of dollars on video cards so pay Sam/Dario your 20 bucks a month.
Ask the LLM to build things you have already done before. You need to know the correct answer so you can evaluate the solution the LLM provides you.
Start with single-file scripts you have written, you can do this in just the ChatGPT or Claude web interface. Start with a vague prompt which doesn't specify many constraints or details. See what they get right and wrong. Try being much more specific and detailed. Make it ask you questions about what you want before it starts work.
Try the small/old/dumb model with no thinking and try the big/new/smart model with full thinking and notice the difference in capability.
Learn to setup an agent software in a sandbox of some kind. I prefer Pi in a Podman container, with only access to the project directory, but there are a million options out there these days.
From here, ask for explanations on a codebase you already know. Again the purpose is to know the answer in advance so you can evaluate the answer provided. Ask it to just tell you some answers. For other answers, ask it to give you a code flow and reference to read more yourself.
Build something larger, like a program across multiple files. Again try being vague and being specific, see the difference in what it spits out.
Use it to troubleshoot a problem you have solved, like your favourite bug, and also just a boring regular bug. Try different types of issue.
Learn to create an AGENTS.md for a project or SKILL.md for reusable instructions anywhere, but don't stuff it full of useless nonsense you think the model needs. Build it from experience of only what the thing gets wrong.
Some people say you can ask LLMs to write their own, but imo these things generate way too much text. You could ask it to do that as a starting point then trim it down to remove all the extra unnecessary crap.
That seems to be 90% of what people use these things for. This set of skills would have been useful for the last 18 months and seems to be the way the industry momentum is moving.
- Be able to analyze a situation into processes and discrete steps - Be able to describe the actual (!) problem you want to be solved - Be able to describe how to verify that the problem is actually solved - Be able to ask the right questions regarding how the problem has been solved in order to gain understanding and to identify the next steps - Be able to identify when the AI gets stuck or down a rabbit hole and to guide it out of there.
Disclaimer: This is based on any scientific study or so :-) Just my personal experience! Feel free to disagree.
But my experience comes directly from my work: I have a lot of customers on my platform that are not engineers, but make use of AI to solve business problems through automations. Nearly everytime when they are stuck (or burning through a sh*tload of tokens in no time), it is because one of those skills aren't present. So I try to teach them more and more how to "think like an engineer". And often when it lands, they become much better at using AI in a very short time. The best learners are often those that already had some kind of engineering background.
Now granted, the frontier models can help themselves pretty well and often can produce amazing results with very bad input. But they are also expensive. If you work with less-than-frontier, those skill requirements become very salient again. And thats what we do. We mostly work with models like Deepseek flash or GLM Flash or even local ones like Qwen3.7 .
Sometimes you'll be coaching, others delegating, and on the agent's worst days you'll be directing.
You can flex between them: for example start in a high trust mode, using an open ended communication style with the agent - thenif the work sucks shift to a more direct / blunt style. Try to avoid asking for exactly what you want, though. Working through curiosity and guiding questions gives the agent its best chance to surprise you - and an open ended conversation could uncover solutions you hadn't thought of.
That's a huge opportunity for you.
Learn the many techniques the industry has developed for scaling verification.
I don't mean infra; CI is solved. I mean Red-green & test-driven development, the testing pyramid (most unit tests/fewer functional/integration tests/happy-path e2e tests), learn how to get the most out of tools like Playwright and test frameworks like pytest and vitest and go and rust's testing tooling. Learn when to mock and when to include your code's dependencies in a test (test boundaries). Learn patterns like dependency injection so your tests can have no I/O and be FAST.
Learn to scale verification, and you'll solve your team's biggest bottleneck.
1. Building your own model - this will cost you a bit, but is without a doubt the best way to earn concrete AI skills. From picking the data to training, seeing how it evolves, improves and scales. You will understand the "hyperplanes" of a model's inner world better than anything else can teach you. You will learn how to pick data, how it impacts model quality, way of thinking, scores, personality. If training your own is too much, start with a completed pretrain and toy with it.
2. Building a harness around a completion model - I'd put this as second best to understand the inner worlds of LLM's. It helps you build intuition around how prompting really works, how they form the answers, how to build around that. Then you can do an SFT for your own harness.
3. Building a harness and an orchestrator around finished models - this takes the bronze. It teaches you how harnesses, agents and swarms actually work, how they shape the model's thoughts and actions, how models interact with them, how they can be applied and how they can work without humans.
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A small model, i.e. a few hundred million params might take you a few days of training, while bigger ones (0.5-1B) would take you months probably.
But you can go for it until you reach the scale where the time tradeoff isn't worth it and use kaggle, colab or such free GPU resources to continue trianing it.
- Agent coding with Claude Code, I rarely see Codex mentioned. Sometimes Cursor is mentioned along with Claude
- Prompt engineering and evaluation techniques, few shot, chain of thought, roles, llm as judge, decision models, code based eval, etc
- RAG, mostly I see "familiar" with it rather than "built something in production"
- Sometimes I see mention of using an agent SDK or implementing LLMs within a product rather than using LLM for coding. So like, LLM chatbot
- Sometimes I see Langchain, n8n, or similar agent orchestration tools
That's easy: Bullshitting. And public speaking.
Approach it as an art form, sort of like a cross between Elim Garak and Jimmy Swaggart. You also need charisma, of course. That's confidence (you'll need to clear that basic hurdle to bullshit properly anyway) plus good looks. If ya lack the latter, and have no means or stomach for cosmetic enhancements, there's still the e/acc-VTuber scene. Good luck!