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- Hacker News
- Sorry, programming is still fun. LLMs can't change that.by lolakutty
- I agree. Possibly more fun than ever. Bugs are being caught earlier through AI review, higher quality and quantity of tests, features are going out faster, bad decisions can be easily course corrected and bike shedding is dropping dramatically.
I miss the romanticism of trad coding but shipping better solutions to my customers was the goal. Hard to argue things aren’t better when AI is used intelligently by experienced people.
by asp_hornet - Fully agree. With LLM being able to solve every problem, getting deep into a problem all by yourself becomes a passion side project. Now might be a real test of how much you love programming.
Your enterprise wants the work done, done fast and reliably. Your productivity goals have increased, just like invention of motors would increased goals of carriers who were earlier doing their job via more manual efforts like pedaling. But still people love cycling, but they largely "don't have to" rely on it to do their job.
Similarly, now you simply don't have a dependency to love programming to increase your productivity.
by gps372 - I'm curious - how do you explain the fact that as you look all around you, lesser number of devs are having fun?by unified101
- I certainly hope so.
But LLMs do not need to change that. Sufficient marketing can change the perception of decision takers and middle managers to: Oooh, they can do that.
That is all that it takes.
by vasvir - I'm doing this. After getting started with LLM coding, I became super interested in learning to code, just out of passion. I walked out of engineering thinking physics was elite, but now I understand how passionate I am about building things, and how boring quantum mechanics was. Better late than never.by sreekanth850
- I am starting to see how many developers actually need to re-learn programming in the age of LLMs.
A while back Claude went down in the middle of a somewhat frantic initial deployment of a product to production at a company where a friend of mine works. And suddenly nobody was able to do anything. Because nobody had actually read the code and had no idea how it worked.
So essentially: much of their day to day work now depends entirely on the availability of a couple of frontier LLMs.
by bborud - (sorry pasting from 4 days ago but IMHO perfect fit)
The same reasons we keep on teaching kids to read, write, do math, sketch, take photos, etc ... because it is fun, empowering and important.
Most of them will never become professional actors, authors, mathematicians, artists, photographers, etc ... and yet they will rely on those skills, on their own or combined, pretty much every single day of their lives.
by utopiah - "The same kind of argument was used when China was admitted to the World Trade Organization. And indeed, lots of new jobs were created, just not in the Western world."
China's entry into the WTO is really not a good evidentiary example for AI causing mass unemployment. Unemployment in the U.S. had already been increasing at the time, peaked soon after, decreased to well below the point it had been at China's entry, and only went up again during the Great Financial Crisis, which had nothing -- or at least very little -- to do with competition from China. That's not to say that jobs weren't lost, even en masse, but they were replaced, and U.S. unemployment has been near record lows in recent years. China's WTO entry is a supporting point, not a counterpoint, to the idea that jobs lost to AI will be replaced by new ones.
by AnodicElegy - > That's not to say that jobs weren't lost, even en masse, but they were replaced
That's what the article was implying with:
> Yes, new jobs were created, but often not for those people who lost their jobs. Coal miners didn't just become programmers overnight.
The point is that middle-income (industrial) jobs were replaced with less, higher-income (service/knowledge/advanced industrial) jobs, with virtually zero overlap between the two in terms of workers. This is why certain regions across the West (US Rust belt, Northern England etc.) were devastated and have become so immiserated.
by wuisce - This is an issue that's very real for me right now because I'm in the middle of teaching my own kids programming, and we've been working on it for years at this point. I'm watching AI seemingly invalidate the premise behind learning all of it. It's been a pretty depressing change to be honest, because I love programming and watching this happen is hard.
So, I've changed my approach with my own teaching. I'm having them do three things that I'm hoping will prepare them to work in the world they're going to be entering soon:
1. Always have a handmade project going, and work on it without AI assistance even if it's painfully slow.
2. When working with AI, treat it as a compiler that operates on data structures, algorithms, and architectural requirements rather than source code. You have to understand the theoretical pieces of what you're building before you instruct it to assemble them.
3. Use the AI as a powerful tool to grow your own knowledge of algorithms and troubleshooting. Whenever a problem crops up, it's an opportunity to come up with a few hypotheses of what the actual issue is. Don't let the model do it first. I have them actually write these down in our custom kanban tool as issue cards, and then they use the model to investigate how close they came to the true issue.
I'm hoping this will give them something similar to my experience of debugging over a lifetime of coding. The knowledge that seems to be most valuable that I bring to the table when coding with AI is in recognizing quality/maintainable code architectures, and "seen that before" debugging experience.
We just started this so I don't have much in the way of results yet, and it feels risky to even allow it into the curriculum. I don't think that it makes sense to hide from it though, so this is our best shot.
by aethertap - The real problem is that the environment for healthy learning is gone. At least when I was a kid, there's something magical about having an environment where nobody knows what the hell is going on and the learning is, to use your words "painfully slow". I guess we've been trained to think that way with all this modern technology, but back in the day, it didn't matter whether the final result was accomplished or not. All that mattered was the fun of trying to get something to work.
Nowadays AI shifts the emphasis to the final product, and frankly that's boring. Kids will use it because it's there, because it gives that initial result really quickly and we're hardwired for that. But it removes that cool environment of having a blank slate and just seeing things happen all on your own accord.
Companies might like AI for the short-term gains but we're doing the entire planet a disservice by having even invented AI, in my opinion. We're all being trained to be producers.
Can a person like you create a little microcosm of learning that can work? Maybe, but I get the feeling that programmers these days are being blinded by the fun of AI and forgetting that there's actually more to life satisfaction than having fun.
by vouaobrasil - I came back from retirement for a couple reasons, one as a hedge against the stock market due to AI. Two, the camaraderie built by working with other people. Three, the opportunity to mentor young people.
AI has made the whole situation suck. Stock market is stressful due to energy and AI. AI has left people working alone managing agents. Any advice regarding development best practices I give to young people might have made sense 3 years ago, but hardly matters now. I look around at all these young people and wonder if they are going to be okay. It has only served to make me feel depressed and put a name and face to any future displacement.
It all seems so pointless.
by sesteel - A good question might be: what do I want to be ? A good prompter ? It's like someone who used to bee a good chef but is now good at ordering at uber eats. Even for a junior: being a good prompter may not be enough.
We tend to think that we make a program, but (writing) the program also makes us what we are. It is what we do that defines what we are. "I think, therefore I am".
I don't see a career path for a prompter. I don't see the benefit for an organization to rely on people who don't understand what they do nor how the programs that keep the organization running works.
That being said, "the man and the machine" can be a powerful combination, like when we drive a motorbike. For me the cooperation with a LLM could work like this: if there is 50 features I write the code for 25 of them and write stubs (classes, methods) for the 25 others, with comments that will be the prompt. The AI audits what I write, gives suggestion, find blind spots and learn best practices from my code. Then I do the same kind of review on the generated code and the AI learns from my review. An AI can even, with luck, suggest a completely new way to solve a problem (see AlphaGo vs Lee Sedol move#37 in game#2) and then I learn something.
Working like that will make me better at reading code (after all Linus spend a lot of time in reading code, it is a good skill to have) while the AI gets better as well. I don't delegate everything and keep practicing, I keep myself up to date, the AI learns from me and I get peer review from the AI. And the codebase does not become a mess (=technical debt) that only another AI can maintain while token prices increases.
by ah1508 - Prompting is an intermediate situation at best anyway. I think the most lucrative AI field going forward is going to be liability litigation since there will be only a small handful of AI companies, each the size of small nation states, who will need to be held accountable for what they unleash.by LastTrain
- I'm a software engineer, I do software development but also system maintenance, and I do handle networking and telephony systems, and work with some juniors. Working with AI is problematic. It can speed up you but at the same time delay you. For the system maintenance part sometimes you need to do a lot of stuff fast and in various machines and you can't just count on a cloud based AI oracle (that takes time) to do your job for you. And the same time, the more you use it as a oracle, the less competent you get. If you are an expert, I would say in any area, you do benefit from using AI as a tool but it easily can become a double edged sword and make you less proficient. For juniors, it can make them rapidly produce stuff that is impressive and works ok for sites and some visual stuff, but it's impossible for a junior to become an expert if they get stuck in the AI using loop. For AI to cause a clean impact, I would say that we would have to live in a world where software engineering didn't matter. That is, the choice of databases, high availability systems, the programming languages themselves.by duendefm
- > we would have to live in a world where software engineering didn't matter.
As an engineer who works very hard to do the right thing, I'm beginning to worry that software engineering doesn't matter. I write code that i think about a lot, understanding every line. It's not perfect, but I try to make sure my code is maintainable and well structured. I work much slower then my colleagues who produce unmaintainable slop at an alarming rate. In my career, no customer has ever complained about code structure or quality. It feels like I'm sinking in quicksand in an industry that's dying.
- I answered this to myself - stop worrying about LLMs. It's pretty simple: due to Curry-Howard isomorphism, programming languages are just notations for some type of formal logic.
Now ask yourself a question, what language do you want to maintain the programs in? Do you think natural language is going to be easier and more maintainable than formal logic?
The answer is no. So you need programmers, people who can read the formal description and adapt it to new requirements.
LLMs are amazing technology, but the truth is - natural language just kinda sucks. Therefore, you don't really need them (see also https://en.wikipedia.org/wiki/AI_effect ).
I think people love LLMs for the same reasons they love magicians. But just like the magician employs a hidden trick, LLM just runs some algorithm you don't see or understand.
So worrying about LLMs taking programming job is kinda like worrying that a magician will take a warehouse worker job, because they can levitate stuff. Meanwhile, we already have automated programmer - it's called a compiler.
by js8 - No offense, but "natural language sucks" is the refuge of the illiterate logician.
Complex language is clearly superior in nature. We're now finding out that that is true in computation as well.
by jappgar - I agree with this but what I've been trying to answer the last few months is if there was an optimal language for the spec. As with you, I don't think it's English Markdown, but I don't think it's Java either. I also don't think it's Gherkin, Lisp perhaps? I'm still searching.by 0x445442
- LLMs do not execute natural language. Natural language describes a problem or request, and LLMs generate and test formal logic they predict will satisfy the request.by jeremyjh
- re: natural language sucks.
prof.dr.Edsger W.Dijkstra's views: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
I humbly add my own.
Natural language is valuable for the things it doesn't say. The ambiguity is core to the functionality. Which can be very helpful when navigating social complexities.
And then written language is also valuable for the things IT doesn't say. Under the theory that 90% of communication is non-verbal, then writing lets you say things without having to communicate that other 90%. Which can be very helpful when negotiating something, for example.
by ds_opseeker - “I built a thing with AI and I don’t understand it. I want to make changes and fix things and have no ability to theorize why it fails or how to fix it.”
Oh my. See, you have to be able to program it yourself before you can build it with an LLM. Otherwise you have no way of judging the output. AI isn’t going to make you a programmer.
Learning to program is.
Interesting article. My advice is to do it the old fashioned, hard way. There’s no royal road to knowledge, skill, and learning.
by agentultra - > Interesting article. My advice is to do it the old fashioned, hard way. There’s no royal road to knowledge, skill, and learning.
I had a colleague tell me "it's not vibe coding when a programmer does it."
I fully believe there's still value in understanding the internals of software and the patterns developers use to structure it, but I do wonder for how much longer any of this knowledge will be more than of academic interest.
by JeremyNT - I'm the author of Python Crash Course, and I got this exact same email this week. I was thinking of writing a public response as well, because any attempt to sincerely answer these questions takes something along the lines of a full post. It's also worth a public response because many people who are getting into programming for the first time right now are asking variations of these same questions.
> Do I think that AI enables people to develop faster than they can keep up?
Absolutely. That's the core of this person's email, and everyone else who asks similar questions. Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP. Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
I don't think anyone has clear answers to all the questions brought up in this email. I think people can learn faster than they used to, because they can make connections between different areas faster than they used to. But it requires skill and discipline in how you learn, and how you work. You have to intentionally build your understanding as you build your projects.
by japhyr - > I think people can learn faster than they used to
Agree, but that only applies for people who were experienced developers before AI took over. Let's see in 5-10 years what our caliber looks like when you skip the foundations.
by pluc - > Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP.
> Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
Somewhat agree. Five years ago you could build an MVP without understanding how to open TCP sockets or how to parse HTTP headers. You didn't need to understand relational databases, let alone B-trees or cache locality. You didn't need to know how to install Linux.
Now you don't need to understand the details of connecting to Stripe or Auth0 or setting up a Kubernetes cluster.
> You have to intentionally build your understanding as you build your projects.
Some things you need to understand-others, not so much. Depends on what you're doing, the scale, risks, etc, but that's always been the case.
by senko