[lmorchard]

What you consider fun isn't universal. Some folks don't want to just tinker for half an hour, some folks enjoy getting a particular result that meets specific goals. Some folks don't find the mechanics of putting lines of code together as fun as what the code does when it runs. That might sound like paid work to you, but it can be gratifying for not-you.

[chung8123]

For me it all the build stuff and scaffolding I have to get in place before I can even start tinkering on a project. I never formally learned all the systems and tools and AI makes all of that 10x easier. When I hit something I cannot figure out instead of googling for 1/2 hour it is 10 minutes in AI.

[mbirth]

The difference is that after you’ve googled it for ½ hour, you’ve learned something. If you ask an LLM to do it for you, you’re none the wiser.

[qudat]

Wrong. I will spend 30 minutes having the LLM explain every line of code and why it's important, with context-specific follow-up questions. An LLM is one of the best ways to learn ...

[Akronymus]

So far, eqch and every time I used an LLM to help me with something it hallucinated non-existant functions or was incorrect in an important but non-obvious way.

Though, I guess I do treat LLM's as a last resort longshot for when other documentation is failing me.

[naasking]

Knowing how to use LLMs is a skill. Just winging it without any practice or exploration of how the tool fails can produce poor results.

[b112]

"You're holding it wrong"

99% of an LLM's usefulness vanishes, if it behaves like an addled old man.

"What's that sonny? But you said you wanted that!"

"Wait, we did that last week? Sorry let me look at this again"

"What? What do you mean, we already did this part?!"

[oblio]

Which LLMs have you tried? Claude Code seems to be decent at not hallucinating, Gemini CLI is more eager.

I don't think current LLMs take you all the way but a powerful code generator is a useful think, just assemble guardrails and keep an eye on it.

[Akronymus]

Mostly chatgpt because I see 0 value in paying for any llm, nor do I wish to gice up my data to any llm provider

[_ikke_]

As long as what it says is reliable and not made up.

[qudat]

That's true for internet searching. How many times have you gone to SO, seen a confident answer, tried it, and it failed to do what you needed?

[Anamon]

Then you write a comment, maybe even figure out the correct solution and fix the answer. If you're lucky, somebody already did. Everybody wins.

That's what LLMs take away. Nothing is given back to the community, nothing is added to shared knowledge, no differing opinions are exchanged. It just steals other people's work from a time when work was still shared and discussed, removes any indication of its source, claims it's a new thing, and gives you no way to contribute back, or even discuss it and maybe get confronted with different opinions of even discovering a better way.

Let's not forget that one of the main reasons why LLMs are useful for coding in the first place, is that they scraped SO from the time where people still used it.

[anakaine]

I feel like we are just covering whataboutism tropes now.

You can absolutely learn from an LLM. Sometimes.documentation sucks and the LLM has learned how to put stuff together feom examples found in unusual places, and it works, and shows what the documentation failed to demonstrate.

And with the people above, I agree - sometimes the fun is in the end process, and sometimes it is just filling in the complexity we do not have time or capacity to grab. I for one just cannot keep up with front end development. Its an insurmountable nightmare of epic proportions. Im pretty skilled at my back end deep dive data and connecting APIs, however. So - AI to help put together a coherent interface over my connectors, and off we go for my side project. It doesnt need to be SOC2 compliant and OWASP proof, nor does it need ISO27001 compliance testing, because after all this is just for fun, for me.

[hyperadvanced]

You can study the LLM output. In the “before times” I’d just clone a random git repo, use a template, or copy and paste stuff together to get the initial version working.

[inferiorhuman]

Studying gibberish doesn't teach you anything. If you were cargo culting shit before AI you weren't learning anything then either.

[ben_w]

Necessarily, LLM output that works isn't gibberish.

The code that LLM outputs, has worked well enough to learn from since the initial launch of ChatGPT. This even though back then you might have to repeatedly say "continue" because it would stop in the middle of writing a function.

[inferiorhuman]

  Necessarily, LLM output that works isn't gibberish.

Hardly. Poorly conjured up code can still work.

[oblio]

It's not gibberish. More than that, LLMs frequently write comments (some are fluff but some explain the reasoning quite well), variables are frequently named better than cdx, hgv, ti, stuff like that, plus looking at the reasoning while it's happening provides more clues.

Also, it's actually fun watching LLMs debug. Since they're reasonably similar to devs while investigating, but they have a data bank the size of the internet so they can pull hints that sometimes surprise even experienced devs.

I think hard earned knowledge coming from actual coding is still useful to stay sharp but it might turn out the balance is something like 25% handmade - 75% LLM made.

[inferiorhuman]

  they have a data bank the size of the internet so they can
  pull hints that sometimes surprise even experienced devs.

That's a polite way of phrasing "they've stolen a mountain of information and overwhelmed resources that humans would use to other find answers." I just discovered another victim: the Renesas forums. Cloudflare is blocking me from accessing the site completely, the only site I've ever had this happen to. But I'm glad you're able to have your fun.

  it might turn out the balance is something like 25% handmade - 75% LLM made.

Doubtful. As the arms race continues AI DDoS bots will have less and less recent "training" material. Not a day goes by that I don't discover another site employing anti-AI bot software.

[dcre]

This is just not true. I have wasted many hours looking for answers to hard-to-phrase questions and learned very little from the process. If an LLM can get me the same result in 30 seconds, it's very hard for me to see that as a bad thing. It just means I can spend more time thinking about the thing I want to be thinking about. I think to some extent people are valorizing suffering itself.

[codr7]

Learning means friction, it's not going to happen any other way.

[onemoresoop]

Some of it is friction, some of it is play. With AI you can get faster to the play part where you do learn a fair bit. But in a sense I agree that less is retained. I think that is not because of lack of friciton, instead is the fast pace of getting what you want now. You no longer need to make a conscious effort to remember any of it because it's effortless to get it again with AI if you ever need it. If that's what you mean by friction then I agree.

[dcre]

I agree! I just don’t think the friction has to come from tediously trawling through a bunch of web pages that don’t contain the answer to your question.

[mountain_peak]

"What an LLM is to me is the most remarkable tool that we've ever come up with, and it's the equivalent of a e-bike for our minds"

[codr7]

Which is about as useful as a bike for our airplanes.

[CamperBob2]

I don't want to waste time learning how to install and configure ephemeral tools that will be obsolete before I ever need to use them again.

[ggggffggggg]

Exactly, the whole point is it wouldn’t take 30 minutes (more like 3 hours) if the tooling didn’t change all the fucking time. And if the ecosystem wasn’t a house of cards 8 layers of json configuration tall.

Instead you’d learn it, remember it, and it would be useful next time. But it’s not.

[Akronymus]

And I don't want to use tools I don't understand at least to some degree. I always get nervous when I do something but don't know why I do that something

[xnx]

Depends on what level of abstraction you're comfortable with. I have no problem driving a car I didn't build.

[Akronymus]

I didnt build my car either. But I understand a bit of most of the main mechanics, like how the ABS works, how powered steering does, how an ICE works and so on.

[qualifck]

Not necessarily. The end result of googling a problem might be copying a working piece of code off of stack exchange etc. without putting any work into understanding it.

Some people will try to vibe out everything with LLMs, but other people will use them to help engage with their coding more directly and better understand what's happening, not do worse.

[spankibalt]

I don't think "learning" is a goal here...

[enraged_camel]

>> The difference is that after you’ve googled it for ½ hour, you’ve learned something.

I've been programming for 15+ years, and I think I've forgotten the overwhelming majority of the things I've googled. Hell, I can barely remember the things I've googled yesterday.

[rob74]

Additionally, in the good/bad old days of using StackOverflow, maybe 10% of the answers actually explained how that thing you wanted to do actually worked, the rest just dumped some code on you and left you to figure it out by yourself, or more likely just copy & paste it and be happy when it worked (if you were lucky)...

[ragequittah]

Usually The thing you've learned after googling for half an hour is mostly that google isn't very useful for search anymore.

[ajmurmann]

I don't think I'll learn anything by yet again implementing authentication, password reset, forgotten password, etc.

[visarga]

Why train to pedal fast when we already got motorcycles? You are preparing for yesterday's needs. There will never be a time when we need to solve this manually like it's 2019. Even in 2019 we would probably have used Google, solving was already based on extensive web resources. While in 1995 you would really have needed to do it manually.

Instead of manual coding training your time is better invested in learning to channel coding agents, how to test code to our satisfaction, how to know if what AI did was any good. That is what we need to train to do. Testing without manual review, because manual review is just vibes, while tests are hard. If we treat AI-generated code like human code that requires a line-by-line peer review, we are just walking the motorcycle.

How do we automate our human in the loop vibe reactions?

[oblio]

> Why train to pedal fast when we already got motorcycles? You are preparing for yesterday's needs.

This is funny in the sense that in properly built urban environment bycicles are one of the best ways to add some physical activity in a time constrained schedule, as we're discovering.

[philipwhiuk]

> Instead of manual coding training your time is better invested in learning to channel coding agents

All channelling is broken when the model is updated. Being knowledgeable about the foibles of a particular model release is a waste of time.

> how to test code to our satisfaction

Sure testing has value.

> how to know if what AI did was any good

This is what code review is for.

> Testing without manual review, because manual review is just vibes

Calling manual review vibes is utterly ridiculous. It's not vibes to point out an O(n!) structure. It's not vibes to point out missing cases.

If your code reviews are 'vibes', you're bad at code review

> If we treat AI-generated code like human code that requires a line-by-line peer review, we are just walking the motorcycle.

To fix the analogy you're not reviewing the motorcycle, you're reviewing the motorcycle's behaviour during the lap.

[visarga]

> This is what code review is for.

My point is that visual inspection of code is just "vibe testing", and you can't reproduce it. Even you yourself, 6 months later, can't fully repeat the vibe check "LGTM" signal. That is why the proper form is a code test.

[ben_w]

Yes and no.

Yes, I recon coding is dead.

No, that doesn't mean there's nothing to learn.

People like to make comparisons to calculators rendering mental arithmetic obsolete, so here's an anecdote: First year of university, I went to a local store and picked up three items each costing less than £1, the cashier rang up a total of more than £3 (I'd calculated the exact total and pre-prepared the change before reaching the head of the queue, but the exact price of 3 items isn't important enough to remember 20+ years later). The till itself was undoubtedly perfectly executing whatever maths it had been given, I assume the cashier mistyped or double-scanned. As I said, I had the exact total, the fact that I had to explain "three items costing less than £1 each cannot add up to more than £3" to the cashier shows that even this trivial level of mental arithmetic is not universal.

I now code with LLMs. They are so much faster than doing it by hand. But if I didn't already have experience of code review, I'd be limited to vibe-coding (by the original definition, not even checking). I've experimented with that to see what the result is, and the result is technical debt building up. I know what to do about that because of my experience with it in the past, and I can guide the LLM through that process, but if I didn't have that experience, the LLM would pile up more and more technical debt and grind the metaphorical motorbike's metaphorical wheels into the metaphorical mud.

[visarga]

> But if I didn't already have experience of code review, I'd be limited to vibe-coding (by the original definition, not even checking).

Code review done visually is "just vibe testing" in my book. It is not something you can reproduce, it depends on the context in your head this moment. So we need actual code tests. Relying on "Looks Good To Me" is hand waving, code smell level testing.

We are discussing vibe coding but the problem is actually vibe testing. You don't even need to be in the AI age to vibe test, it's how we always did it when manually reviewing code. And in this age it means "walking your motorcycle" speed, we need to automate this by more extensive code tests.

[ben_w]

I agree that actual tests are also necessary, that code review is not enough by itself. As LLMs can also write tests, I think getting as close as is sane to 100% code coverage is almost the first thing people should be doing with LLM assistance (and also, "as close as is sane": make sure that it really is a question of "I thought carefully and have good reason why there's no point testing this" rather than "I'm done writing test code, I'm sure it's fine to not test this", because LLMs are just that cheap).

However, code review can spot things like "this is O(n^2) when it could be O(n•log(n))", or "you're doing a server round trip for each item instead of parallelising them" etc.

You can also ask an LLM for a code review. They're fast and cheap, and whatever the LLM catches is something you get without having to waste a coworker's time. But LLMs have blind spots, and more importantly all LLMs (being trained on roughly the same stuff in roughly the same way) have roughly the same blind spots, whereas human blind spots are less correlated and expand coverage.

And code smells are still relevant for LLMs. You do want to make sure they're e.g. using a centralised UI style system and not copy-pasting style into each widget, because duplication wastes tokens and is harder to correctly update with LLMs for much the same reason it is with humans: stuff gets missed during the process when it's copypasta.

[jimbokun]

The difference is whether or not you find computers interesting and enjoy understanding how they work.

For the people who just want to solve some problem unrelated to computers but require a computer for some part of the task, yes AI would be more “fun”.

[phil21]

I don’t find this to be true. I enjoy computers quite a bit. I enjoy the hardware, scaling problems, theory behind things, operating systems, networking, etc.

Most of all I find what computers allow humanity to achieve extremely interesting and motivating. I call them the worlds most complicated robot.

I don’t find coding overly fun in itself. What I find fun is the results I get when I program something that has the result I desire. Maybe that’s creating a service for friends to use, maybe it’s a personal IT project, maybe it’s having commercial quality WiFi at home everyone is amazed at when they visit, etc. Sometimes - even often - it’s the understanding that leads to pride in craftsmanship.

But programming itself is just a chore for me to get done in service of whatever final outcome I’m attempting to achieve. Could be delivering bits on the internet for work, or automating OS installs to look at the 50 racks of servers humming away with cable porn level work done in the cabinets.

I never enjoyed messing around with HTML at that much in the 90s. But I was motivated to learn it just enough to achieve the cool ideas I could come up with as a teenager and share them with my friends.

I can appreciate clean maintainable code, which is the only real reason LLMs don’t scratch the itch as much as you’d expect for someone like me.

[tjr]

What I really enjoy in programming is algorithms and bit-twiddling and stuff that might be in Knuth or HAKMEM or whatever. That’s fun. I like writing Lisp especially, and doing cool, elegant functional programs.

I don’t enjoy boilerplate. I don’t necessarily enjoy all of the error checking and polishing and minutia in turning algorithms into shippable products.

I find AI can be immensely helpful in making real things for people to use, but I still enjoy doing what I find fun by hand.

[girvo]

See, I do though. I enjoy the act, the craft of programming. It's intrinsically fun for me, and has been for the 25 years I've been doing it at this point, and it still hasn't stopped being fun!

Different strokes I guess

[phil21]

Oh I totally agree! I have a lot of fun chatting with friends/coworkers who are super into programming as an art and/or passion.

I just was pushing back on the “you aren’t into computers if you don’t get intrinsic joy out of programming itself” bit.

[ben_w]

> The difference is whether or not you find computers interesting and enjoy understanding how they work.

I'm a stereotypical nerd, into learning for its own sake.

I can explain computers from the quantum mechanics of band gaps in semiconductors up to fudging objects into C and the basics of operating systems with pre-emptive multitasking, virtual memory, and copy-on-write as they were c. 2004.

Further up the stack it gets fuzzy (not that even these foundations are not; "basics" of OSes, I couldn't write one); e.g. SwiftUI is basically a magic box, and I find it a pain to work with as a result.

LLM output is easier to understand than SwiftUI, even if the LLM itself has much weirder things going on inside.

[jiveturkey]

So, can you tell me everything that happens after you type www.google.com<RET> into the browser? ;)

[ben_w]

Nope, but that was the example I had in mind when I chose my phrasing :)

I think I can describe the principles at work with DNS, but not all of how IP packets are actually routed; the physics of beamforming and QAM, but none of the protocol of WiFi; the basics of error correction codes, but only the basics and they're probably out of date; the basic ideas used in private key crypto but not all of HTTPS; I'd have to look up the OSI 7-layer model to remember all the layers; I understand older UI systems (I've even written some from scratch), but I'm unsure how much of current web browsers are using system widgets vs. it all being styled HTML; interrupts as they used to be, but not necessarily as they still are; my knowledge of JavaScript is basic; and my total knowledge of how certificate signing works is the conceptual level of it being an application of public-private key cryptography.

I have e.g. absolutely no idea why Chrome is famously a memory hog, and I've never learned how anything is scheduled between cores at the OS level.

[jimbokun]

Curious if anyone has turned answering this question into an entire book, because it could be a great read.