Cursor told me I should learn coding instead of asking it to generate it

Sometimes you get a composition from the specific prompt+seed+etc. And it has an alien element that is surprisingly stable and consistent throughout “settings wiggling”. I guess it just happens that training may smear some ideas across some cross-sections of the “latent space”, which may not be that explicit in the datasets. It’s a hyper-dimensional space after all and not everything that it contains can be perceived verbatim in the training set (hence the generation capabilities, afaiu). A similar thing can be seen in sd lora training, where you get some “in” idea to be transformed into something different, often barely interepretable, but still stable in generations. While you clearly see the input data and know that there’s no X there, you sort of understand what the precursor is after a few captioning/training sessions and the general experience. (How much you can sense in the “AI” routine but cannot clearly express is another big topic. I sort of like this peeking into the “latent unknown” which skips the language and sort of explodes in a mute vague understanding of things you’ll never fully articulate. As if you hit the limits of language and that is constraining. I wonder what would happen if we broke through this natural barrier somehow and became more LLMy rather than the opposite). /sot

There's actually two things going on when I'm coding at work:

1) I'm writing the business code my company needs to enable a feature/fix a bug/etc. 2) I'm getting better as a programmer and as someone who understands our system, so that #1 happens faster and is more reliable next time.

Using AI codegen can (arguably, the jury is still out on this if we include total costs, not just the costs of this one PR) help with #1. But it _appreciably bad_ at #2.

In fact, the closest parallel for #2 I can think of us plagiarism in an academic setting. You didn't do the work, which means you didn't actually learn the material, which means this isn't actually progress (assuming we continue to value #2, and, again, arguably #1 as well), it is just a problem in disguise.

Agreed. Most of us aren’t washing our clothes with a washboard, yet the washboard not long ago was a timesaver. Technology evolves.

Now, if AI rises up against the government and Cursor becomes outlawed, then maybe your leet coder skills will matter again.

But when a catastrophic solar storm takes out the grid, a washboard may be more useful, to give you something to occupy yourself with while the hard spaghetti of your dwindling food supply slowly absorbs tepid rainwater, and you wish that you’d actually moved to Siberia and learned to live off the land as you once considered while handling a frustrating bug in production that you could’ve solved with Claude.

No. In learning, there is no substitution to practice. Once you jump to the conclusion, you stop understanding the "why".

Throughout various stages of technological advancement, we've come with tools to help relieve us of tedious efforts, because we understood the "why", the underlying reason of what the tools were helping us solve in the first place. Those that understood the "why" could build upon those tools to further advance civilization. The others were left behind to parrot about something they did not understand.

But -and, ironically in this case- as with most things in life, it is about the journey and not the destination. And in the grand scheme of things, it doesn't really matter if we take a more efficient road, or a less efficient road, to reach a life lesson.

Enjoy your journey!

yeah you can be more effective. Just have to pay the price of becoming more stupid.

a bit hard to justify

This has been going on for decades. It is called outsourcing development. We've just previously passed the work to people in countries with lower wages and conditions, now people are increasingly passing the work to a predictive text engine (either directly, or because that is what the people they are outsourcing to are doing).

Personally I'm avoiding the “AI” stuff¹, partly because I don't agree with how most of the models were trained, partly because I don't want to be beholden to an extra 3rd party commercial entity to be able to do my job, but mainly because I tinker with tech because I enjoy it. Same reason I've always avoided management: I want to do things, not tell others to do things. If it gets to the point that I can't work in the industry without getting someone/something else to do the job for me, I'll reconsider management or go stack shelves!

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[1] It is irritating that we call it AI now because the term ML became unfashionable. Everyone seems to think we've magically jumped from ML into _real_ AI which we are having to make sure we call something else (AGI usually) to differentiate reasoning from glorified predictive text.

Whereas what you're responding to is using AI to do the work for you, which weakens your own faculties the more you do it, right?

while both are a consequence of a natural or logical tendency, neither is good. Once a player optimises all the fun out of a game, they stop playing it, often without experiencing it in its entirety (which I would regard as a negative outcome for both player and game)

I am not able to extrapolate with confidence what the analogous outcome would be in the company/worker side of the equation. I would confidently say, out of just anecdotal experience, it's not trending in a good direction.

edit: typo corrections

The human brain is easily manipulated, easily hacked.

The progress comes from amplification of effot, which comes from leverage (output comes from input), not magic (output comes from nowhere) or theft (output comes from someone else).

You've arrived at the reason for compilers. You could go produce all that pesky machine code yourself or you could learn to use the compiler to its optimal potential to do the work for you.

LLMs and smart machines alike will be the same concept but potentially capable of a wider variety of tasks. Engineers that know how to wield them and check their work will see their productivity increase as the technology gets better. Engineers that don't know how to check their work or wield them will at worst get less done or produce volumes of garbage work.

It's a tradeoff. What we call progress is actually a tradeoff for what we value and prioritize more. It necessarily doesn't have to be good - just a state of the world we create.

It's just people ranting about another level of abstraction, like always.

That's not how any previous advancement has worked. "Work expands to fill the time saved" and all that.

I'm not sure what effect that will have on the hierarchy, since it seems like they will replace the top half of the company as well.

There's a reason these technologies are so controversial. They question our entire existence.

These threads of conversation border on religious debate.

(The parallel that comes to my mind is that programmers raised without garbage collection learned to write more efficient code, and now a text editor uses several gigabytes. But yeah, someone called me a "dinosaur" already in this thread ;)

Though this "increase in incompetence" might be a case of selection effect rather than degradation of ability, i.e. people who wouldn't even have gotten the job before are now getting into the industry. I'm not sure, it's probably a bit of both.

I guess it's nothing new! See this great article from nearly 20 years ago: https://blog.codinghorror.com/why-cant-programmers-program/

* for some it is a form of art or craftsmanship, they take pride of their work and view it not just as a means to an end, they hone their skills just like any craftsman will, they believe that the quality of the work and the goal or purpose it serves are intrinsically linked and can never be separated

* for others its a means to an end, the process or object it produces is irrelevant EXCEPT for the purpose it serves, only the end goal matters, things like software quality are irrelevant distractions that must be optimized away since they serve no inherent purpose

There is no way to reconcile this, these are just radically different perspectives.

If you only care about the goal then of course raising questions about the quality of the output of current state of generative AI will be of no concern to you.

The problem with this answer is that there is little to learn from a lot of code that is written in a corporate environment. You typically only learn from brutally hard bleeding-edge challenges that you set to yourself, which you will barely ever find at work.

You want to schedule things. What is the basic tool we use to schedule on Linux? Cron. Do you need to install it separately? No, it usually comes with most Linux images. What is your container, functionally speaking? A working Linux system. So you can run scripts on it. Lot of these scripts run binaries that come with Linux. Is there a cron binary available? Try using that.

Of course, hindsight is 20/20 but breaking objectives down to their basic core can be helpful.

I really fail to see the usefulness in typing out long winded prompts then waiting for information to stream in. And repeat...

Examples include things like, referring to LLM nicely ("my dear"), saying "please" and asking nicely, or thanking.

Do these actually work?

In my experience, before "reasoning" became an option, if you ask it a question that takes a decent amount of thinking to solve, but also tell the model "Just give me the answer", you're FAR more likely to get an incorrect answer.

So "reasoning" just tells the model to first come up with a plan to solve a problem before actually solving it. It generates its own context for coming up with a more complete solution.

"Planning" would be a more accurate term for what LLMs are doing.

https://en.m.wikipedia.org/wiki/Google_Books

As other AI companies argue, copyright doesn't apply when training, it should give Google a huge advantage to be able to use all the worlds books they scanned.

It’s not just bad optics; it’s destructive. It discourages folks from learning.

AI is just another tool. There’s folks that sneer at you if you use an IDE, a GUI, a WYSIWYG editor, or a symbolic debugger.

They aren’t always boomers, either. As a high school dropout, with a GED, I’ve been looking up noses, my entire life. Often, from folks much younger than me.

It’s really about basic human personal insecurity, and we all have that, to some degree. Getting around it, is a big part of growing up, so a lot of older folks are actually a lot less likely to pull that crap than you might think.

And it kind of was a problem. There was an underclass of people who simply could not get anything done if it wasn't already done for them in a Stackoverflow answer or a blog post or (more recently and bafflingly) a Youtube video. These people never learned to read a manual and spent their days flailing around wondering why programming seemed so hard for them.

Now with AI there will be more of these people, and they will make it farther in their careers before their lack of ability will be noticeable by hiring managers.

No, it wasn't.

What such comments were directed at, and with good reason, where 'SO-"Coders"', aka. people when faced with any problem, just googled a vague description of it, copypasted the code from the highest scoring SO answer into their project, and called it a day.

SO is a valueable resource. AI Systems are a valueable resource. I use both every day, same as I almost always have one screen dedicated to some documentation page.

The problem is not using the tools available. The problem is relying 100% on these tools, with no skill or care of ones own.

i think that represents a huge paradigm shift that we need to contend with. it isn't just "better" research. and i say this as someone who welcomes all of this that has come.

IMO the skill gap just widens exponentially now. you will either have the competent developers who use these tools accelerate their learning and/or output some X factor, and on the other hand you will have literally garbage being created or people who just figure out they can now expend 1/10 the effort and time to do something and just coast, never bother to even understand what they wrote.

just encountered that with some interviews where people can now scaffold something up in record time but can't be bothered to refine it because they don't know how. (ex. you have someone prompting to create some component and it does it in a minute. if you request a tweak, because they don't understand it they just keep trying re-prompt and micromanage the LLM to get the right output when it should only take another minute for someone experienced.)

from what i gather, the training data often contains those same poorly written docs and often a lot of poorly written public code examples, so… YMMV with this statement as it is often fruit from the same tree.

(to me LLMs are just a different interface to the same data, with some additional issues thrown in, which is why i don’t care for them).

> I think we have a lot of old timers ramping up on their version of "I walked 10 miles to school uphill both ways". Not a good look. We old timers need to do better.

it’s a question of trust for me. with great power (new tools) comes great responsibility — and juniors ain’t always learned enough about being responsible yet.

i had a guy i was doing arma game dev with recently. he would use chatgpt and i would always warn him about not blindly trusting the output. he knew it, but i would remind him anyway. several of his early PRs had obvious issues that were just chatgpt not understanding the code at all. i’d point them out at review, he’d fix them and beat himself up for it (and i’d explain to him it’s fine don’t beat yourself up, remember next time blah blah).

he was honest about it. he and i were both aware he was very new to coding. he wanted to learn. he wanted to be a “coder”. he learned to mostly use chatgpt as an expensive interface for the arma3 docs site. that kind of person using the tools i have no problem with. he was honest and upfront about it, but also wanted to learn the craft.

conversely, i had a guy in a coffee shop recently claim to want to learn how to be a dev. but after an hour of talking with him it became increasingly clear he wanted me to write everything for him.

that kind of short sighted/short term gain dishonesty seems to be the new-age copy/pasting answers from SO. i do not trust coffee shop guy. i would not trust any PR from him until he demonstrates that he can be trusted (if we were working together, which we won’t be).

so, i get your point about doom and gloom naysaying. but there’s a reason for the naysaying from my perspective. and it comes down whether i can trust individuals to be honest about their work and how they got there and being willing to learn, or whether they just want to skip to end.

essentially, it’s the same copy/pasting directly from SO problem that came before (and we’re all guilty of).

So far, AI cannot do that. But it can pretend to, very convincingly.

I think the same tendency of some programmers to just script kiddie their way out of problems using SO answers without understanding the issue will be exacerbated by the proliferation of AI which is much more convincing about wrong answers.

It's not a binary. You don't have to hate or welcome chatbots, no in between. We all use them, but we all also worry about the negatives, same with SO.

So now we can get wrong code but written in a more confident language.

If you are lucky it’s a hallucination and the error is obvious.

Its one thing to have the AI work through the materials and explain it.

Its another thing to have lost a lot of the background information required to sustain that explanation.

Greater productivity, is of course, a subjective measure. If that's all that matters, then being glib is perfectly acceptable. But, the value of that productivity may change in a different context - for example, I find it very difficult to go back to AI-generated code some months later, and understand it - unless I already took the time earlier to discern the details.

I once got that question in an interview for a small startup and told the interviewer: with all due respect what does that have to do with the job I’m going to do and we moved on to the next question (still passed).

I think it will be a hot minute before nothing has to be known and all human knowledge is irrelevant, but, specially in CS, there is going to be a tremendous amount of rethinking to do, of what is actually important to know.

The reason we ask people to do fizzbuzz is often just to weed out the shocking number of people who cannot code at all.

Unfortunately they also project their ignorance, so there’s massive pushback from more senior employees when anyone who does understand tries to untangle the mess and make it more robust.

The same thing will happen with these ML tools in the future, mark my words: writing code will come to be seen as “too complex and error prone” and barely working, massively inefficient and fragile generated code bases will be revered and protected with “don’t fix what isn’t broken”

In the starting section there was an "engineer" going around fixing stuff. He just pointed his AI tool at the thing, and followed the instructions, while not knowing what he's doing at any point. That's what I see happening

So while learning basic stuff is definitely necessary just like it's necessary to have understanding how to multiply or divide numbers of any size (kids learn that nowadays, right?), actually mastering those skills may be wasted time?

If I need to parse a file I can just chuck it a couple lines, ask it to do it with a recommended library and get the output in 15 minutes total assuming I don't have a library in mind and have to find one I like.

Of course verification is still needed but I'd need to verify it even if I wrote it myself anyway, same for optimization. I'd even argue it's better since it's someone else's code so I'm more judgemental.

The issue comes when you start trying to do complex stuff with LLMs since you then tend to halfass the analysis part and get led down the development path the LLM chose, get a mish mash of the AIs codestyle and yours from the constant fixes and it becomes a mess. You can get things implemented quickly like that, which is cool, but it feels like it inevitably becomes spaghetti code and sometimes you can't even rewrite it easily since it used something that works but you don't entirely understand.

LinkedIn is awash with posts about being a ‘product engineer’ and ‘vibe coding’ and building a $10m startup over a weekend with Claude 3.5 and a second trimester foetus as a cofounder, and the likely end result there is simply just another collection of startups whose founding team struggles to execute beyond that initial AI prototyping stage. They’ll mistake their prompting for actual experience not realising just how many assumptions the LLM will make on their behalf.

Won’t be long before we see the AI startup equivalent of a rug-pull there.

Asimov's "three laws of robotics" beg to differ.

May be it would make sense to ask AI to use those tools instead of doing edits directly?

Just because everyone's doing it (after being told by those who will profit from it that it will work) doesn't mean it's not insane. It's far more likely that they're just a rube.

At the end of using whatever tool one uses to help refactor one's codebase, you still have to actually understand what is getting moved to production, because you will be the one getting called at midnight on Saturday to fix the thing.

And they've been at the company working with the FE stack longer than me by a few months!

I'm not even on the frontend team and decided to take the matter into my hands because I was pretty sure my ask wasn't too onerous so I wanted to double check. It had an out-of-the-box, first party add on package to do exactly what we needed to get data in the right shape on the backend, but the dev made it seem like I was pushing more work on FE. I'm just trying to get the right data format...

The gist of it was to take the JSON representation of an editor and convert it to Markdown. Every popular editor library has an add on or option to export Markdown as well as import Markdown. But on import/export, you then need to almost always write a small transformer for any custom visual elements encoded as text.

Why MD? Because the user is writing a prompt so we need it as MD so it makes sense to transact this data in MD. It just so happens that the library is newish and docs are sparse in some areas. But totally manageable just by looking at the source how to do it.

I can't say for certain where the disconnect is in this whole thing, but to me it felt like "this isn't easy (because the LLM can't do it), so we shouldn't do it this way".

It might be a conflict between shorter outputs and the "soft reasoning" feature that version of Sonnet has, where it stops mid-reply and reflects on what it has written, in an attempt to reduce hallucinations. I don't know what exactly triggers it, but if it triggers in the middle of the long reply, it notices that it's already too long (which is an error according to its training) and stops immediately.

(Or maybe I'm entirely wrong here!)

From there these proompters will have 2 choices, do I learn how to actually code? or do I pay more for the AI to code for me?

Also it stops working well after the project grows too large, from there they'd need an actual understanding to be able to direct the AI - but going back and forth with an AI when the costs are insane isn't going to be feasible.