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I second this. This* is the matter against which we form understanding. This here is the work at hand, our own notes, discussions we have with people, the silent walk where our brain kinda process errors and ideas .. it's always been like this since i was a kid, playing with construction toys. I never ever wanted somebody to play while I wait to evaluate if it fits my desires. Desires that often come from playing.

Outsourcing this to an LLM is similar to an airplane stall .. I just dip mentally. The stress goes away too, since I assume the LLM will get rid of the "problem" but I have no more incentives to think, create, solve anything.

Still blows my mind how different people approach some fields. I see people at work who are drooling about being able to have code made for them .. but I'm not in that group.

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I'll push it back against this a little bit. I find any type of deliberative thinking to be a forcing function. I've recently been experimenting with writing very detailed specifications and prompts for an LLM to process. I find that as I go through the details, thoughts will occur to me. Things I hadn't thought about in the design will come to me. This is very much the same phenomenon when I was writing the code by hand. I don't think this is a binary either or. There are many ways to have a forcing function.
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I think it's analogous to writing and refining an outline for a paper. If you keep going, you eventually end up at an outline where you can concatenate what are basically sentences together to form paragraphs. This is sort of where you are now, if you spec well you'll get decent results.
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I agree, I felt this a bit. The LLM can be a modeling peer in a way. But the phase where it goes to validate / implement is also key to my brain. I need to feel the details.
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> I see people at work who are drooling about being able to have code made for them .. but I'm not in that group.

people seem to have a inability to predict second and third order effects

the first order effect is "I can sip a latte while the bot does my job for me"... well, great I suppose, while it lasts

but the second order effect is: unless you're in the top 10%, you will now lose your job, permanently

and the third order effect is the economy collapses as it is built on consumer spending

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I wonder over the long term how programmers are going to maintain the proficiency to read and edit the code that the LLM produces.
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Personally I planned to allocate weekly challenges to stay sharp.
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Everything you have said here is completely true, except for "not in that group": the cost-benefit analysis clearly favors letting these tools rip, even despite the drawbacks.
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Maybe.

But it's also likely that these tools will produce mountains of unmaintainable code and people will get buried by the technical debt. It kind of strikes me as similar to the hubris of calling the Titanic "unsinkable." It's an untested claim with potentially disastrous consequences.

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> But it's also likely that these tools will produce mountains of unmaintainable code and people will get buried by the technical debt.

It's not just likely, but it's guaranteed to happen if you're not keeping an eye on it. So much so, that it's really reinforced my existing prejudice towards typed and compiled languages to reduce some of the checking you need to do.

Using an agent with a dynamic language feels very YOLO to me. I guess you can somewhat compensate with reams of tests though. (which begs the question, is the dynamic language still saving you time?)

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Companies aren't evaluating on "keeping an eye on technical debt", but then ARE directly evaluating on whether you use AI tools.

Meanwhile they are hollowing out work forces based on those metrics.

If we make doing the right thing career limiting this all gets rather messy rather quickly.

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Tests make me faster. Dynamic or not feels irrelevant when I consider how much slower I’d be without the fast feedback loop of tests.
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Static type checking is even faster than running the code. It doesn't catch everything, but if finding a type error in a fast test is good, then finding it before running any tests seems like it would be even better.
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Oh I'm well aware of this. I admitted defeat in a way.. I can't compete. I'm just at loss, and unless LLM stall and break for some reason (ai bubble, enshittification..) I don't see a future for me in "software" in a few years.
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Somehow I appreciate this type of attitude more than the one which reflects total denial of the current trajectory. Fervent denial and AI trash-talking being maybe the single most dominant sentiment on HN over the last year, by all means interspersed with a fair amount of amazement at our new toys.

But it is sad if good programmers should loose sight of the opportunities the future will bring (future as in the next few decades). If anything, software expertise is likely to be one of the most sought-after skills - only a slightly different kind of skill than churning out LOCs on a keyboard faster than the next person: People who can harness the LLMs, design prompts at the right abstraction level, verify the code produced, understand when someone has injected malware, etc. These skills will be extremely valuable in the short to medium term AFAICS.

But ultimately we will obviously become obsolete if nothing (really) catastrophic happens, but when that happens then likely all human labor will be obsolete too, and society will need to be organized differently than exchanging labor for money for means of sustenance.

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I get crazy over the 'engineer are not paid to write loc', nobody is sad because they don't have to type anymore. My two issues are it levels the delivery game, for the average web app, anybody can now output something acceptable, and then it doesn't help me conceptualize solution better, so I revert to letting it produce stuff that is not maleable enough.
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If the world comes to that it will be absolutely catastrophic, and it’s a failure of grappling with the implications that many of the executives of AI companies think you can paper over the social upheaval with some UBI. There will be no controlling what happens, and you don’t even need to believe in some malicious autonomous AI to see that.
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I feel the same.

Frankly, I am not sure there is a place in the world at all for me in ten years.

I think the future might just be a big enough garden to keep me fed while I wait for lack of healthcare access to put me out of my misery.

I am glad I am not younger.

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Yep, its a rather depressing realization isnt it. Oh well, life moves on i suppose.

I think we realistically have a few years of runway left though. Adoption is always slow outside of the far right of the bell curve.

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i'm sorry if I pulled everybody down .. but it's been many months since gemini and claude became solid tools, and regularly i have this strong gut feeling. i tried reevaluating my perception of my work, goals, value .. but i keep going back to nope.
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That's also how I feel.

I think you have every right to doubt those telling us that they run 5 agents to generate a new SAAS-product while they are sipping latté in a bar. To work like that I believe you'll have to let go of really digging into the code, which in my experience is needed if want good quality.

Yet I think coding agents can be quite a useful help for some of the trivial, but time consuming chores.

For instance I find them quite good at writing tests. I still have to tweak the tests and make sure that they do as they say, but overall the process is faster IMO.

They are also quite good at brute-forcing some issue with a certain configuration in a dark corner of your android manifest. Just know that they WILL find a solution even if there is none, so keep them on a leash!

Today I used Claude for bringing a project I abandoned 5 years ago up to speed. It's still at work in progress, but the task seemed insurmountable (in my limited spare time) without AI, now it feels like I'm half-way there in 2-3 hours.

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I think we really need to have a serious think of what is "good quality" in the age of coding agents. A lot of the effort we put into maintaining quality has to do with maintainability, readability etc. But is it relevant if the code isn't for humans? What is good for a human is not what is good for an AI necessarily (not to say there is no overlap). I think there are clearly measurable things we can agree still apply around bugs, security etc, but I think there are also going to be some things we need to just let go of.
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You can’t drop anything as long as a programmer is expected to edit the source code directly. Good luck investigating a bug when the code is unclear semantically, or updating a piece correctly when you’re not really sure it’s the only instance.
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I think that's the question. Is a programmer expected to ever touch the source code? Or will AI -- and AI alone -- update the code that it generated?

Not entirely unlike other code generation mechanisms, such as tools for generating HTML based on a graphical design. A human could edit that, but it may not have been the intent. The intent was that, if you want a change, go back to the GUI editor and regenerate the HTML.

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So like we went from assembler to higher level programming languages, we will now move to specifications for LLMs? Interesting thought... Maybe, once the "compilers" get good enough, but for mission critical systems they are not nearly good enough yet.
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This is exactly what is happening from a levels of abstraction standpoint.

The difference being that compilers and related tools are deterministic, and we can manage the outputs using mathematical proof of correctness.

The LLM's driving this new abstraction layer are another beast entirely.

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Right. I work in aerospace software, and I do not know if this option would ever be on the table. It certainly isn't now.

So I think this question needs to be asked in the context of particular projects, not as an industry-wide yes or no answer. Does your particular project still need humans involved at the code level? Even just for review? If so, then you probably ought to retain human-oriented software design and coding techniques. If not, then, whatever. Doesn't matter. Aim for whatever efficiency metric you like.

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Then again, would anyone have guessed we’d even be seriously discussing this topic 10, 20, 40 years ago?
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Maybe. This book from 1990

https://mitpress.mit.edu/9780262526401/artificial-intelligen...

envisions a future of AI assistance that looks not too far off from today.

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It’s also pretty close to Steve Jobs initial vision of computing in the future (https://stevejobsarchive.com/stories/objects-of-our-life, 1983) but my point is that whatever it is we call AI now became reality so much faster than anyone really saw coming. Even if the pace slows down, and it didn’t yet, things are improving so massively all the time that the world can’t keep up changing to accommodate.
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> I think you have every right to doubt those telling us that they run 5 agents to generate a new SAAS-product while they are sipping latté in a bar. To work like that I believe you'll have to let go of really digging into the code, which in my experience is needed if want good quality.

Also we live in a capitalist society. The boss will soon ask: "Why the fuck am I paying you to sip a latte in a bar? While am machine does your work? Use all your time to make money for me, or you're fired."

AI just means more output will be expected of you, and they'll keep pushing you to work as hard as you can.

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> AI just means more output will be expected of you, and they'll keep pushing you to work as hard as you can.

That’s a bit too cynical for me. After all, yes, your boss is not paying you for sipping lattes, but for producing value for the company. If there is a tool that maximises your output, why wouldn’t he want you to use that to great efficiency?

Put differently, would a carpenter shop accept employees rejecting the power saw in favour of a hand saw to retain their artisanal capability?

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If the power saw ran itself without any oversight, the carpenter shop wouldn't accept any type of employees.
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But that’s the exact opposite of what the GP was arguing; you will be expected to stick with the agent more, not less.
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I still do this, but when I'm reviewing what's been written and / or testing what's been built.

How I see it is we've reverted back to a heavier spec type approach, however the turn around time is so fast with agents that it still can feel very iterative simply because the cost of bailing on an approach is so minimal. I treat the spec (and tests when applicable) as the real work now. I front load as much as I can into the spec, but I also iterate constantly. I often completely bail on a feature or the overall approach to a feature as I discover (with the agent) that I'm just not happy with the gotchas that come to light.

AI agents to me are a tool. An accelerator. I think there are people who've figured out a more vibey approach that works for them, but for now at least, my approach is to review and think about everything we're producing, which forms my thoughts as we go.

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> A lot of how I form my thoughts is driven by writing code, and seeing it on screen, running into its limitations.

If you need that, don't use AI for it. What is it that you don't enjoy coding or think it's tangential to your thinking process? Maybe while you focus on the code have an agent build a testing pipeline, or deal with other parts of the system that is not very ergonomic or need some cleanup.

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Exactly. 30 years ago a mathematician I knew said to me: "The one thing that you can say for programming is that it forces you to be precise."

We vibe around a lot in our heads and that's great. But it's really refreshing, every so often, to be where the rubber meets the road.

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That's because many developers are used to working like this.

With AI, the correct approach is to think more like a software architect.

Learning to plan things out in your head upfront without to figure things out while coding requires a mindset shift, but is important to work effectively with the new tools.

To some this comes naturally, for others it is very hard.

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I think what GP is referring too are technical semantics and accidental complexity. You can’t plan for those.

The same kind of planning you’re describing can and do happen sans LLM, usually on the sofa, or in front of a whiteboard. Or by reading some research materials. No good programmer rushes to coding without a clear objective.

But the map is not the territory. A lot of questions surface during coding. LLMs will guess and the result may be correct according to the plan, but technically poor, unreliable, or downright insecure.

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I also second this. I find that I write better by hand, although I work on niche applications it’s not really standard crud or react apps. I use LLMs in the same way i used to used stack overflow, if I go much farther to automate my work than that I spend more time on cleanup compared to if I just write code myself.

Sometimes the AI does weird stuff too. I wrote a texture projection for a nonstandard geometric primitive, the projection used some math that was valid only for local regions… long story. Claude kept on wanting to rewrite the function to what it thought was correct (it was not) even when I directed to non related tasks. Super annoying. I ended up wrapping the function in comments telling it to f#=% off before it would leave it alone.

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Using AI or writing your own code isn't an xor thing. You can still write the code but have a coding assistant or something an alt/cmd-tab away. I enjoy writing code, it relaxes me so that's what I do but when I need to look something up or i'm not clear on the syntax for some particular operation instead of tabbing to a browser and google.com I tab to the agent and ask it to take a look. For me, this is especially helpful for CSS and UI because I really suck at and dislike that part of development.

I also use these things to just plan out an approach. You can use plan mode for yourself to get an idea of the steps required and then ask the agent to write it to a file. Pull up the file and then go do it yourself.

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I sometimes wonder if the economics of AI coding agents only work if you totally ignore all the positive externalities that come with writing code.

Is the entire AI bubble just the result of taking performance metrics like "lines of code written per day" to their logical extreme?

Software quality and productivity have always been notoriously difficult to measure. That problem never really got solved in a way that allowed non technical management to make really good decisions from the spreadsheet level of abstraction... but those are the same people driving adoption of all these AI tools.

Engineers sometimes do their jobs in spite of poor incentives, but we are eliminating that as an economic inefficiency.

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In 1987 when I first started coding, I would either write my first attempt in BASIC and see it was too slow and rewrite parts in assembly or I would know that I had to write what I wanted from the get go in assembly because the functionality wasn’t exposed at all in BASIC (using the second 64K of memory or using double hires graphics).

This past week, I spent a couple of days modifying a web solution written by someone else + converting it from a Terraform based deployment to CloudFormation using Codex - without looking at the code as someone who hasn’t done front in development in a decade - I verified the functionality.

More relevantly but related, I spent a couple of hours thinking through an architecture - cloud + an Amazon managed service + infrastructure as code + actual coding, diagramming it, labeling it , and thinking about the breakdown and phases to get it done. I put all of the requirements - that I would have done anyway - into a markdown file and told Claude and Codex to mark off items as I tested each item and summarize what it did.

Looking at the amount of work, between modifying the web front end and the new work, it would have taken two weeks with another developer helping me before AI based coding. It took me three or four days by myself.

The real kicker though is while it worked as expected for a couple of hundred documents, it fell completely to its knees when I threw 20x documents into the system. Before LLMs, this would have made me look completely incompetent telling the customer I now wasted two weeks worth of time and 2 other resources.

Now, I just went back to the literal drawing board, rearchitected it, did all of the things with code that the managed services abstracted away with a few tweaks, created a new mark down file and was done in a day. That rework would have taken me a week by itself. I knew the theory behind what the managed service was doing. But in practice I had never done it.

It’s been over a decade where I was responsable for a delivery that I could do by myself without delegating to other people or that was simple enough that I wouldn’t start with a design document for my own benefit. Now within the past year, I can take on larger projects by myself without the coordination/“mythical man Month” overhead.

I can also in a moment of exasperation say to Codex “what you did was an over complicated stupid mess, rethink your implementation from first principles” without getting reported to HR.

There is also a lot of nice to have gold plating that I will do now knowing that it will be a lot faster

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Any sufficiently detailed specification converges on code.
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I couldn't agree more. It's often when you are in the depth of the details that I make important decisions on how to engineer the continuation.
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Yes, I look at this in a similar vein to the (Eval <--> Appply) Cycle in SICP textbook, as a (Design <--> Implement) cycle.
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Sounds like the coders equivalent of the Whorfian hypothesis.
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I was just thinking this the other day after I did a coding screen and didn't do well. I know the script for the interviewee is your not suppsed to write any code until you talk through the whole thing, but I think i woukd have done better if I could have just wrote a bunch of throw away code to iterate on.
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Are there still people under the impression that the correct way to use Stack Overflow all these years was to copy & paste without analyzing what the code did and making it fit for purpose?

If I have to say, we're just waiting for the AI concern caucus to get tired of performing for each other and justifying each other's inaction in other facets of their lives.

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Lab-grown meat slop producer defends AI slop.
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So now we're pro-slaughter and low-yield agriculture as long as we get to ride the keyboard eh?
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