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Comment by averyvery | Hacker News Reader

Yeah, "Engineers don't try" is a frustrating statement. We've all tried generative AI, and there's not that much to it — you put text in, you get text back out. Some models are better at some tasks, some tools are better at finding the right text and connecting it to the right actions, some tools provide a better wrapper around the text-generation process. Certain jobs are very easy for AI to do, others are impossible (but the AI lies about them).

A lot of us tried it and just said, "huh, that's interesting" and then went back to work. We hear AI advocates say that their workflow is amazing, but we watch videos of their workflow, and it doesn't look that great. We hear AI advocates say "the next release is about to change everything!", but this knowledge isn't actionable or even accurate.

There's just not much value in chasing the endless AI news cycle, constantly believing that I'll fall behind if I don't read the latest details of Gemini 3.1 and ChatGPT 6.Y (Game Of The Year Edition). The engineers I know who use AI don't seem to have any particular insights about it aside from an encyclopedic knowledge of product details, all of which are changing on a monthly basis anyway.

New products that use gen AI are — by default — uninteresting to me because I know that under the hood, they're just sending text and getting text back, and the thing they're sending to is the same thing that everyone is sending to. Sure, the wrapper is nice, but I'm not paying an overhead fee for that.

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> Yeah, "Engineers don't try" is a frustrating statement. We've all tried generative AI, and there's not that much to it — you put text in, you get text back out.

"Engineers don't try" doesn’t refer to trying out AI in the article. It refers to trying to do something constructive and useful outside the usual corporate churn, but having given up on that because management is single-mindedly focused on AI.

One way to summarize the article is: The AI engineers are doing hype-driven AI stuff, and the other engineers have lost all ambition for anything else, because AI is the only thing that gets attention and helps the career; and they hate it.

“Lost all ambition for anything else” is a funny way for the article to frame “hate being forced to run on the hampster wheel on ai, because an exec with the power to fire everyone is foaming at the mouth about ai and seemingly needs everyone to use it”

To add another layer to it, the reason execs are foaming at the mouth is because they are hoping to fire the as many people as possible. Including those who implemented whatever AI solution in the first place.

> the other engineers have lost all ambition for anything else

Worse, they've lost all funding for anything else.

Industries are built upon shit people built in their basements, get hacking

I think it should be noted that a garage or basement in California costs like a million dollars.

That was true before Crypto and AI.

Yes, it just puts the whole "I started Apple in my garage"-style narrative into context.

I am! No one's interested in any of it though...

You need to buy fake stars on github, fake download it 2 millions time a day and ask an AI to spam about it on twitter/linkedin.

ZIRP is gone, and so are the Good Times when any idiot could get money with nothing but a PowerPoint slide deck and some charisma.

That doesn't mean investors have gotten smarter, they've just become more risk averse. Now, unless there's already a bandwagon in motion, it's hard as hell to get funded (compared to before at least).

Are you sure it refers to that? Why would it later say:

> now believes she's both unqualified for AI work

Why would she believe to be unqualified for AI work if the "Engineers don't try" wasn't about her trying to adopt AI?

The most ironic part is that AI skills won't really help you with job security.

You touched on some of the reasons; it doesn't take much skill to call an API, the technology is in a period of rapid evolution, etc.

And now with almost every company trying to adopt "AI" there is no shortage of people who can put AI experience on their resume and make a genuine case for it.

Maybe not what the OP or article is talking about, but it's super frustrating recently dealing with non/less technical mgrs, PMs, etc who now think they have this Uno card to bypass technical discussion just because they vibe coded some UI demo. Like no shit, that wasn't the hard part. But since they don't see the real/less visible past like data/auth/security, etc they act like engineers "aren't trying", less innovative, anti-AI or whatever when you bring up objections to their "whole app" they made with their AI snoopy snow cone machine.

My experience too. They are so convinced that AI is magical that pushing back makes you look bad.

Then things don't turn out as they expected and you have to deal with a dude thinking his engineers are messing with him.

It's just boring.

It reminds me of how we moved from "mockups" to "wireframes" -- in other words, deliberately making the appearance not look like a real, finished UI, because that could give the impression that the project was nearly done

But now, to your point: they can vibe-code their own "mockups" and that brings us back to that problem

Hmm, (whatever is in execs' head about) AI appears to amplify the same kind of thinking fallacies that are discussed in the eternal Mythical Manmonth essay, which was written like half a century ago. Funny how some things don't change much...

> We hear AI advocates say that their workflow is amazing, but we watch videos of their workflow, and it doesn't look that great. We hear AI advocates say "the next release is about to change everything!", but this knowledge isn't actionable or even accurate.

There's a lot of disconnected-from-reality hustling (a.k.a lying) going on. For instance, that's practically Elon Musk's entire job, when he's actually doing it. A lot of people see those examples, think it's normal, and emulate it. There are a lot of unearned superlatives getting thrown around automatically to describe tech.

Yes, much the way some used to (still do?) try and emulate Steve Jobs. There's always some successful person these types are trying to be.

This isn’t “unfair”, but you are intentionally underselling it.

If you haven’t had a mind blown moment with AI yet, you aren’t doing it right or are anchoring in what you know vs discovering new tech.

I’m not making any case for anything, but it’s just not that hard to get excited for something that sure does seem like magic sometimes.

Edit: lol this forum :)

> If you haven’t had a mind blown moment with AI yet, you aren’t doing it right or are anchoring in what you know vs discovering new tech.

Or your job isn't what AI is good at?

AI seems really good at greenfield projects in well known languages or adding features.

It's been pretty awful, IME, at working with less well-known languages, or deep troubleshooting/tweaking of complex codebases.

> It's been pretty awful, IME, at working with less well-known languages, or deep troubleshooting/tweaking of complex codebases.

This is precisely my experience.

Having the AI work on a large mono repo with a front-end that uses a fairly obscure templating system? Not great.

Spinning up a greenfield React/Vite/ShadCN proof-of-concept for a sales demo? Magic.

> It's been pretty awful, IME, at working with less well-known languages

Well, there’s your problem. You should have selected React while you had the chance.

> If you haven’t had a mind blown moment with AI yet, you aren’t doing it right

I AM very impressed, and I DO use it and enjoy the results.

The problem is the inconsistency. When it works it works great, but it is very noticeable that it is just a machine from how it behaves.

Again, I am VERY impressed by what was achieved. I even enjoy Google AI summaries to some of the questions I now enter instead of search terms. This is definitely a huge step up in tier compared to pre-AI.

But I'm already done getting used to what is possible now. Changes after that have been incremental, nice to have and I take them. I found a place for the tool, but if it wanted to match the hype another equally large step in actual intelligence is necessary, for the tool to truly be able to replace humans.

So, I think the reason you don't see more glowing reviews and praise is that the technical people have found out what it can do and can't, and are already using it where appropriate. It's just a tool though. One that has to be watched over when you use it, requiring attention. And it does not learn - I can teach a newbie and they will learn and improve, I can only tweak the AI with prompts, with varying success.

I think that by now I have developed a pretty good feel for what is possible. Changing my entire workflow to using it is simply not useful.

I am actually one of those not enjoying coding as such, but wanting "solutions", probably also because I now work for an IT-using normal company, not for one making an IT product, and my focus most days is on actually accomplishing business tasks.

I do enjoy being able to do some higher level descriptions and getting code for stuff without having to take care of all the gritty details. But this functionality is rudimentary. It IS a huge step, but still not nearly good enough to really be able to reliably delegate to the AI to the degree I want.

This shit right here is why people hate AI hype proponents. It's like it never crosses their mind that someone who disagrees with them might just be an intelligent person who tried it and found it was lacking. No, it's always "you're either doing it wrong or weren't really trying". Do you not see how condescending and annoying that is to people?

The more I use AI for coding the more I realize that its a toy for vibe coding/fun projects. Its not for serious work.

When you work with a large codebase which have a very high complexity level, then the bugs put in there by AI will not worth the cost of the easily added features.

Many people also program and have no idea what a giant codebase looks like.

I know I don't. I have never been paid to write anything beyond a short script.

I actually can't even picture what a professional software engineer actually works on day to day.

From my perspective, it is completely mind blowing to write my own audio synth in python with Librosa. A library I didn't know existed before LLMs and now I have a full blown audio mangling tool that I would have never been able to figure out on my own.

It seems to me professional software engineering must be at least as different to vibe coding as my audio noodlings are to being a professional concert pianist. Both are audio and music related but really two different activities entirely.

I work on a stock market trading system in a big bank, in Hong Kong.

The code is split between a backend in Java (no GC allowed during trading) and C++ (for algos), a frontend in C# (as complex as the backend, used by 200 traders), and a "new" frontend in Javascript in infinite migration.

Most of the code was made before 2008 but that was the cvs to svn switch so we lost history before that. We have employees dating back 1997 who remembers that platform already existing.

It's made of millions of lines of code, hundreds of people worked on it, it does intricate things in 10 stock markets across Asia (we have no clue how the others in US or EU do, not really at least - it's not the same rules, market vendors, protocols etc)

Sometimes I need to configure new trading robots for random little thing we want to do automatically and I ask the AI the company is shoving down our throat. It is HOPELESS, literally hopeless. I had to write a review to my manager who will never pass it along up the ladder for fear of their response that was absolutely destructive. It cannot understand the code let alone write some, it cannot write the tests, it cannot generate configuration, it cannot help in anything. It's always wrong, it never gets it, it doesn't know what the fuck these 20 different repos of thousands of files are and how they connect to each other, why it's in so many languages, why it's so quirky sometimes.

Should we change it all to make it AI compatible, or give up ? Fuck do I know... When I started working on it 7 years ago coming from little startups doing little things, it took me a few weeks to totally get the philosophy of it all and be productive. It's really not that hard, it's just really really really really large, so you have to embrace certain ways of working (for instance, you'll do bugs, and you'll find them too late, and you'll apologize in post mortems, dont be paralized by it). AIs costing all that money to be so dumb and useless, are disappointing :(

There’s a reason why it’s so much better at writing JavaScript than HFT C++.

The latter codebase doesn’t tend to be in github repos as much.

The big problem is AI is amazing at doing the rote boilerplate stuff that generally wasn't a problem to begin with, but if you were to point a codebot at your trouble ticket system and tell it to go fix the issues it will be hopeless. Once your system gets complex enough the AI effectiveness drops off rapidly and you as the engineer have to spend more and more time babysitting every step to make sure it doesn't go off the rails.

In the end you can save like 90% of the development effort on a small one-off project, and like 5% of the development effort on a large complex one.

I think too many managers have been absolutely blown away by canned AI demos and toy projects and have not been properly disappointed when attempting to use the tools on something that is not trivial.

I think the 90/90 rule comes into play. We all know Tom Cargill quote (even if we’ve never seen it attributed):

The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time.

It feels like a gigantic win when it carves through that first 90%… like, “wow, I’m almost done and I just started!”. And it is a genuine win! But for me it’s dramatically less useful after that. The things that trip up experienced developers really trip up LLMs and sometimes trying to break the task down into teeny weeny pieces and cajole it into doing the thing is worse than not having it.

So great with the backhoe tasks but mediocre-to-counterproductive with the shovel tasks. I have a feeling a lot of the impressiveness depends on which kind of tasks take up most of your dev time.

The other problem is that if you didn't actually write the first 90% then the second 90% becomes 2x harder since you have to figure out wtf is actually going on.

Right— that’s bitten me ‘whipping up’ prototypes. My assumption about the way the LLM would handle done minutiae ends up being wrong and finding out why something isn’t working ends up taking more time than doing it right the first time by hand. The worst part about that is you can’t even factor it in to your already inaccurate work time estimates because it could strike anywhere — including things you’d never mess up yourself.

I wonder if this issues isn't caused by people who aren't programmers, and now they can churn out AI generated stuff that they couldn't before. So to them, this is a magical new ability. Where as people who are already adept at their craft just see the slop. Same thing in other areas. In the before-times, you had to painstakingly handcraft your cat memes. Now a bot comes along and allows someone to make cat memes they didn't bother with before. But the real artisan cat memeists just roll their eyes.

AI is better than you at what you aren’t very good at. But once you are even mediocre at doing something you realize AI is wrong / pretty bad at doing most things and every once in awhile makes a baffling mistake.

There are some exceptions where AI is genuinely useful, but I have employees who try to use AI all the time for everything and their work is embarrassingly bad.

>AI is better than you at what you aren’t very good at.

Yes, this is better phrased.

> If you haven’t had a mind blown moment with AI yet...

Results are stochastic. Some people the first time they use it will get the best possible results by chance. They will attribute their good outcome to their skill in using the thing. Others will try it and will get the worst possible response, and they will attribute their bad outcome to the machine being terrible. Either way, whether it's amazing or terrible is kind of an illusion. It's both.

> If you haven’t had a mind blown moment with AI yet, you aren’t doing it right or are anchoring in what you know vs discovering new tech.

Much of this boils down to people simply not understanding what’s really happening. Most people, including most software developers, don’t have the ability to understand these tools, their implications, or how they relate to their own intelligence.

> Edit: lol this forum :)

Indeed.

You whole comment reads like someone who is a victim of hype.

LLMs are great in their own way, but they're not a panacea.

You may recall that magic is way to trick people into believing things that are not true. The mythical form of magic doesn't exist.

I’ve been an engineer for 20 years, for myself, small companies, and big tech, and now working for my own saas company.

There are many valid critiques of AI, but “there’s not much there” isn’t one of them.

To me, any software engineer who tries an LLM, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems. Maybe AI isn’t the right tool for the job, but that kind of shallow dismissal indicates a closed mind, or perhaps a fear-based reaction. Either way, the market is going to punish them accordingly.

> There are many valid critiques of AI, but “there’s not much there” isn’t one of them.

"There's not much there" is a totally valid critique of a lot of the current AI ecosystem. How many startups are simple prompt wrappers on top of ChatGPT? How many AI features in products are just "click here to ask Rovo/Dingo/Kingo/CutesyAnthropomorphizedNameOfAI" text boxes that end up spitting out wrong information?

There's certainly potential but a lot of the market is hot air right now.

> Either way, the market is going to punish them accordingly.

I doubt this, simply because the market has never really punished people for being less efficient at their jobs, especially software development. If it did, people proficient in vim would have been getting paid more than anyone else for the past 40 years.

> simply because the market has never really punished people for being less efficient at their jobs

In fact, it tends to be the opposite. You being more efficient just means you get "rewarded" with more work, typically without an appropriate increase in pay to match the additional work either.

Especially true in large, non-tech companies/bureaucratic enterprises where you are much better off not making waves, and being deliberately mediocre (assuming you're not a ladder climber and aren't trying to get promoted out of an IC role).

In a big team/org, your personal efficiency is irrelevant. The work can only move as fast as the slowest part of the system.

This is very true. So you can't just ask people to use AI and expect better output even if AI is all the hype. The bottlenecks are not how many lines of code you can produce in a typical big team/company.

I think this means a lot of big businesses are about to get "disrupted" because small teams can become more efficient because for them sheer generation of somtimes boilerplate low quality code is actually a bottleneck.

Sadly capitalism rewards scarcity at a macro level, which in some ways is the opposite of efficiency. It also grants "social status" to the scarce via more resources. As long as you aren't disrupted, and everyone in your industry does the same/colludes, restricting output and working less usually commands more money up to a certain point (prices are set more as a monopoly in these markets). Its just that scarcity was in the past correlated with difficulty which made it "somewhat fair" -> AI changes that.

Its why unions, associations, professional bodies, etc exist for example. This whole thread is an example -> the value gained from efficiency in SWE jobs doesn't seem to be accruing value to the people with SWE skills.

What's "there" though is that despite being wrappers of chat gpt, the product itself is so compelling that it's essentially got a grip on the entire american economy. That's why everyone's crabs in a bucket about it, there's something real that everyone wants to hitch on to. People compare crypto or NFTs to this in terms of hype cycle, but it's not even close.

>there's something real that everyone wants to hitch on to.

Yeah, stock prices, unregulated consolidation, and a chance to replace the labor market. Next to penis enhancement, it's a CEO's wet dream. They will bet it all for that chance.

Granted, I think its hastiness will lead to a crash, so the CEO's played themselves short term.

Sure, but under it all there's something of value... that's why it's a much larger hype wave than dick pills

IMO if the market is going to punish anyone it’s the people who, today, find that AI is able to do all their coding for them.

The skeptics are the ones that have tried AI coding agents and come away unimpressed because it can’t do what they do. If you’re proudly proclaiming that AI can replace your work then you’re telling on yourself.

> If you’re proudly proclaiming that AI can replace your work then you’re telling on yourself.

That's a very interesting observation. I think I'm safe for now ;)

> it can’t do what they do

That's asking the wrong question, and I suspect coming from a place of defensiveness, looking to justify one's own existence. "Is there anything I can do that the machine can't?" is the wrong question. "How can I do more with the machine's help?" is the right one.

I think part of this is that there is no one AI and there is no one point in time.

The other day Claude Code correctly debugged an issue for me, that was seen in production, in a large product. It found a bug a human wrote, a human reviewed, and fixed it. For those interested the bug had to do with chunk decoding, the author incorrectly re-initialized the decoder in the loop for every chunk. So single chunk - works. >1 chunk fails.

I was not familiar with the code base. Developers who worked on the code base spent some time and didn't figure out what was going on. They also were not familiar with the specific code. But once Claude pointed this out that became pretty obvious and Claude rewrote the code correctly.

So when someone tells me "there's not much there" and when the evidence says the opposite I'm going to believe my own lying eyes. And yes, I could have done this myself but Claude did this much faster and correctly.

That said, it does not handle all tasks with the same consistency. Some things it can really mess up. So you need to learn what it does well and what it does less well and how and when to interact with it to get the results you want.

It is automation on steroids with near human (lessay intern) capabilities. It makes mistakes, sometimes stupid ones, but so do humans.

>So when someone tells me "there's not much there" and when the evidence says the opposite I'm going to believe my own lying eyes. And yes, I could have done this myself but Claude did this much faster and correctly.

If the stories were more like this where AI was an aid (AKA a fancy auto complete), devs would probably be much more optimistic. I'd love more debugging tools.

Unfortunately, the lesson an executive here would see is "wow AI is great! fire those engineers who didn't figure it out". Then it creeps to "okay have AI make a better version of this chunk decoder". Which is wrong on multiple levels. Can you imagine if the result for using Intellisense for the first time was to slas your office in half? I'd hate autocomplete too?

Or maybe it indicates that the person looking at the LLM and deciding there’s not much there knows more than you do about what they are and how they work, and you’re the one who’s wrong about their utility.

> To me, any software engineer who tries an LLM, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job,

I don't understand why people seem so impatient about AI adoption.

AI is the future, but many AI products aren't fully mature yet. That lack of maturity is probably what is dampening the adoption curve. To unseat incumbent tools and practices you either need to do so seamlessly OR be 5-10x better (Only true for a subset of tasks). In areas where either of these cases apply, you'll see some really impressive AI adoption. In areas where AI's value requires more effort, you'll see far less adoption. This seems perfectly natural to me and isn't some conspiracy - AI needs to be a better product and good products take time.

> I don't understand why people seem so impatient about AI adoption.

We're burning absurd, genuinely farcical amounts of money on these tools now, so of course they're impatient. There's Trillions (with a "T") riding on this massive hypewave, and the VCs and their ilk are getting nervous because they see people are waking up to the reality that it's at best a kinda useful tool in some situations and not the new God that we were promised that can do literally everything ever.

Well that's capital's problem. Don't make it mine!

Well said!

I use AI all the time, but the only gain they have is better spelling and grammar than me. Spelling and grammar has long been my weak point. I can write the same code they write just as fast without - typing has never been the bottleneck in writing code. The bottleneck is thinking and I still need to understand the code AI writes since it is incorrect rather often so it isn't saving any effort, other than the time to look up the middle word of some long variable name.

> that kind of shallow dismissal indicates a closed mind, or perhaps a fear-based reaction

Or, and stay with me on this, it’s a reaction to the actual experience they had.

I’ve experimented with AI a bunch. When I’m doing something utterly formulaic it delivers (straightforward CRUD type stuff, or making a web page to display some data). But when I try to use it with the core parts of my job that actually require my specialist knowledge they fall apart. I spend more time correcting them than if I just write it myself.

Maybe you haven’t had that experience with work you do. But I have, and others have. So please don’t dismiss our reaction as “fear based” or whatever.

> To me, any software engineer who tries an LLM, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.

I would argue that the "actual job" is simply to solve problems. The client / customer ultimately do not care what technology you use. Hell, they don't really care if there's technology at all.

And a lot of software engineers have found that using an LLM doesn't actually help solve problems, or the problems it does solve are offset by the new problems it creates.

Again, AI isn’t the right tool for every job, but that’s not the same thing as a shallow dismissal.

What you described isn't a shallow dismissal. They tried it, found it to not be useful in solving the problems they face, and moved on. That's what any reasonable professional should do if a tool isn't providing them value. Just because you and they disagree on whether the tool provides value doesn't mean that they are "failing at their job".

It is however much less of a shallow dismissal of a tool than your shallow dismissal of a person, or in fact a large group of persons.

I mean, this is the other extreme to the post being replied to (either you think it's useless and walk away, or you're failing at your job for not using it)

I personally use it, I find it helpful at times, but I also find that it gets in my way, so much so it can be a hindrance (think losing a day or so because it's taken a wrong turn and you have to undo everything)

FTR The market is currently punishing people that DO use it (CVs are routinely being dumped at the merest hint of AI being used in its construction/presentation, interviewers dumping anyone that they think is using AI for "help", code reviewers dumping any take home assignments that have even COMMENTS massaged by AI)

> There are many valid critiques of AI, but “there’s not much there” isn’t one of them.

I have solved more problems with tools like sed and awk, you know, actual tools, more than I’ve entered tokens into an LLM.

Nobody seemed to give a fuck as long as the problem was solved.

This it getting out of hand.

Just because you can solve problems with one class of tools doesn’t mean another class is pointless. A whole new class of problems just became solvable.

> A whole new class of problems just became solvable.

This is almost by definition not really true. LLMs spit out whatever they were trained on, mashed up. The solutions they have access to are exactly the ones that already exist, and for the most part those solutions will have existed in droves to have any semblance of utility to the LLM.

If you're referring to "mass code output" as "a new class of problem", we've had code generators of differing input complexity for a very long time; it's hardly new.

So what do you really mean when you say that a new class of problems became solvable?

But sed and awk are problems.

Punishment eh? Serves them right for being skeptical.

I've been around long enough that I have seen four hype cycles around AI like coding environments. If you think this is new you should have been there in the 80's (Mimer, anybody?), when the 'fourth generation' languages were going to solve all of our coding problems. Or in the 60's (which I did not personally witness on account of being a toddler), when COBOL, the language for managers was all the rage.

In between there was LISP, the AI language (and a couple of others).

I've done a bit more than looking at this and saying 'huh, that's interesting'. It is interesting. It is mostly interesting in the same way that when you hand an expert a very sharp tool they can probably carve wood better than with a blunt one. But that's not what is happening. Experts are already pretty productive and they might be a little bit more productive but the AI has it's own envelope of expertise and the closer you are to the top of the field the smaller your returns in that particular setting will be.

In the hands of a beginner there will be blood all over the workshop and it will take an expert to sort it all out again, quite possibly resulting in a net negative ROI.

Where I do get use out of it: to quickly look up some verifiable fact, to tell me what a particular acronym stands for in some context, to be slightly more functional than wikipedia for a quick overview of some subfield (but you better check that for gross errors). So yes, it is useful. But it is not so useful that competent engineers that are not using AI are failing at their job, and it is at best - for me - a very mild accelerator in some use cases. I've seen enough AI driven coding projects strand hopelessly by now to know that there are downsides to that golden acorn that you are seeing.

The few times that I challenged the likes of ChatGPT with an actual engineering problem to which I already knew the answer by way of verification the answers were so laughably incorrect that it was embarrassing.

I'm not a big llm booster, but I will say that they're really good for proof of concepts, for turning detailed pseudocode into code, sometimes for getting debugging ideas. I'm a decade younger than you, but I've programmed in 4GLs (yuch), lived through a few attempts at visual programming (ugh), and ... LLM assistance is different. It's not magic and it does really poorly at the things I'm truly expert at, but it does quite well with boring stuff that's still a substantial amount of programming.

And for the better. I've honestly not had this much fun programming applications (as opposed to students stuff and inner loops) in years.

> but it does quite well with boring stuff that's still a substantial amount of programming.

I'm happy that it works out for you, and probably this is a reflection of the kind of work that I do, I wouldn't know how to begin to solve a problem like designing a braille wheel or a windmill using AI tools even though there is plenty of coding along the way. Maybe I could use it to make me faster at using OpenSCAD but I am never limited by my typing speed, much more so by thinking about what it is that I actually want to make.

I've used it a little for openscad with mixed results - sometimes it worked. But I'm a beginner at openscad and suspect if I were better it would have been faster to just code it. It took a lot of English to describe the shape - quite possibly more than it would have taken to just write in openscad. Saying "a cube 3cm wide by 5cm high by 2cm deep" vs cube([5, 3, 2]) ... and as you say, the hard part is before the openscad anyway.

OpenSCAD has a very steep learning curve. The big trick is not to think sequentially but to design the part 'whole'. That requires a mental switch. Instead of building something and then adding a chamfered edge (which is possible, but really tricky if the object is complex enough) you build it out of primitives that you've already chamfered (or beveled). A strategic 'hull' here and there to close the gaps helps a lot.

Another very useful trick is to think in terms of vertices of your object rather than the primitives creates by those vertices. You then put hulls over the vertices and if you use little spheres for the vertices the edges take care of themselves.

This is just about edges and chamfers, but the same kind of thinking applies to most of OpenSCAD. If I compare how productive I am with OpenSCAD vs using a traditional step-by-step UI driven cad tool it is incomparable. It's like exploratory programming, but for physical objects.

It really depends on what you’re doing. AI models are great at kind of junior programming tasks. They have very broad but often shallow knowledge - so if your job involves jumping between 18 different tools and languages you don’t know very well, they’re a huge productivity boost. “I don’t write much sql, or much Python. Make a query using sqlalchemy which solves this problem. Here’s our schema …”

AI is terrible at anything it hasn’t seen 1000 times before on GitHub. It’s bad at complex algorithmic work. Ask it to implement an order statistic tree with internal run length encoding and it will barely be able to get off the starting line. And if it does, the code will be so broken that it’s faster to start from scratch. It’s bad at writing rust. ChatGPT just can’t get its head around lifetimes. It can’t deal with really big projects - there’s just not enough context. And its code is always a bit amateurish. I have 10+ years of experience in JS/TS. It writes code like someone with about 6-24 months experience in the language. For anything more complex than a react component, I just wouldn’t ship what it writes.

I use it sometimes. You clearly use it a lot. For some jobs it adds a lot of value. For others it’s worse than useless. If some people think it’s a waste of time for them, it’s possible they haven’t really tried it. It’s also possible their job is a bit different from your job and it doesn’t help them.

My dismissal I think indicates exhaustion from the additional work I’d need to do to make an LLM write my code, annoyance at its inaccuracies, and disgust at the massive scam and grift that is the LLM influencers.

Writing code via a LLM feels like writing with a wet noodle. It’s much faster and write what I mean, myself, with the terse was and precision of my own thought.

> with the terse was and precision of my own thought

Hehe. So much for precision ;)

Autocorrected “terse-ness”

Autocorrect is my nemesis. And I suspect it has teamed up with email address completion.

I would've thought that in 20 years you would have met other devs who do not think like you?

something I enjoy about our line of work is there are different ways to be good at it, and different ways to be useful. I really enjoy the way different types of people make a team that knows its strengths and weaknesses.

anyway, I know a few great engineers who shrug at the agents. I think different types of thinker find engagement with these complex tools to be a very different experience. these tools suit some but not all and that's ok

This is the correct viewpoint (in my opinion, of course). There are many ways that lead to a solution, some are better, some are worse, some are faster, some much slower. Different tools and different strokes for different folks and if it works for you then more power to you. That doesn't mean you get to discard everybody for whom it does not work in exactly the same way.

I think a big mistake junior managers make is that they think that their nominal subordinates should solve problems the way that they would solve them, without recognizing that there are multiple valid paths and that it doesn't so much matter which path is chosen as long as the problem is solved on time and within the allocated budget.

>To me, any software engineer who tries an LLM, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems

This feels like a mentality of "a solution trying to find a problem". There's enough actual problems to solve that I don't need to create more.

But sure, the extension of this is "Then they go home and research more usages and see a kerfluffle of legal, community, and environmental concerns". Then decides to not get involved in the politics".

>Either way, the market is going to punish them accordingly.

If you want to punish me because I gave evaluations you disagreed with, you're probably not a company I want to work for. I'm not a middle manager.

To me, any software engineer who tries Haskell, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.

To me, any software engineer who tries Emacs, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.

To me, any software engineer who tries FreeBSD, shrugs and says “huh, that’s interesting” and then “gets back to work” is completely failing at their actual job, which is using technology to solve problems.

We're getting paid to solve the problem, not to play with the shiniest newest tools. If it gets the job done, it gets the job done.

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