Comment by allthetime
1 month ago
AI is just revealing the two types of people in this line of work. Those who don’t actually like software and just do it because it’s lucrative, and the actual nerds who care.
1 month ago
AI is just revealing the two types of people in this line of work. Those who don’t actually like software and just do it because it’s lucrative, and the actual nerds who care.
You are probably talking about people who just crunch out some half baked solutions for the sake of getting somewhere.
But there are other nerds who care, just not about the code quality, but about conversion, testing out business ideas quickly, getting to know their customers better.
There are nerds who care about business strategy.
There are nerds who care about accounting principles and clean financial reporting.
There are nerds who care about sales targets and partnerships.
There are many types of nerds out there. Don’t limit nerds to engineers, because “tech” world is not just an engineering world anymore. All these nerds you can team up with to build meaningful things, because they do care.
They very clearly weren't talking about nerds in general but rather nerds who care about software.
This resonates with me. I'm a Mechanical Engineer who loves the process of coding. I did take an intro to business class in undergraduate though, and my professor said one thing that has stuck with me for 30+ years - 'The fundamental goal of a business is to make profit now and in the future'. Vibe coded slop might get some traction and make money now, but high quality code will reduce technical debt and allow it to be made in the future. So, in some ways, both camps are right. The PM/Manager/VP want to make money now, but if they completely disregard the nerdy engineer, they will sabotage their future.
I see a disconnect between these two camps that will probably cause a lot of chaos in the near future. Those that figure it out will thrive.
But also time to market matters.
While Company A is building their product in perfect hand-coded Rust with zero defects, Company B is on their third iteration of vibe'd "slop" and getting actual customer feedback - which helps them iterate further.
It's mostly a matter whether Company B is smart enough to refactor the code to a stabler and more maintainable form or do they run headlong into a vibeslop wall.
A much more charitable framing: people who enjoy the process vs people who enjoy the result.
(Though, granted, the results are a lot better if you craft it by hand)
I am not really sure. I wrote some scripts that aggregated data from several APIs with an LLM and the LLM had the foresight to create a caching layer for the API responses as it properly inferred that I would need the results over and over again as well as using asyncio to accelerate fetch speed. This would have been a v2 or v3 and it one-shotted it perfectly.
Yeah, they are good at applying generic patterns, but often it can be overkill/YAGNI that lead to more maintenance work in places that are fine with a much simpler/straightforward solution. But this is what the engineer can decide and with LLMs they wont be forced to make the trade off because it takes longer to build, but rather whether it is really necessary or not.
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When it works, it feels genuinely miraculous. Working in a common problem space, like gluing together APIs, it generally does well. Doing something novel or even a little complicated, it can really lead you astray.
But business people always cared only about thr result. My PM (who speaks like a salesman) only cares about the results. My “head of” same. My ceo same. The only ones who ever cared about the process and quality were us the engineers… if we don’t have that care, well, to hell with everything
That's not true as a simple statement, many business people really do care about quality and process, and you may find you care much more about them than you think.
How often have engineers decried yet another rewrite that some project is doing? Or talked about "over-engineering" something that isn't needed, or that another person in a team has setup a full kubernetes gitops thing that's glorious to them but you just want to scp a go binary and be done with it?
I've seen truly excellent engineers hit this issue, I worked in a team years ago and people disagreed on the approach to take on a new project. So we all made a prototype and presented it, so we could pick a direction. There was a requirement that it be done in ruby since that was the language most of the developers were most fluent in. One of the engineers, remarkably smart, wrote a lisp interpreter in ruby so that technically it'd be "in ruby" but have the benefits of lisp.
He cared about the quality and process in one area. Deeply. However focussing on that would be at the detriment to the rest of the actual product we wanted to ship. If you considered the quality of the product as a whole and the process at the level of the organisation, you'd do something very different.
Now, none of this means all business people are good at this or long term vision or anything, just as it doesn't mean all engineers have a very narrow focus. But I've seen engineers focus on the quality or engineering of some component without looking at what it is you're actually trying to achieve as a business, and so push for a worse overall process and lower "quality" result. It's the same sort of disconnect that leads a lot of engineers to rail against meetings and PMs that slow them down without seeing from the other side that it's often better to build the right thing more slowly than the wrong thing more quickly.
Assuming it is accurate, the logical conclusion is that the race is over. The management can get their $result and fast. Now, whether it is good or bad, is a separate story, and only time will tell whether they will be forced to learn anything. Right now, the expectation is to push for results and management seems to ascribe current set of failures to: people not embracing AI enough.
I think that's a common experience but not universal.
Just about everyone cares about process and quality when things start falling apart. And at least with current technology, it seems like vibe coding your way into a large project will inevitably land you in that spot.
> enjoy the process
This means different things to different people, lot of people enjoy the process of engineering solutions with LLM agents, build out tailored skilled, custom approaches that make up their own flavour "agentic" workflow. There are also people who find joy in Javascript that other people cannot understand why. And other people again love system languages or even tinkering with assembly etc.
What I wanted to say is that LLM use does not automatically mean people just want to get results faster, there are still nerds enjoying the process of working with these new tools.
The results being a lot better crafted by hand I would agree with, if one removes any notion of a time constraint. Sometimes the comparison point is between the LLM authored software or nothing at all.
Can we build a list of the actual nerds who care? Need it for my future recruitment needs lol.
The benchmark is "do they do it for fun", i.e. personal projects.
But the real trick isn't "number of personal projects", but how weird they are. There's no "rational" reason to do them, they don't increase the person's marketability / hireability. They are done purely for intrinsic reasons.
(On reflection, this also seems to be a pretty robust predictor of autism. :)
It goes for all professions really, people who do it for work and people who care. Apply to any profession, plumbers, doctors, carpenters, cleaners, etc etc. Most of us have experienced both types and I haven’t heard of anyone preferring the ”do it for work” over the ones who care. And like those other professions, in software we accept the worse of the two because finding people who care is both time consuming and often much more expensive.
>in software we accept the worse of the two
and the whole world suffers for it.
No disagreement from me
I care a lot about software and I use LLMs extensively. There are some things I deeply understand yet I don't care for doing anymore because I've done them for years and there's nothing to be gained from doing them manually.
That’s just you using the tools responsibly. Not using LLMs to perform well defined virtually deterministic tasks that you fully understand is simply a waste of time. There’s a big difference between that and just letting agents go wild and do your design for you.
This is such a naive take. Most of the nerdiest and most "quality" oriented engineers are hard leaning in to agentic coding. I feel like the most impressive engineers I know have always leaned in to learning how to "sharpen the axe" and AI is really the biggest axe we have seen.
Where did I say they weren’t? We’re all using LLMs now, it would be stupid not to. It’s how we use them and how much care we’re willing to give up for speed that is at issue here.
I take software engineering and production reliability very seriously. But coding is just a small part of my job. It's not really the meat and potatoes. I'll vibe code (responsibility) where I can.
Your category of "nerds who care" is actually "nerds who only want to be coders" and not "nerds who care about solving problems".
I care about solving problems for and delivering value to my users. The software is simply a means to that end. It needs to work well, but that does not mean every line of code requires an artisanal touch and high attention to detail.
I think there's some ambiguity in the discussion around what people mean when they say "good code".
Good code for a business is robust code, that's functionally correct, efficient where it needs to be and does not cost too much.
I believe most developers who care about good code are trying to articulate this, they care about a strong system that delivers well, which comes from good architecture.
LLMs actually deliver pretty well on the more trivial code cleanlines stuff, or can be made to pretty trivially with linters, so I don't think devs working with it should be worried about that aspect.
What is changing fast is that last point I mentioned, "that doesn't cost too much" because if you can get 70% of the requirements for 10% of the perceived up front cost, that calculus has changed. But you are not going to be getting the same level of system architecture for that time/cost ratio. That can bite you later, as it does often enough with human coders too.
I think the other aspect to this which you allude to at the end is that all of these arguments start with the assumption that all human software engineers produce high quality code that meets the requirements, but obviously that’s very much not the case in the real world. After all, 80-90% of drivers rate themselves as above average.
If one compares a single competent software engineer directing a number of agents against a random group of engineers (not necessarily working at FAANG or a YC startup), then those quality arguments are going to be significantly less compelling.
But the trick is that if / when you can define "good code" in a deterministic manner, then the LLM can also deliver "good code".
But if it's just based on feels, then of course it can't do it because it's not a mind reading machine.
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I think there's a continuum here, too. I've heard it said, in jest, mind, that LLM's square the dev. It turns a 1.5x dev into a 2.25x dev, but it also turns a 0.75x dev into a ~0.56x dev.
I think the exponent of 2 is probably too high, but it's not a bad approximation of a very messy reality.
There is also the division of people who value the thing being produced vs. valuing the actual production of that thing, whether or not its used. I don't see one side here being "right", necessarily, but when a company is behind it one is certainly more valued, and I think not incorrectly.
There are more types of people. I do it because it is lucrative, because it turns out I'm good at being a professional software engineer, but I also enjoy it more than other things I could be doing.
However, ultimately, I got into software because I was intellectually curious and programming was a tool I could use to explore that curiosity. When I stop working professionally, I will stop caring about the sorts of stuff I care about today and go back to using programming for what I love. A tool to explore.
I am a nerd who cared. Caring is not putting my food on the table though, delivering stuff is.
I still enjoy diving into documentation but AI has transformed how I work. I can quickly get code examples I can debug. I learn new things as sometimes AI generates approaches I haven't used before.
I've posited for a while now that the people who find spicy autocomplete to be exciting are the people who can't really do what it does.
I played with Image Playground last year some time. It was really fun. You know why? I can't draw, and I can't paint, to save my life. It's letting me do something I can't do well/at all on my own.
Using an LLM to do something I can do, with the caveat that it's pretty mediocre at the task, and needs to be constantly monitored to check it isn't doing stupid things? If I wanted that I'd just get an intern and watch them copy crappy examples from StackOverflow all day.
The same logic explains the use of LLM's to write emails/other long form text.
It makes accessible something that people otherwise cannot do well. Go look at submissions on community writing sites. The people who write because they're good at it, are adamant they don't use an LLM.
People use LLM's to do things they're otherwise not able to do. I will die on this hill.
"I've posited for a while now" and you post the most lukewarm and outdated take like it's an enlightenment. I've been coding for 20 years and can very well do everything the AI does, and so can all devs I know. We use it because it amplifies us, not because we couldn't otherwise. You've chosen a very ridiculous hill to die on.
Is your argument that there is no imaginable situation where someone who was competent at software development could find use for a semi-automated tool for writing software?
That would imply that either the person in question has infinite time, or has access to all software that could ever be of utility to them, which seems unlikely.
There's a reason I call it spicy autocomplete.
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While you are dying on a hill, with the help of LLMs, I'm shipping quality software and features to my customers at a pace I haven't been able to before. And no, not some nextjs slop. If you are letting your LLM look at StackOverflow, you are doing it wrong - it needs to be grounding in your stacks official docs and any other style/rules you prefer wired with other tooling like linting/formatting, duplication checking, etc. And yes, you have to constantly monitor the output and review every line of code - but it's still faster and if managed correctly, produces better code and (this is the hill I will die on) better test suites and documentation than I would have written.
> If you are letting your LLM look at StackOverflow, you are doing it wrong
So you've evaluated all the sources that the model was trained on initially have you? How long did that take you?
> I'm shipping quality software and features to my customers at a pace I haven't been able to before.
I'm sorry are you agreeing with me or not? It sounds like you're agreeing with me.
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Initially I wanted to write more but I can boil it down to taste and context mismatch. By that I mean some people see LLM output as tasteless or kitsch (which I ascribe to generally) and another set of people (though sometimes overlapping more often than not) hold disdain or at the very least look funny at heavy LLM users like gym-goers would look at someone in the middle of the gym loudly suggesting using a dolly or forklift instead of barbell training.
So yeah, I guess the value of doodles has shot up simply because of optics.
Somewhere else in this comment section someone tried to broaden the definition of nerd so much so that pretty much anybody who is a consummate professional is also a nerd. The hill I will die on is that people don't actually dislike all this new AI stuff but more so the attitude of people heavily invested in it.
And to add another data point regarding your hill my drawing/painting moment was NLP stuff. Now if I want to do (rudimentary) sentiment analysis or keyword extraction I can lean on a local LLM. Yet I don't go around yelling Snowball (I think?) is obsolete.
> more so the attitude of people heavily invested in it
Exactly.
LLM bros are just the new blockchain/crypto bros, but they aren't necessarily even writing their own spruiking comments any more.
I've been a software nerd all my life (and there was a time where I worked 60 hours a week at a startup working hard to make mobile games), but there's just been so much extra crap associated with it (especially web development, and especially corporate web development, what currently pays my bills) over the years that it's worn me down and I'm happy to let A.I. churn through the hard or frustrating or endless amounts of boilerplate bits, and let me focus on other things.
Part of me still wishes we were making websites with just HTML, CSS, PHP, and a little Javascript here and there (before AJAX). I'm still not convinced all this extra SPA functionality is really needed for most corporate website needs (something like Google maps or real-time chatting, sure, other things not so much), but I do it because they insist.
I also really like game design, and I had a fairly simple game idea that I prototyped a physical version of and playtested a few times and thought, 'yeah, this is pretty fun'.
But I don't have the energy to code it in my spare time anymore. Was curious how close to a working MVP it could get with me writing up a specification yesterday with the help of ChatGPT (after I brainstormed a few aspects of the design), and dumped that spec into a new repo on GitHub, and about 20 minutes later, it had a fully functional game that worked exactly like my physical prototype.
It was still missing other features, like tutorials and stats and sharing abilities and the like, and I'd like to adjust the presentation some, and the computer opponent A.I. was a bit weak and could have been stronger, but it was fully functional and even looked pretty good, kind of like a Wordle presentation, which was what I was going for anyway.
Something that would have taken me probably 40 hours of dedicated work at least to get everything working and looking as nice as it did.
So yeah, it's kind of like 'well what's the point of me manually coding this anymore'.
What I really like about software was solving puzzles, but now I can focus on the more interesting puzzle of what makes a good game design and 'how best to present this to players' instead of how to get five different libraries and/or APIs to play nice together and learn how it all works.
If coding hadn't become some labyrinthian monstrosity and got out of your way when coding, I probably would want to keep coding more.
Some languages/frameworks get close to that, Lua/Love2D is pretty smooth except when it gets to you wanting to distribute it on platforms other than PC/Mac/Linux, or integrate with external libraries, or for me work with shaders since I'm still pretty weak with shaders.
But even then, it was hard to deny how much faster A.I. could code a feature and I've started getting more hands-off there as well.
That being said, work has gotten less fulfilling, since I'm not doing any actual design work really, just implementing features and making them look according to Figma specifications or fixing bugs, so that's gotten less fulfilling without the busywork of solving coding puzzles (now it's 'how to say this to the A.I. to get it to fix this right, which is still a puzzle but a much weaker one). I'm starting to get tempted to make a go of starting my own business so I can have more autonomy again.
Why exactly does "actual nerds who care" stipulate writing code?