Senior Developer Skills in the AI Age
322 comments
·April 3, 2025necovek
milicat
The more I browse through this, the more I agree. I feel like one could delete almost all comments from that project without losing any information – which means, at least the variable naming is (probably?) sensible. Then again, I don't know the application domain.
Also…
def _save_current_date_time(current_date_time_file: str, current_date_time: str) -> None:
with Path(current_date_time_file).open("w") as f:
f.write(current_date_time)
The scary thing is: I have seen professional human developers write worse code.
Aurornis
> I feel like one could delete almost all comments from that project without losing any information
I far from a heavy LLM coder but I’ve noticed a massive excess of unnecessary comments in most output. I’m always deleting the obvious ones.
But then I started noticing that the comments seem to help the LLM navigate additional code changes. It’s like a big trail of breadcrumbs for the LLM to parse.
I wouldn’t be surprised if vibe coders get trained to leave the excess comments in place.
cztomsik
More tokens -> more compute involved. Attention-based models work by attending every token with each other, so more tokens means not only having more time to "think" but also being able to think "better". That is also at least part of the reason why o1/o3/R1 can sometimes solve what other LLMs could not.
Anyway, I don't think any of the current LLMs are really good for coding. What it's good at is copy-pasting (with some minor changes) from the massive code corpus it has been pre-trained. For example, give it some Zig code and it's straight unable to solve even basic tasks. Same if you give it really unique task, or if you simply ask for potential improvements of your existing code. Very, very bad results, no signs of out-of-box thinking whatsoever.
BTW: I think what people are missing is that LLMs are really great at language modeling. I had great results, and boosts in productivity, just by being able to prepare the task specification, and do quick changes in that really easily. Once I have a good understanding of the problem, I can usually implement everything quickly, and do it in much much better way than any LLM can currently do.
lolinder
It doesn't hurt that the model vendors get paid by the token, so there's zero incentive to correct this pattern at the model layer.
dkersten
What’s worse, I get a lot of comments left saying what the AI did, not what the code does or why. Eg “moved this from file xy”, “code deleted because we have abc”, etc. Completely useless stuff that should be communicated in the chat window, not in the code.
nostromo
LLMs are also good at commenting on existing code.
It’s trivial to ask Claude via Cursor to add comments to illustrate how some code works. I’ve found this helpful with uncommented code I’m trying to follow.
I haven’t seen it hallucinate an incorrect comment yet, but sometimes it will comment a TODO that a section should be made more more clear. (Rude… haha)
ramesh31
>The scary thing is: I have seen professional human developers write worse code.
This is kind of the rub of it all. If the code works, passes all relevant tests, is reasonably maintainable, and can be fitted into the system correctly with a well defined interface, does it really matter? I mean at that point its kind of like looking at the output of a bytecode compiler and being like "wow what a mess". And it's not like they can't write code up to your stylistic standards, it's just literally a matter of prompting for that.
mjr00
> If the code works, passes all relevant tests, is reasonably maintainable, and can be fitted into the system correctly with a well defined interface, does it really matter?
You're not wrong here, but there's a big difference in programming one-off tooling or prototype MVPs and programming things that need to be maintained for years and years.
We did this song and dance pretty recently with dynamic typing. Developers thought it was so much more productive to use dynamically typed languages, because it is in the initial phases. Then years went by, those small, quick-to-make dynamic codebases ended up becoming unmaintainable monstrosities, and those developers who hyped up dynamic typing invented Python/PHP type hinting and Flow for JavaScript, later moving to TypeScript entirely. Nowadays nobody seriously recommends building long-lived systems in untyped languages, but they are still very useful for one-off scripting and more interactive/exploratory work where correctness is less important, i.e. Jupyter notebooks.
I wouldn't be surprised to see the same pattern happen with low-supervision AI code; it's great for popping out the first MVP, but because it generates poor code, the gung-ho junior devs who think they're getting 10x productivity gains will wisen up and realize the value of spending an hour thinking about proper levels of abstraction instead of YOLO'ing the first thing the AI spits out when they want to build a system that's going to be worked on by multiple developers for multiple years.
dilyevsky
what are you going to do when something suddenly doesn't work and cursor endlessly spins without progress no matter how many "please don't make mistakes" you add? delete the whole thing and try to one-shot it again?
ManuelKiessling
Good insight, and indeed quite exactly my state of mind while creating this particular solution.
Iin this case, I did put in the guard rails to ensure that I reach my goal in hopefully a straight line and a quickly as possible, but to be honest, I did not give much thought to long-term maintainability or ease of extending it with more and more features, because I needed a very specific solution for a use case that doesn't change much.
I'm definitely working differently in my brown-field projects where I'm intimately familiar with the tech stack and architecture — I do very thorough code reviews afterwards.
necovek
I think this code is at least twice the size than it needs to be compared to nicer, manually produced Python code: a lot of it is really superfluous.
People have different definitions of "reasonably maintainable", but if code has extra stuff that provides no value, it always perplexes the reader (what is the point of this? what am I missing?), and increases cognitive load significantly.
But if AI coding tools were advertised as "get 10x the output of your least capable teammate", would they really go anywhere?
I love doing code reviews as an opportunity to teach people. Doing this one would suck.
stemlord
Right, and the reason why professional developers are writing worse code out there is most likely because they simply don't have the time/aren't paid to care more about it. The LLM is then mildly improving the output in this brand of common real world scenario
FeepingCreature
> there is a lot of obviously useful abstraction being missed, wasting lines of code that will all need to be maintained.
This is a human sentiment because we can fairly easily pick up abstractions during reading. AIs have a much harder time with this - they can do it, but it takes up very limited cognitive resources. In contrast, rewriting the entire software for a change is cheap and easy. So to a point, flat and redundant code is actually beneficial for a LLM.
Remember, the code is written primarily for AIs to read and only incidentally for humans to execute :)
fzeroracer
At the very least, if a professional human developer writes garbage code you can confidently blame them and either try to get them to improve or reduce the impact they have on the project.
With AI they can simply blame whatever model they used and continually shovel trash out there instantly.
Hojojo
I don't see the difference there. Whether I've written all the code myself or an AI wrote all of it, my name will be on the commit. I'll be the person people turn to when they question why code is the way it is. In a pull request for my commit, I'll be the one discussing it with my colleagues. I can't say "oh, the AI wrote it". I'm responsible for the code. Full stop.
If you're in a team where somebody can continuously commit trash without any repercussions, this isn't a problem caused by AI.
jstummbillig
> The scary thing is: I have seen professional human developers write worse code.
That's not the scary part. It's the honest part. Yes, we all have (vague) ideas of what good code looks like, and we might know it when we see it but we know what reality looks like.
I find the standard to which we hold AI in that regard slightly puzzling. If I can get the same meh-ish code for way less money and way less time, that's a stark improvement. If the premise is now "no, it also has to be something that I recognize as really good / excellent" then at least let us recognize that we have past the question if it can produce useful code.
merrywhether
I think there’s a difference in that this is about as good as LLM code is going to get in terms of code quality (as opposed to capability a la agentic functionality). LLM output can only be as good as its training data, and the proliferation of public LLM-generated code will only serve as a further anchor in future training. Humans on the other hand ideally will learn and improve with each code review and if they don’t want to you can replace them (to put it harshly).
necovek
I do believe it's amazing what we can build with AI tools today.
But whenever someone advertises how an expert will benefit from it yet they end up with crap, it's a different discussion.
As an expert, I want AI to help me produce code of similar quality faster. Anyone can find a cheaper engineer (maybe five of them?) that can produce 5-10x the code I need at much worse quality.
I will sometimes produce crappy code when I lack the time to produce higher quality code: can AI step in and make me always produce high quality code?
That's a marked improvement I would sign up for, and some seem to tout, yet I have never seen it play out.
In a sense, the world is already full of crappy code used to build crappy products: I never felt we were lacking in that department.
And I can't really rejoice if we end up with even more of it :)
gerdesj
My current favourite LLM wankery example is this beauty: https://blog.fahadusman.com/proxmox-replacing-failed-drive-i...
Note how it has invented the faster parameter for the zpool command. It is possible that the blog writer hallucinated a faster parameter themselves without needing a LLM - who knows.
I think all developers should add a faster parameter to all commands to make them run faster. Perhaps a LLM could create the faster code.
I predict an increase of man page reading, and better quality documentation at authoritative sources. We will also improve our skills at finding auth sources of docs. My uBlacklist is getting quite long.
Henchman21
What makes you think this was created by an LLM?
I suspect they might actually have a pool named faster -- I know I've named pools similarly in the past. This is why I now name my pools after characters from the Matrix, as is tradition.
taurath
This really gets to an acceleration of enshittification. If you can't tell its an LLM, and there's nobody to verify the information, humanity is architecting errors and mindfucks into everything. All of the markers of what is trustworthy has been coopted by untrustworthy machines, so all of the way's we'd previously differentiated actors have stopped working. It feels like we're just losing truth as rapidly as LLMs can generate mistakes. We've built a scoundrels paradise.
How useful is a library of knowledge when n% of the information is suspect? We're all about to find out.
lloeki
The pool is named backups according to zpool status and the paragraph right after.
But then again the old id doesn't match between the two commands.
rotis
How can this article be written by LLM? Its date is November 2021. Not judging the article as a whole but the command you pointed out seems to be correct. Faster is the name of the pool.
gruez
>Its date is November 2021
The date can be spoofed. It first showed up on archive.org in December 2022, and there's no captures for the site before then, so I'm liable to believe the dates are spoofed.
bdhcuidbebe
There was alot going on in the years before ChatGPT. Text generation was going strong with interactive fiction before anyone were talking about OpenAI.
victorbjorklund
I used LLM:s for content generation in july 2021. Of course that was when LLM:s were pretty bad.
selcuka
GPT-2 was released in 2019. ChatGPT wasn't the first publicly available LLM.
rybosome
Ok - not wrong at all. Now take that feedback and put it in a prompt back to the LLM.
They’re very good at honing bad code into good code with good feedback. And when you can describe good code faster than you can write it - for instance it uses a library you’re not intimately familiar with - this kind of coding can be enormously productive.
imiric
> They’re very good at honing bad code into good code with good feedback.
And they're very bad at keeping other code good across iterations. So you might find that while they might've fixed the specific thing you asked for—in the best case scenario, assuming no hallucinations and such—they inadvertently broke something else. So this quickly becomes a game of whack-a-mole, at which point it's safer, quicker, and easier to fix it yourself. IME the chance of this happening is directly proportional to the length of the context.
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bongodongobob
This typically happens when you run the chat too long. When it gives you a new codebase, fire up a new chat so the old stuff doesn't poison the context window.
aunty_helen
Nah. This isn’t true. Every time you hit enter you’re not just getting a jr dev, you’re getting a randomly selected jr dev.
So, how did I end up with a logging.py, config.py, config in __init__.py and main.py? Well I prompted for it to fix the logging setup to use a specific format.
I use cursor, it can spit out code at an amazing rate and reduced the amount of docs I need to read to get something done. But after its second attempt at something you need to jump in and do it yourself and most likely debug what was written.
skydhash
Are you reading a whole encyclopedia each time you assigned to a task? The one thing about learning is that it compounds. You get faster the longer you use a specific technology. So unless you use a different platform for each task, I don't think you have to read that much documentation (understanding them is another matter).
necovek
I do plan on experimenting with the latest versions of coding assistants, but last I tried them (6 months ago), none could satisfy all of the requirements at the same time.
Perhaps there is simply too much crappy Python code around that they were trained on as Python is frequently used for "scripting".
Perhaps the field has moved on and I need to try again.
But looking at this, it would still be faster for me to type this out myself than go through multiple rounds of reviews and prompts.
Really, a senior has not reviewed this, no matter their language (raciness throughout, not just this file).
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barrell
I would not say it is “very good” at that. Maybe it’s “capable,” but my (ample) experience has been the opposite. I have found the more exact I describe a solution, the less likely it is to succeed. And the more of a solution it has come up with, the less likely it is to change its mind about things.
Every since ~4o models, there seems to be a pretty decent chance that you ask it to change something specific and it says it will and it spits out line for line identical code to what you just asked it to change.
I have had some really cool success with AI finding optimizations in my code, but only when specifically asked, and even then I just read the response as theory and go write it myself, often in 1-15% the LoC as the LLM
BikiniPrince
I’ve found AI tools extremely helpful in getting me up to speed with a library or defining an internal override not exposed by the help. However, if I’m not explicit in how to solve a problem the result looks like the bad code it’s been ingesting.
mjr00
I "love" this part:
def ensure_dir_exists(path: str) -> None:
"""
Ensure a directory exists.
Args:
path: Directory path
"""
# Ensure the directory exists and is writable
ensure_dir_exists(work_dir)
work_path = Path(work_dir)
if not work_path.exists() or not os.access(work_dir, os.W_OK):
This might not matter here because it's a small, trivial example, but if you have 10, 50, 100, 500 developers working on a codebase, and they're all thoughtlessly slinging code like this in, you're going to have a dumpster fire soon enough.
I honestly think "vibe coding" is the best use case for AI coding, because at least then you're fully aware the code is throwaway shit and don't pretend otherwise.
edit: and actually looking deeper, `ensure_dir_exists` actually makes the directory, except it's already been made before the function is called so... sigh. Code reviews are going to be pretty tedious in the coming years, aren't they?
johnfn
Not all code needs to be written at a high level of quality. A good deal of code just needs to work. Shell scripts, one-offs, linter rules, etc.
jayd16
It'll be really interesting to see if the tech advances fast enough that future AI can deal with the tech debt of present day AI or if we'll see a generational die off of apps/companies.
bdhcuidbebe
I expect some of the big companies that went all in on relying on AI to fall in the coming years.
It will take some time tho, as decision makers will struggle to make up reasons why why noone on the payroll is able to fix production.
jjice
You’re objectively correct in a business context, which is what most software is for. For me, seeing AI slop code more and more is just sad from a craft perspective.
Software that’s well designed and architected is a pleasure to read and write, even if a lower quality version would get the job done. I’m watching one of the things I love most in the world become more automated and having the craftsmanship stripped out of it. That’s a bit over dramatic from me, but it’s been sad to watch.
hjnilsson
It’s probably the same way monks copying books felt when the printing press came along. “Look at this mechanical, low-quality copy. It lacks all finesse and flourish of the pen!”
I agree with you that it is sad. And what is especially sad is that the result will probably be lower quality overall, but much cheaper. It’s the inevitable result of automation.
deergomoo
I feel exactly the same way, it’s profoundly depressing.
Aperocky
Having seen my fair share of those, they tend to work either until they don't, or you need to somehow change it.
layoric
Also somewhat strangely, I've found Python output has remained bad, especially for me with dataframe tasks/data analysis. For remembering matplotlib syntax I still find most of them pretty good, but for handling datagframes, very bad and extremely counter productive.
Saying that, for typed languages like TypeScript and C#, they have gotten very good. I suspect this might be related to the semantic information can be found in typed languages, and hard to follow unstructured blobs like dataframes, and there for, not well repeated by LLMs.
datadrivenangel
Spark especially is brutal for some reason. Even databrick's AI is bad at spark, which is very funny.
It's probably because spark is so backwards compatible with pandas, but not fully.
nottorp
Here's a rl example from today:
I asked $random_llm to give me code to recursively scan a directory and give me a list of file names relative to the top directory scanned and their sizes.
It gave me working code. On my test data directory it needed ... 6.8 seconds.
After 5 min of eliminating obvious inefficiencies the new code needed ... 1.4 seconds. And i didn't even read the docs for the used functions yet, just changed what seemed to generate too many filesystem calls for each file.
bongodongobob
Nice, sounds like it saved you some time.
nottorp
You "AI" enthusiasts always try to find a positive spin :)
What if I had trusted the code? It was working after all.
I'm guessing that if i asked for string manipulation code it would have done something worth posting on accidentally quadratic.
raxxorraxor
In my opinion this isn't even too relevant. I am no python expert but I believe defining a logger at the top for the average one file python script is perfectly adequate or even very sensible in many scenarios. Depends on what you expect the code to do. Ok, the file is named utils.py...
Worse by far is still the ability of AI to really integrate different problems and combine them into a solution. And it also seems to depend on the language. In my opinion especially Python and JS results are often very mixxed while other languages with presumably a smaller training set might even fare better. JS seems often fine with asynchronous operation like that file check however.
Perhaps really vetting a training set would improve AIs, but it would be quite work intensive to build something like that. That would require a lot of senior devs, which is hard to come by. And then they need to agree on code quality, which might be impossible.
necovek
This is a logging setup being done top-level in an auxiliary module "utils": you might import it into one command and not another, and end up surprised why is one getting the logging setup and the other isn't. Or you might attempt to configure it and the import would override it.
As for getting a lot of code that was vetted by senior engineers, that's not so hard: you just have to pay for it. Basically, any company could — for a price — consider sharing their codebase for training.
conductr
As a long time hobby coder, like 25 years and I think I’m pretty good(?), this whole LLM /vibecoding thing has zapped my creativity the past year or so. I like the craft of making things. I used tools I enjoy working with and learn new ones all the time (never got on the JS/react train). Sometimes I have an entrepreneur bug and want to create a marketable solution, but I often just like to build. Im also the kind of guy that has a shop he built, builds his own patio deck, home remodeling, Tinker with robotics, etc. Kind of just like to be a maker following my own creative pursuit.
All said, it’s hard on me knowing it’s possible to use llm to spit out a crappy but functional version of whatever I’ve dreamt up with out satisfaction of building it. Yet, it also seems to now be demotivating to spend the time crafting it when I know I could use llm to do a majority of it. So, I’m in a mental quagmire, this past year has been the first year since at least 2000 that I haven’t built anything significant in scale. It’s indirectly ruining the fun for me for some reason. Kind of just venting but curious if anyone else feels this way too?
carpo
I'm the complete opposite. After being burnt out and feeling an almost physical repulsion to starting anything new, using AI has renewed my passion. I've almost finished a side project I started 4 weeks ago and it's been awesome. Used AI from the beginning for a Desktop app with a framework I'd never heard of before and the learning curve is almost non-existent. To be able to get the boring things done in minutes is amazing.
crm9125
Similar sentiment here. I taught myself python a decade ago after college, and used it in side projects, during my masters degree, in a few work projects. So it's been handy, but also required quite a bit of time and effort to learn.
But I've been using Claude to help with all kinds of side projects. One recently was to help create and refine some python code to take the latest Wikipedia zipped XML file and transform/load it locally into a PostgreSQL DB. The initial iteration of the code took ~16 hours to unzip, process, and load into the database. I wanted it to be faster.
I don't know how to use multiple processes/multi-threading, but after some prompting, iterating, and persistent negotiations with Claude to refine the code (and an SSD upgrade) I can go from the 24gb zip file to all cleaned/transformed data in the DB in about 2.5 hours. Feels good man.
Do I need to know exactly what's happening in the code (or at lowers levels, abstracted from me) to make it faster? not really. Could someone who was more skilled, that knew more about multi-threading, or other faster programming languages, etc..., make it even faster? probably. Is the code dog shit? it may not be production ready, but it works for me, and is clean enough. Someone who better knew what they were doing could work with it to make it even better.
I feel like LLMs are great for brainstorming, idea generation, initial iterations. And in general can get you 80%+ the way to your goal, almost no matter what it is, much faster than any other method.
carpo
That's awesome! That's a lot of data and a great speed increase. I think that as long as you test and don't just accept exactly what it outputs without a little thought, it can be really useful.
I take it as an opportunity to learn too. I'm working on a video library app that runs locally and wanted to extract images when the scene changed enough. I had no idea how to do this, and previously would have searched StackOverflow to find a way and then struggled for hours or days to implement it. This time I just asked Aider right in the IDE terminal what options I had, and it came back with 7 different methods. I researched those a little and then asked it to implement 3 of them. It created an interface, 3 implementations and a factory to easily get the different analyzers. I could then play around with each one and see what worked the best. It took like an hour. I wrote a test script to loop over multiple videos and run each analyzer on them. I then visually checked the results to see which worked the best. I ended up jumping into the code it had written to understand what was going on, and after a few tweaks the results are pretty good. This was all done in one afternoon - and a good chunk of that time was just me comparing images visually to see what worked best and tweaking thresholds and re-running to get it just right.
theshrike79
I'm on this boat. I can use LLMs to skip the boring bits like generating API glue classes or simple output functions.
Example:
I'm building a tool to grab my data from different sites like Steam, Imdb, Letterboxd and Goodreads.
I know perfectly well how to write a parser for the Goodreads CSV output, but it doesn't exactly tickle my brain. Cursor or Cline will do it in minutes.
Now I've got some data to work with, which is the fun bit.
Again if I want to format the output to markdown for Obsidian, the LLM can do it in a few minutes and maybe even add stuff I didn't think about at first.
carpo
Yeah, I've found it great at XML too. All sorts of boilerplate stuff works so well. As it the project gets bigger Ive had to think for longer about things and step in more, but as you said, that's usually the fun, meaty bit that you want to work on anyway. My app is about 15k lines of code now, and it's a bit more work than at the beginning.
tediousgraffit1
this is the key idea right here. LLMs are not replacing good coders, they're electric yak shavers that we should all learn how to use. The value add is real, and incremental, not revolutionary.
FeepingCreature
Same for me. The lack of effort to get started is amazing, as well as the ability to farm the parts I don't like out to the AI. (As opposed to me, it's actually passable at graphical design.)
ManuelKiessling
I'm very much in the same camp as you. I'm having the time of my (professional) live right now.
dmamills
I can echo your sentiment. Art is the manifestation of creativity, and to create any good art you need to train in whatever medium you choose. For the decade I've been a professional programmer, I've always argued that writing code was a creative job.
It's been depressing to listen to people pretend that LLM generated code is "the same thing". To trivialize the thoughtful lessons one has learned honing their craft. It's the same reason the Studio Ghilbi AI image trend gives me the ick.
carpo
I agree, but only to an extent. For me, the passion changed over time. I used to love getting an O'Reilly tome and learning something new, but now I don't really want to learn the latest UI framework, library/API or figure out how a client configures their DI container. If the AI can do most of that stuff, and I just leverage my knowledge of all the frameworks I've had to use, it's a huge timesaver and means I can work on more things at once. I want to work on the core solution, not the cruft that surrounds it.
I agree though that the Studio Ghibli trend feels off. To me, art like this feels different to code. I know that's probably heresy around these parts of the internet, and I probably would have said something different 15-20 years ago. I know that coding is creative and fulfilling. I think I've just had the fun of coding beat out of me over 25 years :) AI seems to be helping bring the fun back.
bigpeopleareold
Just think what the 19th century craftsmen were thinking! :D (i.e. they were right, but really good stuff is hard to make at scale)
hombre_fatal
I know what you mean. It takes the satisfaction out of a certain class of problems knowing that you can just generate the solution.
On the other hand, most tasks aren't fun nor satisfying and frankly they are a waste of time, like realizing you're about to spend the afternoon recredentializing in some aspect of Webpack/Gradle/BouncyCastle/Next.js/Combine/x/y/z just to solve one minor issue. And it's pure bliss when the LLM knows the solution.
I think the best antidote to the upset in your comment is to build bigger and more difficult things. Save your expertise for the stuff that could actually use your expertise rather than getting stuck wasting it on pure time burn like we had to in the past.
conductr
I like the antidote and does remind me that I tried to create a game, which gamedev has always been a challenge for me. I’ve attempted a few times and didn’t get very far with this one either. I think I could do the coding even though scale is large, but I’m not artistic and asset generation/iteration is my block. I tried a handful of ai tools specifically for this and found they were all really far behind. I don’t particularly like working with asset store art, maybe for parts but characters and the vibe of the game usually would excite and motivate me early on and I can’t quite get there to sustain the effort
hombre_fatal
I had this in my comment but deleted it, maybe you were responding to it, but in case you didn't see it: I found multiplayer browser games to be a good example of a hard project that LLMs help a lot with so that you can focus on the rewarding part.
LLMs can one-shot pretty good server-authority + client-prediction + rollback netcode, something I've probably spent weeks of my life trying to build and mostly failing. And they can get a basic frontend 'proof' working. And once you verify that the networked MVP works, you can focus on the game.
But the cool thing about multiplayer games is that they can be really small in scope because all of the fun comes from mechanics + playing with other people. They can be spaceships shooting at each other in a single room or some multiplayer twist on a dumbed down classic game. And that's just so much more feasible than building a whole game that's expected to entertain you as a single player.
Hojojo
I've become so much more productive in my hobby programming projects, because it lets me skip over the tedious parts that suck the life out of me (boilerplate, figuring out library dependencies, etc), and I can focus on the parts that are actually interesting like experimenting with different solutions, iterating and learning new things.
It also lets me explore other libraries and languages without having to invest too much time and effort before knowing if it's right for what I want to do. I know that if I want to continue in a different direction, I haven't wasted tons of time/effort and getting started in the new direction will be much less effortful and time consuming.
woah
Try being a drummer!
conductr
I’ve actually been considering it. Although I think I’m probably tone deaf or rhythmically challenged and my rotator cuff is kinda messed up from an old injury, I’ve been trying to convince my 6 year old to take it up so I could justify having a kit around the house
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johnnyanmac
I'm mixed on the "craft" aspect of it. I don't hate coding, but there's definitely times where it feels like it's just some time plumbing. I don't get satisfaction out of that kind of stuff.
I'm pragmatic and simply don't trust current LLM's to do much in my domain. All that tribal knowledge is kept under lock and key at studios, so good luck scraping the net to find more than the very basic samples of how to do something. I've spent well over a decade doing that myself; the advanced (and even a lot of intermediate) information is slim and mostly behind paywalls or books.
fragmede
Fascinating. it's gone the other way for me. because I can now whip up a serious contender to any SaaS business in a week, it's made everything more fun, not less.
Aurornis
I followed a lot of Twitter people who were vibecoding their way to SaaS platforms because I thought it would be interesting to follow.
So far none of them are having a great time after their initial enthusiasm. A lot of it is people discovering that there’s far more to a business than whipping up a SaaS app that does something. I’m also seeing a big increase in venting about how their progress is slowing to a crawl as the codebase gets larger. It’s interesting to see the complaints about losing days or weeks to bugs that the LLM introduced that they didn’t understand.
I still follow because it’s interesting, but I’m starting to think 90% of the benefit is convincing people that it’s going to be easy and therefore luring them into working on ideas they’d normally not want to start.
fragmede
absolutely! It turns out that the code is just this one little corner of the whole thing. A critical corner, but still just one piece of many.
conductr
Yeah, I see that perspective bu I guess my thought process is “what’s the point, if everyone else can now do the same”
I had long ago culled many of those ideas based on my ability to execute the marketing plan or the “do I really even want to run that kind of business?” test. I already knew I could build whatever I wanted to exist so My days of pumping out side projects ended long ago and I became more selective with my time.
carpo
I guess it depends why you're writing the code. I'm writing a local video library desktop app to categorise my home and work videos. I'm implementing only the features I need. No one else will use it, I'll be finished the first version after about 4 weeks of weekend and night coding, and it's got some pretty awesome features I never would have thought possible (for me). Without AI I probably never would have done this. I'm sold, even just for the reduction of friction in getting a project off the ground. The first 80% was 80% AI developed and the last 20% has flipped to 80% coded by me. Which is great, because this part is the meat of the app and where I want most input.
fragmede
which turns it into passion. the side project that I'm only interested in because it could maybe make some money? eh.
a project in a niche where I live and breath the fumes off the work and I can help the whole ecosystem with their workflow? sign me up!
caseyohara
> I can now whip up a serious contender to any SaaS business in a week
This reminds me of the famous HN comment when Drew Houston first announced Dropbox here in 2007: https://news.ycombinator.com/item?id=9224
fragmede
you don't get to choose why you get Internet famous, it chooses you.
thankfully, I'm not important enough for my comment to amount to the same thing.
cglace
So you can create a serious contender to Salesforce or Zapier in a week?
fragmede
like an Eventbrite or a shopmonkey. but yeah, you don't think you could? Salesforce is a whole morass. not every customer uses every corner of it, and Salesforce will nickel and dime you with their consultants and add ons and plugins. if you can be more specific as to which bit of Salesforce you want to provide to a client we can go deep.
only-one1701
Increasingly I’m realizing that in most cases there is a SIGNIFICANT difference between how useful AI is on greenfield projects vs how useful it is on brownfield projects. For the former: pretty good! For the brownfield, it’s often worse than useless.
Aurornis
It’s also interesting to see how quickly the greenfield progress rate slows down as the projects grow.
I skimmed the vibecoding subreddits for a while. It was common to see frustrations about how coding tools (Cursor, Copilot, etc) were great last month but terrible now. The pattern repeats every month, though. When you look closer it’s usually people who were thrilled when their projects were small but are now frustrated when they’re bigger.
Workaccount2
The real issue is context size. You kinda need to know what you are doing in order to construct the project in pieces, and know what to tell the LLM when you spin up a new instance with fresh context to work on a single subsection. It's unwieldy and inefficient, and the model inevitably gets confused when it can effectively look at the whole code base.
Gemini 2.5 is much better in this regard, it can make decent output up to around 100k tokens compared to claude 3.7 starting to choke around 32k. Long term it remains to see if this will remain an issue. If models can get to 5M context and perform like current model with 5k context, it would be a total game changer.
echelon
I think there's a similar analogy here for products in the AI era.
Bolting AI onto existing products probably doesn't make sense. AI is going to produce an entirely new set of products with AI-first creation modalities.
You don't need AI in Photoshop / Gimp / Krita to manipulate images. You need a brand new AI-first creation tool that uses your mouse inputs like magic to create images. Image creation looks nothing like it did in the past.
You don't need Figma to design a webpage. You need an AI-first tool that creates the output - Lovable, V0, etc. are becoming that.
You don't need AI in your IDE. Your IDE needs to be built around AI. And perhaps eventually even programming languages and libraries themselves need AI annotations or ASTs.
You don't need AI in Docs / Gmail / Sheets. You're going to be creating documents from scratch (maybe pasting things in). "My presentation has these ideas, figures, and facts" is much different than creating and editing the structure from scratch.
There is so much new stuff to build, and the old tools are all going to die.
I'd be shocked if anyone is using Gimp, Blender, Photoshop, Premiere, PowerPoint, etc. in ten years. These are all going to be reinvented. The only way these products themselves survive is if they undergo tectonic shifts in development and an eventual complete rewrite.
dghlsakjg
Just for the record, Photoshop's first generative 'AI' feature, Content Aware Fill, is 15 years old.
That's a long time for Adobe not to have figured out what your are saying.
echelon
Photoshop is unapproachable to the 99%.
A faster GPT 4o will kill Photoshop for good.
carpo
I've been thinking about this a lot and agree. I think the UI will change drastically, maybe making voice central and you just describe what you want done. When language, image and voice models can be run locally things will get crazy.
SkyPuncher
Oh, I find almost the exact opposite.
On Greenfield projects there's simply too many options for it to pursue. It will take one approach in one place then switch to another.
On a brownfield project, you can give it some reference code and tell it about places to look for patterns and it will understand them.
the-grump
My experience on brownfield projects is the opposite.
bdcravens
I find that feeding in a bunch of context can help you refactor, add tests to a low coverage application pretty quickly, etc in brownfield apps.
layer8
And greenfield turns into brownfield pretty quickly.
whiplash451
Right, but AI could change the ratio of greenfield vs brownfield then (« I’ll be faster if I rewrite this part from scratch »)
robenkleene
I struggle to wrap my head around how this would work (and how AI can be used to maintain and refine software in general). Brownfield code got brown by being useful and solving a real problem, and doing it well enough to be maintained. So the AI approach is to throwaway the code that's proved its usefulness? I just don't get it.
rs186
That prompt looks horrifying.
I am not going to spend half an hour coming up with that prompt, tweaking it, and then spend many hours (on the optimistic side) to track down all the hallucinated code and hidden bugs. Have been there once, never going to do that again.
I'd rather do it myself to have a piece of mind.
skydhash
I wonder how much time it would take with some samples from GitHub, and various documentation about python laying around (languages, cheatsheet, libraries)...
miningape
10 minutes tops, once you have the idea and you've thought it through a bit just spamming out the code isn't that hard nor does it take that long. If there's anything surprising (e.g. function call returns something a bit different than expected) it's fast enough to just read the docs and change your mental model slightly.
yeahitsgreat12
[flagged]
JohnMakin
I'm not quite 40 but starting to feel the effects of age, AI has been a great tool if not for the fact it saves my hands. I don't have it write the logic for me, mostly just stuff like smart autocomplete etc. I battle really severe tendonitis, I've noticed a definite improvement since I started using code complete.
As far as knowledge/experience, I worry about a day where "vibe coding" takes over the world and it's only the greybeards that have any clue WTF is going on. Probably profitable, but also sounds like a hellscape to me.
I would hate to be a junior right now.
curiousllama
I would love to be a junior right now. I would just hate becoming a senior, after having been a junior right now.
JohnMakin
well said
spoonfeeder006
Well, whats the difference between Vibe Coding in 5 - 10 years vs coding in C 5 - 10 years after compilers came out?
alternatex
Compilers produce verifiable results. Are we seriously comparing compilers to LLMs? It's like comparing math to sociology.
spoonfeeder006
Well, arguably so do LLMs. You put in the same input prompt, out comes the same code. But yeah, it is kinda different, but I'm just saying that as LLMs become better at understanding how to solve sub-problems, they may become reliable enough that coding via LLMs becomes the new norm, and learning how to effectively prompt will become a new skill for coders
burntcaramel
I agree, and I really like the concrete examples here. I tried relating it to the concept of “surprise” from information theory — if what the LLM is producing is low surprise to you, you have a high chance of success as you can compare to the version you wrote in your experienced head.
If it’s high surprise then there’s a greater chance that you can’t tell right code from wrong code. I try to reframe this in a more positive light by calling it “exploration”, where you can ask follow up questions and hopefully learn about a subject you started knowing little about. But it’s important for you to realize which mode you are in, whether you are in familiar or unfamiliar waters.
https://royalicing.com/2025/infinite-bicycles-for-the-mind
The other benefit an experienced developer can bring is using test-driven development to guide and constrain the generated code. It’s like a contract that must be fulfilled, and TDD lets you switch between using an LLM or hand crafting code depending on how you feel or the AI’s competency at the task. If you have a workflow of writing a test beforehand it helps with either path.
sivaragavan
Aah.. I have been repeating this to my team for several days now. Thanks for putting this together.
I start every piece of work, green or brown, with a markdown file that often contains my plan, task breakdown, data models (including key fields), API / function details, and sample responses.
For the tool part, though, I took a slightly different approach. I decided to use Rust primarily for all my projects, as the compile-time checks are a great way to ensure the correctness of the generated code. I have noticed many more errors are detected in AI-generated Rust code than in any other language. I am happy about it because these are errors that I would have missed in other languages.
manmal
> I have noticed many more errors are detected in AI-generated Rust code than in any other language.
Is that because the Rust compiler is just a very strong guardrail? Sounds like it could work well for Swift too. If only xcodebuild were less of a pain for big projects.
sivaragavan
Yes, exactly. AI isn’t gonna check things like memory management, data type overflows and such, during generation. It would be great if we catch them at compile time.
Regarding swift, totally hear you :) Also I haven’t tried generating swift code - wondering how well that would be trained as there are fewer open source codebases for that.
amflare
> The very experience and accumulated know-how in software engineering and project management — which might seem obsolete in the age of AI — are precisely what enable the most effective use of these tools.
I agree with the author here, but my worry is that by leaning on the LLMs, the very experience that allows me to uniquely leverage the LLMs now will start to atrophy and in a few years time I'll be relying on them just to keep up.
jonnycoder
Very good concrete examples. AI is moving very fast so it can become overwhelming, but what has held true is focusing on writing thorough prompts to get the results you want.
Senior developers have the experience to think through and plan out a new application for an AI to write. Unfortunately a lot of us are bogged down by working our day jobs, but we need to dedicate time to create our own apps with AI.
Building a personal brand is never more important, so I envision a future where dev's have a personal website with thumbnail links (like a fancy youtube thumbnail) to all the small apps they have built. Dozens of them, maybe hundreds, all with beautiful or modern UIs. The prompt they used can be the new form of blog articles. At least that's what I plan to do.
owebmaster
> Building a personal brand is never more important
the low-hanging fruit is to create content/apps to help developers create their personal brands through content/apps.
quantadev
As of 2025, it's no longer the case that older developers (like me at 57) are at a disadvantage just due to potentially lessened sheer brain power, as we had in our 20s. The reason is simple: We know what all the terminologies are, how to ask for things with proper and sufficient levels of detail and context, we know what the pitfalls and common error patterns are, and on and on, from decades of experience. Working with AI has similarities to management positions. You need to be a generalist. You need to know a little about everything, more so than a lot about one thing. All this can ONLY come with age, just like wisdom can only come thru experience.
I just hope that most hiring managers now realize this. With AI the productivity of younger developers has gone up by a factor of 10x, but the productivity of us "Seasoned" developers has gone up 100x. This now evens the playing field, I hope, where us experienced guys will be given a fair shake in the hiring process rather than what's been happening for decades where the 20-somethings pretend to be interviewing the older guys, because some boss told them to, but they never had any actual intentions of hiring anyone over 40, just on the bases of age alone, even if some older guy aces the interview.
ManuelKiessling
This is a great observation and a beautiful perspective.
Would it be okay for you if I quote this on a revised version of the article (with proper attribution, of course)?
quantadev
Sure, you have my permission. However, in my own case, I'm not currently looking for a job, and never in my life did it take more than 2 weeks to FIND a job, when looking. Been coding 24x7x365 since 1981. :)
...however I'm absolutely certain there are many interviews I've aced and not gotten the job, primarily for being in an several undesirable categories that Silicon Valley progressive wokesters despise with a passion: 1) Over 40. 2) White 3) Male 4) Straight 5) Handsome 6) Rich 7) American, speaking perfect English.
All 7 of those demographic stats about me are absolutely despicable to Silicon Valley hiring managers, and any one alone is enough for them to pass by a candidate over a different one lacking the trait.
You can also quote, this post about Silicon Valley too, and there was even a time when such rants like this would've been CENSORED immediately by HackerNews. Thankfully they've lightened up a bit.
pphysch
I'm skeptical that
1. Clearly define requirements
2. Clearly sketch architecture
3. Setup code tool suite
4. Let AI agent write the remaining code
Is better price-performance than going lighter on 1-3 and instead of 4, spending that time writing the code yourself with heavy input from LLM autocomplete, which is what LLMs are elite at.
The agent will definitely(?) write the code faster, but quality and understanding (tech debt) can suffer.
IOW the real takeaway is that knowing the requirements, architecture, and tooling is where the value is. LLM Agent value is dubious.
istjohn
We're just in a transitional moment. It's not realistic to expect LLM capabilities to leapfrog from marginally better autocomplete to self-guided autocoder without passing through a phase where it shows tantalizing hints of being able to go solo yet lacks the ability to follow through. Over the next couple years, the reliability, versatility, and robustness of LLMs as coders will steadily increase.
yoyohello13
I’ve been pretty moderate on AI but I’ve been using Claude cli lately and it’s been pretty great.
First, I can still use neovim which is a massive plus for me. Second it’s been pretty awesome to offload tasks. I can say something like “write some unit tests for this file, here are some edge cases I’m particularly concerned about” then I just let it run and continue with something else. Come back a few mins later to see what it came up with. It’s a fun way to work.
ramon156
This sounds scarily like tasking interns, which only makes me empathize more and more with people studying CS. At what point is it even profitable anymore to teach a junior dev and keep them long enough until they can be considered senior
The premise might possibly be true, but as an actually seasoned Python developer, I've taken a look at one file: https://github.com/dx-tooling/platform-problem-monitoring-co...
All of it smells of a (lousy) junior software engineer: from configuring root logger at the top, module level (which relies on module import caching not to be reapplied), over not using a stdlib config file parser and building one themselves, to a raciness in load_json where it's checked for file existence with an if and then carrying on as if the file is certainly there...
In a nutshell, if the rest of it is like this, it simply sucks.