I only work on it for a few hours during the week. And it’s progressing at a reasonable pace that I’m happy with. I got cross-compilation from Linux to Windows going early on in a couple of hours. Wasn’t that hard.
I’ve had to rework parts of the code as I’ve progressed. I’ve had to live with decisions I made early on. It’s code. It’s fine.
I don’t really understand the, “more, better, faster,” cachet to be honest. Writing the code hasn’t been the bottle neck to developing software for a long time. It’s usually the thinking that takes most of the time and if that goes away well… I dunno, that’s weird. I will understand it even less.
Agree with writing less code though. The economics of throwing out 37k lines of code a week is… stupid in the extreme. If we get paid by the line we could’ve optimized for this long before LLM’s were invented. It’s not like more lines of code means more inventory to sell. It’s usually the opposite: the more bugs to fix, the more frustrated customers, the higher churn of exhausted developers.
This is what I've always found confusing as well about this push for AI. The act of typing isn't the hard part - its understanding what's going on, and why you're doing it. Using AI to generate code is only faster if you try and skip that step - which leads to an inevitable disaster
It’s more than just typing though. A simple example remembering the exact incantation of CSS classes to style something that you can easily describe in plain English.
Yes, you could look them up or maybe even memorize them. But there’s no way you can make wholesale changes to a layout faster than a machine.
It lowers the cost for experimentation. A whole series of “what if this was…” can be answered with an implementation in minutes. Not a whole afternoon on one idea that you feel a sunk cost to keep.
I think it's a few things converging. One is that software developers have become more expensive for US corporations for several reasons and blaming layoffs on a third party is for some reason more palatable to a lot of people.
Another is that a lot of decision makers are pretty mediocre thinkers and know very little about the people they rule over, so they actually believe that machines will be able to automate what software developers do rather than what these decision makers do.
Then there's the ever-present allure of the promise that middle managers will somehow wrestle control over software crafts from the nerds, i.e. what has underpinned low-code business solutions for ages and always, always comes with very expensive consultants, commonly software developers, on the side.
They want you to pay for their tokens at their casino and rack up a 5 - 6 figure bill.
This is a very superficial and simplistic analysis of the whole domain. Programmers don't "type". They apply changes to the code. Pressing buttons in a keyboard is not the bottleneck. If that was the case, code completion and templating would have been a revolutionary, world changing development in the field.
The difficult part is understanding what to do and how to do it, and why. It turns out LLMs can handle all these types of task. You are onboarding onto a new project? Hit a LLM assistant with /explain. You want to implement a feature that matches a specific requirement? You hit your LLM assistant with /plan followed by apply. You want to cover some code with tests? You hit your LLM assistant with /tests.
In the end you review the result,and do with it whatever you want. Some even feel confident enough to YOLO the output of the LLM.
So while you still try to navigate through files, others already have features out.
I have heard these words, almost verbatim, from manager-yes-men coming from a FAANG background, and surprisingly concentrated in a certain demography (if someone find this offensive, I'll remove this part).
My CTO wants us to "deliver as fast as possible", and my VP wants us to "go much faster, and more ownership". "Better" or anything related with quality was definitely mentioned, too, but always at a second place.
To this day, I consider these yes-men to be a major red flag, so I always tried to probe for such information during interviews.
I haven't needed to change jobs but I'm worried that one day I'll have to
One of the largest speedups is from how much of the codebase I can keep in my head. Because I started from an empty C++ file, the engine reflects how I reason and organize concepts (lossless compression). Thus most of the codebase is in my brains RAM.
I don't see how LLM agents are going to improve my productivity in the long run. The less a person understands their code (organized logic), the more abstracted the conversation is going to become when directing an agent. The higher up the abstraction ladder you go, the less distinct your product becomes.
[1] And very, very rarely have I wished I had it for a moment. Not using git simplifies abstracted parts of development. No branches, no ballooning of conceptual tangents, etc. Focus on one thing at a time. Daily backups and a log of what I worked on for the day suffices should I need to revisit/remember earlier changes. I've never been in a situation where I change I made over a week ago interfered with todays work.
My game is just in my spare time while I'm looking for work and the scope of the project is small so that I can finish it, release it, and start working on the next one. I'm not trying to build an engine or anything. Just a game. Not even the best game I can make.
I can iterate on it fast because I know the structures. I can refactor it fast because I've built an intuition for a process over time that keeps code amenable to changes over time. I know I'm not going to make the right decisions at the start so I avoid committing to generalizations, etc.
Editing code is pretty fast for me. Again, years working with a particular setup. I still have expandable snippets, multi-cursor editing and a host of macros for common editing motions.
Checking changes... pretty fast. I'm getting to the point where I might invest in using dynamic reloading for my in-development builds. I suspect it will take a few hours to do at most. Not a big deal. For now I have a basic system that just watches for file changes and recompiles/re-runs the program.
In a different context, working on a team in a large multi-million line codebase... I dunno what other people find it's like but I've never found it terribly slow to write/edit code or ship features. I can usually knock most tasks out at a reasonable pace especially when my familiarity with the area of the code I'm working in increases. I usually find my priorities shift with the demands of users, the business, etc. Some times I work on shipping new features quick. Other times it's making sure what we ship the right things and done well so that we don't leak PII.
Either way... actually writing the code isn't the slowest part, in my experience. It's all the other stuff: the meetings, the design, maintenance, documentation, understanding the problem domain, actually shipping the code to production, etc that takes up the most time for me.
Then we're doing different things.
I didn't like GitHub so I wrote my own. 60k lines of code later... yes writing code was the bottleneck which has been eliminated. The bottleneck is now design, review, and quality assessments that can't be done trivially.
This isn't even the project I wanted to be doing, the tools that were available were holding me back so I wrote my own. It also consumes a few hours a week.
If you think writing code isn't the bottleneck then you aren't thinking big enough. If you don't WANT to think big enough, that's fine, I also do things for the joy of doing them.
Once we tried shipping features and updates every week, because we could ideate, code, test and deploy that fast.
No user wanted that - product owners and business wanted that or they thought they wanted, until users came with torches and pitchforks.
Don’t forget there is user adoption and education.
Churning out features no one will use because they don’t know about is useless.
> I don’t really understand the, “more, better, faster,” cachet to be honest
And this:
> I’m working as a single solo developer
...I believe explain it all here. You likely are not beholden to PMs, CEOs and the like. Of course you can go at your own pace. I am actually puzzled that you don't understand that aspect yourself.
> The economics of throwing out 37k lines of code a week is… stupid in the extreme
Again, bosses. CEOs have 14 calls a week with potential prospects and sometimes want demos, sometimes they sign quickly and want a prototype, and sometimes they arrange a collab with a friend or family. Then 3 weeks later the whole thing falls apart and you have to throw it away because it's getting in the way of delivering what actually still pays the bills.
I am not the CEO. I try making his visions come true. I don't get to make the calls on whether 37k of lines will be quickly churned out and then deleted some weeks later.
I think your comment is overly focused only on the coding/programming aspect of things. We don't exist in a vacuum. May I ask how do you make your living? That might shed extra light on your trouble understanding the inevitable churn when writing code for money.
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All of this does not even mention the fact that I 100% agree that less coding lines == less trouble. Code is generally a liability, I believe every mature dev understands that. But often we are not given a choice so we have to produce more code and periodically compress it / re-architect it (while never making the mistake of asking to be given time to do so because we never will).
Code may not be the bottleneck, but writing it absolutely does consume time.
Especially with solo game dev, I can prototype ideas, try them out, and then refine or scrap them at a rate I could never do without AI. This type of experimentation is a perfect use-case for AI. It’s actually super fun, and if I pay attention and give the AI decent instructions, I don’t really lose out on code quality.
Numbers, it is all about numbers.
I have worked in a company where one release a week would drive the managers insane, by them we should have one release every minute.
Things break all the time because in order to deploy faster and faster, corners were cut and now one of the most visited online pet store in the country is down :)
Does your coding not involve thinking? And if not, why are you not delighted to have AI take that over? Writing unthinking boilerplate is tedious garbage work.
Today I wanted to address a bug I found on a product I work on. At the intersection of platform migration and backwards compatibility I found some customers getting neither. I used an LLM to research the code paths and ensure that my understanding of the break was correct and what the potential side effects of my proposed fix would be. AI saved me stepping through code for hours to understand the side effects. I asked it for a nice description of the flow and it gave it to me, including the pieces I didn’t really know because I’d never even touched that code before. I could have done this. Would it have been a better use of my time than moving on to the next thing? Probably not. Stepping through function calls in an IDE is not my idea of good “thinking” work. Tracing through glue to understand how a magical property gets injected is a great job for a machine.
> I used an LLM to research the code paths and ensure that my understanding of the break was correct and what the potential side effects of my proposed fix would be.
Using the LLM for understanding is very different to using the LLM for codegen.
You are not really disagreeing with the author here; it's just that for the specific project he is talking about, he already understands it just fine so the advantages of LLM help in understanding is tiny.
For who? There's no lack of professional programmers who couldn't clear FizzBuzz now coding up company-sized systems using Agents. This is all good as long as agents can stick to the spec/req & code it all up with decent enough abstractions... as the professional approving it is in no position to clue it on code organization or bugs or edge cases. I think, we (as a society) are looking at something akin to "reproducibility crisis" (software full of Heisenbugs) as such "vibe coded" systems get widely sold & deployed, 'cause the "pros" who excel at this are also good at... selling.
The one-shot vibe-coded C compiler is a good example. Sure it created a compiler that could pass the basic tests. But it was no where near a plausible or useful compiler you’d use in a production system.
Someone who knows compilers reviewed it and was able to prompt Claude or Gemini to fix the issues. But still… you’re not going to be able to do that unless you know what to look for.
On an enterprise development team doing boring, Line of Business software? Might have a chance at rolling the dice and trusting the agents and tests and processes to catch stuff for you but I’d still be worried about people who don’t know what questions to ask or have deep expertise to know what is “good,” etc.
I see this on HN just so much and I am not sure what this is, almost seems like a political slogan that followers keep repeating.
I had to do some rough math in my head but in the last 5 years I have been involved with hiring roughly 40 SWEs. Every single one of them was hired because writing the code was THE bottleneck (the only one) and we needed more people to write the code
I’ve seen it time and again: startups move from their market-fit phase into an operational excellence phase on the backing of VC funding and they start hiring a ton of people. Most of those developers are highly educated, specialized people with deep technical skills and they’re often put to work making the boxes more blue or sitting in meetings with PMs for hours. Teams slow down output when you add more people.
You don’t have a quota. It’s not like you’ll have fewer units to sell if you don’t add that 30k lines of code by Friday.
This is knowledge work. The work is understanding problems and knowing how to develop solutions to them. You add more people and you end up adding overhead. Communication, co-ordination, operations overhead.
The real bottle necks are people and releasing systems into production. Every line of code change is a liability. There’s risk tolerance to manage in order to achieve five-nines.
A well-sized team that has worked together a long time can outperform a massive team any day in my experience.
The difference, I think is:
- Code factories where everything is moving fast - there's no time to think about how to simplify a problem, just gotta get it done. These companies tended to hire their way out of slowness, which led to more code, more complexity, and more code needed to deal with and resolve edge cases introduced by the complexity. I can count many times I was handed directives to implement something that I knew was far more complex than it had to be, but because of the pressure to move forward it was virtually impossible to push back. Maybe it's the only way they can make the business case work, but IMO it undoubtedly led to far, far more code than would've been necessary if it were possible to consider problems more carefully and if engineers had more autonomy. In these companies also a lot of time was consumed by meetings trying to "sync up" with the 100 other people moving in one direction.
- Smaller shops, open source projects, or indie development where there isn't a rush to get something out the door. Here, it's possible to think through a problem and come up with a solution that reduces code surface area. This was about solving the largest number of problems with the least amount of complexity. Most of my time at this company was spent thinking through how to solve the problem and considering edge cases and exploratory coding, the actual implementation was really quick to write. It really helped that I had a boss who understood and encouraged this, and we were working on safety critical systems. My boss liked to say "you can't birth a baby in less than 9 months just by adding another woman".
I think most of the difference is in team size. A larger team inherently results in more code to do less, because of the n*(n-1)/2 communication overhead [1].
Recently I learned the Navy SEALs saying "Slow is smooth, smooth is fast" which I feel sums up my experience well.
On the extreme end to prove the point, the suits intentionally abstract out reality into neat forecasts and spreadsheet cells.
It's hard for me to think of something concrete that will convince you. Does code map directly to business outcomes in your experience? Because it's overwhelmingly not even remotely true in my experience.
even just "all lines of code are not created equal" tells me there's no direct correlation with business value.
There is some truth to it, like Brooks' Law (https://en.wikipedia.org/wiki/Brooks's_law) about how adding people to an already late project will just make it later. There are many factors in how long a software engineering task takes beyond pure typing speed, which suggests there are factors beyond code produced per day as well. But some typing has to be done, and some code has to be produced, and those can absolutely be bottlenecks.
Another way of looking at it that I like is Hickey's hierarchy of the problems of programming and their relative costs, from slide 22: https://github.com/matthiasn/talk-transcripts/blob/master/Hi... If you have inherent domain complexity, or a misconception on how to apply programming to a domain, those are 10x worse costs than any day-to-day practice of programming concerns ("the code"), and there's a 10x further reduction for trivialisms like typos.
I think some of it must be cope since so many are in organizations where the more they get promoted the less they program, trending towards (and sometimes reaching) 0. In such an organization sure, code isn't the bottleneck per se, it's a symptom of an underlying cause. The bottleneck would be the bad incentives that get people to schedule incessant unnecessary meetings with as many people as they can to show leadership of stakeholders for promotion doc material, and other questionable things shoved on the best engineers that take them away from engineering. Remove those, and suddenly productivity can go way up, and code produced will go up as well.
I've also always been amused by estimates of what constitutes "good" productivity if you try to quantify it in lines of code. There's a paper from 1994 by Jim Coplien, "Borland Software Craftsmanship: A New Look at Process, Quality, and Productivity". It's summarized in the free book by Richard Gabriel, "Patterns of Software". (https://www.dreamsongs.com/Files/PatternsOfSoftware.pdf pg 135) They were making new spreadsheet software for Windows, and had a group of "very high caliber" professionals, with a core group of 4 people (2 with important prior domain expertise) and then 4 more team members added after a year. "The QPW group, consisting of about eight people, took 31 months to produce a commercial product containing 1 million lines of code. This elapsed time includes the prototypes, but the line count does not. That is, each member of this group produced 1,000 lines of final code per week."
Later on, Coplien was asked "what he thought was a good average for US software productivity", and the answer was "1000 to 2000 non-commentary source lines per programmer per year". Also: "this number was constant for a large in-house program over its 15-year lifetime -- so that original development and maintenance moved at the same pace: slowly". An average of 1k lines a year is 19 lines a week, or about 4 lines a day for a work-week. This was considered acceptable for an average, whereas for an exceptional team you could get 200 a day. Might not there be ways to boost the average from 4 to something like 12 or 20? If your organization is at 4, there is clearly a bottleneck. (For extra context, the QPW group was in C++, and Gabriel notes he had personal experience with several groups demonstrating similar productivity levels. "I watched Lisp programmers produce 1000 lines of Lisp/CLOS code per month per person, which is roughly equivalent to 350 to 1000 lines of C++ code per week." Of course language matters in lines of code comparisons.)
It was!
pre-2022 people needed developers to build software for them, now with platforms like Replit, Lovable - people are creating their own tiny software projects, which wasn't easily accessible in the past.
If you say coding wasn't a bottleneck, then indirectly you could also say, you don't need developers. If you need developers, outcome of their other type of work (thinking, designing based on existing tools and so on) is actually CODE.
In my current role (and by no means that is unique), I don't know how to write less code.
Here are problems I am facing: - DS generating a lot of code - Managers who have therapy sessions with Gemini, and in which their ideas have been validated - No governance on DS (you want this package? import it) - No governance on Infrastructure (I spent a couple of months upskilling in a pipeline technology that were using: reading documentation and creating examples, until I became very good it...just for the whole tech to be ditched) - Libraries and tools that have been documentation, or too complex (GCP for example)
The cognitive overload is immense.
Back few years ago, when I was doing my PhD, immersing in PyTorch and Scipy stack had a huge return on investment. Now, I don't feel it.
So, how do I even write less code? Slowly, I am succumbing to the fact that my tools and methods are inappropriate. I am steadily shifting towards offloading this to Claude and its likings.
Is it introducing risks? For sure. It's going to be a disaster at one point. But I don't know what to do. Do I need a better abstraction? Different way to think about it? No clue
(Disclosure: I'm a corporate trainer)
I think "config first" is an understatement. The more general term here is "data driven".
It's sort of obvious that agents are way better and faster when writing data that can be validated easily against a schema and understood and reviewed in far less time. Data driven also gives you leverage, because it is far easier to for a program to produce data than code.
The same applies to humans as well. Sort of ironic that we are now rediscovering and celebrating robust approaches like writing well designed CLIs, data driven programming, actionable error messages and good documentation.
Maybe AI agents are a sort of reality check or even evolutionary pressure that forces us to do the right things.
And I can tell all of the nay-sayers in this thread, from first-hand experience, that the AI tools can be useful. When you use them well, they can save time. If you're writing just a dinky webapp for your "radio on the internet" startup, it can do a lot of grunt work. It's better auto completion, at a minimum.
Last week I was struggling with an annoying, interlocking-race-condition/-stale-state bug. Fixing one issue kept reintroducing others that I'd just fixed. Skill issue, right? Right. And Clause 4.6 Opus diagnosed the problem and fixed it with just a little bit of coaxing.
Then I asked it to fix another issue and it wound up chasing its tail, as it tried to apply the same principle to unrelated code with unrelated problems.
Call these tools stochastic parrots. Call them autocorrect on steroids. Call them whatever you want. If you think they're worthless or have no use, you're living either in a fantasy land or in 2022 just after openai released its first, hilariously stupid chatbot.
1. what you have identified here, thinking they're useless
2. wanting them to be useless because they like the process of writing code itself, and AI makes that less important, so it's a form of wishful thinking.
3. having ethical concerns about AI, so they want it to fail. And part of that is dismissing their usefulness (after all, it's easier to get rid something which isn't that useful...)
I personally find the third one quite fascinating -- like the cognitive dissonance about how the whole free software movement started out as a way to subvert copyright and nowadays they're almost the biggest defenders of it... but I do understand the reasoning here.
You hold them accountable.
Once upon a time we used to fire people from their jobs for doing things poorly. Perhaps we could return to something approximating this model.
Pitching this is the exact opposite of the maintainer burden of expectation.
> Sometimes I discover a project that is truly wonderful but visibly vibe-coded. I start using it without the guarantee of next release not running rm -rf and wipe my system.
For me this is on you, not the developer.
Sure you can let Claude have a field day and churn out whatever you want but the question is: a) Did you read the diffs and provide the necessary oversight to make sure it actually does what you want properly, b) Is the feature actually useful?
If you've worked on legacy systems you know there's so much garbage floating around that the bar isn't that high generally for code as long as it seems to work. If you read the code and documentation Claude makes thoroughly and aren't blindly accepting every commit there is not really a problem as long as you are responsible and can put your stamp of approval on it. If you are pushing garbage through it doesn't matter if a junior dev, yourself, or Claude wrote it, the problem isn't the code but your CI/CD process.
I think the problem is expectations. I know some devs at 'AI-native' organizations that have Claude do a lot for them. Which is fine, for a lot of boiler plate or standard requests they can now ship 2X code. The problem is the expectation is now that they ship 2X code. I think if you leave timelines relatively the same as pre-AI then having an agent generate, document, refactor, test, and evaluate code with you can lead to a better product.
- small codebases (whole thing is injected into context)
- small, fast models (so it's realtime)
- a custom harness (cause everything I tried sucks, takes 10 seconds to load half my program into context instead of just doing it at startup lmao)
The result is interactive, realtime, doesn't break flow (no waiting for "AI compile", small models are very fast now), and most importantly: active, not passive.
I make many small changes. The changes are small, so small models can handle them. The changes are small, so my brain can handle them. I describe what I want, so I am driving. The mental model stays synced continuously.
Life is good.
There are still consequences, however. Even with an agent, development slows, cost increases, bugs emerge at a higher rate, etc. It's still beneficial to focus on code quality instead of raw output. I don't think this is limited writing it yourself, mind - but you need to actually have an understanding of what's being generated so you can critique and improve it.
Personally, I've found the accessibility aspect to be the most beneficial. I'm not always writing more code, but I can do much more of it on my phone, just prompting the agent, which has been so freeing. I don't feel this is talked about enough!
The website seems to at the least be semi-generated via AI. But I think the statement that the quality of many projects went downwards, is true.
I am not saying all projects became worse, per se, but if you, say, search for some project these days, often you land on a github page only. Or primarily. How is the documentation there? Usually there is README.md and some projects have useful documentation. But in most cases that I found, open source projects really have incredibly poor documentation for the most part. Documentation is not code, so the code could be great, but I am increasingly noticing that even if the code gets better, the documentation just gets worse; rarely updated, if at all. Even when you file requests for specific improvements, often there is no response or change, probably because the author just lacks time to do so, anyway.
But I am also seeing that the code also gets worse. AI generated slop is often unreadable and unmaintainable. I have even recently seen AI spam slop used on mailing lists - look here:
https://lists.ffmpeg.org/archives/list/ffmpeg-devel@ffmpeg.o...
Michael Niedermayer does not seem to understand why AI slop is a problem. One comment reveals that. I don't read mailing lists myself really (never was able to keep up with traffic) but I would be pissed to no ends if AI spam like that would land into my mailbox and waste my time. Yet the people who use AI spam, don't seem to understand mentally why that is a problem. This is interesting. They suddenly think spam is ok if AI generated it. So the overall trend is that quality goes down more and more. Not in all projects but in many of them.
AI making code cheaper to produce doesn’t make the decisions around it any cheaper. What to build, for whom, and why — that’s still fully on you. It should free up more time for strategy, user understanding, and saying “no” to things that shouldn’t exist regardless of how easy they are to ship.
The maintainability concern Orhun raises is real, but I think the root cause isn’t AI — it’s ownership. If you don’t understand what was built, you can’t evolve it. It’s the same failure mode as a PM who doesn’t grasp the technical implementation — they end up proposing expensive features that fight the architecture instead of working with it. Eventually, someone has to pay for that disconnect, and it’s usually the team
