this post was submitted on 07 Dec 2025
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AI isn't good at changing code, or really even understanding it... It's good at writing it, ideally 50-250 lines at a time
I'm just not following the mindset of "get ai to code your whole program" and then have real people maintain it? Sounds counter productive
I think you need to make your code for an Ai to maintain. Use Static code analysers like SonarQube to ensure that the code is maintainable (cognitive complexity)!and that functions are small and well defined as you write it.
I don't think we should be having the AI write the program in the first place. I think we're barreling towards a place where remotely complicated software becomes a lost technology
I don't mind if AI helps here and there, I certainly use it. But it's not good at custom fit solutions, and the world currently runs on custom fit solutions
AI is like no code solutions. Yeah, it's powerful, easier to learn and you can do a lot with it... But eventually you will hit a limit. You'll need to do something the system can't do, or something you can't make the system do because no one properly understands what you've built
At the end of the day, coding is a skill. If no one is building the required experience to work with complex systems, we're going to be swimming in a world of endless ocean of vibe coded legacy apps in a decade
I just don't buy that AI will be able to take something like a set of State regulations and build a complaint outcome. Most of our base digital infrastructure is like that, or it uses obscure ancient systems that LLMs are basically allergic to working with
To me, we're risking everything on achieving AGI (and using it responsibly) before we run out of skilled workers, and we're several game changing breakthroughs from achieving that
I think complicated software has been an art more than a science, for the past 30 years we have been developing formal processes to make it more of a procedural pursuit but the art is still very much in there.
I think if AI authored software is going to reach any level of valuable complexity, it's going to get there with the best of our current formal processes plus some more that are being (rapidly) developed specifically for LLM based tools.
And how do we surpass those limits? Generally: research. And for the past 20+ years where do we do most of that research? On the internet. And where were the LLMs trained, and what are they relatively good at doing quickly? Internet research.
So is semiconductor design, application of transistors to implement logic gates, etc. We still have people who can do that, not very many, but enough. Not many people work in assembly language anymore, either...
Yeah, that's a lost tech. We still use the same decades, even century old, frameworks
They're not perfect. But they are unchangeable. We no longer have the skills to adapt them to modern technology. Improvements are incremental, despite decades of effort you still can't reliably run a system on something like RISK.
I find Claude Sonnet 4.5 to be good up to 800 lines at a chunk. If you structure your project into 800ish line chunks with well defined interfaces you can get 8 to 10 chunks working cooperatively pretty easily. Beyond about 2000 lines in a chunk, if it's not well defined, yeah - the hallucinations start to become seriously problematic.
The new Opus 4.5 may have a higher complexity limit, I haven't really worked with it enough to characterize... I do find Opus 4.5 to get much slower than Sonnet 4.5 was for similar problems.
Okay, but if it's writing 800 lines at once, it's making design choices. Which is all well and good for a one off, but it will make those choices, make them a different way each time, and it will name everything in a very generic or very eccentric way
The AI can't remember how it did it, or how it does things. You can do a lot... Even stuff that hasn't entered commercial products like vectorized data stores to catalog and remind the LLM of key details when appropriate
2000 lines is nothing. My main project is well over a million lines, and the original author and I have to meet up to discuss how things flow through the system before changing it to meet the latest needs
But we can and do it to meet the needs of the customer, with high stakes, because we wrote it. These days we use AI to do grunt work, we have junior devs who do smaller tweaks.
If an AI is writing code a thousand lines at a time, no one knows how it works. The AI sure as hell doesn't. If it's 200 lines at a time, maybe we don't know details, but the decisions and the flow were decided by a person who understands the full picture
I’ve made full-ass changes on existing codebases with Claude
It’s a skill you can learn, pretty close to how you’d work with actual humans
That has been my experience as well. It's like working with humans who have extremely fast splinter skills, things they can rip through in 10 minutes that might take you days, weeks even. But then it also takes 5-10 minutes to do some things that you might accomplish in 20 seconds. And, like people, it's not 100% reliable or accurate, so you need to use all those same processes we have developed to help people catch their mistakes.
What full ass changes have you made that can't be done better with a refactoring tool?
I believe Claude will accept the task. I've been fixing edge cases in a vibe colleague's full-ass change all month. Would have taken less time to just do it right the first time.
I just did three tasks purely with Claude - at work.
All were pretty much me pasting the Linear ticket to Claude and hitting go. One got some improvement ideas on the PR so I said “implement the comments from PR 420” and so it did.
These were all on a codebase I haven’t seen before.
The magic sauce is that I’ve been doing this for a quarter century and I’m pretty good at reading code and I know if something smells like shit code or not. I’m not just YOLOing the commits to a PR without reading first, but I save a ton of time when I don’t need to do the grunt work of passing a variable through 10 layers of enterprise code.
True that LLMs will accept almost any task, whether they should or not. True that their solutions aren't 100% perfect every time. Whether it's faster to use them or not I think depends a lot on what's being done, and what alternative set of developers you're comparing them with.
What I have seen across the past year is that the number of cases where LLM based coding tools are faster than traditional developers has been increasing, rather dramatically. I called them near useless this time last year.
It's a skill this "fractional CTO" lacks
Definitely
heh