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- Hacker News
- I've personally adopted doing multiple refactoring passes after any large code implementation done by AI. In pretty much any scenario where I'm adding code using AI, it's 1 turn to add the feature and and then another 4-5 turns to refactor and clean everything up.
Often times it's not even that the code is bad but rather that it's overengineered. I see it happen so much that I'm tempted to actually go the other way on a toy project. Like what would Claude or Codex come up with if I told it I wanted an enterprise grade, globally scalable, compliant and auditable tic-tac-toe game.
by _fat_santa - Reminds me of the "entreprise grade fizzbuzz"
https://github.com/enterprisequalitycoding/fizzbuzzenterpris...
by goldenarm - I find the basic premise for low level code quality to be true, in my experience, but counterintuitively I can now police the overall structure and system architecture MUCH more heavily.
As ever, no one is willing to allocate time for this, but (with unlimited work tokens) I can parallel path massive cleanup refactors all the time now.
by glaslong - As long as you're working alone, that makes sense. Otherwise, some team will have to review all your refactoring PRs...by rosenfeld
- The very first thing I do on a Greenfield project or when taking on an existing codebase is implement strict LoC rules in CI/linters. Anything over 800 lines gets a line freeze.
Anything over 600 lines is considered for a refactor when it's touched if appropriate, anything over 800 lines must be refactored when touched. (Combination of AGENTS.md and custom lint warnings/errors).
Anything over 1000 lines forms the refactoring backlog. This way, the bleeding stops immediately and a lot of debt that would otherwise sit in a refactoring backlog forever gets chipped away at over time. The debt that doesn't get chipped is code that isn't touched often anyway and has been battle tested, so might as well be left alone.
by blairharper - > Here’s the problem. AI agents are not bound by human context limits in the same way. An agent can read the tangled function, trace every caller, and make sense of the mess that would have stopped a human cold. It can add the next branch correctly, and the one after that, working confidently inside code that no human on the team fully understands anymore.
They're not bound by the same limits but they're still bound by some limits, yeah?
I'm not an AI expert, so I don't honestly understand why LLM driven agents are as good as they are. But my impression is "trace every caller", most of the time, is still an approximation. Once the code has gotten convoluted enough, cases are going to get dropped.
by Sivart13 - Yes, of course, they're limited, but it doesn't compare to the limit humans face. We can't hold a lot of context on our minds when investigating a bug when the code is too complex with huge methods, lots of branches, several callers and so on. It doesn't mean agents are perfect but they don't care as much as humans if the code is a mess because they can find logic in mess, we can't.by rosenfeld
- They also very often do not actually read the fucking files! Even after specifying to „read in full the fucking files“. LLMs are so lazy, they take any shortcut they can.
Sorry, just thinking about it is reviving my frustration…
by dgellow - > The same modularity that keeps a system inside a human’s head keeps each change inside a well-defined boundary the agent can reason about reliably.
Not only that. Good modularity also:
- improves code reusability, reduces unnecessary code duplication
- helps agents and engineers make better data model, data structure, design pattern, naming, and algorithm choices
- surfaces incorrect irregularities or outdated exceptions to a rule
- enables clean, independent upgrades of parts of a system to improve performance
- reduces stale references in code and comments (and the confusion that results, both from agents and humans)
Bad modularity is basically a summary of what usually constitutes pathological code in general, but AI systems seem particularly disposed to sling lots of it (at least humans are constrained in their output rate). How often have you tried to grok an AI-built project and found trivially unreusable code, unnecessary duplication (everywhere!), bad data structure and algorithm choices, and stale references?
by TimTheTinker - > AI agents don’t get lost
Thats… not my experience. Like, not at all. They very regularly get lost
by dgellow - Same.
Also this article reads like it was written by Sonnet.
by Ancalagon - Yes and they go down rabbit holes, and have to be yanked back to focus on the actual goal.by SoftTalker
- AI agents get lost all the time, particularly if the codebase is already sprawling out of control.
Your discipline only pays off if you already understand your code and/or established clear baseline for your standards before launching into a feature development mania. And it needs to be enforced every turn, or the firehose of code generation knocks the front door down easily.
by moezd - A lot of this feels like it comes down to the training of the agents to produce code that satisfies the various benchmarks combined with reactions to things which were previously maladaptive. I.e. things which were explicitly trained out of the model in post training. I think there's a lot of missing long term software engineering principles that don't seem to be baked into the way the models tend to write code by default.
I have some speculation that maybe the people doing the model post-training tend to be younger researchers that haven't worked on large complex software systems, so their taste isn't as developed in this regard about what things are important here.
But this is an area that can be steered with appropriate early instructions ("When choosing tradeoffs of implementation, build for long term maintainability and understandability of code over implementing just the exact code necessary to solve the issues. etc. chain of thought often includes information that would have to be repeated in a future agent session, make sure to persist it to code or external docs so that future sessions and user understanding is respected.")
It can also be done as a post-change step with similar effects. And you can use your agents to build this layer into your general modus operandi for dealing with the crimes of generated code. But one of the things that all AI labs should be doing is looking at AGENTS.md on real project as being hard expressions of what failure modes real projects have noticed in models generally. Don't wait fo the bugs to be raised on these things, use express preferences that show that there's a problem. Go trawl github for these in bulk to use for future post-training.
by joshka - I've found that access to coding agents has helped me be far less tolerant of bad code patterns that can be refactored.
Refactoring used to have a very real cost - it was substantial amounts of time that would have to be carved away from working on new features.
Now I can spot a potential refactor, fire off a prompt in an asynchronous coding agent (or on a worktree or whatever), then come back 20 minutes later and either accept it, poke it a bit, or abandon it. Costs me almost nothing.
by simonw - Do you work with other, potentially unmotivated devs/managers a lot? I wonder how much of this is an incentive issue. I don’t think refactoring itself is as much of an issue than working with 2-3 other engineers that DGAF and a manager who is only looking at LOC to determine who to promote.
But it also doesn’t mean these things aren’t problems, they’re obviously huge problems.
by boron1006 - All sorts of efforts that used to be put off indefinitely can now be handled largely by LLMs. Yesterday I took a codebase (~50 source files) and spawned a sub-agent (GLM 5.3 Flash) for every single file. Each file was analyzed for test coverage issues, inconsistencies between comments and implementations, and all call sites against the implementation. Then issues were aggregated. I reviewed the list manually, had Opus and K3 review as well to prune the list, and then had a commit made for every minor issue found. 70 commits with maybe 30 minutes of manual work.
Not exactly a refactor, but high degrees of consistency are what I strive for in a codebase. LLMs get confused by inconsistencies, as do humans.
- And for me, it has created a giant pile of unmaintainable code from my coworkers because of stupid management people that think they can code.
Refactoring was never a substantial amounts of time for me before llms. Before I could spot a potential refactor and refactor it in 20 minutes and less. Never abandon it. Just constant improvement to the point the previous tech startup that I was working for just drive from itself (I am still paid a a few hours per months for it)
by rafaelRiv - Meanwhile I'm over here refactoring as much as I can from years (or decades) of human-slung code. Turning the mess I either inherited, helped create, or built on top of into something clean and pristine might be my favorite LLM use. Same for personal projects, codebases that evolved over many years when I happened to have time that weren't kept quite as "clean" as I wish that finally been cleaned up.
I've always _wanted_ my code to be clean and easy to follow but life, deadlines, shifting-priorities, etc have stood in the way of that. Now I can finally realize my personal nirvana.
That said, I've had to steer models away from too-heavy of abstraction or similar because it made the code too hard to follow.
by joshstrange - Same here. It's been very freeing mentally to get the 90% of things I always felt needed done actually done.by dboreham
- Hear, hear. As a lazy dev, LLMs allow me to write so much better code than ever before, and refactor all the crap I never got around to fixing.
- I feel your pain, that's what I've been doing for the past whole week. I'm trying to fix some ancient bugs (there are lots of them in this codebase) but each Claude review detects so many issues that might happen on some rare cases that it takes days until it stops complaining and I can get some peer approval to get the fix merged.by rosenfeld
- It's been fun reviving some abandoned personal/side projects and seeing them through. I have a bunch of random partially completed code that I never got around to finishing and it's fun seeing it work even if I have no use for it. Tons of "proof of concepts" left at "concept" that never reached "proof" haby nijave
- I disagree with the claim that "AI agents don't get lost." What I've observed instead is that they don't experience the sensation of feeling lost. Which is quite different.
This summer I spent quite a while using a coding agent to help me untangle a deep and complicated data processing pipeline. It had itself been built by agents, in a remarkably short amount of time. But it had also become clear that it was riddled with errors and was producing lots of bad data.
What I quickly discovered was that upwards of half of my questions would receive very confidently wrong answers. And even once I had finally diagnosed whatever problem I was currently working on, it was difficult to trust the agent with any bug fixes. Since it was having an even harder time tracing data flows than I was (I'll take this chance to submit for your consideration that faster is not necessarily better), it was proving to be a bit of a monkey's paw. Yes, it would fix the exact bug I asked it to fix, but typically introduce new defects in the process. And yes, I was having this struggle with all of the latest & greatest models.
I ultimately concluded that, in this codebase, the agent was indeed deeply, hopelessly lost. (edit: And probably this code got so bad in the first place because the agents that were used to build it had been lost for a while, but unable to recognize this problem and call their operators' attention to it.)
by bunderbunder - This is a really good take. Thanks for sharing this. I havent been able to put to words how I have felt about agents being untrustworthy.by twosdai
- I'm curious about your exact case. In my experience I often had luck with evidence based approaches where the agent had to prove something first in order to make a statement (or write/do some tests first before making claims).
I agree though that one has to be very careful when trying to "fix" things with agents in a big codebase without it introducing new defects.
by julien_dev - Yes, I've also noticed a few occasions when Claude would get it wrong, but in most cases it's able to find issues I didn't even consider because of a very deep analysis in a confusing (to humans) code. It detects some rare situations where a defect could exist. This happens when I explicitly ask Opus to review a PR and there's some harness around this ability, but I'm really impressed at how deep their analysis can be and correct as well. Of course, sometimes they're going to fail, but I don't see them getting lost often.by rosenfeld
- "Confidently wrong" is definitely a standard behavior model for LLMs.
I agree with the other reply that you're likely to get better results if it has some kind of test case to run that's more authoritative than its own reasoning.
by Sivart13 - > Teams, em dash bla bla bla em dash have quietly bla bla bla. It's not this, it's _that_.
I find it hard to read articles where the agentic writing is this obvious. It's a distraction from the message of the text, which I'm sure is worth my time. Is there no way to stop generated writing from sounding like this?
by wesselbindt - Why believe it's worth your time? Upfront it reads like:
> Claude, generate a 500 word blog post about how people don't refactor anymore because of AI.
Just send me the prompt!
by getnormality