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guhcampos 5 hours ago [-]
I'm a convert. I was 100% skeptical about LLM code generation, now over 80% of the professional code I write is generated.
That said, the limitations are kind of obvious and are starting to show in some of my projects, and this article seems to confirm my suspicions. If it's just confirmation bias or not, I can't say yet.
In my experience, for anything complex enough, I have to start adding more and more constraints, style guides, corner cases, error handling, optimization guidelines and all this good stuff to my Markdown specifications, rules and skills. At some point this starts to look like we're all just moving complexity from the more formal and deterministic world of programming languages to the informal and non-deterministic world of natural language. The writing speed gains are enormous, yeah, and business sees this as productivity gains, of course - and we do it because the pressure for increased productivity is there, as it's always been; yet the trade off seems to be clear and a lot of people are just ignoring it.
mike_hock 26 minutes ago [-]
> moving complexity from the more formal and deterministic world of programming languages to the informal and non-deterministic world of natural language
It's like using a compiler that generates semantically different code every time you run it. Basically like compiling a program that's full of UB but "seems to work" most of the time.
> business sees this as productivity gains
Back to LoC/s as a measure of "productivity."
somewhatgoated 13 minutes ago [-]
> Back to LoC/s as a measure of "productivity."
IMO this doesn’t follow from what OP wrote.
I personally measure it with a more abstract “how long does it take me to ship something that is useful in production and solving a real problem” and the increase in speed there has been massive for me.
But of course I’m not a bigbrain 10x coder that is doing bleeding edge novel stuff like most people here, so gains might be more obvious for me than for others.
dominotw 3 hours ago [-]
if 80% of code you are generating is from llm then you are merely remixing whats out there. Aka slop.
llms cannot generate anything novel.
naveen99 9 minutes ago [-]
Unless you mean remixing the alphabet / tokens, this is mathematically false. 2^256 gets you to unique very fast, and that’s like 252 bytes or like 80 tokens. Remember almost all numbers are irrational. Complexity is infinite.
lurking_swe 8 minutes ago [-]
> you are merely remixing whats out there
So basically 90% of programming in an enterprise environment? lol. Sounds useful to me...
giwook 2 hours ago [-]
And how much of pre-LLM code was just copy pasta from Stack Overflow?
Code doesn't need to be novel to be useful. There's a reason why design patterns are a thing in software.
archagon 2 hours ago [-]
That’s why we abstract the useful code away as libraries, frameworks, etc.
AI is not an abstraction.
ryan_lane 2 hours ago [-]
You generally need to wire libraries in to your service, and you may be using the library in a slightly different way than normal. AIs are perfectly capable of doing this.
Back to the original point, though: most software engineering work isn't novel. Most people are working on slightly different iterations of the same thing, but with the aim of different products. You can have completely different products that use nearly the same patterns as most other services.
To put it bluntly: we don't need AI to generate novel code for the vast majority of the software being built.
apsec112 3 hours ago [-]
LLMs recently solved a major, famous open mathematical problem in combinatorial geometry:
Most code written are not novel. Actually most code written should not be novel. Eg: the number of lines of code written isn't spent on writing git, it's spent on writing that landing page no one ever sees.
hmmokidk 17 minutes ago [-]
they can take novel things. so my novel
pkulak 18 minutes ago [-]
If we were doing novel things, we’d be scientists. I’m an engineer though. I don’t think I’ve been writing slop for 30 years.
loeg 3 hours ago [-]
There is nothing new under the sun.
son_of_gloin 2 hours ago [-]
But there are other suns :)
runhelm 4 hours ago [-]
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jdlshore 15 hours ago [-]
“Our systematic study exposes a phenomenon of constraint decay in LLM-based coding agents. While current models excel at unconstrained generation, their performance drops when forced to navigate explicit architectural rules. For end-users, this dichotomy implies that agents are reliable for rapid prototyping but remain unreliable for production-grade backend development.”
One major weakness of this study is that they didn’t fully test frontier models for cost reasons, so the specific performance results should be taken with a grain of salt. But the overall conclusion that models degrade when both behavior and architecture must be correct is interesting, and something to keep an eye on.
qsort 14 hours ago [-]
I think it's downstream of "you can't optimize for two different objectives".
If you only have functional requirements, then in effect you're doing some form of program synthesis, and RL can optimize that very hard.
If you have a mixture of functional and non-functional requirements, you are basically giving the model an incomplete specification, and it must in some way guess at the user's intent to fill in the blanks. This is also why adding to the prompt examples of the style of code you want (hats off to antirez for this particular tip ;)) is phenomenally powerful.
apsurd 14 hours ago [-]
Would you mind sharing antirez' suggestion?
qsort 14 hours ago [-]
I am obviously paraphrasing, but the general idea is that trying to synthesize style from a codebase into e.g. a markdown guide generally doesn't work very well. What achieves style transfer is providing the model with a lot of examples of the style, conventions, patterns you want.
To put it in practice: if you point claude/codex to a repository and you ask it to implement feature X using style guide Y, the code will probably work, but you can usually get better results by saying "do it in the style of this file, it was done well there".
brandensilva 13 hours ago [-]
Right more simply put it's great at being a copy cat, exploring similar data points that match your token needs.
It is not great at decision making or judgment calls that don't have a well defined spec or plan in place yet; like unofficial or unapproved tokens if you will. A lot of this stuff simply never has had specs as it has been internal to how companies work and their secret sauce.
The closest thing we have are governance and compliance policies due to legal/business needs requiring it so it's far more well documented than operational ones in how we work. It is more about the how versus the what here I guess is what I'm saying.
But yeah this is why it does great when there are tests, design systems, evals, and other artifacts to mirror. Far more reckless and unpredictable without these things, but still great for exploration and finding the data output you seek.
withinboredom 11 hours ago [-]
Doesn't that make sense? Its text prediction. If you give it examples, it can predict. Synthesizing "put semi-colons on new lines" requires it to generate its own examples 'in its head' (so to speak) and remember that. It won't.
It's like when I see people feeding it a whole bunch of "best practices" and expect it to follow them. It won't. But you could ask it questions about the best practices all day long.
brandensilva 10 hours ago [-]
Yes, exactly. Any engineer deep on this stuff right now understands that grounded predictive engine sprinkled with RL training and are discovering what that means in terms of its strengths and weaknesses for company use.
dnautics 7 hours ago [-]
Supposing an unspecified or poorly specified function f(x), and example "f(A)=>B", "given C tell me what f(C) is" lies at the core of creativity.
Idk, calling it "just text prediction " seems unfairly dismissive of this capability
withinboredom 5 hours ago [-]
Saying that it’s dismissive is like saying writing (insert language) is dismissive that you’re just writing assembly.
at the end of the day, it presents a vector field and predicts the next vector. That’s literally the heart of intelligence just like assembly is the heart of execution. When playing table tennis, your brain is literally predicting seconds into the future to get your body into the right position.
But we aren’t discussing intelligence here. We are discussing how best to utilize that intelligence.
dnautics 4 hours ago [-]
You're making my point for me, saying table tennis is "just a proprioceptive predictor" is dismissively reductive (and not a particularly useful framework for understanding table tennis), even if it is strictly speaking accurate. It's the sort of thing someone who has no idea how hard training for table tennis is would say.
coredog64 6 hours ago [-]
I was recently using Copilot to implement a small feature within a very large codebase. About 75-80% of the time, the code that was added matched the current style (warts and all). Copilot would specifically go off and research "How X is already done in the codebase" all the time.
mikeyouse 13 hours ago [-]
I ran into similar issues as we started to roll out LLM generated financials in our org.. I’m so used to the old SQL workflow of “grab this data from this table, that data from that table, combine it into a final result that looks like xxxx” where the tables were outputs from reports in our ERP but I was having terrible results.
Ended up pointing Claude at a few sample files from our existing reporting, gave it read-only oauth access to the ERP and said “build a new report showing the cash by project as calculated by xxxx - yyyy + zzzz in the style of the existing reports” and it basically one-shot from there.
Kind of crazy and I built a bunch of redundant check-sums because I honestly didn’t think it would be able to replace like 6 workdays of effort for the 2 FTEs who generate that kind of thing manually every month but so far so good..
BlueTierOps 11 hours ago [-]
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8note 5 hours ago [-]
You basically get this for free, if the coding agent has read the relevant classes that the legacy code its touching has to match.
just dont break out a plan without also having it read the code again
KaiShips 11 hours ago [-]
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zdragnar 10 hours ago [-]
I've noticed something similar with AI assist authored books as well. Early on it does alright, but after some chapters the beginning of each chapter repeats the end of the previous, and obvious LLM tells become more frequent.
The more it has to go on, the more it relies on repetition of what came before. It's also possible that authors start paying much less attention and put less effort into editing later chapters.
Despite the sheer volume on Amazon, LLMs are not at the point of writing well.
piker 10 hours ago [-]
Holy crap are you reading books that advertised somehow they were written with LLM assistance? Hard no here in 2026.
zdragnar 9 hours ago [-]
Oh no, they were not advertised as such. It's rather painfully obvious in the worst cases.
nijave 13 hours ago [-]
Hmm, I have some anecdotal evidence this is true. Interactively working out a plan with Opus on multiple occasions it'd come up with an incompatible solution, I'll add additional context/requirements, and it has a tendency to "anchor" on it's original architecture and struggles to adapt. Sometimes it tries to sneak in changes for the original plan anyway.
whstl 12 hours ago [-]
Opus does this waaaay too much for my taste. It works fine for vibe-coders but for technical work it is infuriating.
UncleEntity 11 hours ago [-]
I think the problem is they take the shortest path to the goal ...which may or may not coincide with what you have planned. Oh, and generally think instructions are merely suggestions and what you really want this this totally different thing and not the one in the plan you handed them plus, as a stoke of good luck, this other system is a lot easier to implement as well.
I mean, I spend more tokens having them clean up all the places they didn't follow the the plan (if I catch it) or implementing what came out of a 'complete and tested' previous plan where they just stop as soon as all the pathetic new test pass and you discover half of it isn't even there when trying to implement the next thing on top of it.
Though... I have been conducting an experiment, of sorts, where we've been cooking on these fairly complicated projects and I don't ever touch a single line of code, just yell at them a lot, and with suitable amounts of marijuana (they are very frustrating most of the time) it's been going pretty well. I also helps that they need to explain what they're doing to somebody fairly-baked -- maybe not such an HR friendly plan?
Animats 11 hours ago [-]
That may be the same problem seen when prompts try to force "alignment" or "guardrails". There's a performance drop. Seemingly, a big chunk of the potential solution space has been made unreachable.
For example, if you apply "guardrails" to an image generator of about a year ago, all the people start looking alike. Story generators start using only a few standard names.
That was last year. Is it happening with the frontier models?
10 hours ago [-]
jeremyjh 14 hours ago [-]
Even the strongest frontier model they used - GPT 5.2 - I would consider barely usable for agentic programming.
I’m not really interested in analysis of the weaknesses of such models because in my experience many weaknesses disappear entirely as models get stronger and reasoning effort is turned up. Especially if you tell them what you want them to do.
Also, it’s not surprising to learn that when more acceptance criteria are added the failure rate increases.
sigbottle 13 hours ago [-]
Wait isn't gpt 5.2 good? Or is it not thinking / not codex? 5.2 was what sparked the late 2025 openai agentic programming revolution.
mkozlows 2 hours ago [-]
5.2 still had a Codex variant, which this doesn't describe using. It also notably is not using the Codex harness -- it does everything with open source harnesses (which obviously are worse). And while it uses two harnesses with its cheap models, it only uses the worse-performing one of those with GPT 5.2 for cost reasons. (They also don't specify effort/thinking level used for GPT 5.2, but given that it performs worse in their baseline testing than obviously non-SOTA models, I'm guessing it wasn't set to anything high.)
nozzlegear 9 hours ago [-]
Oldheads remember when GPT 5.2 was at the forefront of agentic programming. December 2025 feels like eons ago, but alack it was an entire half year!
ipaddr 4 hours ago [-]
If I'm not using got 5.5 high reasoning I'm wasting time.
nozzlegear 3 hours ago [-]
Well, maybe so, but how did you feel about 5.2 when it was OpenAI's frontier model? That's what I'm getting at – it was the equivalent of your gpt 5.5 high reasoning just six months ago.
xienze 13 hours ago [-]
> their performance drops when forced to navigate explicit architectural rules
Even the best models have trouble adhering to stuff as mundane as rules for how to style generated code (indent this much, name things with these patterns, etc.). Even the most die-hard AI-first coder will admit to that kind of stuff being not unheard-of. Yet they still delude themselves into thinking that these models will follow a sufficiently detailed spec to the letter, every time.
maxbond 15 hours ago [-]
Reminds me of the recent paper about delegating document editing tasks to LLMs across different disciplines [1]. That paper found that programming was the only discipline most LLMs can perform long horizon tasks on without accumulating errors & corrupting the document.
I've only read the abstract of this one so far but it seems like this paper has zoomed in on programming with greater fidelity and shown a similar phenomenon. But not about long horizon tasks, more like "long style horizons" of larger sets of structural constraints.
If it’s not easily verifiable, LLMs aren’t good at it.
jeremyjh 14 hours ago [-]
I think that’s mostly because they get so much more of that reinforcement learning - since it is so economical. I dont know if there is any evidence of a fundamental reason they can’t be just as good at other tasks, but it might be economically infeasible for awhile yet.
mjburgess 13 hours ago [-]
No one is curating vast amounts of data for them in other domains. Programmers send programs with fixes
knollimar 12 hours ago [-]
There's no diff of my excel lambdas being fixed? :(
emp17344 12 hours ago [-]
RLVR doesn’t work for unverifiable tasks, so they won’t be able to effectively use tools to boost reliability for those tasks.
dominotw 7 hours ago [-]
but what does it mean to be good at something that cant be verified. how do you know that they are not good at it, you are obviously using some measure.
sounds like an oxymoron of a claim.
marcosdumay 3 hours ago [-]
You just threw the "easily" away from the comment you are replying.
dominotw 3 hours ago [-]
doesnt make a difference to my comment
maxbond 6 hours ago [-]
It means having taste. People say Picasso was a great painter, but that cannot be verified (at least, not in the sense of a verified reward).
dominotw 4 hours ago [-]
"people say picasso was a great painter" is definitely not hard to verify . lol.
maxbond 3 hours ago [-]
I don't know if you're being factitious or not but that was not what I meant. Picasso being a great painter is an example of "having taste"; "create an artistic image generation model with Picasso-level performance" is a valid problem statement we could attack with RLHF, but not with RLVR, because "taste" is not amenable to modeling with a reward function.
"Write this code in a way that is readable and maintainable" is another example.
The first paragraph ends with "[...] unleashing a flood of ill-informed reactions and muddled discourse. So, you know, it was just another day online."
It's almost as though it's not about the Monet.
4 hours ago [-]
vishvananda 13 hours ago [-]
I've been experimenting quite a bit with long-horizion agentic coding[1] and I have also noticed that agents seem to perform worse when forced into certain architectural patterns. I have found that is a bit better when including the constraints along the way instead of adding them after the fact. There seems to be a side-effect I have been calling "calcification", where a pattern starts appearing in the codebase and the agent follows the pattern to the point where it dominates the context and becomes self-reinforcing. This could potentially be a strength or a weakness for existing code bases depending the codebase quality. I will have more insights on this soon as more from-scratch runs conclude that include architectural guidance from the beginning.
> agents seem to perform worse when forced into certain architectural patterns.
FWIW I've noticed this too. I've found that the agents/models have their own style, which is mostly summed up as overly verbose.
Additionally, the models are OK at modularization when given space to "plan" their implementation, but rarely decide that abstracting something would be helpful after the fact (i.e. after many iterations on a greenfield codebase or when being dropped into a legacy codebase).
This often leads to "god files" which, when pointed to by the user/architect, causes the models to correctly critique (humorously when they're the ones that wrote the code in the first place).
yomismoaqui 15 hours ago [-]
Also they used languages with dynamic typing like Python & JS. In my experience a statically typed codebase is easier to maintain for humans so maybe it is also for agents.
When using Codex/Claude Code with Go code I cannot count the times the agent does some change, runs a build to check for errors, find some and fix them.
acbart 14 hours ago [-]
It's crazy to me that people think of Python as dynamically typed by default. Strong static typing has been an option in Python for years now, and it should just be the default.
mrob 12 hours ago [-]
>Strong static typing has been an option in Python for years now, and it should just be the default.
"The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc."
Which third-party enforcement mechanism do you propose become the default?
shepherdjerred 8 hours ago [-]
There are plenty of options for static type checking in Python. Choose your favorite or just use Ty
epgui 14 hours ago [-]
The python type hints are useful for static analysis (and yes, should be the default) but it’s a joke compared to the utility of types in a language like Haskell.
shepherdjerred 8 hours ago [-]
If you're comparing type systems against Haskell you're excluding all mainstream languages except maybe Scala and Rust
antonvs 12 hours ago [-]
Typing with tools like Pyright doesn't come close to providing what a good statically typechecked language provides.
There are many reasons for this. A big one is that many libraries are only partially typed at best, and dynamic types tend to propagate, weakening the guarantees you get from type checking.
Dynamic idioms in general, including something as common as string-indexed dictionaries, negate type checking. Runtime metaprogramming is the same. All of these things have equivalents in a good statically checked language, but Python doesn't follow those models.
Fundamentally, in Python static typing is an optional analysis layer over a dynamic language, and the consequences of that can't be fully mitigated. The result is a big difference in what types can guarantee.
shepherdjerred 8 hours ago [-]
TypeScript had this _exact_ same problem when it started out. As more libraries add annotations, the ecosystem will become stronger, and it will eventually be about as good as a "real" statically typed language.
> Dynamic idioms in general, including something as common as string-indexed dictionaries, negate type checking.
Do you have any proof of this? It hasn't been a problem in TypeScript, and I doubt it's an issue in Python
alexwwang 5 hours ago [-]
I am trying to avoid this by building a plugin based on my memory management project Aristotle. I add a status machine to monitor the activities of LLM while it does jobs following my tdd-pipeline skills, which begins with requirements clarification and ends up with delivery.
These two projects are on GitHub, you may search alexwwang/aristotle and alexwwang/tdd-pipeline to dive into the details or just ask your LLM to scan them to tell you the points you are interested in.
dwa3592 14 hours ago [-]
This sounds like another version of "As a chat becomes longer, the guardrails seem to become fuzzy". You can't use all of the context window bc at the end, the output would not respect the constraints (or guardrails) but to reliably produce production grade code you want the model to have expansive awareness which fills up the context window pretty quickly. It's like saying "Keep everything in mind from these 6 directories - and make this <insert ticket> change" - but keeping everything in mind already fills it's context window which makes it lose it's ability to follow the constraints (or guardrails).
whatever1 14 hours ago [-]
This is not a new problem though. This is why we started writing modular code, strict interfaces etc
lanstin 14 hours ago [-]
And doing incremental dev, so once a feature is done you can mostly ignore it.
Silhouette 13 hours ago [-]
If there is one good thing that the generative AI tools have shown beyond any doubt it's that the classic "good programming" practices are still useful and effective. Self-documenting code. Modular design. Clearly defined architecture. Incremental development. Coding standards. Automated tests. Automated everything.
If there's a second thing the generative AI tools have shown beyond any doubt it's that many of the more modern (relatively speaking) "best practices" that have always been over-hyped and questionably-evidenced really do tend to produce worse results. LLMs take these methods to their logical conclusions and show us the end result much sooner. You can't just iterate your way to a solution when you don't even know what problem you're trying to solve. If you don't have a clear spec then you don't know what a correct product looks like. You need to invest time in reviewing code properly. If you don't keep the big picture in mind then the big picture becomes a mess.
Maybe one day the LLMs will leave me out of a job but at least I'll feel validated first!
skydhash 7 hours ago [-]
> If there is one good thing that the generative AI tools have shown beyond any doubt it's that the classic "good programming" practices are still useful and effective
If you apply those practice, then quickly you find yourself using the agent as merely a writing boost. And there’s an inflexion point when coding is no longer a bottleneck. Instead, you spend more time on thinking about design. You can see it in open source projects where most PRs are just a few line diffs. The bottleneck is knowledge and problem solving talent.
Silhouette 7 hours ago [-]
If you apply those practice, then quickly you find yourself using the agent as merely a writing boost.
I don't know what that means but I have seen no evidence so far that if you don't apply those practices then your code will be anything other than unmanageable spaghetti if you leave AI to maintain it for long.
Coding has never been the bottleneck for good developers. Part of the reason for that is that good developers know how to isolate different aspects of a system and so keep each individual aspect relatively simple and self-contained. Another part is that good developers were already standardising and automating a lot of the grunt work. These traits are also advantageous for keeping generative AI on the right track and keeping its proposed changes manageable.
lanstin 7 hours ago [-]
Yeah and that design and insight is the tiring part and while fun a bit less satisfying in the way that writing a nice bit of boiler plate or populating the struct members for your data type can be. One thing is you can work on design and insight while taking a good walk around the block, which is nice.
skydhash 7 hours ago [-]
I spend that time mostly on the sofa, or in front of a whiteboard. Or sometimes a live brainstorming. Typing code is actually relaxing. What looks like relaxing is actually hard thinking.
usrusr 11 hours ago [-]
So give harder guiderails? Sonarcube and the like. But I guess then the failure mode would be appeasing the linter while slowly forgetting the requirements... (or not so slowly, because the try/fail loop won't be nice to context at all..)
siliconc0w 10 hours ago [-]
I recommend spending some time getting a few parts of the codebase idiomatic and then @-ing those files as exemplars. This works a lot better than trying to steer it with markdown. This works reasonably well for like FastAPI but JavaScript seems to be the worst, even with guidance and exemplars it'll prefer in-lining a bunch of garbage rather than use the APIs as directed.
p0w3n3d 14 hours ago [-]
tasks spanning eight web frameworks
Does anyone else have this experience that LLM create better pure html+CSS+js than work with existing frameworks?
bob1029 13 hours ago [-]
I think web frameworks have been "in trouble" as of gpt-5.4. I can't imagine using something like React anymore.
The most incredible combo I've seen lately is progressive enhancement of Razor Pages with javascript. With this arrangement the newest models tend to make a really good call on if something should happen server-side (cshtml) or on the client (js).
AmazingTurtle 11 hours ago [-]
So my finding is:
planning is worth it.
For a little complex changes, I always run codex (5.5-high) in planning mode first.
I have linked various docs/{ARCHITECTURE,BACKEND-GUIDELINES,NESTJS-DI,..}.md etc. from AGENTS.md so they can quickly discover relevant docs at planning time, only if they are needed. No need to know react specific stuff when it's dealing with a backend problem for example. I typically blindly approve plans made by the agent with a fresh context, because that's as if I had prompted it. Works the best for me.
Using /goal however, it's really just constantly compacting and doing it's thing, of course it gets sloppy. If only there was a state machine that would transform tickets into a Planning Mode Prompt, then use, idk. guardian approvals (somehow a "Product Management Perspective Lens" approving or making changes to the plan) and then letting a less capable or less reasoning agent execute the plan, I think that would work the best.
alasano 7 hours ago [-]
I've been building https://engine.build to introduce a proper structured external agent orchestrator that's used to build with clear constraints and make sure the end result is what you wrote in your spec or requirements. Without having to babysit and micromanage the models.
Implementation phases very often go through 5-10 review and fix rounds to actually get the implementation to match the spec. It takes longer but that's what's necessary to get actually good results on long horizon tasks with detailed requirements. I'll be open sourcing it fully soon.
gkfasdfasdf 15 hours ago [-]
Odd they used GPT-5.2 and not GPT-5.2-codex. i.e. the one optimized for coding agent tasks.
maleldil 12 hours ago [-]
Considering this is from academia, there's a chance there were limitations on the available models. My research group accesses OpenAI models via Azure, and until recently (last week) the latest model was GPT 5. We just got 5.4.
beering 10 hours ago [-]
That’s wild. Are you at a university that bans using the OpenAI APIs directly?
dalemhurley 8 hours ago [-]
This is why we as an industry have spent so much effort optimising the code generation process with things like skills, rules, tests, reviews, lints, agentic loops with feedback and sub-agents, and the code-runners. It is not just LLMs building code, it is an eco-system collaborating together.
I would agree too that as the codebase grows the LLM struggles more and more with generating code. It is probably misaligned incentives, it wants to complete the isolated task without too much context consumed, at the POC it can consume most of the app, by about 30K lines of code it is quite complex code base to navigate.
ElenaDaibunny 3 hours ago [-]
Fragility compounds fast when you add visual grounding to the loop. Code agents at least get structured feedback from the compiler.
pianopatrick 11 hours ago [-]
I think someone is going to figure out a framework for using LLMs for coding.
A framework would use static code checking tools to force an architecture on to LLMs instead of trying to do so in markdown.
I don't know exactly what it will look like but for example I could imagine a Java Framework where the LLM could only create subclasses of certain classes.
cheevly 1 hours ago [-]
A lot of us have been doing this for over a year now.
KronisLV 10 hours ago [-]
> For end-users, this dichotomy implies that agents are reliable for rapid prototyping but remain unreliable for production-grade backend development.
Time to start writing linting tools that check the architecture and spoon feed the LLM what exactly it's doing wrong.
(I also wrote a simple linter for architecture/code checks that aren't well encapsulated by ones that just focus on individual files, that uses Go + goja to write rules in ECMAScript and parallelize the read only ones and also allow ones that change files as necessary, in addition to something like Ruff / Oxlint / Oxfmt / whatever is present in each stack; though it's is still in development and not as good of a focused example as ArchUnit is)
If we write software specification docs, bother describing how it evolves with ADRs, enforce code style automatically and require certain test coverage automatically (or at least should), why couldn't we go a step further, formalize those specs and ensure that any new code is also up to snuff? I don't think that's any more of a job for an LLM, than telling it how it should format code is. Also, I'm in the camp that believes that at least many of your ORM mappings and similar stuff should be the output of codegen, since you've already gone through the trouble of describing the schema/migrations to get there.
I don't think this would be only good for LLMs, though - I've seen projects that have like 3 different audit systems built in, not because of some fancy business requirement, but rather cause the devs either didn't know about the previous one(s) or just didn't feel like following what should have been the pre-established conventions, even when there were docs in place (nobody read those).
leecommamichael 15 hours ago [-]
These things don’t think. We’re going to have to reiterate this for a long time, I fear.
sheeshkebab 14 hours ago [-]
…but they reason well enough given enough context (using their matmuls).
noosphr 14 hours ago [-]
To this day frontier models think that A and not B means A and B when the sentence gets pushed far enough back in their context window. The context length that model can reason over without obvious errors is much smaller than the advertised context. Between a 1/4th to a 1/20th what is advertised on the tin.
Npovview 13 hours ago [-]
Do you also happen to remember what you ate last thrusday?
ethin 4 hours ago [-]
Do you have a point? Because last time I checked, AIs were supposed to be better than us fragile faulty humans, and weren't designed to emulate us and all our faults.
UncleEntity 11 hours ago [-]
"If you have a question look in the specification for the answer and don't just guess" seems a fairly important thing to remember for more than a couple of minutes...
Npovview 10 hours ago [-]
I had a coding session where I was doing stuff across two repositories. And CC forgot in exactly which repository a particular file was so it was grepping the parent directory. I just asked it to write all important key-value pairs which it thinks are important to a file and it never did parent directory grepping.
leecommamichael 13 hours ago [-]
Is that the same gap as what you’re responding to? To me, it seems his critique is about advertised capability and logical statements, and your rhetorical(?) question is about memory.
antonvs 11 hours ago [-]
Critiques like this tend to focus very hard on what models can't do. It's true, they have limitations.
But they're also superhuman in so many other ways. It's valid to point out limitations, but that doesn't support the conclusion that models are not incredibly powerful and capable of the functional equivalent of reasoning at human or superhuman levels in many scenarios.
noosphr 7 hours ago [-]
They may be better than humans at reasoning but they are substantially worse than the first generation logic programs from the 1950s.
cheevly 1 hours ago [-]
These types of comments help demonstrate first-hand how human reasoning stacks up against what an LLM would say in this situation.
emp17344 15 hours ago [-]
There is now a trillion-dollar industry bent to the task of convincing people these things can think. It’s gonna cause some damage.
suprfnk 13 hours ago [-]
I don't think they think. I still use them a lot despite that, because they are very powerful parameterised code generators.
akomtu 12 hours ago [-]
There is a movie, Gold (2016), about a fake gold mine. One of its founders is a true believer: he found a few chunks of gold and started digging for more. The other founder is a nihilist: he realised that there is no gold there, but who cares if he makes the investors believe? So he does, and almost sells the company for $300M.
In our story, investors are mining intelligence from GPUs, and they truly believe they are one inch from discovering the biggest goldmine in history. But GPUs, unlike a goldmine, cannot be inspected for traces of gold by independent contractors. To keep the hype up, the nihilists in our story dig up cheap gold-looking metals from time to time and tell investors that with a bit of alchemy - agentic workflows, etc. - those metals can be magically turned into gold.
Investors will keep digging until the end of the age, or until they run out of money.
11 hours ago [-]
bob1029 14 hours ago [-]
> Our findings reveal a phenomenon of constraint decay: as structural requirements accumulate, agent performance exhibits a substantial decline.
I have exactly the inverse findings on my end. The bigger and more legacy the codebase, the more accurate the patches become.
The harness itself seems to be the most important part. I use a recursive loop that primes the root context based on the user prompt each time. My agent will often make over 100 tool calls to sql and git before it finally decides to apply a patch. If I was greenfield, there would be nothing to query or constrain against.
richardlblair 14 hours ago [-]
I find the same. We have abstractions with multiple concrete implementations, examples of patterns and examples of anti patterns.
I usually find I can achieve 90% of the outcome I'm trying to achieve. I use sonnet for planning, qwen for coding, sonnet for review.
xcjsam 14 hours ago [-]
The harness mattering more than the model lines up with my experience too. What this paper measures is within-turn constraint decay. The version that bites in multi-agent setups is across-session — the architectural rules an agent wrote down on Monday don't reach the agent making the next change on Tuesday.
rrook 11 hours ago [-]
As a codebase grows, divergent structural emergence from incidental(lang and lib) details results in prolonged complexity costs. I'm working on a language that enforces structure for agents: https://github.com/hale-lang/hale
rbbydotdev 14 hours ago [-]
This is interesting, anecdotally I have felt like I was having better luck with raw sqlite than using an ORM in a recent typescript project, using raw sqlite queries vs drizzle
oulipo2 13 hours ago [-]
Exactly why you can't remove humans in the loop to assess that the solution is not only correct (which LLMs are quite bad at, once concurrency, logic, etc are involved), but also elegant, maintainable, etc
phrotoma 14 hours ago [-]
"constraint decay" isn't this just another name for the (already well understood) idea of "context rot"?
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spacedoutman 13 hours ago [-]
This research is useless and nearly all other LLM research is too.
gpt 5.2 is the strongest model they tested, a nearly 6 month old model.
Traditional research can not keep up.
abujazar 10 hours ago [-]
Agreed. As Simon Willison points out, November 2025 was a a critical months because that's pretty much when coding agents became «good enough», eliminating most of the problems pointed out in this study.
acgourley 13 hours ago [-]
I disagree, their findings should generalize to the frontier. Even if the latest can deal with the extra complexity, it stands to reason it will take more tokens to do less. This could be a useful insight into the next generation of evals.
That said, the limitations are kind of obvious and are starting to show in some of my projects, and this article seems to confirm my suspicions. If it's just confirmation bias or not, I can't say yet.
In my experience, for anything complex enough, I have to start adding more and more constraints, style guides, corner cases, error handling, optimization guidelines and all this good stuff to my Markdown specifications, rules and skills. At some point this starts to look like we're all just moving complexity from the more formal and deterministic world of programming languages to the informal and non-deterministic world of natural language. The writing speed gains are enormous, yeah, and business sees this as productivity gains, of course - and we do it because the pressure for increased productivity is there, as it's always been; yet the trade off seems to be clear and a lot of people are just ignoring it.
It's like using a compiler that generates semantically different code every time you run it. Basically like compiling a program that's full of UB but "seems to work" most of the time.
> business sees this as productivity gains
Back to LoC/s as a measure of "productivity."
IMO this doesn’t follow from what OP wrote. I personally measure it with a more abstract “how long does it take me to ship something that is useful in production and solving a real problem” and the increase in speed there has been massive for me. But of course I’m not a bigbrain 10x coder that is doing bleeding edge novel stuff like most people here, so gains might be more obvious for me than for others.
llms cannot generate anything novel.
So basically 90% of programming in an enterprise environment? lol. Sounds useful to me...
Code doesn't need to be novel to be useful. There's a reason why design patterns are a thing in software.
AI is not an abstraction.
Back to the original point, though: most software engineering work isn't novel. Most people are working on slightly different iterations of the same thing, but with the aim of different products. You can have completely different products that use nearly the same patterns as most other services.
To put it bluntly: we don't need AI to generate novel code for the vast majority of the software being built.
https://www.reddit.com/r/math/comments/1tj534d/openais_inter...
One major weakness of this study is that they didn’t fully test frontier models for cost reasons, so the specific performance results should be taken with a grain of salt. But the overall conclusion that models degrade when both behavior and architecture must be correct is interesting, and something to keep an eye on.
If you only have functional requirements, then in effect you're doing some form of program synthesis, and RL can optimize that very hard.
If you have a mixture of functional and non-functional requirements, you are basically giving the model an incomplete specification, and it must in some way guess at the user's intent to fill in the blanks. This is also why adding to the prompt examples of the style of code you want (hats off to antirez for this particular tip ;)) is phenomenally powerful.
To put it in practice: if you point claude/codex to a repository and you ask it to implement feature X using style guide Y, the code will probably work, but you can usually get better results by saying "do it in the style of this file, it was done well there".
It is not great at decision making or judgment calls that don't have a well defined spec or plan in place yet; like unofficial or unapproved tokens if you will. A lot of this stuff simply never has had specs as it has been internal to how companies work and their secret sauce.
The closest thing we have are governance and compliance policies due to legal/business needs requiring it so it's far more well documented than operational ones in how we work. It is more about the how versus the what here I guess is what I'm saying.
But yeah this is why it does great when there are tests, design systems, evals, and other artifacts to mirror. Far more reckless and unpredictable without these things, but still great for exploration and finding the data output you seek.
It's like when I see people feeding it a whole bunch of "best practices" and expect it to follow them. It won't. But you could ask it questions about the best practices all day long.
Idk, calling it "just text prediction " seems unfairly dismissive of this capability
at the end of the day, it presents a vector field and predicts the next vector. That’s literally the heart of intelligence just like assembly is the heart of execution. When playing table tennis, your brain is literally predicting seconds into the future to get your body into the right position.
But we aren’t discussing intelligence here. We are discussing how best to utilize that intelligence.
Ended up pointing Claude at a few sample files from our existing reporting, gave it read-only oauth access to the ERP and said “build a new report showing the cash by project as calculated by xxxx - yyyy + zzzz in the style of the existing reports” and it basically one-shot from there.
Kind of crazy and I built a bunch of redundant check-sums because I honestly didn’t think it would be able to replace like 6 workdays of effort for the 2 FTEs who generate that kind of thing manually every month but so far so good..
just dont break out a plan without also having it read the code again
The more it has to go on, the more it relies on repetition of what came before. It's also possible that authors start paying much less attention and put less effort into editing later chapters.
Despite the sheer volume on Amazon, LLMs are not at the point of writing well.
I mean, I spend more tokens having them clean up all the places they didn't follow the the plan (if I catch it) or implementing what came out of a 'complete and tested' previous plan where they just stop as soon as all the pathetic new test pass and you discover half of it isn't even there when trying to implement the next thing on top of it.
Though... I have been conducting an experiment, of sorts, where we've been cooking on these fairly complicated projects and I don't ever touch a single line of code, just yell at them a lot, and with suitable amounts of marijuana (they are very frustrating most of the time) it's been going pretty well. I also helps that they need to explain what they're doing to somebody fairly-baked -- maybe not such an HR friendly plan?
For example, if you apply "guardrails" to an image generator of about a year ago, all the people start looking alike. Story generators start using only a few standard names.
That was last year. Is it happening with the frontier models?
I’m not really interested in analysis of the weaknesses of such models because in my experience many weaknesses disappear entirely as models get stronger and reasoning effort is turned up. Especially if you tell them what you want them to do.
Also, it’s not surprising to learn that when more acceptance criteria are added the failure rate increases.
Even the best models have trouble adhering to stuff as mundane as rules for how to style generated code (indent this much, name things with these patterns, etc.). Even the most die-hard AI-first coder will admit to that kind of stuff being not unheard-of. Yet they still delude themselves into thinking that these models will follow a sufficiently detailed spec to the letter, every time.
I've only read the abstract of this one so far but it seems like this paper has zoomed in on programming with greater fidelity and shown a similar phenomenon. But not about long horizon tasks, more like "long style horizons" of larger sets of structural constraints.
[1] https://arxiv.org/abs/2604.15597
Discussion: https://news.ycombinator.com/item?id=48073246
sounds like an oxymoron of a claim.
"Write this code in a way that is readable and maintainable" is another example.
It's almost as though it's not about the Monet.
[1]: https://medium.com/@vishvananda/i-spent-2-billion-tokens-wri...
FWIW I've noticed this too. I've found that the agents/models have their own style, which is mostly summed up as overly verbose.
Additionally, the models are OK at modularization when given space to "plan" their implementation, but rarely decide that abstracting something would be helpful after the fact (i.e. after many iterations on a greenfield codebase or when being dropped into a legacy codebase).
This often leads to "god files" which, when pointed to by the user/architect, causes the models to correctly critique (humorously when they're the ones that wrote the code in the first place).
When using Codex/Claude Code with Go code I cannot count the times the agent does some change, runs a build to check for errors, find some and fix them.
https://docs.python.org/3/library/typing.html
"The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc."
Which third-party enforcement mechanism do you propose become the default?
There are many reasons for this. A big one is that many libraries are only partially typed at best, and dynamic types tend to propagate, weakening the guarantees you get from type checking.
Dynamic idioms in general, including something as common as string-indexed dictionaries, negate type checking. Runtime metaprogramming is the same. All of these things have equivalents in a good statically checked language, but Python doesn't follow those models.
Fundamentally, in Python static typing is an optional analysis layer over a dynamic language, and the consequences of that can't be fully mitigated. The result is a big difference in what types can guarantee.
> Dynamic idioms in general, including something as common as string-indexed dictionaries, negate type checking.
Do you have any proof of this? It hasn't been a problem in TypeScript, and I doubt it's an issue in Python
These two projects are on GitHub, you may search alexwwang/aristotle and alexwwang/tdd-pipeline to dive into the details or just ask your LLM to scan them to tell you the points you are interested in.
If there's a second thing the generative AI tools have shown beyond any doubt it's that many of the more modern (relatively speaking) "best practices" that have always been over-hyped and questionably-evidenced really do tend to produce worse results. LLMs take these methods to their logical conclusions and show us the end result much sooner. You can't just iterate your way to a solution when you don't even know what problem you're trying to solve. If you don't have a clear spec then you don't know what a correct product looks like. You need to invest time in reviewing code properly. If you don't keep the big picture in mind then the big picture becomes a mess.
Maybe one day the LLMs will leave me out of a job but at least I'll feel validated first!
If you apply those practice, then quickly you find yourself using the agent as merely a writing boost. And there’s an inflexion point when coding is no longer a bottleneck. Instead, you spend more time on thinking about design. You can see it in open source projects where most PRs are just a few line diffs. The bottleneck is knowledge and problem solving talent.
I don't know what that means but I have seen no evidence so far that if you don't apply those practices then your code will be anything other than unmanageable spaghetti if you leave AI to maintain it for long.
Coding has never been the bottleneck for good developers. Part of the reason for that is that good developers know how to isolate different aspects of a system and so keep each individual aspect relatively simple and self-contained. Another part is that good developers were already standardising and automating a lot of the grunt work. These traits are also advantageous for keeping generative AI on the right track and keeping its proposed changes manageable.
The most incredible combo I've seen lately is progressive enhancement of Razor Pages with javascript. With this arrangement the newest models tend to make a really good call on if something should happen server-side (cshtml) or on the client (js).
For a little complex changes, I always run codex (5.5-high) in planning mode first. I have linked various docs/{ARCHITECTURE,BACKEND-GUIDELINES,NESTJS-DI,..}.md etc. from AGENTS.md so they can quickly discover relevant docs at planning time, only if they are needed. No need to know react specific stuff when it's dealing with a backend problem for example. I typically blindly approve plans made by the agent with a fresh context, because that's as if I had prompted it. Works the best for me.
Using /goal however, it's really just constantly compacting and doing it's thing, of course it gets sloppy. If only there was a state machine that would transform tickets into a Planning Mode Prompt, then use, idk. guardian approvals (somehow a "Product Management Perspective Lens" approving or making changes to the plan) and then letting a less capable or less reasoning agent execute the plan, I think that would work the best.
Implementation phases very often go through 5-10 review and fix rounds to actually get the implementation to match the spec. It takes longer but that's what's necessary to get actually good results on long horizon tasks with detailed requirements. I'll be open sourcing it fully soon.
I would agree too that as the codebase grows the LLM struggles more and more with generating code. It is probably misaligned incentives, it wants to complete the isolated task without too much context consumed, at the POC it can consume most of the app, by about 30K lines of code it is quite complex code base to navigate.
A framework would use static code checking tools to force an architecture on to LLMs instead of trying to do so in markdown.
I don't know exactly what it will look like but for example I could imagine a Java Framework where the LLM could only create subclasses of certain classes.
Time to start writing linting tools that check the architecture and spoon feed the LLM what exactly it's doing wrong.
I reckon something like this would be good for every project out there: https://www.archunit.org/getting-started
They expand a bit more on the reasoning behind it: https://www.archunit.org/motivation
(I also wrote a simple linter for architecture/code checks that aren't well encapsulated by ones that just focus on individual files, that uses Go + goja to write rules in ECMAScript and parallelize the read only ones and also allow ones that change files as necessary, in addition to something like Ruff / Oxlint / Oxfmt / whatever is present in each stack; though it's is still in development and not as good of a focused example as ArchUnit is)
If we write software specification docs, bother describing how it evolves with ADRs, enforce code style automatically and require certain test coverage automatically (or at least should), why couldn't we go a step further, formalize those specs and ensure that any new code is also up to snuff? I don't think that's any more of a job for an LLM, than telling it how it should format code is. Also, I'm in the camp that believes that at least many of your ORM mappings and similar stuff should be the output of codegen, since you've already gone through the trouble of describing the schema/migrations to get there.
I don't think this would be only good for LLMs, though - I've seen projects that have like 3 different audit systems built in, not because of some fancy business requirement, but rather cause the devs either didn't know about the previous one(s) or just didn't feel like following what should have been the pre-established conventions, even when there were docs in place (nobody read those).
But they're also superhuman in so many other ways. It's valid to point out limitations, but that doesn't support the conclusion that models are not incredibly powerful and capable of the functional equivalent of reasoning at human or superhuman levels in many scenarios.
In our story, investors are mining intelligence from GPUs, and they truly believe they are one inch from discovering the biggest goldmine in history. But GPUs, unlike a goldmine, cannot be inspected for traces of gold by independent contractors. To keep the hype up, the nihilists in our story dig up cheap gold-looking metals from time to time and tell investors that with a bit of alchemy - agentic workflows, etc. - those metals can be magically turned into gold.
Investors will keep digging until the end of the age, or until they run out of money.
I have exactly the inverse findings on my end. The bigger and more legacy the codebase, the more accurate the patches become.
The harness itself seems to be the most important part. I use a recursive loop that primes the root context based on the user prompt each time. My agent will often make over 100 tool calls to sql and git before it finally decides to apply a patch. If I was greenfield, there would be nothing to query or constrain against.
I usually find I can achieve 90% of the outcome I'm trying to achieve. I use sonnet for planning, qwen for coding, sonnet for review.
gpt 5.2 is the strongest model they tested, a nearly 6 month old model.
Traditional research can not keep up.