It's an interesting review but I really dislike this type of techno-utopian determinism: "When models inevitably improve..." Says who? How is it inevitable? What if they've actually reached their limits by now?
To have their minds changed drastically, sure..
https://www.merriam-webster.com/dictionary/radical
Deciding that AI is going nowhere to suddenly deciding that coding agents are how they will work going forward is a radical change. That is what they meant.
If your second paragraph makes that reply irrelevant, are you saying the meaning was "Your use of 'radicalized' is technically correct but I still think you shouldn't have used it here"?
ChatGPT came out in Nov 2022. Attention Was All There Was in 2017, we were already 5 years in the past. Or 5 years of research to catch up to, and then from 2022 to now ... papers and research have been increasing exponentially. Even in if SOTA models were frozen, we still have years of research to apply and optimize in various ways.
Lately I spend all day post-training models for my product, and I want to say 99% of the research specific to LLMs doesn't reproduce and/or matter once you actually dig in.
We're getting exponentially more papers on the topics and they're getting worse on average.
Every day there's a new paper claiming an X% gain by post-training some ancient 8B parameter model and comparing it to a bunch of other ancient models after they've overfitted on the public dataset of a given benchmark and given the model a best of 5.
And benchmarks won't ever show it, but even ChatGPT 3.5-Turbo has better general world knowledge than a lot models people consider "frontier" models today because post-training makes it easy to cover up those gaps with very impressive one-prompt outputs and strong benchmark scores.
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It feels like things are getting stuck in a local maxima: we are making forward progress, the models are useful and getting more useful, but the future people are envisioning takes reaching a completely different goal post that I'm not at all convinced we're making exponential progress towards.
There maybe exponential number of techniques claiming to be ground breaking, but what has actually unlocked new capabilities that can't just as easily be attributed to how much more focused post-training has become on coding and math?
Test time compute feels like the only one and we're already seeing the cracks form in terms of its effect on hallucinations, and there's a clear ceiling for the performance the current iteration unlocks as all these models are converging on pretty similar performance after just a few model releases.
We are just back on track.
I just read Oracular Programming: A Modular Foundation for Building LLM-Enabled Software the other day.
We don't even have a new paradigm yet. I would be shocked that in 10 years I don't look back at this time of writing a prompt into a chatbot and then pasting the code into an IDE as completely comical.
The most shocking thing to me is we are right back on track to what I would have expected in 2000 for 2025. In 2019 those expectations seemed like science fiction delusions after nothing happening for so long.
It feels a bit like Halide, where the goal and the strategy are separated so that each can be optimized independently.
Those new paradigms are being discovered by hordes of vibecoders, myself included. I am having wonderful results with TDD and AI assisted design.
IDEs are now mostly browsers for code, and I no longer copy and paste with a chatbot.
Curious what you think about the Oracular paper. One area that I have been working on for the last couple weeks is extracting ToT for the domain and then using the LLM to generate an ensemble of exploration strategies over that tree.
LLMs have been continually improving for years now. The surprising thing would be them not improving further. And if you follow the research even remotely, you know they'll improve for a while, because not all of the breakthroughs have landed in commercial models yet.
It's not "techno-utopian determinism". It's a clearly visible trajectory.
Meanwhile, if they didn't improve, it wouldn't make a significant change to the overall observations. It's picking a minor nit.
The observation that strict prompt adherence plus prompt archival could shift how we program is both true, and it's a phenomenon we observed several times in the past. Nobody keeps the assembly output from the compiler around anymore, either.
There's definitely valid criticism to the passage, and it's overly optimistic - in that most non-trivial prompts are still underspecified and have multiple possible implementations, not all correct. That's both a more useful criticism, and not tied to LLM improvements at all.
It should be obvious to anyone who with a cursory interest in model training, you can't trust benchmarks unless they're fully private black-boxes.
If you can get even a hint of the shape of the questions on a benchmark, it's trivial to synthesize massive amounts of data that help you beat the benchmark. And given the nature of funding right now, you're almost silly not to do it: it's not cheating, it's "demonstrably improving your performance at the downstream task"
If the LLM hallucinates, then the code it produces is wrong. That wrong code isn't obviously or programmatically determinable as wrong, the agent has no way to figure out that it's wrong, it's not as if the LLM produces at the same time tests that identify that hallucinated code as being wrong. The only way that this wrong code can be identified as wrong is by the human user "looking closely" and figuring out that it is wrong.
You seem to have this fundamental belief that the code that's produced by your LLM is valid and doesn't need to be evaluated, line-by-line, by a human, before it can be committed?? I have no idea how you came to this belief but it certainly doesn't match my experience.
An agent lints and compiles code. The LLM is stochastic and unreliable. The agent is ~200 lines of Python code that checks the exit code of the compiler and relays it back to the LLM. You can easily fool an LLM. You can't fool the compiler.
I didn't say anything about whether code needs to be reviewed line-by-line by humans. I review LLM code line-by-line. Lots of code that compiles clean is nonetheless horrible. But none of it includes hallucinated API calls.
Also, from where did this "you seem to have a fundamental belief" stuff come from? You had like 35 words to go on.
The subtext is pretty obvious, I think: that standards, on message boards, are being set for LLM-generated code that are ludicrously higher than would be set for people-generated code.
The LLM can easily hallucinate code that will satisfy the agent and the compiler but will still fail the actual intent of the user.
> I review LLM code line-by-line. Lots of code that compiles clean is nonetheless horrible.
Indeed most code that LLMs generate compiles clean and is nevertheless horrible! I'm happy that you recognize this truth, but the fact that you review that LLM-generated code line-by-line makes you an extraordinary exception vs. the normal user, who generates LLM code and absolutely does not review it line-by-line.
> But none of [the LLM generated code] includes hallucinated API calls.
Hallucinated API calls are just one of many many possible kinds of hallucinated code that an LLM can generate, by no means does "hallucinated code" describe only "hallucinated API calls" -- !
I think at this point our respective points have been made, and we can wrap it up here.
The parent seems to be, in part, referring to "reward hacking", which tends to be used as a super category to what many refer to as slop, hallucination, cheating, and so on.
https://courses.physics.illinois.edu/ece448/sp2025/slides/le...
There is an obvious and categorical difference between the "bugs" that an LLM produces as part of its generated code, and the "bugs" that I produce as part of the code that I write. You don't get to conflate these two classes of bugs as though they are equivalent, or even comparable. They aren't.
That's not how I use it. I see hallucination as a very specific kind of mistake: one where the LLM outputs something that is entirely fabricated, like a class method that doesn't exist.
The agent compiler/linter loop can entirely eradicate those. That doesn't mean the LLM won't make plenty of other mistakes that don't qualify as hallucinations by the definition I use!
It's newts and salamanders. Every newt is a salamander, not every salamander is a newt. Every hallucination is a mistake, not every mistake is a hallucination.
https://simonwillison.net/2025/Mar/2/hallucinations-in-code/
This is a mistaken understanding. The person you responded to has written on these thoughts already and they used memorable words in response to this proposal:
> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you?
It should be obvious that one would read and verify the code before they commit it. Especially if one works on a team.
If you want a model that's getting better at helping you as a tool (which for the record, I do), then you'd say in the last 3 months things got better between Gemini's long context performance, the return of Claude Opus, etc.
But if your goal post is replacing SWEs entirely... then it's not hard to argue we definitely didn't overcome any new foundational issues in the last 3 months, and not too many were solved in the last 3 years even.
In the last year the only real foundational breakthrough would be RL-based reasoning w/ test time compute delivering real results, but what that does to hallucinations + even Deepseek catching up with just a few months of post-training shows in its current form, the technique doesn't completely blow up any barriers that were standing the way people were originally touting it.
Overall models are getting better at things we can trivially post-train and synthesize examples for, but it doesn't feel like we're breaking unsolved problems at a substantially accelerated rate (yet.)