It feels like a lot of people keep falling into the trap of thinking we’ve hit a plateau, and that they can shift from “aggressively explore and learn the thing” mode to “teach people solid facts” mode.

A week ago Scott Hanselman went on the Stack Overflow podcast to talk about AI-assisted coding. I generally respect that guy a lot, so I tuned in and… well it was kind of jarring. The dude kept saying things in this really confident and didactic (teacherly) tone that were months out of date.

In particular I recall him making the “You’re absolutely right!” joke and asserting that LLMs are generally very sycophantic, and I was like “Ah, I guess he’s still on Claude Code and hasn’t tried Codex with GPT 5”. I haven’t heard an LLM say anything like that since October, and in general I find GPT 5.x to actually be a huge breakthrough in terms of asserting itself when I’m wrong and not flattering my every decision. But that news (which would probably be really valuable to many people listening) wasn’t mentioned on the podcast I guess because neither of the guys had tried Codex recently.

And I can’t say I blame them: It’s really tough to keep up with all the changes but also spend enough time in one place to learn anything deeply. But I think a lot of people who are used to “playing the teacher role” may need to eat a slice of humble pie and get used to speaking in uncertain terms until such a time as this all starts to slow down.

I used to get made up APIs in functions, now I get them in modules. I used to get confidently incorrect assertions in files now I get them across codebases.

Hell, I get poorly defined APIs across files and still get them between functions. LLMs aren't good at writing well defined APIs at any level of the stack. They can attempt it at levels of the stack they couldn't a year ago, but they're still terrible at it unless the problem is so well known enough that they can regurgitate well reviewed code.

LLMs are bad at creating abstraction boundaries since inception. People have been calling it out since inception. (Heck, even I got a twitter post somewhere >12 months old calling that out, and I'm not exactly a leading light of the effort)

It is in no way size-related. The technology cannot create new concepts/abstractions, and so fails at abstraction. Reliably.

I agree somewhat, but more when it comes to its use of logic - it only gleans logic from human language which as we know is a fucking mess.

I've commented before on my belief that the majority of human activity is derivative. If you ask someone to think of a new kind of animal, alien or random object they will always base it off things that they have seen before. Truly original thoughts and things in this world are an absolute rarity and the majority of supposed original thought riffs on what we see others make, and those people look to nature and the natural world for inspiration.

We're very good at taking thing a and thing b and slapping them together and announcing we've made something new. Someone please reply with a wholly original concept. I had the same issue recently when trying to build a magic based physics system for a game I was thinking of prototyping.

better mental model: it's a lossy compression of human knowledge that can decompress and recombine in novel (sometimes useful, sometimes sloppy) ways.

classical search simply retrieves, llms can synthesize as well.

This is not true.

Calling it "just search" is like calling a compiler "just string manipulation". Not false, but aggressively missing the point.

  > Once you understand its just search, you can get really good results.

What's problematic with these types of claims is that they just come off as calling anyone who thinks differently dumb. It's as disconnected as saying "It's intuitive" in one breath and "You're holding it wrong" in another. It's a bad mindset to be in as an engineer because someone presents a problem and instead of trying to address it is dismissed. If someone is holding it wrong, it probably isn't intuitive[0]. Even if they can't explain the problem correctly, they are telling you a problem exists[1]. That's like 80% of the job of an engineer: figuring out what the actual problem is.

As maybe an illustrative example people joke that a lot of programming is "copy pasting from stack overflow". We all know the memes. There's definitely times where I've found this to be a close approximation to writing an acceptable program. But there's many other times where I've found that to be far from possible. There's definitely a strong correlation to what type of programming I'm doing, as in what kind of program I'm writing. Honestly, I find this categorical distinction not being discussed enough with things like LLMs. Yet, we should expect there to be a major difference. Frankly, there are just different amounts of information on different topics. Just like how LLMs seem to be better with more common languages like Python than less common languages (and also worse at just more complicated languages like C or Rust).

[0] You cannot make something that's intuitive to all people. But you can make it intuitive for most people. We're going to ignore the former case because the size should be very small. If 10% of your users are "holding it wrong" then the answer is not "10% of your users are absolute morons" it is "your product is not as intuitive as you think." If 0.1% of your users are "holding it wrong" then well... they might be absolute morons.

[1] I think I'm not alone in being frustrated with the LLM discourse as it often feels like people trying to gaslight me into believing the problems I experience do not exist. Why is it so surprising that people have vastly differing experiences? *How can we even go about solving problems if we're unwilling to acknowledge their existence?*

It’s a bit like the shift from film to digital in one very specific sense: the marginal cost of trying again virtually collapsed. When every take cost money and setup time, creators pre-optimized in their head and often never explored half their ideas. When takes became cheap, creators externalized thought as they could try, look, adjust, and discover things they wouldn’t otherwise. Creators could wander more. They could afford to be wrong because of not constantly paying a tax for being clumsy or incomplete, they became more willing to follow a hunch and that's valuable space to explore.

Digital didn’t magically improve art, but it let many more creatives enter the loop of idea, attempt and feedback. LLMs feel similar: they don’t give you better ideas by themselves, but they remove the friction that used to stop you from even finding out whether an idea was viable. That changes how often you learn, and how far you’re willing to push a thought before abandoning it. I've done so many little projects myself that I would have never had time for and feel that I learned something from it, of course not as much if I had all the pre LLM friction, but it should still count for something as I would never have attempted them without this assistance.

Edit: However, the danger isn’t that we’ll have too many ideas, it’s that we’ll confuse movement with progress.

When friction is high, we’re forced to pre-compress thought, to rehearse internally, to notice contradictions before externalizing them. That marination phase (when doing something slowly) does real work: it builds mental models, sharpens the taste and teaches us what not to bother to try. Some of that vanishes when the loop becomes cheap enough that we can just spray possibilities into the world and see what sticks.

A low-friction loop biases us toward breadth over depth. We can skim the surface of many directions without ever sitting long enough in one to feel its resistance. The skill of holding a half formed idea in our head, letting it collide with other thoughts, noticing where it feels weak, atrophies if every vague notion immediately becomes a prompt.

There’s also a cultural effect. When everyone can produce endlessly, the environment fills with half-baked or shallow artifacts. Discovery becomes harder as signal to noise drops.

And on a personal level, it can hollow out satisfaction. Friction used to give weight to output. Finishing something meant you had wrestled with it. If every idea can be instantiated in seconds, each one feels disposable. You can end up in a state of perpetual prototyping, never committing long enough for anything to become yours.

So the slippery slope is not laziness, it is shallowness, not that people won’t think, but people won’t sit with thoughts. The challenge here is to preserve deliberate slowness inside a world that no longer requires it: to use the cheap loop for exploration, while still cultivating the ability to pause, compress, and choose what deserves to exist at all.

> I feel that I'm learning faster

Yes, you are feeling that. But is that real? If I take all LLMs from you right now, is your current you still better than your pre-LLM you? When I dream I feel that I can fly and as long as I am dreaming, this feeling is true. But the subject of this feeling never was.

> Being able to type quickly and accurately reduces

LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate.

> I feel that I'm learning faster because I'm not tripping over silly little things.

I'm curious: what have you actually learned from using LLMs to generate code for you? My experience is completely the opposite. I learn nothing from running generated code, unless I dig in and try to understand it. Which happens more often than not, since I'm forced to review and fix it anyway. So in practice, it rarely saves me time and energy.

I do use LLMs for learning and understanding code, i.e. as an interactive documentation server, but this is not the use case you're describing. And even then, I have to confirm the information with the real API and usage documentation, since it's often hallucinated, outdated, or plain wrong.

My experience with Claude (and other agents, but mostly Claude) is such a mixed bag. Sometimes it takes a minimal prompt and 20 minutes later produce a neat PR and all is good, sometimes it doesn't. Sometimes it takes in a large prompt (be it your own prompt, created by another LLM or by plan mode) and also either succeed and fail.

For me, most of the failure cases are where Claude couldn't figure something out due to conflicting information in context and instead of just stopping and telling me that it tries to solve in entirely wrong way. Doesn't help that it often makes the same assumptions as I would, so when I read the plan it looks fine.

Level of effort also hard to gauge because it can finish things that would take me a week in an hour or take an hour to do something I can in 20 minutes.

It's almost like you have to enforce two level of compliance: does the code do what business demands and is the code align with codebase. First one is relatively easy, but just doing that will produce odd results where claude generated +1KLOC because it didn't look at some_file.{your favorite language extension} during exploration.

Or it creates 5 versions of legacy code on the same feature branch. My brother in Christ, what are you trying to stay compatible with? A commit that about to be squashed and forgotten? Then it's going to do a compaction, forget which one of these 5 versions is "live" and update the wrong one.

It might do a good junior dev work, but it must be reviewed as if it's from junior dev that got hired today and this is his first PR.

> I don't think we'll get to the point where all you have is a CEO and a massive Claude account but it's not completely science fiction the more I think about it.

At that point, why do you even need the CEO?

> In my experience Claude is like a "good junior developer"

We've been saying this for years at this point. I don't disagree with you[1], but when will these tools graduate to "great senior developer", at the very least?

Where are the "superhuman coders by end of 2025" that Sam Altman has promised us? Why is there such a large disconnect between the benchmarks these companies keep promoting, and the actual real world performance of these tools? I mean, I know why, but the grift and gaslighting are exhausting.

[1]: Actually, I wouldn't describe them as "good" junior either. I've worked with good junior developers, and they're far more capable than any "AI" system.

(Author here)

> I'm not entirely convinced by the anecdote here where Claude wrote "bad" React code

Yeah, that's fair - a friend of mine also called this out on Twitter (https://x.com/konstiwohlwend/status/2010799158261936281) and I went into more technical detail about the specific problem there.

> I've seen Claude make mistakes like that too, but then the moment you say "you can modify the calling code as well" or even ask "any way we could do this better?" it suggests the optimal solution.

I agree, but I think I'm less optimistic than you that Claude will be able to catch its own mistakes in the future. On the other hand, I can definitely see how a ~more intelligent model might be able to catch mistakes on a larger and larger scale.

> I expect that adding a CLAUDE.md rule saying "always look for more efficient implementations that might involve larger changes and propose those to the user for their confirmation if appropriate" might solve the author's complaint here.

I'm not sure about this! There are a few things Claude does that seem unfixable even by updating CLAUDE.md.

Some other footguns I keep seeing in Python and constantly have to fix despite CLAUDE.md instructions are:

- writing lots of nested if clauses instead of writing simple functions by returning early

- putting imports in functions instead of at the top-level

- swallowing exceptions instead of raising (constantly a huge problem)

These are small, but I think it's informative of what the models can do that even Opus 4.5 still fails at these simple tasks.

> My guess is that Claude is trained to bias towards making minimal edits to solve problems.

I don't have the same feeling. I find that claude tends to produce wayyyyy too much code to solve a problem, compared to other LLMs.

I expect that adding instructions that attempt to undo training produces worse results than not including the overbroad generalization in the training in the first place. I think the author isn’t making a complaint they’re documenting a tradeoff.

Definitely, The training parameters encourage this. The AI is actually deliberately also trying to trick you and we know for that for a fact.

Problems with solutions too complicated to explain or to output in one sitting are out of the question. The AI will still bias towards one shot solutions if given one of these problems because all the training is biased towards a short solution.

It's not really practical to give it training data with multi step ultra complicated solutions. Think about it. The thousands of questions given to it for reinforcement.... the trainer is going to be trying to knock those out as efficiently as possible so they have to be readable problems with shorter readable solutions. So we know AI biases towards shorter readable solutions.

Second, Any solution that tricks the reader will pass training. There is for sure a subset of questions/solution pairs that meet this criteria by definition because WE as trainers simply are unaware we are being tricked. So this data leaks into the training and as a result AI will bias towards deception as well.

So all in all it is trained to trick you and give you the best solution that can fit into a context that is readable in one sitting.

In theory we can get it to do what we want only if we had perfect reinforcement data. The reliability we're looking for seems to be just right over this hump.

Well yes but the wider point is that it takes new Human skills to manage them - like a pair of horses so to speak under your bridle

When it comes down to it these AI tools are like going to power tools or machines from the artisanal era

- like going from surgical knife to a machine gun- so they operate at a faster pace without comprehending like humans - and without allowing humans time to comprehend all side effects and massive assumptions they make on every run in their context window

humans have to adapt to managing them correctly and at the right scale to be effective and that becomes something you learn

I don't know how someone can look at what you build and conclude LLMs are still google search. It boggles the mind how much hatred people have for AI to the point of self deception. The evidence is placed right in front of you and on your lap with that link and people still deny it.

your github repo was highly entertaining. thanks for make my day a bit brighter:)

Well I feel like this is because a better system would distill such learning into tokens not associated with a human language and that that could represent logic better than using English etc for it.

I don't have the GPUs or time to experiment though :(

(Author here)

I have! I agree it's very good at applying abstractions, if you know exactly what you want. What I notice is that Claude has almost no ability to surface those abstractions on its own.

When I started having it write React, Claude produced incredibly buggy spaghetti code. I had to spend 3 weeks learning the fundamentals of React (how to use hooks, providers, stores, etc.) before I knew how to prompt it to write better code. Now that I've done that, it's great. But it's meaningful that someone who doesn't know how to write well-abstracted React code can't get Claude to produce it on their own.