[bangaroo]

every time i say "the tech seems to be stagnating" or "this model seems worse" based on my observations i get this response. "well, it's better for other use cases." i have even heard people say "this is worse for the things i use it for, but i know it's better for things i don't use it for."

i have yet to hear anyone seriously explain to me a single real-world thing that GPT5 is better at with any sort of evidence (or even anecdote!) i've seen benchmarks! but i cannot point to a single person who seems to think that they are accomplishing real-world tasks with GPT5 better than they were with GPT4.

the few cases i have heard that venture near that ask may be moderately intriguing, but don't seem to justify the overall cost of building and running the model, even if there have been marginal or perhaps even impressive leaps in very narrow use cases. one of the core features of LLMs is they are allegedly general-purpose. i don't know that i really believe a company is worth billions if they take their flagship product that can write sentences, generate a plan, follow instructions and do math and they are constantly making it moderately better at writing sentences, or following instructions, or coming up with a plan and it consequently forgets how to do math, or becomes belligerent, or sycophantic, or what have you.

to me, as a user with a broad range of use cases (internet search, text manipulation, deep research, writing code) i haven't seen many meaningful increases in quality of task execution in a very, very long time. this tracks with my understanding of transformer models, as they don't work in a way that suggests to me that they COULD be good at executing tasks. this is why i'm always so skeptical of people saying "the big breakthrough is coming." transformer models seem self-limiting by merit of how they are designed. there are features of thought they simply lack, and while i accept there's probably nobody who fully understands how they work, i also think at this point we can safely say there is no superintelligence in there to eke out and we're at the margins of their performance.

the entire pitch behind GPT and OpenAI in general is that these are broadly applicable, dare-i-say near-AGI models that can be used by every human as an assistant to solve all their problems and can be prompted with simple, natural language english. if they can only be good at a few things at a time and require extensive prompt engineering to bully into consistent behavior, we've just created a non-deterministic programming language, a thing precisely nobody wants.

[48terry]

The simple explanation for all this, along with the milquetoast replies kasey_junk gave you, is that to its acolytes, AI and LLMs cannot fail, only be failed.

If it doesn't seem to work very well, it's because you're obviously prompting it wrong.

If it doesn't boost your productivity, either you're the problem yourself, or, again, you're obviously using it wrong.

If progress in LLMs seems to be stagnating, you're obviously not part of the use cases where progress is booming.

When you have presupposed that LLMs and this particular AI boom is definitely the future, all comments to the contrary are by definition incorrect. If you treat it as a given that this AI boom will succeed (by some vague metric of "success") and conquer the world, skepticism is basically a moral failing and anti-progress.

The exciting part about this belief system is how little you actually have to point to hard numbers and, indeed, rely on faith. You can just entirely vibe it. It FEELS better and more powerful to you, your spins on the LLM slot machine FEEL smarter and more usable, it FEELS like you're getting more done. It doesn't matter if those things are actually true over the long run, it's about the feels. If someone isn't sharing your vibes about the LLM slot machine, that's entirely their fault and problem.

[mwigdahl]

And on the other side, to detractors, AI and LLMs cannot ever succeed. There's always another goalpost to shift.

If it seems to work well, it's because it's copying training data. Or it sometimes gets something wrong, so it's unreliable.

If they say it boosts their productivity, they're obviously deluded as to where they're _really_ spending time, or what they were doing was trivial.

If they point to improvements in benchmarks, it's because model vendors are training to the tests, or the benchmarks don't really measure real-world performance.

If the improvements are in complex operations where there aren't benchmarks, their reports are too vague and anecdotal.

The exciting part about this belief system is how little you have to investigate the actual products, and indeed, you can simply rely on a small set of canned responses. You can just entirely dismiss reports of success and progress; that's completely due to the reporter's incompetence and self-delusion.

[kasey_junk]

Claude Sonnet 4.5 is _way_ better than previous sonnets and as good as Opus for the coding and research tasks I do daily.

I rarely use Google search anymore, both because llms got that ability embedded and the chatbots are good at looking through the swill search results have become.

[bangaroo]

"it's better at coding" is not useful information, sorry. i'd love to hear tangible ways it's actually better. does it still succumb to coding itself in circles, taking multiple dependencies to accomplish the same task, applying inconsistent, outdated, or non-idiomatic patterns for your codebase? has compliance with claude.md files and the like actually improved? what is the round trip time like on these improvements - do you have to have a long conversation to arrive at a simple result? does it still talk itself into loops where it keeps solving and unsolving the same problems? when you ask it to work through a complex refactor, does it still just randomly give up somewhere in the middle and decide there's nothing left to do? does it still sometimes attempt to run processes that aren't self-terminating to monitor their output and hang for upwards of ten minutes?

my experience with claude and its ilk are that they are insanely impressive in greenfield projects and collapse in legacy codebases quickly. they can be a force multiplier in the hands of someone who actually knows what they're doing, i think, but the evidence of that even is pretty shaky: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

the pitch that "if i describe the task perfectly in absolute detail it will accomplish it correctly 80% of the time" doesn't appeal to me as a particularly compelling justification for the level of investment we're seeing. actually writing the code is the simplest part of my job. if i've done all the thinking already, i can just write the code. there's very little need for me to then filter that through a computer with an overly-verbose description of what i want.

as for your search results issue: i don't entirely disagree that google is unusable, but having switched to kagi... again, i'm not sure the order of magnitude of complexity of searching via an LLM is justified? maybe i'm just old, but i like a list of documents presented without much editorializing. google has been a user-hostile product for a long time, and its particularly recent quality collapse has been well-documented, but this seems a lot more a story of "a tool we relied on has gotten measurably worse" and not a story of "this tool is meaningfully better at accomplishing the same task." i'll hand it to chatgpt/claude that they are about as effective as google was at directing me to the right thing circa a decade ago, when it was still a functional product - but that brings me back to the point that "man, this is a lot of investment and expense to arrive at the same result way more indirectly."

[theshrike79]

The biggest issue with Sonnet 4.5 is that it's chatty as fuuuck. It just won't shut up, it keeps producing massive markdown "reports" and "summaries" of every single minor change, wasting precious context.

With Sonnet 4 I rarely ran out of quota unexpectedly, but 4.5 chews through whatever little Anthropic gives us weekly.

[orwin]

Gpt5 isn't an improvement to me, but Claude sonnet4.5, handle terragrunt way, way better than the previous version did. It also go search AWS documentation by itself, and parse external documents way better. That's not LLM improvement, to be clear (except the terragrunt thing), I think it's improvement in data acquisition and a better inference engine. On react project it seems way, way less messy also, I have to use it more but the inference engine seems clearer. At least less prone to circular code, where it's stuck in a loop. It seems to be exiting the loop faster, even when the output isn't satisfactory (which isn't an issue to me, most of my prompt have more or less 'only write functions template, do not write the inside logic if it has to contain more than a loop', I fill the blanks myself)