A friend worked in an audiophile shop during his physics master and he'd swear the customer base was the most gullible bunch he ever saw... And mostly unswayable by rational arguments.
I suspect some of that disconnect is because hearing itself isn’t standardized. Differences in frequency perception, hearing loss, and training can make two people genuinely hear different things.
Of course people have different hearing, but the audiophile market is overflowing with snake-oil stuff like 'oxygen free copper' cables to 'acoustic resonator discs'.
Nobody's proven any of that stuff results in better sound quality (or even different quality after you graduate from junk stuff to reasonable equipment). Seems like an awfully expensive way of experiencing the placebo effect to me.
I know someone who spent upwards of $10k on a single 3-foot HDMI cable that was 'infused with Peruvian copper'. He says it makes the colors "more true".
It’s also possible that people more experienced, knowledgable and skilled than you can see fundamental flaws in using LLMs for software engineering that you cannot. I am not including myself in that category.
I’m personally honestly undecided. I’ve been coding for over 30 years and know something like 25 languages. I’ve taught programming to postgrad level, and built prototype AI systems that foreshadowed LLMs, I’ve written everything from embedded systems to enterprise, web, mainframes, real time, physics simulation and research software. I would consider myself an 7/10 or 8/10 coder.
A lot of folks I know are better coders. To put my experience into context: one guy in my year at uni wrote one of the world’s most famous crypto systems; another wrote large portions of some of the most successful games of the last few decades. So I’ve grown up surrounded by geniuses, basically, and whilst I’ve been lectured by true greats I’m humble enough to recognise I don’t bleed code like they do. I’m just a dabbler. But it irks me that a lot of folks using AI profess it’s the future but don’t really know anything about coding compared to these folks. Not to be a Luddite - they are the first people to adopt new languages and techniques, but they also are super sceptical about anything that smells remotely like bullshit.
One of the most wise insights in coding is the aphorism“beware the enthusiasm of the recently converted.” And I see that so much with AI. I’ve seen it with compilers, with IDEs, paradigms, and languages.
I’ve been experimenting a lot with AI, and I’ve found it fantastic for comprehending poor code written by others. I’ve also found it great for bouncing ideas. And the code it writes, beyond boiler plate, is hot garbage. It doesn’t properly reason, it can’t design architecture, it can’t write code that is comprehensible to other programmers, and treating it as a “black box to be manipulated by AI” just leads to dead ends that can’t be escaped, terrible decisions that will take huge amounts of expert coding time to undo, subtle bugs that AI can’t fix and are super hard to spot, and often you can’t understand their code enough to fix them, and security nightmares.
Testing is insufficient for good code. Humans write code in a way that is designed for general correctness. AI does not, at least not yet.
I do think these problems can be solved. I think we probably need automated reasoning systems, or else vastly improved LLMs that border on automated reasoning much like humans do. Could be a year. Could be a decade. But right now these tools don’t work well. Great for vibe coding, prototyping, analysis, review, bouncing ideas.
Yes, that is the analogy I am making. People argued that bicycles (a tool for humans to use) could not possibly work - even as people were successfully using them.
People use drugs as well but I'm not sure I'd call that successful use of chemical compounds without further context. There are many analogies one can apply here that would be equally valid.
Please tell me which one of the headings is not about increased usage o LLMs and derived tools and is about some improvement in the axes of reliability or or any kind of usefulness.
> The coding agent heading. Claude Code and tools like it represent a huge improvement in what you can usefully get done with LLMs.
Does it? It's all prompt manipulation. Shell script are powerful yes, but not really huge improvement over having a shell (REPL interface) to the system. And even then a lot of programs just use syscalls or wrapper libraries.
> can try the code, see that it doesn't work and fix the problem.
If you mean correct often enough that you can expect it to be a productive assistant that helps solve all sorts of problems faster than you could solve them without it, and which makes mistakes infrequently enough that you waste less time fixing them than you would doing everything by yourself then yes, it's plenty reliable enough now.
You're welcome to try the LLM's yourself and come up with your own conclusions. By what you've posted it doesn't look like you've tried the anything in the last 2 years. Yes LLM's can be annoying, but there has been progress.
I know it seems like forever ago, but claude code only came out in 2025.
Its very difficult to argue the point that claude code:
1) was a paradigm shift in terms of functionality, despite, to be fair, at best, incremental improvements in the underlying models.
2) The results are an order of magnitude, I estimate, better in terms of output.
I think its very fair to distill “AI progress 2025” to: you can get better results (up to a point; better than raw output anyway; scaling to multiple agents has not worked) without better models with clever tools and loops. (…and video/image slop infests everything :p).
Did more software ship in 2025 than in 2024? I'm still looking for some actual indication of output here. I get that people feel more productive but the actual metrics don't seem to agree.
I'm still waiting for the Linux drivers to be written because of all the 20x improvements that AI hypers are touting. I would even settle for Apple M3 and M4 computers to be supported by Asahi.
I am not making any argument about productivity about using AI vs. not using AI.
My point is purely that, compared to 2024, the quality of the code produced by LLM inference agent systems is better.
To say that 2025 was a nothing burger is objectively incorrect.
Will it scale? Is it good enough to use professionally? Is this like self driving cars where the best they ever get is stuck with an odd shaped traffic cone? Is it actually more productive?
Who knows?
Im just saying… LLM coding in 2024 sucked. 2025 was a big year.
It's just one-shot AI slop - literally, the prompt was 'make a web based version of [github url of this project]' and it spat this out. It appears to work fine.
I'll keep it up for a couple of months and then it'll be auto-deleted, no sense in keeping it around longer than that.
What did you like more about the re-write of the Antimemetics book? I strongly preferred a few things about the original, especially what happens to (I think?) Bart Hughes and the germ.
Does it allow you to inject js, modify the DOM, and most crucially monitor/modify network requests? I do those things in probably 95-99% of the time I reach for playwright mcp in claude, and from the "For Agents" part of the README, it seems like all this can do is click/type/screenshot?
Thanks. I would love to understand what people are doing with Playwright that doesn't involve those things. I really can't recall ever using it where that wasn't what I was doing. I use it letting Claude fix things. You can't fix what you can't see! What else are people using it for? Obviously there must be a (very popular!) use case for "just clicking", but I can't seem to imagine it.
To me doing network interception in browser driven tests is a smell like that. Unless you’re running vs a full mocked server (like MSW).
I’m a big fan of testing exactly like a user. Users don’t use network intercepts, timeouts, etc. All of my most reliable tests assert on DOM state. If the user doesn’t see it, don’t assert on it.
I guess the issue is that real world does smell terribly. I wish I could just have the perfect World like my side projects always have, but not the case with the commercial ones making money.
In my experience, we've used playwright significantly for unit/integration tests combining it with react-testing-library to verify individual components and also whole (mocked, we used something else that I can't seem to remember for E2E tests) flows within that React application
Just like all those normal people want rid of their bloated day-to-day monster of a web and therefore go and do something like, say, install an ad blocker?
Oh right. 99% of people don't do even that, much less switch their life over to entirely new websites.
In 2025, depending on the study, it is said that 31.5~42.7% of internet users now block ads. Nearly one-third of Americans (32.2%) use ad blockers, with desktop leading at 37%.
reply