Given this is the Netherlands and this appears to be a wealthy area, I'm quite sure they don't. Guessing they either have septic tanks or holding tanks that need to be pumped regularly.

We have known for a very long time that computers and machines are much faster than humans, more accurate, are scalable in certain ways that humans aren't, and they don't tire. I think most people who are not in the "AI cult" would agree that LLMs and modern generative AI are really just an extension of those faster/more accurate/more scalable and never tiring traits. But there does seem to be (and I'm sure folks much smarter than I have quantified this or described it better than I can) a fundamental difference in how humans think, especially as it applies to what true "understanding" really entails, and for the ability to think up truly novel and unique things that are not just a rejiggering/recombination of training data. I believe those skills really are at the heart of human cognition, and as impressive as LLMs are in replicating what this looks like, there are plenty of "LLM failure modes" where it's clear that LLMs lack a true understanding of concepts or the ability to generate useful, completely novel ideas.

I used to do a lot of interviews in my previous career, and I can't tell you the number of times I'd be interviewing someone who could talk a good game, but the second it got to writing code, if the world depended on them being able to write a simple correct loop, we'd all be dead. I know everyone shits on Lee code, and I agree the "brain teaser-y" nature of it doesn't often match real world development, but often times I'd tell folks almost exactly what the code needed to do, in English, and they still couldn't translate that to sensible code.

Last side note, when this topic comes up I feel some folks are really biking it down to the absurd "typing was never the hard part". Well, yeah, sure. But actually writing correct, maintainable, well-structured code is (was?) very much at least one of the hard parts.

For the vast part of my career I worked for good companies that I thought were comparatively very well managed, and I was especially fortunate that overall I think I had excellent bosses. But the reality of the Internet age and CICD in particular is that speed is much more important than quality. I don't even really disagree with the business imperative of "ship, ship, ship", but for people who really value their craft, it can be discouraging shipping stuff you know is always kinda half baked. I was definitely not a "gold plater" either, and time pressure was certainly a thing pre-Internet, but as you say mistakes were a lot more expensive then so there was more business rationale to ensure quality before a release.

And more directly, I believe good software engineering is really hard. I think it takes a certain mindset to begin with that many people just do not and will never possess, and it takes years of experience to get a good sense of what works and what doesn't, especially with respect to the entire relationship between code, business, people and teams. Yet we constantly see all this devaluation of the practice of software engineering ("code was always the easy part" - bull-fucking-shit), often times by our own practitioners of the craft.

The past 5-10 years or so saw me pretty beaten down though with software engineering, and AI was the final nail in the coffin that caused me to leave the profession in later middle age. I thought software as a business had "lost its way" from building great products with attention to detail to "how do we addict as many people as possible as quickly as possible". Think about the degradation in Apple software from say the "it just works" era to now.

With respect to AI, I'm not really against it, and I find it extremely valuable in my personal projects. I just feel in a large group/enterprise context that it's replaced a lot of tasks I actually enjoy doing with becoming an editor for what feels like a slightly inebriated junior developer. Or maybe it's better to say a junior developer on a mild amount of meth, because as you say this developer can churn out semi-but-not-fully-working code at an astonishing rate, and then I feel like it's often my job to mop the slop off the floor. Pass, not interested.

I feel lucky to have had my career during what I consider the golden age of software engineering, but I'd note I don't think that golden age lasted even a full career of one person.

1. A frontier model wrapper that gens the designs 2. A bunch of humans behind the scenes ("AI means Another Indian") actually reviewing and fixing the design.

That is, get to market quickly with people, gather lots of data, and hope that models can be refined eventually so less/no human review is needed.

I recently worked with Claude Opus 5 High to help me build a testing rig for some acoustic tests. There was a lot of back and forth with each time Claude giving me the stereotypical "Great catch! I see my mistake in the previous iteration..." responses.

I agree with the first part of that, but not necessarily the second. For a very long time in America good journalism was essentially paid for by classified ads. They were really two separate (but obviously related) businesses. Journalism could be more "walled off" from the money making side of things since classified ads were bringing in money hand over fist.

The Internet decimated the classified ads business, so now you see more sensational stories that try to make money in their own right. Even on the CNN website, there are ads at the bottom that are confusingly meant to look like stories. They're often embarrassingly dumb (literal "the one trick doctors don't want you to know" type of stuff), but I think that's what it's come to for news businesses to survive.

Before she transitioned, as a man she said she'd get lots of compliments and kudos from customers for basically just doing her job at Subway. As a woman, though, people were much more likely to complain and she was rarely complimented, and only if she went "above and beyond" her normal work duties.

The thing I loved about this is that, like the women who lose a lot of weight, she basically "acted as her own control". When it's different men and women you can argue over whether or not people behaved differently, but it's harder to do that (or at least it should be) if it's the same person.

Yes, "clickbait" was a thing even before people were clicking. "Yellow journalism" helped start the Spanish-American war over 125 years ago. But the pernicious effects on society of social media are way, way worse, if only because social media companies have way more data, and correspondingly more levers to act on that data. They run addiction machines that legacy media could only dream of, and they do it for the sole purpose of making money, all other consequences be damned.

I'm into a rather niche artistic pursuit, and I get lots of reels on this topic, and I noticed that the top comment on these reels is always the dumbest, most asinine comment imaginable. It's like if HN's algorithm were literally reversed. It's like this because of course these comments get lots of "engagement" because it's just rage bait. And all of Facebook is like this - they deliberately push the most rage inducing content because it's profitable.

Zuckerberg deserves all the opprobrium he gets and then some. I'm not necessarily arguing that legacy media is more "moral", but they simply never had the tech to tear society apart like Zuckerberg does.

That's not true. Even early on, most mathematicians doubted he had a proof. But I'd check out the history of the problem as described on Wikipedia. There was considerable research into the problem before Fermat, and general research into Diophantine equations had gone on for over a thousand years before Fermat. I have no doubt there would have been significant interest into solving the problem even if Fermat had never written his "I have a proof but it's too big to fit in the margin" note.

I think you have an odd definition of "responsible". Many (most?) theorems start out as conjectures, and I would strongly disagree that just because someone first formulated a problem that they're "responsible" for the eventual solution.

Opus 5 first built me a detailed plan, but a couple important details were either obviously wrong or felt unnecessary. I went back and forth asking for sources and more information probably like 4 times and every time it did the "in looking at things in more detail it appears my previous advice was incorrect" spiel. It just became exhausting at some point because it feels like it really lays bare how LLMs are just minimizing that loss function but don't actually "understand" anything. It was really useful as a search engine (it correlated some highly relevant source docs), but I just couldn't trust it to believe it was actually done at any step.

I don't know the details of her situation, what else she could have done to protect her investments, or how common an experience that was, but there were many comments in agreement under her post, so I'm just curious if it's widespread.

I think that's BS. User metrics are usually very explicitly defined (e.g. monthly or daily active users have always been clearly defined wherever I've worked, even if just for the sole reason that people collecting those numbers need to know what to count). User quality is definitely a gray area and estimating bot percentage has become increasingly difficult, but user metrics are not some ill-defined, fuzzy math notion.