Problem is that as a provider, if you are sure you are confident you’ll get hit by an outage at some point anyway, it’s actually better for you if a bunch of other big names are brought down at the same time.

Instead of “that one service sucked”, the story is “aws sucked”. If it happens too much people will more widely say “ok they suck for using aws”, but for now the transparency gets them treated more like being affected by an unavoidable external condition.

I’m grateful a lot of sites I like didn’t use aws, but I’m not exactly a common demographic and even I won’t know if she is the services even move or not until another such outage.

Those metrics aren’t any more trustworthy than their own subjective word anyway. If they wanted to say they took more time then they could delay at their whim anyway. If they said their production costs increased, then again, they could spend the money to fit the narrative. On those particular points objective evidence is so susceptible to being gamed that it isn’t really more valuable than their subjective reporting.

Numbers of subscribers/views could be a bit more informative, but then people inclined to disbelieve would claim it’s because of any number of other reasons not because of AI slop.

Killing in this case sounds like the content is becoming harder and harder to create, which they lay out the subjective case for, but that wouldn’t be exactly something they could use figures to present, since it’s so subjective.

The one point they might have been able to show with numbers would be the emergence of AI slop ‘infotainment animations’ diluting the audience, but that wasn’t exactly the biggest point of the video and it might be a bit early to be able to demonstrate statistically credible evidence on that one.

Also, presuming they are sincere and put in all that effort, they are competing with other sources that have no such discipline and they are able to flood the field and grab eyeballs faster than they could.

It’s so fun when it’s so specific about some detail with casual confidence that is based on absolutely nothing at all. I know ultimately it’s architecture is more akin to a predictive word generator, but it seems so much better.

Saw a clear demonstration and it is wild that the output is consistent, but at least in the model I saw being run, every word is generated without it having considered what the word after would be or what the general concept it is going for. For a human one has to already know the concept before he/she starts putting words to it, but at least the models I’ve seen explained with detail, it manages to assemble it word by word without knowing where it is trying to go in advance.

<blink>Welcome to my web page under construction</blink>

Oh man, I remember seeing things go from burned in subs to sub tracks with all kinds of nice functional markup and managed font color schemes and such to… Slapping white text with no border/shadow in the bottom middle of the video unconditionally without formatting…

Kind of miss how much effort went into fan subs, with notes explaining things that didn’t exactly translate without context…

Thing is both statements can be true.

Used appropriately and in the right context, LLMs can accelerate some select work.

But the hype level is ‘human replacement is here (or imminent, depending on if the company thinks the audience is willing to believe yet or not)’. Recently Anthropic suggested someone could just type ‘make a slack clone’ and it’ll all be done and perfect.

Mad Maze…

I will concur with the whole ‘llm keeps suggesting to reinvent the wheel’

And poorly. Not only did it not use a pretty basic standard library to do something, it’s implementation is generally crap. For example it offered up a solution that was hard coded to IPv4, and the context was very ipv6 heavy

A VP and his taking head sycophants at my work has not shut up about it. They went through some trouble to automatically measure employee use of the AI and made it a performance measure. So now we have it generate code so we don’t get fired and mostly throw it away.

I thought that as well and got some code from someone that left the company and asked it to comment it.

It did the obvious “x= 5 // assign 5 to x” crap comments and then it got to the actually confusing part and just skipped that mess entirely…

Yeah if I use it and it generatse more than 5 lines of code, now I just immediately cancel it out because I know it’s not worth even reading. So bad at repeating itself and falling to reasonably break things down in logical pieces…

With that I only have to read some of it’s suggestions, still throw out probably 80% entirely, and fix up another 15%, and actually use 5% without modification.

Yep, going through that at work, they hired several consultant companies and near as I can tell, they just asked employees how the company was screwing up, we largely said the same things we always say to executives, they repeated them verbatim, and executives are now praising the insight on how to fix our business…

Based on my experience with claude sonnet and gpt4/5… It’s a little useful but generally annoying and fails more often than works.

I do think moderate use still comes out ahead, as it saves a bunch of typing when it does work, but I still get annoyed at the blatantly stupid suggestions I keep having to decline.

I sometimes get up to five lines of viable code. Then upon occasion what should have been a one liner tries to vomit all over my codebase. The best feature about AI enabled IDE is the button to decline the mess that was just inflicted.

In the past week I had two cases I thought would be “vibe coding” fodder, blazingly obvious just tedious. One time it just totally screwed up and I had to scrap it all. The other one generated about 4 functions in one go and was salvageable, though still off in weird ways. One of those was functional, just nonsensical. It had a function to check whether a certain condition was present or not, but instead of returning a boolean, it passed a pointer to a string and set the string to “” to indicate false… So damn bizarre, hard to follow and needlessly more lines of code, which is another theme of LLM generated code.

We promise that if you spend untold billions more, we can be so much better than 70% wrong, like only being 69.9% wrong.

Another interesting thing to consider.

To be clear, he is rich. But he’s not crazy crazy rich, like nowhere near billionaire status.

With that in mind, his kernel is a key component of RedHat’s, SuSE’s and Canonical whole business, with at least two of those being multi billion dollar businesses.

His kernel is a key component of Android phones, which represent over 50 billion a year in hardware spend, and a bunch of software money on top of that.

His kernel is foundational to most hosting/cloud services with just mind blowing billions of revenue quarterly.

It’s used in almost every embedded device on the planet, networking gear, set top boxes, thermostats, televisions, just nearly everything.

People with a fraction of that sort of relevance are billionaires several times over. A number of billionaires owe much of their success to him. Yet he is not among their numbers.

Now there’s more to things than just a kernel to be sure, but across the hundreds of billions of dollars made while running Linux, there was probably plenty of room for him to carve out a few billion for himself were he that sort of person, but he cares about the work more than gaming the dollars. I have a great deal of respect for that.

Means that while he may not always be right, but I at least believe his assessments are sincere and not trying to drive some grift or cover some insecurity about being left behind.