Personally, at first I thought these sorts of tools were dumb and wouldn't really work, but I think it works because it just isn't designed to be "adversarial". If you want your AI to trick Pangram, you can make an AI to trick Pangram. It just catches people who are cutting and pasting from the AIs without putting any more effort into hiding it.
Think of it this way, how would you turn a set into a vector in the first place? We solve this in programming a lot, for example, the "one-hot" encoding for neural networks. Here, a set turns into a vector that has a zero for every item that isn't in the set, a one for every item that is.
Now, there are a lot of things that |v| for a vector can mean. In the L1 distance you just add up the absolute value of each dimension. You could argue that that's a simpler sort of |v| than L2.
And there you go! |S| on a set actually means exactly the same thing as |x| on a vector, if you interpret sets as vectors in the right way.
"Falling behind schedule" doesn't really seem like the right term, for a sector of the economy that has been accelerating for the past few years.
You could easily describe this trend positively rather than negatively, like:
"Google has built an incredible amount of datacenters in the past few years, which makes sense since Google Cloud revenue has tripled since 2021. But they are trying to grow even faster and add more revenue."
I don't think it's nonsensical, it's just another name for the same thing. E.g. in the Haskell wiki it says, "the Error monad, also called the Exception monad".
Yeah, and rate-limiting is only one of the things a PaaS needs to handle, to avoid looking like a bad actor to the underlying platform. The trickiest thing to handle is people using your PaaS to host malware, because:
1. There may be no simple rule of thumb like "suddenly using tons of bandwidth"
2. Bad actors can open up so many accounts, you have to close them automatically
3. Malware can infect a good actor, who is unaware or struggling to deal with it
The specific code I was working on, I had a general idea of the sort of performance improvement that would be possible. I just thought that it would be too hard for the models to figure out without a lot of hand-holding.
But it ended up being not "too hard ever", but more like, in 1 out of every 5 tries, the model did in fact manage to get a large refactoring to the point where it improved performance. So once I set it up to try something, use the perf test, see if it worked, if not, throw it away, repeat. Then it started, slowly, finding some useful things.
It's an especially awkward situation because Railway is a competitor of Google Cloud, with many third parties involved. So, I just think it will take them a little more time to figure out how to message things.
To me, what it sounds like is that a Google Cloud system identified Railway as a misbehaving customer. Spam, hackers, that sort of thing. Often this happens for "platform as a service" companies, because Railway themselves probably do host some spammers and hackers, and they have their own systems for dealing with it.
So, it's quite possible that according to the Google team, Railway violated the terms of something or other, and according to the Railway team, they did not, and now everyone has to argue about it.
But who knows, this is just me guessing based on some experience running a PaaS that itself was running on top of AWS.
I didn't dig into what the actual repository was doing, but personally, I took some inspiration from the idea after reading about it and realizing that I might have been underestimating the ability of LLMs. I put a bit more work into a performance harness I was using locally and just set some agents to brainstorming and they did seem to find some great stuff. So I don't really have a stance one way or another on this specific repo, but the general idea seems like a really good one.
“In his presence, reality is malleable. He can convince anyone of practically anything. It wears off when he’s not around, but it makes it hard to have realistic schedules.”
The conclusion that "insurance companies using algorithmic tools have failed Californians who lost their homes to fire by systematically undervaluing their properties" seems pretty dubious to me. Everyone is shooting the messenger by getting angry at the insurance companies when fire insurance isn't cheaper. Meanwhile many insurance companies are leaving California entirely.
It isn't the "evil algorithms" at fault here - it's the high risk of fire.
> There is no avenue by which you make GitHub better by continuing to use it as it has been.
I feel like in a very mundane sense, I pay GitHub for a service, and they use that money to pay developers, to then make GitHub better.
It's tough to be working somewhere when usage is booming, and your service is crashing all the time. It's also tough to migrate your infrastructure between platforms, which it sounds like GitHub finally has to do in order to scale to the next level, to really take advantage of being part of Microsoft, although that has to feel pretty frustrating in the short term.
So hang in there GitHub team. Just keep fixing things.
ChatGPT is a great resource for learning things, if you really want to learn.
I hope that this leads us to shift education towards helping people learn things, when they do want to learn. Instead of forcing people to learn things that they do not want to learn.
If you write the police and ask them to delete all their data about you, that isn't a thing that they do. It shouldn't matter if the police store their data on AWS or their own servers.
Flock is a tool used by the police so it should work the same way.
I've been making games with JS in the browser with my kids, ages 7-13. Very simple games, the sort where we can just use emojis instead of real game assets. Even just building a game inside a Claude Artifact is pretty fun.
The nice thing about JS is that there is not very much overhead in setting things up, debugging weird things, restarting.
This part of the essay makes me feel moved by the author's situation.
> I am sitting down after a long walk outdoors. It should have been relaxing, but I was processing - processing another interview pipeline that has fallen through. I'm in my 6th month of unemployment, despite job hunting 40 - 60 hours a week, starting literally the day I was laid off - because the company needed to make cuts and remote workers were top of the list.
That sounds really tough, and I'm sorry the author finds themselves in this situation. Six months sounds grueling.
I think the interview process is likely to be completely overhauled in the age of AI. I don't really know what will happen. I used to be in favor of the standard code-at-a-whiteboard approach, but nowadays the actual work is even further from that. But I haven't seen an AI-aware interview process yet that seems like an improvement.
At any rate, these systematic changes are likely to come too late for the author. Hang in there. Maybe it's time to consider a bigger change, like moving cities and looking for in-person work. I like working remotely but it's harder to get a remote job, and the in-person stuff does have upsides. Good luck out there.
https://www.theargumentmag.com/p/why-everyone-loves-amazon