This is just how authoritarian governments operate. It’s not some Machiavellian set of dominos. It’s more of a test of loyalty to The Administration than anything else.
It’s a better code reader built on top of sem (treesitter). I’m getting a lot of massive PRs at work now, and this has helped a lot with reading them. It decomposes the changes into entities and sorts based on what has the most dependencies. This tends to put the most important functions first. Plus I can click through the dependencies for each function and mark things as reviewed as I’m reading them. It’s a big improvement over the GitHub review flow for me at least.
Sure, but let’s not pretend that people treated the statements of these ceos as strategic messaging. People very clearly treated what Altman, Zuck, Amodei etc have been saying as predictions, and it hasn’t been until they’ve been proven wrong that people have started with the counter-narrative.
I’d argue most businesses have had their start through yak shaving.
I’ve found that the overuse of AI papers over a lot of problems. Then if things start failing people have no idea where or what to start fixing. I’m very much a destination person, but I’ve been on enough rides which crashed and burned to be cautious about it.
Part of this is definitely cultural and class snobbery. Europeans are seen as being high class. So if you put cream in your Alfredo you are seen as being as ignorant and not knowing any better. Yet any sort of fusion with cultural foods that are viewed as lower class only serves to “elevate” the dishes.
The rule of thumb I have in my head right now is that AI will benefit people with deep specialized knowledge a lot, but people with shallow knowledge or skills can’t build anything that your average SWE with a Claude code subscription can’t replicate in a few hours.
Most LinkedIn influencers, startup bros and consultancies kind of fall into the latter.
I think a lot of the discourse around LLMs fails because of organizational differences.
I work in science, and I’ve recently worked with a couple projects where they generated >20,000 LOC before even understanding what the project was supposed to be doing. All the scientists hated it and it didn’t do anything that it was supposed to. But I still felt like I was being “anti-ai” when criticizing it.
I understand that it’s way better when you deeply understand the problem and field though.
Ovens are a special occasion thing in my house because our oven is huge and I can usually do the same thing in the air fryer, which is just a small convection oven.
For me it’s that usually I can figure out if I’m going to like something way more easily if I’m just clicking through and watching samples of a show. I don’t want to be constrained to a predetermined algorithmic category.
This essay somehow sounds worse than AI slop, like ChatGPT did a line of coke before writing this out.
I use AI everyday for coding. But if someone so obviously puts this little effort into their work that they put out into the world, I don’t think I trust them to do it properly when they’re writing code.
Having lived in Vancouver and NYC and now LA I think I’ve seen both sides of things, and I don’t think these things are quite as insurmountable as you think.
I don’t think public transit is ever that pleasant, but I rarely felt unsafe in Vancouver or even NYC compared to LA.
One thing that I disagree with is the timing. In a lot of cases I’d rather spend 20 minutes more on the bus than driving. It’s much easier to hop on a bus, listen to music and walk to my destination than deal with traffic or parking. Also, in cities that have properly invested in transit, there are things to do around the transit points. Grocery stores, coffee shops, general stores etc, so I’m often doing 2-3 things in a single trip. Whereas in LA, each of those things is a separate car journey away for me, so overall things are less efficient.
As with most continuous arguments in SWE, it really depends. I used to do a lot of debugging of random (i.e. not written by me) bioinformatics tools and being able to just fire up gdb and get an immediate landscape of what the issues were in the program was just invaluable.
Times when I was more familiar with the program or there were fewer variables to track it was less helpful
I agree that’s kind of what should happen. What seems to have happened is that people have figured out it’s easier to game the system than produce more complicated or technical projects.
Also when you factor in the age of the wedding participants it almost seems like a regression. A couple in their 30s should be able to afford more than a couple in their 20s a generation ago.
I don’t think my concern is that AI is going to make everything too awesome for people to cope. The fact that I can now DoorDash lunch doesn’t really matter when I can’t afford a place to live or healthcare.