It's always relevant but the NTSB's recommendations sometimes fall on deaf ears in a way that endangers future safety.
1,259 karma · joined October 7, 2016
It's always relevant but the NTSB's recommendations sometimes fall on deaf ears in a way that endangers future safety.
I think it's rooted in the idea that AI is going to "solve" software engineering when it's really only another tool that magnifies leverage. Same thing with traditional engineering and their computer-assisted tools. It doesn't "solve" engineering, but it does automate some of the repetitive things that aren't the biggest value-add.
Software isn't a manufactured product and that's where the disconnect is. You can't build software factories unless you don't care about the quality. For some companies, that might be fine, but I imagine that's a much smaller subset of the market than folks think.
I use Ghostty, so new tabs and splits don't feel that strange to me. I'm guessing the feature I'm missing out on is session resumption and presets? If I leaned into that more, then a multiplexer would be worth the potential drawbacks?
It might simply be the economy. The vibes are bad and have been bad since the late 70s because wealth distribution is lopsided and income growth is stagnant.
Second job was remote, so I outfitted my own home office. I upgraded from a 34" 21:9 monitor to a 32:9 monster and enjoyed having the equivalent of 2x27" monitors.
I've since upgraded that setup to add a 28" LG DualUp to the left side of my 32:9 ultrawide. It strikes a nice balance of a productivity display for calendar/email and one for my main workspace that's plenty wide for spreadsheets (because I write less code than spreadsheets for FinOps modeling work).
I'm a huge fan of wider aspect ratios beyond 16:9 or 16:10. The dream is a 32:10 monitor in tandem OLED.
Eventually, I’d love to modify the exhaust to make it slightly louder. The turbo noise from the raised air intake is awesome enough and I’m curious if other drivers on the road can hear the turbo noise when I drive by them.
I understand the bar for deployment would need to be high to ensure that side effects are even rare compared to typical voluntary vaccinations.
Models don't actually reason in the same sense, so recalling rote from their training data is "cheating" in the sense that the training data cheated, not the model. So many of those benches have snaked their way into training data to make them less useful benchmarks. That, I think, is going to be a long-term difficulty in quantitatively assessing model quality and "intelligence." So it is cheating, in a sense of what we expect from the models and training data, but not in a human sense.
Consolidation is inevitable, so let’s lean in and ensure society, not shareholders, reap those benefits.
If you have most of the work and conversation is done in public, you're not hiring very curious people.
Per-token pricing is totally sensible from the provider-perspective on mapping COGS to revenue, but for a consumer, different models will produce more or less tokens, meaning the cost calculation is multi-dimensional.
Also, I love how my 4th gen Tacoma drives! So glad I got a long-bed Trailhunter last year.
It's interesting that Toyota has two hybrid models: one for efficiency and one for low-end torque performance.
It also helped that Neovim tends to consume minuscule amounts of resources compared to IntelliJ. I like the product Jetbrains developed, but I don't like how resource hungry it is—running JVM software is something I try to avoid, if possible.
I think it's worse than that. The frontier labs are purporting that there won't be any "new work" required in the future—starting with knowledge workers and then eventually snaking down into more manual labor jobs via robotics.
The irony is that these same labs are still hiring engineers to build the machinery they're so convinced will make engineers obsolete. It's so paradoxical it's not true.
The only true things are that AI is a bubble, the current technology is unsustainable given the amount of compute required, and LLMs are overhyped in what they can do well versus what they need to be closely supervised with.
It's learned-helplessness on a large scale.
There are really two "core" issues at play:
1. The prudish nature of US society
2. The fact that we don't have data privacy laws and restrictions on digital surveillance by private companies