Dual numbers are basically big/little O notation. They combine beautifully with Robinson's NSA to give the most 18th century-like approach to deriving integral/derivative formulas that I know. And it's fully rigorous!
> Students have to memorize a diversity of processes for essentially performing the same task.
Is that true for differentiation ? I don't recall having to memorize many things, just how differentiation composes over +,-,×,÷, function composition and the differential of a few standard forms.
Symbolic integration, on the other hand, is a whole can of worms.
Another anecdote: I am a big nvim user and set everything up three years ago and basically haven't touched it since. At this point I'm a bit afraid I won't be able to fix it if something breaks lol.
I do think the culture of tweaking constantly without ever doing real work exists, but it's far less common than it seems online. People who make posts about their neovim configs all the time are certainly more likely to be in this camp than people not posting at all.
I was super into vim in college (20 years ago now, jeez). I started off like that but then transitioned to basically using “default vim” (plus support for a few external tools like ctags or cscope). For basically this reason!
You know, it'd have been amazing if TFA has not opened with that video. So instead of clicking the link to view TFA, you went off and dug up the exact same link in TFA???
The main linked article actually does not have that video; the article linked from in the description does have it. Not surprising that someone missed it.
I made a nice little CLI tool for testing the subjunctive in Italian. Claude code spun off a bunch of Claude API calls to generate example sentences with fill-in-the-blank spots for the correctly conjugated verb. Having an AI generate a prompt for and call out to other AIs was a bit surreal!
Nice article! The generated images make me so nostalgic for the early days of AI image generation. DeepDream and others had such uncanny, interesting generations.
Yeah, generative AI used to be wild, alien creativity and not something that made art kids furious.
I wonder if models can be trained for "high-temperature" purposes. I'd rather have a model which can surprise me than one which can predicably produce generic mediocre results. I mean you can run them on high temperature of course, but it doesn't seem like it's optimized for that.
I believe D. A. Jimenez and C. Lin, "Dynamic branch prediction with perceptrons" is the paper which introduced the idea. It's been significantly refined since and I'm not too familiar with modern improvements, but B. Grayson et al., "Evolution of the Samsung Exynos CPU Microarchitecture" has a section on the branch predictor design which would talk about/reference some of those modern improvements.
