665 karma · joined May 28, 2020
Intentionality is a big theme in math research (so i've heard), where solving "useful" problems isn't the ideal goal. The goal is to solve interesting problems, which might seem useless, but along the way achieve results with much wider implications that would have been impossible to discover directly. Or, how inventions like toothpaste came from space travel research.
(rhetorically) Does an indirect result "justify" a longer, slower project? Is speed an inherent property of the problem, or is it only knowable once it's complete? Or both, in the cases of misused funds?
Seriously, this does such a good job of capturing the feeling of MAGIC that code is capable of -- both in its process and in its output. Textbook "craft". It's hard to experience that sometimes when surrounded by dependency hell, environments, build systems, certain dynamic programming languages, and the modern web ecosystem.
[0]: https://pkl-lang.org
If someone is releasing a model that claims to have a level of reasoning, one would hope that their training dataset was scrutinized and monitored for unintended bias (as any statistical dataset is susceptible to: see overfitting). But if the graph on the announcement page is literally unreadable to seemingly anyone but the creator... that's damning proof that there is little empathy in the process, no?
I find these kinds of problems the most fun and the most educational! I tried building a grid layout system from scratch in SwiftUI, and it was similarly tricky to map out:
- what the "ideal" behavior one expects is,
- what edge cases exist,
- how to handle the edge cases,
- maintaining ideal behavior while handling edge cases.
(It was tricky b/c SwiftUI lays out its children, then its parent -- so the parent needs to ask its children for its view size, and iterate and set rows/columns that way.)
Maybe because the problem seem simple, it is that much more fun to dig into. Some good ol' time with a whiteboard.
Currently in TestFlight, but 1.0 is launching soon :) Feel free to give it a spin, and drop thoughts on the feedback board.
(Another idea I'm starting is an easy-learn, hard-to-master, productivity app: split calendar and todo list, with fluid drag/drop and power user features. Targeting the iPad at first, and will hopefully bring to more platforms. Keep an eye on my website[1] for launch!)
[1]: https://peterkos.me
If the intern "had no experience with the AI lab", is it the right thing to do to fire them, instead of admitting that there is a security/access fault internally? Can other employees (intentionally, or unintentionally) cause that same amount of "damage"?
One would assume that choosing to file litigation against Amazon would be done thoughtfully / with a plausible rate of success; they have an army of lawyers. What informs the opinion that a PIPed employee isn't worth even listening to, esp. from a lawsuit? Amazon is not exactly a shining star for work culture, and this situation doesn't sound unfathomable.
What’s your use case? I’d imagine the macOS display extending is wildly cool.
Also, I’d wager the people buying this can mostly Certainly afford it, given how expensive it is. For those people the money is meh, or justified (business expense, app platform). For others who are Maybe affording it, returns seem like an easy failsafe if it doesn’t meet expectations.
Algorithmic music composition has usually been split into two:
1. Generate notes (re: theory, genre)
2. Generate sound
(i.e., EMI[0], Kulitta[1], MusicNet[2])
Now we are doing both at the same time, and backwards. The model isn't (necessarily) going "write melody, then generate the sound", but rather, "here are 500 songs that are described with X, 500 with Y, and you want XY, so we'll combine these two" :)
(This is my best understanding, so feel free to correct)
[0]: http://artsites.ucsc.edu/faculty/cope/experiments.htm
It reminds me how Best Buy used to have horrible customer experience, it was all commission-based, and you would be hounded when first walking in the store. Then Apple came along with the model of "don't force someone to do anything, and the right product for them might not be in the store, and that's fine". (Notably, Best Buy seems to have gotten better since.)
> Most course material is covered in video lectures recorded in 2010 (already watched by over 350,000 people), which you can conveniently play at faster speed than real time. There may also be some new material presented by the professor and/or guest lecturers, which will be recorded for asynchronous viewing.
They may have meant "2010s"?
I was able to do a variety of "good first" things: readme updates, typo fixes, adding small features -- but it was hard to feel really _validated_ that my work was valuable, or that contributing to open-source was an impressive thing to do, because I (felt like I was) was surrounded by engineers, and nobody ever told me that OSS was "cool", until I met a company 3 years into college who valued OSS (more than just "wow, great job, you fixed a typo!")
The website didn't really suffice because the UX was bad, and wrestling with it got tiring. Apple+Google's hours were never quite correct.
My biggest struggle was skipping some of the basics and having to go back and re-learn after really struggling. But struggling helped in a way because then I really understood why X or Y solution didn’t work. If there’s anything i’d suggest, it’s that once you get some confidence in solving problems, try to approach new ones without looking through the entire lesson first, and that will help the actual way to solve the problem really stick. (And it’ll help you ask more focused questions!)