is the kobo store not good/convenient compared to kindle? I thought the kobo store was pretty good, but it is my first and only e-reader.

Kobo store is convenient but feels pricey sometimes (I don't have experience with the Kindle store). I don't mind paying them though, because it's still easy enough to strip the DRM and make backup copies of my books. If that changes, I'll take my business elsewhere.

I make a lot of use of my local library through the native Overdrive integration.

There's nothing wrong with the Kobo store itself, but some titles are only published via Amazon. Especially from self-published authors or participants in Kindle Unlimited. Whereas the major releases from the bigger publishers are usually widely available.

This is somewhat annoying. Please don't offer only one storefront as a place to buy your work.

can very much agree about not writing stuff like reductions yourself, unless you have good reason to. but this sort of feels like another "implement everything with <nvidia stuff> and you'll have a great time!! (but also coincidentally get locked in even more to Nvidia hardware)"

I really wish there were better options to PMPP... It's by far the most up-to-date book, but I totally agree the writing is sort of bad and some of the code examples are straight up incorrect.

So tl;dr, you have at least one person who would pay for a better book :-)

I think Jensen Huang said this recently, and I've had a similar opinion for a while, but a lot of companies seem uncreative with how they use their employees. like google probably has >10k people working on stuff like "ensure gmail refresh button is the correct size", but why not fund teams to take on new and more risky projects... maybe part of it is that the type of people who work at big tech companies are not interested in risky projects :shrug:

> like google probably has >10k people working on stuff like "ensure gmail refresh button is the correct size", but why not fund teams to take on new and more risky projects... maybe part of it is that the type of people who work at big tech companies are not interested in risky projects

Wow, I really hope that you're just too young to remember when Google famously invested in tons of "let's see what happens" projects. Google X projects is still a thing: https://x.company/projects/. And important things like Waymo came out of these Google moonshot projects.

Still, I think your comment also just shows how much Google has changed in the past 15 years. A lot of those projects on that x.company website are dead, and of course Google is also famous for killing projects that don't become huge, even if they're useful to lots of people.

yeah I was being very hyperbolic (and am on the younger side, so tbh wasn't very aware of a lot the x projects... I think those are even riskier than I meant.)

google was probably the worst example for me to use tbh, especially since it still has such a good culture of funding researchers. There was a "meme" a few years ago saying gmail's UI has dozens teams working on each of the different buttons, so that was why I said google/gmail.

huang's original comment was referencing layoffs due to AI, and I think a lot of the "maintaining/replacing existing stuff" engineers are at the most risk atm. But why lay people off why they could be pushed to work on new risky projects :-/

I do sort of think the stereotype of killing projects is kind in the vein of what I meant. like idk, google has so much money I feel like they don't need ~everything to clearly and immediately fit into their ai / data / advertising / search stuff. earnings - expenses is so huge, I think it should be fine to just allow some things to stay "small" without being a more "distinguished" moonshot-style project.

I bet Jensens thinks everyone working at Nvidia is crucial, and his reports hit the perfect balance of efficiency, continuity and growth. Classic Fundamental attribution error.

it's so great to see people boosting "security" in a way that also just happens to require locking in to big-tech approved apps that send all your data to big-tech so that they can deliver ads to you via your big-tech approved device using your big-tech approved os running your big tech approved browser showing your big-tech approved video platform with your big-tech approved content (oh, and also sends your data to your big-tech approved government)

literally watched it last night and was struck by how much "personality" it has.

Input (Eminem lyrics): there's vomit on my sweater already, mom's spaghetti.

Output: Facing some early-stage operational challenges today. Already dealing with some unexpected "output" on my professional attire, but it’s all part of the high-stakes journey. Embracing the mess and staying focused on the mission. #Resilience #GrowthMindset #StartupLife

I feel like that's true when the font is insanely small, which I guess was good when people would print entire proceedings. Reading two column super small font on a computer is super annoying though tbh.

If you have a hiDPI monitor, then reading even small fonts is IMO less annoying. But no monitor can still beat the DPI of a good printer.

A personal guideline for a lot of stuff is that a function may be too long when people add comments to mark what sections of it do. (ofc not really a hard rule). I just think it's easier to see "oh this is calling the load_some_stuff function, which I can easily see returns some data from a file." Rather than <100 lines of stuff, inlined in a big function, that you have to scan through to realize it loads some stuff and/or find the comment saying it loads some stuff>. That is to say, descriptive functions names are easier to read than large chunks of code!

smaller functions are also usually easier to test :shrug:

I kind of dislike the benchmarkification of AI for science stuff tbh. I've encountered a LOT of issues with benchmark datasets that just aren't good... In a lot of cases they are fine and necessary, but IMO, the standard for legit "success" in a lot of ML for science applications should basically be "can this model be used to make real scientific or engineering insights, that would have been very difficult and/or impossible without the proposed idea."

Even if this is a super high bar, I think more papers in ML for science should strive to be truly interdisciplinary and include an actual science advancement... Not just "we modify X and get some improvement on a benchmark dataset that may or may not be representative of the problems scientists could actually encounter." The ultimate goal of "ml for science" is science, not really to improve ML methods imo