278 karma · joined March 30, 2015
meet.hn/city/us-Honolulu
Socials: - x.com/spyhi - linkedin.com/in/spyhi - bsky.app/profile/edfff.com - github.com/spyhi
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The link above also has an example of the use of multiple shadows to create a more naturalistic feel, namely that in the real world we are accustomed to, one source of light creates multiple kinds of shadows because of reflections from clouds, objects, etc.
This is all linked to physical realism, to help carry over our expectations of what the world looks like into a (in this case) digital space.
Perhaps the most egregious is your position on black shadows being the best and physically most realistic color to blend with, when in reality pure black doesn't exist in nature almost at all, it's just how our brains process things and if you actually use pure black then things will look subtly wrong--I know because I used to do that and learning to stay away from black where possible after taking a basic design course was one of the things that best improved the feel of my designs. Like, there's decades of research in the arts, sciences, and design community about this! It works for a reason.
For the sake of brevity, I'll leave this blog post here that explains it better and more in depth: https://ianstormtaylor.com/design-tip-never-use-black/
One thing that people also aren’t pointing out is that Space is considered a multi-trillion dollar market for whoever can access it due to the Wild West “if you can reach it, it’s yours” resource policies, which is why it attracts so much investment for long term projects. Many sectors that need investment in fundamental research don’t have those properties (untapped, unclaimed resources you can look to the sky and see), and therefore don’t attract the same kind of money.
[1] https://www.theverge.com/2019/6/18/18683455/nasa-space-angel...
Also, investors have things thing about inventing the future by funding it. Generally, individually or as a group, they'll decide something is going to be the next big thing and start cultivating deals in that area. One you can see happening in real time is space: Randomly in the last couple of years, investors have put together huge funds around space ventures, even though serious commercialization still seems to be a ways off. Even OP referenced it in his blog post.
As for why a founder vs a compensated product owner? Because a founder presumably has spent a lot of time thinking about the problem and is intrinsically motivated to solve it--so much so that they've done all the legwork to assemble a team and gather enough data to be investment-worthy. If as an investor you're wrong about the opportunity in this space, it's a whole lot of risk and cost and time you don't have to take on yourself. By advertising a pot of money, instead of searching for the right person, the hope is that the right person and team will eventually come to your door.
"I very quickly realized that" <- Implying a prior assumption that was proven false
"very smart people" <- The set of very smart people OP has met, which hinges on perspective
"often" <- The key phrase here, implying that it is generally (but not always) true
"have very little knowledge of finance and business." <- The assumption at hand
I'm assuming OP thinks of STEM people as the definition of "very smart," based on his description of his degree, and his feelings about coding. OP may have assumed that since his knowledge is comparatively trivial for STEM people to learn, the knowledge might be widespread among what he thinks of as smart people, and was surprised when this valuable knowledge wasn't. In my experience, the average kind of person I'm assuming OP would consider "very smart" is either averse to business, or didn't bother learning business because it took time away from the hard problems they were more interested in, even though they easily could have.
More generally, the total set of obviously and profoundly smart people is bound to be (much) bigger than the set of obviously and profoundly intelligent people with knowledge of finance. Are there smart business people? Absolutely. But it's probably a small percentage when compared with the total set of Very Smart People. Also, at least in my school, the smartest people tend to go into harder disciplines, so that may further dilute the numbers of finance knowledge among the pool of Very Smart People.
So, there are multiple ways OP's statement can be true without being specifically about the capacity of intelligent people to learn finance through study or experience.
This was exactly why I chose to do a BBA alongside my CS degree. If I was gonna make something, I wanted to make sure it would have some impact on the real world. I tell people: CS is a tool for solving problems, but business is a tool for finding them.
For what it's worth, I was one of those people. I didn't go for my undergrad until 8 years into my adult life (I'm a veteran), but I had done extensive reading about business to the point that most of my BBA (which I was taking as a bonus alongside CS) felt like review. Except for finance. I knew the basics, but even undergrad courses revealed that my knowledge was very shallow. I think it's just something that people don't expect to have that much complexity. I wouldn't have thought to learn it if I hadn't been forced to take it in school, but I took as many finance courses as I could once I realized the knowledge deficit.
Is finance hard? The sophisticated stuff can be, but generally in comparison to engineering it's not. But nonetheless, nearly none of the computer scientists in my class or in my professional circles have bothered to learn...probably because they're more interested in compilers ;)
Just as a small example, why do you need to read financial report at all? Because that is the language of investors and managers. Why read financial reports for some random company? Benchmarking, so you can tell investors and stakeholders "this is how well we are doing vs comparable companies." A template generator won't help you with this kind of analysis, and those services you mention won't help you if you can't really understand them.
I'm not saying you need an MBA to be a good business person, and I felt like my BBA (which I'm told is mostly the same material as an MBA, at least at my school) only confirmed that my instincts and self-learning after 8 years in the workforce was pretty good...but man, those little bits I didn't know and hadn't thought to learn (especially around finance and proper marketing) were the ones that have ended up mattering the most.
Here is an example of TechFolios in the wild [1] which I built while I was in his class. https://spyhi.github.io/
(I’ve let it fall a bit out of date since the class, but this thread is good inspiration!)
This is a dubious claim.
There have been periodic blowups about Yelp offering to manipulate how reviews are presented for money. [1][2] Yelp portrays this as advertising [3], like putting a "favorite" review "above the fold" so to speak, but many business owners consistently tell the same story about extortion over negative reviews.
I never paid it much attention before, but recently someone I personally know mentioned that her boss (at the time) was paying to remove negative Yelp reviews, so ¯\_(ツ)_/¯
Point is, even if they aren't directly deleting negative reviews, reviews are their main product and pressure over negative reviews seems to be at the very least a sales tactic, so don't be so sure they have no financial benefit from manipulating at least certain aspects of reviews--it's not inconceivable.
[1] https://thenextweb.com/insider/2009/02/20/yelp-remove-bad-re... [2] https://www.searchenginejournal.com/yelp-filter-positive-rev... [3] https://www.quora.com/Does-Yelp-remove-negative-reviews-in-e...
http://www.ics.hawaii.edu/welcome/academics/graduate-degree-...
Yeah, that's basically it. In short, the operations listed are pretty computationally intensive on a CPU, but pretty easy to parallelize for the data structures that are commonly used in machine learning (matrices and tensors). I presume that, by creating an API, Google can abstract out these common ML operations to work on the most efficient hardware on the device, whether a GPU or custom ML hardware such as Apple's Neural Engine. In theory, this should make on-device machine learning and inference more power-efficient, and could reduce reliance on server communication, improving privacy and probably making lightweight ML applications feel faster.
I'm gonna try pointing to the relevant CSS, but be warned, I am not a CSS wizard. I am also probably not a smart man, posting about CSS on HN while not being a wizard.
The relevant classes seem to be .sidebar, .sidebar-toggle, .motion-element, and .posts-expand, the last of which seems to control the transforms and durations. This is where my knowledge breaks down, since I'm not sure how they .sidebar or .motion-element trigger .posts-expand or any of the other animation CSS. Hopefully someone can help fill in the rest! ¯\_(ツ)_/¯