blauditore

If you can't stitch polygons together seamlessly, how can you be sure the background doesn't bleed through with sampling? Isn't computing the exact coverage the same as having infinitely many point samples? The bleed-through of the background would then also be proportional to the gap between polygons, so if that one's small, the bleeding would be minor as well.

But as I understand it, the article is about rasterization, so if we filter after rasterization, the sampling has already happened, no? In other words: Isn't this about using the intersection of polygon x square instead of single sample per pixel rasterization?

This kind of makes sense from a mathematical point of view, but how would this look implementation-wise, in a scenario where you need to render a polygon scene? The article states that box filters are "the simplest form of filtering", but it sounds quite non-trivial for that use case.

By that logic, any product/organization that generates revenue mainly through ads would be an ad company. Search is the product that (probably) carries most of the ads revenues, so that's the main product, and ads are the means for generating revenue around it.

I feel personally attacked

Do I understand correctly that a low swappiness basically also means higher priority of anon pages over disk caching, simply because the former uses more space and the latter "runs out" eventually? I.e. low swappiness would achieve what you want, sort of?

Never though of it that way: With enough data, it's likely to find something that looks suspicious, for some definition thereof, even if just due to software errors. When looked at in isolation, this could convince people like real evidence.

This would basically be the same as p-hacking.

Although this author seems pretty reasonable with regards to actual privacy (preferring self-hosted software), I find the popular echo chamber of "avoid Google, they steal your data" quite misguided.

Yes, Google accumulates data and does stuff with it. But Google also has rigorous processes to lock down data and access to it, unlike virtually any small-to-medium cloud software provider. I've heard crazy stories like people looking up their friends' health insurance details for fun, just because almost everyone in the engineering part of that company had access to the production database.

Plus, Google is so large that it constantly receives attention by public institutions, which makes it harder to pull off shady stuff without getting caught. If <random SME> sells your data to the highest bidder who will spam you with cold calls, no one's gonna bat an eye.

I wonder what the chances are that a simple painting like this (a few large rectangular blocks) is a valid program. From skimming the documentation, I tend to think even that every such image would run without error, given that "Any operations which cannot be performed (such as popping values when not enough are on the stack) are simply ignored, and processing continues with the next command."

However, another question is how many of such random images would actually do something "meaningful".

The binary code at boot time spells "hello world s":

  01101000 01100101
  01101100 01101100
  01101111 00100000
  01110111 01101111
  01110010 01101100
  01100100 00100000
  01110011

I wonder where that sound/song is coming from, is there a Youtube channel or similar?

Why? I had fairly good experiences with Cordova, once I figured out the right way to use it (documentation tends to lack a bit IIRC).

Reminds me of a time where I almost certainly found a bug in Safari Mobile's JS implementation. Some variable ended up undefined in a place where it was provably impossible. It didn't happen consistently and was prone to right timing, so extremely hard to pin down and debug. Given the fact that all debugging had to be done tediously through a phone emulator and remote dev tools (it was inside a Cordova app), I eventually just gave up and added some sort of if-else for that case.

When buying a bike, I kind of used this method by looking at the cost-vs-spec-level plot on 99Spokes. Although it should be taken with a grain of salt, as spec level is a heuristic and not always very accurate. Found a nice MTB for a good price that way, but struggled with other types of bicycles.

I think you're falling for the exact same fallacy that I was describing. Also note that the human level of intelligence is not arbitrary at all: Most LLMs are trained on human-generated data, and since they are statistical models, they won't suddenly come up with truly novel reasoning. They're generally just faster at generating stuff than humans, because they're computers.

> Furthermore I don't think people have a conception of how good these models are going to get within 5-10 years.

Pretty sure it's quite the opposite of what you're implying: People see those LLMs who closely resemble actual intelligence on the surface, but have some shortcomings. Now they extrapolate this and think it's just a small step to perfection and/or AGI, which is completely wrong.

One problem is that converging to an ideal is obviously non-linear, so getting the first 90% right is relatively easy, and closer to 100% it gets exponentially harder. Another problem is that LLMs are not really designed in a way to contain actual intelligence in the way humans would expect them to, so any apparent reasoning is very superficial as it's just language-based and statistical.

In a similar spirit, science fiction stories playing in the near future often tend to have spectacular technology, like flying personal cars, in-eye displays, beam travel, or mind reading devices. In the 1960s it was predicted for the 80s, in the 80s it was predicted for the 2000s etc.

Just came here to wonder who came up with the beautiful name of CLOWNASS... er, I mean GLONASS.

Also, there are some websites targeting users with little money as well.

IIUC they never have db transactions across shards, because customers are basically data silos, which makes sharding quite straightforward.

I'm stuck trying to understand what exactly a home lab is: Apparently a computer at home that has similarities with common production servers?

Here's my naive question: What is the point of a home lab, i.e. what makes it better than running stuff on the machine you're developing? I can see some advantages in networking applications (as mentioned in the article), but I have the impression this is not too critical for most applications and devs.

Don't get me wrong, I'm a fan of building stuff like this, and will always be able to find an excuse to do it anyway. :)

Alright, but supersampling would exactly achieve another approximation to (real) motion blur, but still suffer from the same issues (like not becoming sharp when tracking with your eyes).

I'm pretty sure there's some bias present due to adaption. I'm very much used to some non-MB touchpad, and whenever I use a MB it feels worse (too slow and mushy). I feel similar pain even when switching operating systems on the same laptop, which almost certainly has to do with muscle memory. In that sense "getting it right" for Apple users would mean other manufacturers would need to exactly copy Apple's behavior, and probably make other users unhappy.

That's not true at all. When moving multiple pixels per frame, there will always be visible jumps. Aliasing only becomes relevant at the pixel level.

> And I'm someone that would take 9 attempts to do FizzBuzz

Hehe

I get the same impression: There are many head-in-the-cloud architects who see themselves as artists. The equivalent totally exists in the software world; it's people who want to be pure "software architects", designing what others should implement. In my experience, this dictating mindset never works - designers (technical or not) should evolve their ideas with the implementors/builders, otherwise such disconnects happen.

...and so do programmers, increasingly so nowadays (at least it's very tangible in Europe).

Well, any tea/infusion without caffeine is probably better, especially when consumed in high amounts. Without sweetening it, it's really close to just "water with taste".

The paper tries to make it sound like it solved a long-standing mystery, while it basically just confirms the most obvious explanation: Insects use artificial (close) light sources like a natural (distant) light source for navigation, which makes them eventually spiral towards the light most of the time.

The methodology and resulting graphics are interesting, but the underlying geometry of the effect is not really new or surprising.

Funnily enough, my brain still associates IEEE with ethernet, or networking in general. I know what it is, but I think the first few times I read the term was on some network config view in an old Windows version, probably next to some protocol selector. Now whenever I see IEEE, my brain loads a picture of a network config page, or an ethernet cable & port.