jake-low

Whoops, I misread your comment! Sorry about that.

Just FYI, most of the content on Wikipedia is actually dual licensed under CC-BY-SA and GFDL. Both licenses are copyleft licenses; CC0 is more permissive.

https://en.wikipedia.org/wiki/Wikipedia:Reusing_Wikipedia_co...

One advantage is accessibility for people with limited vision. A folding phone can have about the same screen area as a small tablet, while still fitting in a pocket or handbag. A larger screen is immensely more comfortable to use if your font size and UI has been scaled up by 300%.

This only applies in a hypothetical future where someone figures out how to make a durable folding display though.

I've heard that cities also charge for parking because of an interesting social factor: many people will drive in circles for half an hour looking for a free spot rather than pay for parking in a private lot. Everyone driving around in circles creates more traffic. So the city charges for curbside parking, in order to make the cost more comparable to parking in a private lot, with the hope that it will lower the psychological switching cost from "finding a curbside spot" to "giving up and paying for a garage spot".

Here's the paper [0]. The answer to your question is in Tables 4 and 5 (page 9). To summarize, darker skinned women are misclassified far more often than any other group, in all three of the tested facial recognition systems. Error rates for lighter skinned women are between 5x and 10x lower, depending on the system. Lighter skinned men have the lowest classification error rates by a wide margin in the IBM and Microsoft systems, and are tied with darker skinned men for lowest error rate in the Face++ system.

[0]: http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a...

Are you referencing the ESLint backdoor? From my recollection, the two incidents seem really similar. Both were noticed and unpublished quickly. Both could have been prevented by 2FA. Can you elaborate on why you think this incident was handled better?

The client is open source; you can build just the CLI client if you want. Check out https://github.com/keybase/client/tree/master/go

> "My left shoe won't even reboot."

The future is somehow both weirder and more mundane than I ever dreamed.

I think OP's point was that if your company provides a service, and runs 10 containers to do it, then each container provides 1/10th of the total functionality of the service and is therefore a "deciservice". The joke being that you then can't say you use "microservices" until you've got 10^6 of them.

I just watched a recording of a talk Chris gave a few months ago. It's a retrospective about building Lighttable/Aurora/Eve. If you were interested in the project(s) I suspect you'll find it interesting.

Part 1: https://www.youtube.com/watch?v=WT2CMS0MxJ0

Part 2: https://www.youtube.com/watch?v=ThjFFDwOXok

While both your question and the GP's are worth pondering, I'm not sure either is identical to the original question. The new job doesn't necessarily come with less free time (or less free coffee and foosball).

Probably not, from the approximate sizes noted in the article it sounds like Ultima Thule is a bit bigger than Diemos, which XCKD says you could escape with a bicycle and a ramp.

And just for fun, I also worked out a best guess for the force between the two "lobes": 1.5 x 10^13 N (assuming Ultima Thule is the same density as our Moon), which is about equal to the (Earth) weight of all the ships in the world. [0]

[0]: https://what-if.xkcd.com/33/

Worth noting that the use of black and white isn't just an Instagram-style filter being applied in Photoshop; the photographer is making these images on black and white film, using a large format camera that's not too different from the ones people were using to take portraits a hundred years ago. You can see a picture of the artist and his camera on this page [0].

[0]: https://www.ericpickersgill.com/about-eric-pickersgill-remov...

Think about it this way: imagine standing at the bottom of the Challenger Deep. Draw a small circle around yourself on the ocean floor. Now step outside the circle. Because you're standing on a spherical object (Earth), the area outside your circle isn't infinite like it would be on a plane. It's another finite region, which covers almost the entire Earth, and contains both yourself (standing just beside the lowest point of the Challenger Deep) and also the peak of Mount Everest. Therefore the circle is a base contour line for Everest, and Everest's wet prominence can be measured from the circle.

However, this same contour line cannot be used for the wet prominence measure of Mauna Kea, since it contains every mountain on Earth, many of which are taller. Mauna Kea's lowest contour line that doesn't contain another taller mountain would probably enclose a sizeable portion of the floor of the Pacific Ocean, but wouldn't include the Andes, Japan, or the Sierra Nevada, since those peaks are taller. The contour line would therefore be deep below the ocean surface, but not as deep as the Challenger Deep.

The article is likely referring to the estimated dry prominence of Mauna Kea. Topographic prominence [1] measures the height of a peak relative to its lowest surrounding contour line (that doesn't also contain another, taller peak). Usually the ocean's surface is considered "flat ground" when computing this measure (called a "wet prominence"), but one can alternatively imagine the Earth to have no water, and compute a "dry prominence" that permits the lowest surrounding contour line to be below sea level.

[1]: https://en.wikipedia.org/wiki/Topographic_prominence

The above Wikipedia article specifically mentions the dry prominence of Mauna Kea, estimating it to be 9330m which is taller than Everest's conventional height of 8848m. However, an apples-to-apples comparison would use the dry prominence of Everest too, which is the distance from the bottom of the Challenger Deep (-10,911m) to the summit. This would make Mauna Kea the second tallest mountain by topographic prominence if the Earth had no oceans.

If you don't mind me asking, did the book do any explaining of why the magic computer words were in a foreign language (English)? Had you been exposed to much English already when you picked up the book?

As a native English speaker I feel fairly ignorant of how programming is taught in other (human) languages. I recall a quote [1] by Eric S. Raymond commenting (or perhaps speculating?) that Linus commented his kernel code in English because it never occurred to him to do otherwise. But I'm curious for your perspective as I imagine the experience of a child learning from a book is quite different from that of an adult who is immersed in the English-dominated "hacker culture" on the internet.

[1]: http://www.catb.org/~esr/faqs/hacker-howto.html#skills4

Nim is shown in the graph on the bottom left, at approximately X = 5, Y = 13.

You rock for looking into this. When I have a chance I'll merge the Wikipedia data into the dataset I'm using. Thanks!

Thank you! I'm flattered. I like that map too, and I'm definitely interested in making more "difference from trend" choropleth maps in the future.

It's Alaska, as noted in the footnotes of the article; I think each borough is showing the vote ratio for the whole state rather than the borough-level data. Sorry about that; I'll make some time to look into it later.

I agree. The vote dataset doesn't have demographic breakdowns (I believe it was created by scraping post-election reports on _The Guardian_, not by relying on exit polls). But you compare the prediction errors to county-level data on race (the Census Bureau publishes these numbers) and pose the question "is the population-density model more wrong in areas of higher diversity?"

Fixed! Thanks for noticing.

Thanks for pointing that out! It looks like every county in Alaska is just showing the state-level vote ratio. Might have to do with Alaska having boroughs instead of counties? I'll look into this later; I've added a disclaimer at the bottom of the notebook for now.

Hey, author here. Definitely didn't expect to see this on the front page when I woke up. A friend posed the question in the title to me last week after seeing the referenced NYT map, and I spent the last couple evenings making this notebook to try and answer it.

Let me know if you have any questions; I'll try my best to answer them.

What typo? “Beard tax” is indeed correct; it was a tax on those who wore facial hair, as part of Peter the Great’s plan to ‘westernize’ Russia.

Edit: I see now; it’s the year (1987). D’oh!

I’m on mobile so I can’t verify this, but I think you might be able to accomplish what you want by using auto margins on the last child of the flex container? Auto margins have some really cool by-design interactions with flexbox. More info: https://hackernoon.com/flexbox-s-best-kept-secret-bd3d892826...

About 20 million people in northeastern Thailand speak Isan, a dialect of Lao. There's also a long tail of languages spoken in minority communities. Thai is widely spoken in Bangkok but the rural areas are much more of a patchwork in terms of languages used. As I said though, these numbers have large error bars so it's hard to be very certain of what the actual distribution looks like today.

Of the languages on this list, here are the subset where non-native (L2) speakers outnumber native (L1) speakers by a factor of two or more:

- English (378m L1, 744m L2)

- French (77m L1, 208m L2)

- Malay (77m L1, 204m L2)

- Swahili (16m L1, 82m L2)

- Thai (21m L1, 40m L2)

Technically Thai isn't quite 2x, but the error bars on these numbers are generally big.

Some languages have an opposite distribution, where nearly all of the language's speakers are L1 speakers. Bengali, Japanese, Portuguese, and Wu/Shanghainese are in this category, as well as perhaps Spanish (only ~14% of speakers are L2, which surprised me). Many others fall somewhere in between.

This would make an interesting data visualization.

I was working on a PR for a Node project when I read about this. My changes added some new dependencies and I wanted to make sure I hadn't pulled in any packages that were updated after this incident began (since the authors of those packages might have had their credentials compromised and used to push malicious updates to their packages).

I wrote a script to extract all of my changed dependencies from package-lock.json and retrieve the publication date of the resolved version from registry.npmjs.org. It's hacky but here's the steps:

First run this pipeline. You can change the first two lines if you're interested in the whole package-lock.json; I was just interested in my changes.

    git diff master -- package-lock.json \
    | grep '+\s*"resolved":' \
    | awk '$2 == "\"resolved\":" {print $3}' \
    | cut -d '"' -f2 \
    | perl -pe "s/\/-\/(?:\\S+-)((?:[0-9]+)(?:\\.[0-9]+)+\\S+)\\.tgz/ \1/" \
    > dep-urls-and-versions.txt

You now have a file which contains on each line a URL, then a space, then a version string. I ran this python script on the file.

import requests

    with open("dep-urls-and-versions.txt", "r") as f:
        for line in f.readlines():
            url, version = line.strip().split(" ")
            res = requests.get(url)
            body = res.json()
            print(body["time"][version], body["name"])

Run it as `python script.py | sort` and you'll get the most recently published packages in your package-lock.json. Just check that the last (bottom-most) timestamp is older than 2018-07-12 10:25 UTC when the first compromised package was published.

Hope that helps someone.

My public-school education (in the US) definitely never taught the subject. My history classes were divided into clearly-delineated "units" of study with no overlap. "First let's learn about Mesopotamia. Agriculture, Hammurabi, etc. Now recite what you learned on a test. Good, now we're moving on. Greece. City-states, marble statues, Olympics. Another test. Moving on. Rome..."

My "Earth Science" class was structured the same way. We learned about the formation of the solar system, then dinosaurs, then Cro-Magnon Man. Each unit of study was so completely separate from the others chronologically that it never occurred to me to ask "So did Greece and Rome ever have wars?" for the same reason it never did to ask "How did early homonids fight off dinosaurs?". It seemed so obvious at the time that there was no overlap.

Anyway, if I have a point, it's this: never underestimate the inadequacy of the US primary education system.