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WEBVTT
00:00:00.000 --> 00:00:06.120
Hey, YouTube, everyone out there. Thank you so much for showing up for the live show or
00:00:06.120 --> 00:00:09.440
watching the recording afterwards. If you're here live, be sure to throw some comments
00:00:09.440 --> 00:00:15.600
and thoughts into the live stream and we'll try to make it part of the show. All right,
00:00:15.600 --> 00:00:22.280
guys ready to kick this off? Yep. Let's do it. Andre and Aaron, welcome to talk Python
00:00:22.280 --> 00:00:28.880
to me. Thanks for having us. Yeah. It's great. Yeah, it's fantastic to have you both here.
00:00:28.880 --> 00:00:32.920
What a cool topic, symbolic math with Python.
00:00:32.920 --> 00:00:36.000
I think I'd heard of SymPy before,
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but I didn't fully appreciate how neat
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and how advanced it really was.
00:00:41.960 --> 00:00:44.740
So people are gonna enjoy diving into this
00:00:44.740 --> 00:00:47.640
and we can talk some internals and whatnot.
00:00:47.640 --> 00:00:51.520
And if you're doing anything scientific or computational,
00:00:51.520 --> 00:00:54.040
this is certainly a project worth checking out.
00:00:54.040 --> 00:00:56.960
But before we get to that, let's just hear your story.
00:00:56.960 --> 00:00:58.880
How do you get into programming and Python?
00:00:58.880 --> 00:01:01.120
Aaron, you want to go first?
00:01:01.120 --> 00:01:01.620
Sure.
00:01:01.620 --> 00:01:05.640
So I mean, as far as just programming,
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I've always been into computers ever since I was a little kid.
00:01:13.400 --> 00:01:18.120
Actually started messing around with programming
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and AppleScript and my family's Macintosh
00:01:23.360 --> 00:01:26.600
back when I was a really little kid.
00:01:26.600 --> 00:01:28.960
but didn't really do any serious programming
00:01:28.960 --> 00:01:32.120
till college when I started doing,
00:01:32.120 --> 00:01:34.680
taking like computer science courses.
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And actually for Python,
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my college had a free course that they were offering
00:01:40.720 --> 00:01:45.120
to teach this little language called Python.
00:01:45.120 --> 00:01:48.400
So, you know, the CS course that I was taking
00:01:48.400 --> 00:01:52.200
was in like Java and C and those languages
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or I wasn't a huge fan of those languages,
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but I learned about this language called Python
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and yeah, I thought it was great.
00:01:58.880 --> 00:02:05.100
And so, yeah, I was immediately hooked with Python
00:02:05.100 --> 00:02:08.620
just because it's so easy to use.
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It's so easy to just write a program
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and you don't have to worry about compilers
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or all this boilerplate.
00:02:16.700 --> 00:02:19.740
And then--
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- I'd always been a little suspicious of languages
00:02:21.880 --> 00:02:24.100
that described themselves that way,
00:02:24.100 --> 00:02:27.220
which is ironic given how much I do with Python.
00:02:27.220 --> 00:02:29.140
But so often when you hear that,
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it's like, "Oh, it doesn't need all the symbols and it's super easy to get started."
00:02:32.080 --> 00:02:33.500
That means like, "Oh,
00:02:33.500 --> 00:02:37.540
it's really easy to do easy stuff but then you can't do interesting things."
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I think one of the things that's special about Python is like you can keep going.
00:02:40.980 --> 00:02:44.980
You don't have to stop once you need advanced ideas. That's pretty awesome.
00:02:44.980 --> 00:02:47.340
>> Yeah, it really is. You're right.
00:02:47.340 --> 00:02:49.700
A lot of languages stop,
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But Python, I mean, it's got a lot of advanced stuff as well.
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And it's got a huge ecosystem of libraries you can do.
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Just pretty much anything you want in it.
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- Yeah, for sure.
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The big paradox for me is,
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there's a joke T-shirt that I've seen that says,
00:03:08.020 --> 00:03:09.980
I learned Python, it was a great weekend.
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Which I think is kind of true, you can do that.
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But at the same time, for many, many years now,
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I've been studying Python,
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and there's still just so much more to learn.
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One about the language and also just like, as you said,
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all the different libraries like,
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oh, I wanna learn Pydantic now
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or this other thing or so on.
00:03:25.600 --> 00:03:27.560
Very cool.
00:03:27.560 --> 00:03:28.380
All right, how about now?
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What are you working on day to day?
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- Yeah, so I work at a company called Quansight.
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We do consulting mostly around open source,
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open source Python, open source data science stack.
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I work about 50% of my time on SymPy
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as part of a grant, which we might talk about later,
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I think.
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And I also work on different consulting projects.
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Actually, right now I'm part of a project
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called the Data APIs Consortium,
00:04:04.000 --> 00:04:09.000
which is trying to standardize the array API libraries
00:04:09.000 --> 00:04:16.840
for different Python array libraries, the APIs for those.
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like NumPy and X-Ray and those types of things?
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- NumPy, PyTorch, Jax, CuPy.
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So the idea is that all these libraries
00:04:26.280 --> 00:04:29.080
have a very slightly different APIs
00:04:29.080 --> 00:04:32.720
and it makes it hard to write code against that goes,
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that just works against all of them.
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So what we're doing is creating a standard API
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that all of these libraries will target.
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And so you can write code that will just,
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will work with NumPy,
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but you can also just, instead of importing NumPy, you can import Coupy,
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and that same code will run on the GPU, for example.
00:04:52.120 --> 00:04:56.120
Oh, that's fantastic. Yeah, that's a really good, ambitious goal.
00:04:56.120 --> 00:05:00.360
It sounds so easy, I'm sure it's pretty challenging.
00:05:00.360 --> 00:05:04.680
Yeah, well, I think we're being successful. We just released the first version of the
00:05:04.680 --> 00:05:11.560
specification, and we've got several libraries that are implementing it already,
00:05:11.560 --> 00:05:17.880
including NumPy, Coupy, several others. So it's actually, I think it's being a successful project
00:05:17.880 --> 00:05:23.720
and should really help push the Python data science ecosystem forward.
00:05:23.720 --> 00:05:27.400
Yeah, it sounds like definitely a good step. Andrej, how about you? How'd you get into
00:05:27.400 --> 00:05:32.120
programming in Python? You know, probably in high school. I was trying to figure out when I
00:05:32.120 --> 00:05:38.840
got into it. I think in high school, I don't know. I don't remember how I found it, but it was early
00:05:38.840 --> 00:05:45.320
2000s I'm guessing, it was before NumPy, I think there was NumArray, I think, it was
00:05:45.320 --> 00:05:52.040
the array library in Python. And during my undergrad, I was studying physics, I wanted
00:05:52.040 --> 00:05:58.520
to play with mathematical formulas in Python, so that's when I started SymPy. And then I
00:05:58.520 --> 00:06:03.240
used Python, you know, pretty much probably close to 20 years now, or over 20 years.
00:06:05.240 --> 00:06:07.240
And I cannot say I'm an expert.
00:06:07.240 --> 00:06:09.400
You know, it wasn't a good weekend.
00:06:09.400 --> 00:06:11.960
I don't know.
00:06:11.960 --> 00:06:17.400
A lot of stuff I'm not that great at, you know, but the basics I would say I know pretty well.
00:06:17.400 --> 00:06:17.900
Yeah, sure.
00:06:17.900 --> 00:06:24.040
I, after my PhD in physics, I went to work at Los Alamos National Lab for about eight years
00:06:24.040 --> 00:06:25.880
as a computational physicist.
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And I use Python.
00:06:27.320 --> 00:06:30.520
I use a lot of Fortran, a lot of computational codes and a lot of C++.
00:06:30.520 --> 00:06:31.720
That sounds fun.
00:06:31.720 --> 00:06:34.760
There's a lot of Python stuff happening there at Los Alamos.
00:06:34.760 --> 00:06:39.720
Yeah, it is. The NetworkX, I think, library was started here.
00:06:39.720 --> 00:06:48.680
And then, very recently, half a year ago, I changed jobs. I now work at a company
00:06:48.680 --> 00:06:55.400
called GSI Technology, which is a hardware vendor as a compiler developer. And so at Los
00:06:55.400 --> 00:07:01.880
Alamos, I started this compiler for Fortran called LFortran. It's a compiler for Fortran,
00:07:01.880 --> 00:07:06.440
but it also allows you to use it interactively just like you would Python. It runs in a Jupyter
00:07:06.440 --> 00:07:14.280
notebook as well as a kernel. And at this new company, we are also, in addition to LFortran,
00:07:14.280 --> 00:07:19.880
creating a new frontend called LPython, which takes Python, but compiles it through the same
00:07:19.880 --> 00:07:25.160
pipelines, through the same intermediate representation and all the backends, all
00:07:25.160 --> 00:07:31.640
the code is the same. So effectively, it treats Python like Fortran. So the same speed and so
00:07:31.640 --> 00:07:32.920
and so forth.
00:07:32.920 --> 00:07:33.760
- Oh, wow.
00:07:33.760 --> 00:07:34.600
That sounds awesome.
00:07:34.600 --> 00:07:36.280
- It is awesome, yes.
00:07:36.280 --> 00:07:37.980
- Very, very cool.
00:07:37.980 --> 00:07:42.640
All right, well, super fun to be talking about SymPy
00:07:42.640 --> 00:07:45.320
with you all today, symbolic math.
00:07:45.320 --> 00:07:47.660
I wanna start this off though,
00:07:47.660 --> 00:07:50.160
with a write-up from Aaron
00:07:50.160 --> 00:07:52.800
that is maybe not what people would expect
00:07:52.800 --> 00:07:55.640
as the first topic to introduce SymPy.
00:07:55.640 --> 00:07:57.160
SymPy has been in the news a little bit
00:07:57.160 --> 00:08:00.080
for the wrong reasons recently, right?
00:08:00.080 --> 00:08:00.920
Right, Aaron?
00:08:00.920 --> 00:08:05.920
So it was even on the front page of Hacker News
00:08:05.920 --> 00:08:10.960
because this company, HackerRank, reached out to GitHub,
00:08:10.960 --> 00:08:16.200
sounds like through automated ways,
00:08:16.200 --> 00:08:19.800
and said, "We seem to see some math
00:08:19.800 --> 00:08:21.820
"that looks like questions we might ask,"
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or something like that,
00:08:22.960 --> 00:08:25.060
"so we need to completely ban Senpai
00:08:25.060 --> 00:08:28.380
"because they're stealing our interview questions,"
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or something like that, right?
00:08:30.080 --> 00:08:30.920
Give us the story.
00:08:30.920 --> 00:08:33.000
What kind of madness was this?
00:08:33.000 --> 00:08:36.720
>> Yeah, so we sort of just got this notice from GitHub
00:08:36.720 --> 00:08:39.640
that a DMCA takedown had been issued
00:08:39.640 --> 00:08:43.520
against one of the pages on our documentation.
00:08:43.520 --> 00:08:47.400
And the way the DMCA works
00:08:47.400 --> 00:08:49.520
and the way GitHub's DMCA policy works
00:08:49.520 --> 00:08:51.760
is when somebody issues a notice,
00:08:51.760 --> 00:08:54.700
they basically just have to take it down.
00:08:54.700 --> 00:08:58.600
And GitHub basically took down
00:08:58.600 --> 00:09:04.480
the entire documentation site for about 12 hours.
00:09:04.480 --> 00:09:08.760
And so I've got a timeline of everything that happened here
00:09:08.760 --> 00:09:11.080
in this blog post.
00:09:11.080 --> 00:09:18.000
But basically, yeah, they had some company
00:09:18.000 --> 00:09:22.520
that they were working with to try to find their solutions
00:09:22.520 --> 00:09:26.280
on GitHub and issued DMCA takedown notices against them.
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And somehow this worth IT solutions company
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decided that our docs was one of these,
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and they just issued the notice.
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And so it ended up taking down our documentation, which
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immediately, like you said, it made it
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to the top of Hacker News and--
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>> It's too outrageous.
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>> Yeah, so the notice itself, it was completely ridiculous.
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Like there's like the stuff that they were claiming was
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their copyright is like probably not even copyrightable.
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Like if you look at the examples on
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on that documentation page that they took down
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they're just like simple math examples.
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- Yeah, it was like a X squared minus two solution is,
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you know, negative plus or minus radical two, right?
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Like that you shouldn't be able to copyright.
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I think that actually predates HackerRank Foundation.
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- I think it is HackerRank as a company.
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- Yeah, so the CEO of HackerRank,
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I guess noticed the buzz that was being made about it
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and retracted the notice.
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So the docs ended up going back online about 12 hours later.
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If they hadn't done that,
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we would have had to issue a counter notice,
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which would have kept the docs offline for about two weeks
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before that counter notice would have taken effect.
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And that's just because of the way the DMCA law works.
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I also explained how the DMCA law works in this blog post.
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If anyone's wondering like why would GitHub even do this?
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Like, well, they're kind of required
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to do this kind of stuff by law.
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There are some ways I think they could have improved
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what they did here, which I go over as well.
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But this DMCA law is-
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- It's probably like pointing those out.
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- Yeah, so I mean, the DMCA provides
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what's called safe harbor for places
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that host user generated content, basically.
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YouTube, the social media places,
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GitHub clearly has a ton of user generated content
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that's public.
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If you got the source code of Windows or something
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and you posted it up there, right?
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Theoretically, Microsoft might say,
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you know, we don't really want you hosting this.
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It's not supposed to be open source.
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Please take it down, right?
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So that's the idea.
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The way it works though is basically once there's a request to take it down, they just have to, here's the part that I think that got missed, decide that it's a valid request and then just take it down.
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Like they don't really try to negotiate or determine the right, the correctness of that assertion, right?
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Yeah, so they sort of have to stay like as a disinterested third party, I guess.
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And the reason is that in order for them to have the safe harbor, the safe harbor status sort of
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frees them from the liability on either side, if there is a copyright claim or if there's a lawsuit or something,
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they can just say, OK, well, we're just hosting the content.
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The law sort of allows them to do that.
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But in order to do that, they sort of have to just,
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if they get a DMCA claim, they have to just take it down.
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And then if there's a counter notice issued,
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then they can put it back up.
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And the DMCA has these,
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the DMCA sort of tells them that if they get a claim,
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they have to sort of take it down expeditiously.
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And if there's a counter notice,
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they have to put it back up in 10 to 14 days.
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So it's, there's a lot of parts of the law
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that they sort of have to do.
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There's some things I think that they could have done better.
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And there's also some things that they already do,
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which they don't have to do, which are nice,
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including they have a repo where they actually post
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every single DMCA notice that they've received,
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going all the way back to 2011, actually.
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- Interesting, that's good.
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- So you can actually see all the DMCA notices
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that have been issued against GitHub repositories
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on that repo, it's github.com/github/dmca.
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- Yeah, okay.
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You know, I wanna preface this by saying I'm not a lawyer,
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so I'm only speculating here,
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but one of the things that seems very,
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very much like it fell through the cracks,
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and this part I think is both maliciousness
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and laziness on the Worth IT company,
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as well as, you know, sort of either laziness
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or disinterest, I guess, from GitHub.
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I don't really know how to characterize it,
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But this company came and said, on your documentations,