WEBVTT 00:00:00.001 --> 00:00:05.080 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to 00:00:05.080 --> 00:00:12.180 your earbuds, or in this case, your eyeballs also. This is episode 210, recorded November 23rd, 00:00:12.180 --> 00:00:16.120 2020. And I'm Brian Okken. I'm Michael Kennedy. Yeah, I'm Jay. 00:00:16.120 --> 00:00:19.380 Hi. Jay Miller. Welcome to Python Bytes, man. 00:00:19.380 --> 00:00:24.980 I'm glad to be here. I will say this is hard because coming up with picks for 00:00:24.980 --> 00:00:29.700 the show that you normally get all your picks from is quite the challenge. 00:00:30.260 --> 00:00:33.700 Have we pulled the rug out from you? Like normally you would just listen and then go, 00:00:33.700 --> 00:00:35.460 here we go. But now it's... 00:00:35.460 --> 00:00:40.640 I'm not going to lie. I went to the search page and typed in like my first three picks and sure 00:00:40.640 --> 00:00:41.380 enough, they were taken. 00:00:41.380 --> 00:00:46.540 No, there's 210 episodes. So we just have been recycling them for like the last four months. 00:00:46.540 --> 00:00:49.120 We just started with zero and started over again. 00:00:49.120 --> 00:00:52.200 Yeah. If people listen, like, what are we on? I caught in episode 40 again. 00:00:52.200 --> 00:00:57.080 Yeah. Funny. We probably could, but you know, anyway. 00:00:57.220 --> 00:01:01.280 Only our best fans would know. And Brian, we're live streaming as well. We should tell 00:01:01.280 --> 00:01:04.500 the non-live stream listeners that this might be a thing in the future. 00:01:04.500 --> 00:01:08.760 Yeah, this might be a thing. We're going to see how it goes. It's kind of fun. We're live 00:01:08.760 --> 00:01:13.000 streaming right now, but that's boring for people that are listening to us in the future. 00:01:13.000 --> 00:01:13.880 But anyway. 00:01:13.880 --> 00:01:17.460 Yeah, we'll put a link and like a way to know when we're live streaming and stuff like 00:01:17.460 --> 00:01:19.160 that. So we'll talk more about that later. 00:01:19.160 --> 00:01:19.700 Yeah. 00:01:19.700 --> 00:01:23.940 Well, let's start it off. I want to talk about Kickstarter. So you had a Kickstarter campaign 00:01:23.940 --> 00:01:25.640 once a long time ago, right? 00:01:25.640 --> 00:01:30.660 I did. I've actually done three Kickstarter campaigns. One for a bunch of t-shirts, which 00:01:30.660 --> 00:01:35.640 was a ton of work and was fun, but shipping thousand shirts or something was quite the deal. 00:01:35.640 --> 00:01:41.360 Maybe not that many, but many shirts. And I did one to start the training company around 00:01:41.360 --> 00:01:46.060 the Python Jumpstart course, which was awesome. And I did one with Matt Makai for the Python 00:01:46.060 --> 00:01:48.240 for Entrepreneurs course. So yeah, I love Kickstarter. 00:01:48.240 --> 00:01:52.500 So I think Kickstarter is a pretty cool thing. And so I was interested to see, there was an 00:01:52.500 --> 00:01:58.660 article called Kickstarter projects. Do they succeed? But yes, the success or failure of 00:01:58.660 --> 00:02:04.200 them is sort of interesting. I really loved that this was an analysis done with Python data 00:02:04.200 --> 00:02:10.800 science tools. So it looks like, I think it's Aditya. It was the author. And it looks like this 00:02:10.800 --> 00:02:15.520 may have been a university project or something. I'm not quite sure, but I guess it doesn't matter. 00:02:15.520 --> 00:02:22.640 It's a pretty cool article. So he used a Kaggle information that went, that used all the projects 00:02:22.640 --> 00:02:29.440 up through 2018, I think. So it's not, it's not up through today, but that's pretty big data set. 00:02:29.440 --> 00:02:35.560 There's like 370 plus thousand projects in the data set. So that's neat. Came from Kaggle. 00:02:35.560 --> 00:02:41.340 And then the article goes through using pandas data frames to explore the data using describe, 00:02:41.340 --> 00:02:46.060 describe, which is a cool thing for the data frames have to just learn. You can learn quite a bit about 00:02:46.060 --> 00:02:52.160 your data just with describe. But then he goes on and uses matplotlib and Seaborn to analyze the data 00:02:52.160 --> 00:02:57.660 further. And I really, I kind of really liked that. I've seen articles like this before of how to use 00:02:57.660 --> 00:03:03.620 pandas and stuff and graphics to explore data. But this is data I actually really kind of cared about 00:03:03.620 --> 00:03:09.060 learning about Kickstarter. I may want to go back and, and use some of the, some of the techniques 00:03:09.060 --> 00:03:14.980 that he talked about in this article to kind of explore it more. I got to point out, there was a 00:03:14.980 --> 00:03:21.300 interesting line in there that said the data set goes back, has a 1970 listing. And it said the data 00:03:21.300 --> 00:03:28.020 from 1970 seems to be bad or insignificant data. I wasn't sure if this was just like a misunderstanding 00:03:28.020 --> 00:03:33.260 of when Kickstarter started or a joke, but yeah, it wasn't around in 1970. 00:03:33.260 --> 00:03:36.500 That's not when the song kickstart my heart came out, is it? 00:03:36.500 --> 00:03:37.680 Oh, I don't know. 00:03:37.680 --> 00:03:40.420 That was Motley Crue, but I think that was like late eighties. 00:03:40.420 --> 00:03:40.900 Yeah. 00:03:40.900 --> 00:03:42.240 Yeah. 00:03:42.240 --> 00:03:42.900 Yeah. 00:03:42.900 --> 00:03:43.180 Yeah. 00:03:43.180 --> 00:03:49.340 You know, there's a bunch of punchlines. One of the punchlines is 30, about 35% are successful, 00:03:49.340 --> 00:03:53.080 although they describe success as meeting the goal. 00:03:53.640 --> 00:03:54.640 And one of the things- 00:03:54.640 --> 00:03:55.040 Right, it's funded, right? 00:03:55.040 --> 00:03:55.400 Yeah, it's funded. 00:03:55.400 --> 00:03:57.500 Not necessarily shipped, delivered the product, right? 00:03:57.500 --> 00:04:01.920 Yeah. Well, I'm not sure. But one of the things I love about Kickstarter is the feedback. So I 00:04:01.920 --> 00:04:08.120 personally think that a failed Kickstarter is also a success because people, people learn that there's 00:04:08.120 --> 00:04:13.320 not enough demand to push forward. So that's information and that's successful too. But, 00:04:13.320 --> 00:04:14.960 you know, maybe I'm just an optimist. 00:04:14.960 --> 00:04:21.100 There's a lot of research that's been done on like the idea of Kickstarter and how now Kickstarter is almost 00:04:21.100 --> 00:04:28.100 80% marketing and 20% product idea. There have been Kickstarters that have failed, that have 00:04:28.100 --> 00:04:33.440 retried on Kickstarter with a different marketing approach and like wildly succeeded. 00:04:33.440 --> 00:04:34.360 So interesting. 00:04:34.360 --> 00:04:39.680 It would be interesting to see like out of the ones that did succeed, like how many of them had videos, 00:04:39.680 --> 00:04:45.180 like how long were their posts? Because I think a lot of times people want to jump on these projects 00:04:45.180 --> 00:04:50.080 like, hey, fund me, please, please give me money. And it's like, you almost have to do a little bit 00:04:50.080 --> 00:04:54.540 more work than that to even get to the bare minimum opportunity of success. 00:04:54.540 --> 00:04:59.040 I did a lot of research on Kickstarter success and failure and stuff in the early days. And yeah, 00:04:59.040 --> 00:05:03.960 it's, there's a whole thing that you don't see about getting the word out about if you get a high 00:05:03.960 --> 00:05:08.000 enough growth in the early days, Kickstarter will feature you. Like my first one, when I launched 00:05:08.000 --> 00:05:17.840 Talk Python training, it became for like two weeks, my Python course was the number one software product 00:05:17.840 --> 00:05:23.720 on Kickstarter. And we got like 40% of the people who bought the course discovered it on Kickstarter, 00:05:23.720 --> 00:05:27.620 not because I told them about it or someone else told them about it. But the reason it got there is 00:05:27.620 --> 00:05:32.580 the podcast, the podcast listeners who heard about it all went and quickly funded it. And so it, 00:05:32.580 --> 00:05:38.080 Kickstarter saw like this trajectory of going up and yeah, it worked well. 00:05:38.300 --> 00:05:43.260 I think that's good information though. Also, I mean, that's, yes, it's marketing, but yes, 00:05:43.260 --> 00:05:48.560 selling a product is also marketing. It's also having a platform to tell people about it. It's, 00:05:48.560 --> 00:05:53.780 um, you can't really sell stuff into a vacuum and expect to make money. So yeah. 00:05:54.040 --> 00:05:58.200 One really final interesting thought, if people are kind of interested in Kickstarter, there's a 00:05:58.200 --> 00:06:03.960 fantastic movie. One of my favorite documentaries of all time called capital C capital, the letter C, 00:06:03.960 --> 00:06:11.900 the movie you search for that. It's like, it's in 19, not 2015, following people around who got funded. 00:06:11.900 --> 00:06:17.020 And like when Kickstarter was all the brand new rage and it's just super cool to see those people go 00:06:17.020 --> 00:06:18.140 through there. It's, it's amazing. 00:06:18.140 --> 00:06:18.840 I'll check it out. 00:06:18.900 --> 00:06:25.340 But you know, what was not super commonly used back then, at least compared to now is using GPUs 00:06:25.340 --> 00:06:30.100 for machine learning and high end computation. Like that's a pretty new thing, right? Not brand new, 00:06:30.100 --> 00:06:31.780 but it's definitely gaining momentum. 00:06:31.780 --> 00:06:32.760 Yeah. Let's hear about it. 00:06:32.760 --> 00:06:38.800 All right. So often you hear about, Oh, you can just do like PI CUDA this and CUDA that. And CUDA 00:06:38.800 --> 00:06:44.120 means NVIDIA. CUDA is not a general thing. So when something says you can do this cool machine 00:06:44.120 --> 00:06:50.760 learning on GPUs with CUDA. And then I look at my MacBook pro here that has an AMD, I don't know, 00:06:50.760 --> 00:06:58.520 560, whatever graphics card in it. Nope. Don't get to do CUDA. No CUDA. I actually do have a gaming 00:06:58.520 --> 00:07:04.640 sim racing computer that has like a NVIDIA card if I really wanted to. But the point is like a lot of 00:07:04.640 --> 00:07:11.220 these laptops and other machines don't have some way to run NVIDIA stuff on them. Right. And that's, 00:07:11.220 --> 00:07:15.920 you know, that takes the wind out of the sails of a lot of data science and computational projects. 00:07:15.920 --> 00:07:16.180 Yeah. 00:07:16.180 --> 00:07:24.340 Unless you could bring in the Vulcans. So there's an article which talks about an open source set of 00:07:24.340 --> 00:07:30.920 libraries, I guess. Articles entitled GPU accelerated Python for machine learning on cross vendor graphics 00:07:30.920 --> 00:07:32.600 cards. That's pretty cool. Yeah. 00:07:32.660 --> 00:07:39.360 And so apparently it uses this project called Vulcan compute, the Vulcan compute Python framework. 00:07:39.360 --> 00:07:47.100 And the idea is that a lot of the modern machine learning libraries like Google TensorFlow, 00:07:47.100 --> 00:07:53.920 Facebook's PyTorch, a bunch of others, they've all been adopting Vulcan, which is a C++ library, 00:07:54.180 --> 00:08:02.060 as their cross platform GPU SDK. So that means, yeah, I can do it on my computer or I don't have 00:08:02.060 --> 00:08:06.980 to have NVIDIA or whatever. Right. So that's really awesome. However, they say, you know, in order to use 00:08:06.980 --> 00:08:14.160 this Vulcan library is 500 to 2000 lines of C++ code just to get like the boilerplate set up. 00:08:14.520 --> 00:08:18.900 As a Python developer, that strikes me as a lot. And how do you feel, Jay? Does that seem like a lot? 00:08:18.900 --> 00:08:21.000 Yeah, that's a hard pass on my part. 00:08:21.000 --> 00:08:22.280 Yeah, exactly. 00:08:22.280 --> 00:08:26.120 Well, you kind of have like a thousand lines of code just for a print statement. So 00:08:26.120 --> 00:08:33.120 exactly. Yeah. Like that's all the header files and all. So then there's the compute Python package, 00:08:33.120 --> 00:08:40.920 which is built on top of the Vulcan SDK using C++ bindings. And that thing is nice and simple. 00:08:40.920 --> 00:08:46.200 And it's a GPU framework, a general purpose graphics processing unit framework, I guess, 00:08:46.200 --> 00:08:52.340 is the acronym police there. And so if you're interested in building stuff for GPUs and you 00:08:52.340 --> 00:08:58.200 don't want to be tied to CUDA cores, then check out this compute Python package. It sounds pretty cool. 00:08:58.200 --> 00:09:04.160 Yeah. Except for the logo looks like it was like designed in 1983 or something. 00:09:05.460 --> 00:09:11.360 You know what? Maybe hold on, hold on, hold on. I think there's a little animation, 00:09:11.360 --> 00:09:19.020 like the Cylon type animation here for the animated GIF with the Python. And it has like ray tracing 00:09:19.020 --> 00:09:22.160 reflections of the logos. Oh, I kind of dig it, man. 00:09:22.160 --> 00:09:24.160 Well, okay. Yeah. It's a little video. 00:09:24.160 --> 00:09:26.040 Jay split the vote here. Come on. What's it going to be? 00:09:26.040 --> 00:09:30.780 They're probably using all of their GPU power to do something in the background, 00:09:30.780 --> 00:09:32.880 which is why they couldn't come up with a good logo. 00:09:32.880 --> 00:09:37.180 It sounds like a useful framework that people could talk about. And the article talks about, 00:09:37.180 --> 00:09:41.080 walks you through some examples of using it and stuff. I'm not going to go in the details there, 00:09:41.080 --> 00:09:43.140 but yeah, pretty neat. 00:09:43.140 --> 00:09:48.460 I like that this is mobile enabled too. So you can, you can take some of your work on the go. 00:09:48.460 --> 00:09:52.140 Yeah. Quite cool. Put it on your small devices. Quite neat. 00:09:52.140 --> 00:09:52.500 Yeah. 00:09:52.500 --> 00:09:54.900 Speaking of small devices, Jay. 00:09:55.120 --> 00:09:59.380 Oh, you stole my segue. I was going to say we could use some Vulcan technology to open up a portal, 00:09:59.380 --> 00:10:01.900 but we could, what kind of portal? 00:10:01.900 --> 00:10:07.500 A pie portal at that. So my pick, we're getting closer to the holiday season, which 00:10:07.500 --> 00:10:11.540 I can't believe I'm the one that's saying that normally I'm the one that gets angry when 00:10:11.540 --> 00:10:19.960 people talk about Christmas before Thanksgiving. But yeah, I received a little gift from Santa pie. 00:10:21.040 --> 00:10:25.640 And from there, I thought this would be a great gift for the tinkering Pythonista. 00:10:25.640 --> 00:10:31.140 And it is a pie portal, which I'm actually glad we're live streaming this because I can kind of 00:10:31.140 --> 00:10:31.600 show it off. 00:10:31.600 --> 00:10:32.220 Yeah. 00:10:32.220 --> 00:10:32.980 Oh, nice. 00:10:32.980 --> 00:10:37.640 Oh yeah. So when I think of like circuit Python and these devices, like it's usually just little 00:10:37.640 --> 00:10:41.220 chips and maybe you've got an led you can make blink, but that's like a screen. 00:10:41.220 --> 00:10:48.340 This is like all of the little things that we think about when we think of circuit pie on one device. So 00:10:48.340 --> 00:10:55.560 it has a touchscreen. So not just a regular screen, it's a touchscreen. It has the actual IO that's 00:10:55.560 --> 00:11:00.620 running right now as a light sensor. So there's a little sensor on the side that if I cover it up, 00:11:00.620 --> 00:11:04.320 you probably can't see it, but the numbers will actually start changing. There we go. 00:11:04.320 --> 00:11:05.480 Oh yeah. Yeah. 00:11:05.600 --> 00:11:10.900 And of course, since it's running circuit pie, it is like the easiest thing in the world to, 00:11:10.900 --> 00:11:19.040 to work with because at its core foundation, it's just Python and it's a limited scope of Python. So 00:11:19.040 --> 00:11:22.880 you kind of have to figure out, you know, what you're importing, what you're exporting. But 00:11:22.880 --> 00:11:31.180 I was able to display my light sensor output in 20 lines of code and five of those were import 00:11:31.180 --> 00:11:36.560 statements. So about 15 lines. That's pretty awesome. And I've seen some pretty cool projects 00:11:36.560 --> 00:11:43.280 with it. So what's the, graphics programming language look like? Is it like draw text? Is it 00:11:43.280 --> 00:11:52.260 draw pixel? No, actually it's, it's really interesting because of like, they have like actual pie portal 00:11:52.260 --> 00:11:56.540 module that you can import. And from there you actually load the screen and you can display 00:11:56.540 --> 00:12:02.320 full images on. So what you would do is you'll set your background almost like how the old Atari 00:12:02.320 --> 00:12:05.880 systems, you would come like with these paper backgrounds and then you would just overlay 00:12:05.880 --> 00:12:09.940 your actual game data over it. You kind of do that with this. 00:12:09.940 --> 00:12:12.300 It's like a 2d layered sprite type thing almost. 00:12:12.300 --> 00:12:20.320 Exactly. You load up like a small bitmap onto the screen and then you just tell your text where you 00:12:20.320 --> 00:12:25.480 want it to be placed. And I've seen people do like weather sensors. I actually did a thing with 00:12:25.480 --> 00:12:29.980 like Twitter lists so I could check and see like which Twitter lists that I follow for work or 00:12:29.980 --> 00:12:35.960 like having to have the most traffic. You can also throw some audio on there has built in speaker. So 00:12:35.960 --> 00:12:43.260 we played some practical jokes on, on relatives by having this, having it randomly every few seconds, 00:12:43.260 --> 00:12:48.300 just shout names and they didn't know where it was coming from. So, that's always fun. 00:12:48.380 --> 00:12:49.860 That looks really cool. Do you know what it costs? 00:12:49.860 --> 00:12:51.600 I want to say it's 50 bucks. 00:12:51.600 --> 00:12:58.220 Yeah. So not too expensive of a thing. Oh, that's a nice one. I like it. I'm still looking for that 00:12:58.220 --> 00:13:04.320 good use case where I can make some use out of some of these little devices. I think as somebody that 00:13:04.320 --> 00:13:09.080 does a lot of recording, I really want something that I can put on my door that is have like a sign 00:13:09.080 --> 00:13:12.080 like recording, please don't open up and yell and ask if I'm recording. 00:13:12.280 --> 00:13:18.980 That was the game plan, but I need to get a case for it. And this is actually why a wifi enabled. So 00:13:18.980 --> 00:13:25.520 if you are pulling data in, you can, tap into the add a fruit API and they give you your own little 00:13:25.520 --> 00:13:30.180 dashboard. You can store data there to retrieve later. You can pull up data from it. It has its 00:13:30.180 --> 00:13:36.420 own request library. So you can actually pull stuff down from the web. And, that was actually my game 00:13:36.420 --> 00:13:43.500 plan too, was to say, all right, have some little server on my Mac that whenever I open up, you know, 00:13:43.500 --> 00:13:48.960 zoom or Skype or something like that, it changes the status to now recording. And then like, 00:13:48.960 --> 00:13:53.520 because this is wireless, it would just pick up that signal and change on the door. 00:13:53.520 --> 00:13:58.120 Yeah. Maybe some serverless function thing where you talk to a database that literally has one entry 00:13:58.120 --> 00:14:03.640 recording or not, you just push it up there and it's all good to go. Awesome. I'm pretty sure this is 00:14:03.640 --> 00:14:08.420 going to exist for at least one of us someday, right? Like it seems like such a nice thing to 00:14:08.420 --> 00:14:12.580 have. Yeah, we should do it. Even all this COVID work from home stuff. Like you've seen those videos 00:14:12.580 --> 00:14:18.340 of like kids being like crawling in and mom's dragging the kid back out. Like, okay. So maybe it doesn't 00:14:18.340 --> 00:14:21.960 work for a two-year-old, but still it would be nice. So Brian, I'm not the only one with machine 00:14:21.960 --> 00:14:26.780 learning here. What do you got? Well, we've got, yeah. So I guess this is a machine learning stuff. 00:14:26.780 --> 00:14:31.520 We've got an introduction to linear, an article called introduction to linear algebra 00:14:31.520 --> 00:14:38.400 for applied machine learning with Python. Now I do want to do some machine learning in the future and 00:14:38.400 --> 00:14:42.660 definitely, and that's the target for this article. But you still got to learn about it. 00:14:42.660 --> 00:14:51.280 Sorry, Clyde. That was bad. It was very bad. Actually, I use, we use linear, linear algebra is used for 00:14:51.280 --> 00:14:56.820 communication systems as well. And so I kind of would like to brush up on my linear algebra. I didn't, 00:14:56.820 --> 00:15:03.560 it was a long time ago when I took it in college. So this article is from Pablo Ciceras and 00:15:03.560 --> 00:15:08.840 it's a big article. He says it's intended as a reference and not a comprehensive review. 00:15:08.840 --> 00:15:11.440 However, it's a really big reference. 00:15:11.440 --> 00:15:13.520 This is a book. It has an epilogue. 00:15:13.520 --> 00:15:20.900 Yeah, it is a book, but there's also links. He includes links to both free and paid resources that 00:15:20.900 --> 00:15:25.720 where he suggests learning linear algebra, including some video classes that are free, 00:15:25.720 --> 00:15:31.800 which would be neat. But anyway, I think the level that he's got this is really just right for me. 00:15:31.800 --> 00:15:38.740 And he talks about sets and ordered pairs and relations and functions, then gets into vectors 00:15:38.740 --> 00:15:45.460 and matrices and, and mappings and matrix decomposition. So he gets pretty deep into the linear algebra. 00:15:45.920 --> 00:15:50.880 And in some of the examples, he's got diagrams and stuff, which help, help me learn the visuals. 00:15:50.880 --> 00:16:05.100 And, he's using NumPy, pandas and Altair for some of the examples. And, I think that's pretty cool to show not only kind of how you do this, how you use linear algebra or really what it is. 00:16:05.100 --> 00:16:12.960 And then how to apply those, principles with NumPy and, show the examples with Python. It's really kind of nice. 00:16:12.960 --> 00:16:16.560 So anyway, top, it's a really useful thing for me. 00:16:16.560 --> 00:16:17.040 That's cool. 00:16:17.040 --> 00:16:21.860 Your linear algebra, it's one of these weird things that like appears in places that you don't expect it. 00:16:21.860 --> 00:16:27.800 It was definitely one of my favorite parts of my math studies is the, like the advanced linear algebra stuff. 00:16:28.040 --> 00:16:35.980 But like in 3D computer graphics, the way you rotate, like the viewport and stuff is you just multiply everything by like a rotation matrix. 00:16:35.980 --> 00:16:39.320 Or if you move something, you multiply it by a matrix or you make it bigger or smaller. 00:16:39.320 --> 00:16:44.900 Like there's just all these crazy applications of linear algebra. so yeah, it seems pretty neat. 00:16:44.900 --> 00:16:57.180 Yeah. A lot of the, it comes up a lot in, like you said, with, with graphics also with, changing, well, really anything where you've got like massively parallel systems can be described as a linear algebra problems. 00:16:57.180 --> 00:17:07.640 Yeah. Very nice. Next up, I want to talk about a new notebook framework. We seem to be catering to the data science crowd or scientific computing crowd this, this time around, but not always. 00:17:07.640 --> 00:17:18.360 So I recently did a show with Philip Guo and Sam and we talked about, I think it was 60 different notebook frameworks. 00:17:18.360 --> 00:17:28.300 It was, Sam Lau and Philip Guo back on episode 268. So, you know, you think Jupyter and now, is there something else? Oh, there's that Google Colab thing. 00:17:28.300 --> 00:17:29.140 60. 00:17:29.140 --> 00:17:31.660 Well, 60 different things. 00:17:31.660 --> 00:17:32.080 Wow. 00:17:32.220 --> 00:17:36.460 Yeah. And 61 now, because there's another one called deep note. 00:17:36.460 --> 00:17:36.780 Okay. 00:17:36.780 --> 00:17:37.860 Jay, have you heard of deep note? 00:17:37.860 --> 00:17:44.700 I have actually, I'm kind of upset because this was going to be one of my picks, but I hadn't used it yet. 00:17:45.240 --> 00:17:58.560 I was looking at it for a lightning talk that I gave last week and I was like, Oh, this is so cool. Too bad. I don't have anyone to collaborate with on this project. Otherwise I would have tried it. So you beat me to it. 00:17:58.620 --> 00:18:12.740 I sniped. Yeah. I got it out of there. Yeah. So deep note is an interesting one. It's Jupyter compatible, like many of the other ones that I sort of indirectly referenced there, but not exactly Jupyter. It's not like it just runs in Jupyter or JupyterLab or something. 00:18:13.300 --> 00:18:29.460 So it's, I'm pretty sure like that means import export from Jupyter. And then it runs in the cloud, which is kind of cool. A lot of times it's a hosted thing. So you have to maybe pay for it, depending on how you use it. It's free for individuals. But if you're on a team or you're in a company, it's going to cost some money. 00:18:29.460 --> 00:18:57.860 And like Jay was hinting at the main feature of it is real time collaboration, not like just the Jupyter stuff, but you know, let's log in and work on this together and start typing and start reviewing. It has some cool features. Like it has code review within the notebook coming. It has built in version control coming, obviously the real time collaboration that I talked about. And it even has dashboards kind of like streamlet coming soon. So you can like turn a notebook into an interactive dashboard website. 00:18:57.860 --> 00:19:24.640 I'm really excited for the code review stuff. I think that that's something that as Python itself is becoming more and more and more a tool for people outside of the traditional developer space, we still need to have good review tools. And even just a matter of having the ability to do collaboration and say, Hey, can you take a look at this code? And then you can actually see it running instead of just imagining what it's going to do. 00:19:24.860 --> 00:19:38.940 I think adding version tracking, adding code review. That's just the next step to really teach some of the fundamentals of developer work to skill sets that aren't traditionally designed to work that way. 00:19:38.940 --> 00:20:07.580 Yeah. And so much of the Jupyter computation data exploration comes from an individual working on the thing. Like Jupyter notebooks are not particularly friendly to version control, like GitHub. Like every time you make a change, or just the output changes, it clashes in GitHub and things like that. There's like a merge conflict and all those sorts of things. So it can be a challenging. So I think you're right. Like moving to a more collaborative, like sort of Google Docs style of world makes a lot of sense, not for everyone, but for many people. 00:20:07.580 --> 00:20:21.120 Yeah, I'm curious if something like this might be useful for like, say, instructor sort of thing. So you tutor somebody by two people hopping on a on one of these collaborations and working through a problem. 00:20:21.120 --> 00:20:25.780 Yeah, looks cool. Jay, what's this last one you got here? You and I've talked a bit about this offline. 00:20:25.780 --> 00:20:55.760 We have this is a tool that I found working on my own blog. It's called image kit. And I'm sure everyone here, you know, listening might or has a good idea of what a CDN is. And the too long didn't read part of it is they also make CDN specifically for images. So you can actually store your images on something like S3 storage, point this CD into it. And instead of calling that S3 storage, you would just call your CDN. 00:20:55.760 --> 00:21:13.700 URL. And I mean, this is great because you can do things like compression, you can do responsive types and different sizes. But the thing that about image kit that I really liked was that it also does a little bit of machine learningness, like image magic behind the scenes. 00:21:13.760 --> 00:21:30.820 So of course, you can do your own scaling of these images, but you can also do smart cropping. So if you take a picture of your face, and you say, I want the focal point of this image to be my face, you can crop it around your face and not just around the center of the image, which I think is a really good idea. 00:21:30.820 --> 00:21:42.780 Oh, that's cool. That's great. Like if you had a site where you're accepting like user profile images, or people were posting stuff, you could say, emphasize this, and they don't have to know, how do I crop an image? And why won't it accept a BMP? 00:21:42.780 --> 00:22:12.760 Yeah, it's just here. 00:22:12.760 --> 00:22:42.740 I can just say, here's the URL, here's the text that I want. And of course, because this is Python bytes, it has a Python API, it actually has a ton of API. So like, they have like first party API's for most of the primary languages. But if you don't have time to play with the API, you don't have a way to access the API before you render your page. It also has a URL schema. So you can also give all of those commands directly into the URL itself. And it's pretty cheap. 00:22:42.740 --> 00:22:58.540 I mean, right now, I'm not paying for it at all, just using their free tier. But if you want things like a custom domain name, we can say images dot Python bytes, or something else. That's when you start having to look at it. But I think even then, it's not for an image CD. And it's not too bad. 00:22:58.740 --> 00:23:20.400 Yeah, that's cool. And you can put like resizing in there. Just like I want this size to be such and such. That's important for like Google page speed, especially on mobile devices. If you want to put like a 2000, whatever 2000 by whatever picture, you don't want to just show that directly, right? So you can just make it part of the URL, even do like responsive design, like use different URLs for different screen media queries and stuff. 00:23:20.400 --> 00:23:32.560 It also supports fuzzy or lazy loading, which is really cool to where you can get like a blurred image until it finishes rendering the entire site. And then once it's done the entire site, it'll go back and then load the high def image. 00:23:32.560 --> 00:23:33.240 Oh, nice. 00:23:34.380 --> 00:23:35.100 Yeah, this is cool. 00:23:35.100 --> 00:23:40.060 I like it. And you mentioned your podcast, like tell people real quick, your podcast. 00:23:40.060 --> 00:23:54.560 Oh, sure. Yeah. So I do a weekly show called the pit show. Pit is short for productivity and tech. It's been around for a few years. I think once upon a time on some RSS feed far, far away, I had Michael Kennedy on the show. 00:23:54.560 --> 00:23:55.360 That was fun. 00:23:55.360 --> 00:24:00.780 I think I'm still trying to get that Brian fellow on there as well. But I'm sure we'll make that happen at some point. 00:24:00.780 --> 00:24:02.860 I thought we already did that. We haven't done that yet. 00:24:03.520 --> 00:24:32.660 Never did. But I blame my schedule. So it's okay. I guess as a developer advocate now, I get a lot of questions about, you know, hey, what's your journey in tech? How did you do this? Where did you get these things? What's it like, you know, not just for you, but for your family and other things like that. So it's a combination of two things. One, it's my walk through this journey out loud, showing people what I'm going through, some of the things that I'm doing, some of the things that I'm playing with, practicing on, but also interviewing folks from around. 00:24:32.660 --> 00:24:42.380 interviewing folks from around the internet, from all sorts of disciplines and asking the questions because I can, because I have them on my show for 30 minutes at a time. 00:24:42.380 --> 00:24:44.040 Yeah, that's awesome. 00:24:44.040 --> 00:24:44.660 Nice. 00:24:44.660 --> 00:24:58.420 Yeah, Brian, you got to get on there. We got to hear your story. So maybe that's, that was the last item. So maybe we should just roll into the extras. Jay, you want to keep going? You said you were a developer advocate now. Congratulations. That's an exciting new role. You just got, right? 00:24:58.420 --> 00:25:11.500 Yeah, been at that for a few months now, really enjoying it. And of course, because I'm doing this in the middle of the day, I'm going to take this opportunity to promote two of the things that I've been doing a lot, which are our contributor program. 00:25:11.640 --> 00:25:23.760 I work for Elastic, the company behind Elasticsearch. We also are responsible for Logstash and Kibana. I think that was how I got, I snuck onto this show is because I messaged Mike after he said that Elasticsearch wasn't a real database. 00:25:24.260 --> 00:25:39.100 So shots fired. But then from there, yeah, if you're doing anything with Elasticsearch, Kibana, Logstash, Beats, even some of our Python specific clients, we have a contributor program. If you do a blog post video tutorial, let us know. 00:25:39.100 --> 00:25:49.020 We have a whole platform. There's a link here in the show notes. And if you do enough of it, we actually give you credits to Elastic Cloud. So you can host some of your projects for free. 00:25:49.440 --> 00:26:13.920 And then also we have our YouTube channel, the Elastic Community YouTube channel, where you can see meetup talks, lightning talks. I just, the lightning talk I talked about earlier in the show is on there as well. You've got a lot of great folks that are teaching and sharing that information. And here's a single point where you can go and you can watch hours and hours of good information and good content on it. 00:26:13.920 --> 00:26:17.760 Awesome. That's really cool. And people are in Elasticsearch. They should definitely check that out. 00:26:17.760 --> 00:26:18.440 Yeah, definitely. 00:26:18.440 --> 00:26:34.720 So, Michael, I would just bring up, I've been plugging away at testing code at testingcode.com. That's a podcast I do. But I think I'm going to try to change the direction a little bit. So since I really like this short format. 00:26:34.720 --> 00:26:36.840 Oh, look what Jay has. 00:26:36.840 --> 00:26:38.160 In reach. 00:26:38.160 --> 00:26:39.420 Always. 00:26:39.420 --> 00:26:42.820 Do it again, but you got to say something so that you get on the video thing. 00:26:42.820 --> 00:26:50.560 If you like testing code, you should totally check out this book. It's called Python Testing with pytest. It's by this Brian Okken. 00:26:50.560 --> 00:26:52.500 I thought that was how to build rockets with Python. 00:26:52.500 --> 00:27:10.800 At first I was like, man, he needs to talk to those PyTorch characters, you know, maybe they can help him, you know, blast off or something. But no, I really, I genuinely say this is probably the most opened book that I have, like, within reach of me, because I'm always screwing up testing. 00:27:11.340 --> 00:27:16.560 And luckily, I don't have to message Brian every five minutes on how do I do this. I can just use his words. 00:27:16.560 --> 00:27:19.260 I don't need any extras more than that. So thanks, Jay. 00:27:19.260 --> 00:27:21.360 Well, you said you changed your direction. What's your direction? 00:27:21.360 --> 00:27:31.320 I kind of want to hear what people would like to do. I was thinking about possibly doing shorter episodes or more focused, like maybe 10 minute episodes about individual topics. 00:27:31.320 --> 00:27:35.900 But I kind of like to hear from other people about what they'd like to hear. So yeah, just open it up. 00:27:35.900 --> 00:27:40.880 I love a podcast only about mocking, because that's just the one thing that no one understands. 00:27:40.880 --> 00:27:42.660 That's because nobody should do it. 00:27:42.660 --> 00:27:51.660 Hey, how am I going to fix my dependency inversion and inversion control stuff in my Java app without some mocking? Come on. 00:27:52.160 --> 00:27:54.460 Well, yeah. With Java, you might need it. 00:27:54.460 --> 00:28:03.360 All right. I've got a few things. One, my Apple M1 chip, Silicon Apple Mac mini weight continues. 00:28:03.360 --> 00:28:12.240 But eventually, I will give you all. It's like mid-December. Eventually, I'll give you all of like the state of Python on Apple M1 chips. 00:28:12.440 --> 00:28:24.380 But until I know that, I can't share it. So over at Talk Python Training, we are running, we're participating in the Python Software Foundation fundraiser to close out the year. 00:28:24.380 --> 00:28:29.560 So because of time travel, that is not announced. That's probably not even known yet. 00:28:29.560 --> 00:28:33.760 But at the time of the release of this, it should be out. It starts December 1st. 00:28:34.180 --> 00:28:41.080 So if you visit pythonbytes.fm/PSF 2020, right now, it's going to go to a page that says nothing yet. 00:28:41.080 --> 00:28:45.820 But I'm going to change that redirect to end on the right place when it goes live. 00:28:45.820 --> 00:28:49.120 So there's going to be some discounts on some of our courses. 00:28:49.120 --> 00:28:57.160 Half of all the payments will go like half of the revenue, not profits, half the revenue will go to fund the PSF. 00:28:57.160 --> 00:29:04.160 So I teamed up with the PSF team and a couple other folks like RealPython and Dan Bader over there. 00:29:04.160 --> 00:29:07.620 And a few others to try to, you know, give some money to PSF. 00:29:07.620 --> 00:29:09.020 Keep them going strong. 00:29:09.020 --> 00:29:12.880 And last but not least, trying something new as podcasters. 00:29:12.880 --> 00:29:14.880 You guys can tell me what you think of this idea. 00:29:14.880 --> 00:29:19.960 I wanted to give something back to the people that supported the training course the most and the podcast the most. 00:29:19.960 --> 00:29:25.780 So I launched this thing called Talk Python To Me Pro Edition, which basically means it just has no ads. 00:29:25.780 --> 00:29:30.300 And it has all the history of like, you know, back five years worth of shows in the podcast player. 00:29:30.300 --> 00:29:34.800 So anyway, people can check that out as well at talkpython.fm/pro. 00:29:34.800 --> 00:29:38.580 And yeah, anyway, that's kind of fun to put together. 00:29:38.580 --> 00:29:40.540 And those are all my items, I think. 00:29:40.540 --> 00:29:41.600 Yeah, let's do a joke. 00:29:41.600 --> 00:29:42.280 Oh, man. 00:29:42.280 --> 00:29:43.180 All right. 00:29:43.180 --> 00:29:44.420 You ask the question. 00:29:44.420 --> 00:29:45.220 I'll do the answer. 00:29:45.220 --> 00:29:45.680 Okay. 00:29:45.680 --> 00:29:49.080 Why can't SQL and NoSQL developers date one another? 00:29:49.080 --> 00:29:49.660 I don't know. 00:29:49.660 --> 00:29:50.060 It's rough. 00:29:50.060 --> 00:29:52.780 I guess because they don't agree on relationships. 00:29:52.780 --> 00:29:54.300 It's so bad. 00:29:54.300 --> 00:29:55.020 It's so bad. 00:29:55.020 --> 00:30:00.560 Jay, as a professional at a database company, how do you feel about this? 00:30:00.560 --> 00:30:10.100 I would have said because date time formatting is hard, but I can definitely vouch for this. 00:30:10.100 --> 00:30:12.920 But even though they can't date, they can still get along. 00:30:12.920 --> 00:30:14.560 They can still get along. 00:30:14.560 --> 00:30:15.540 They still have ETL. 00:30:15.540 --> 00:30:16.480 Come on. 00:30:16.480 --> 00:30:21.340 To close this out, I'll post the video of the live stream here once it gets recorded and saved. 00:30:21.340 --> 00:30:23.060 So maybe we'll do this again. 00:30:23.060 --> 00:30:24.880 People can let us know if they think that's a cool idea. 00:30:24.880 --> 00:30:30.120 And if people are wondering why I'm at a weird angle in the dark, it's because the power in my neighborhood went out. 00:30:30.120 --> 00:30:31.620 I've been doing this show from my phone. 00:30:31.620 --> 00:30:32.360 So anyway. 00:30:32.360 --> 00:30:33.620 Nice. 00:30:33.620 --> 00:30:35.980 It won't always be like that. 00:30:35.980 --> 00:30:36.340 That's good. 00:30:36.340 --> 00:30:37.540 Well, thanks a lot. 00:30:37.540 --> 00:30:38.820 Thanks, Jay, for showing up. 00:30:38.820 --> 00:30:39.960 I enjoyed having you here. 00:30:39.960 --> 00:30:40.460 Thank you. 00:30:40.460 --> 00:30:41.540 Thank you for having me. 00:30:41.540 --> 00:30:42.080 All right. 00:30:42.080 --> 00:30:42.460 Thanks, Michael. 00:30:42.460 --> 00:30:43.180 Thanks, Brian. 00:30:43.180 --> 00:30:43.860 Bye. 00:30:43.860 --> 00:30:44.120 See you, everyone. 00:30:44.120 --> 00:30:46.100 Thank you for listening to Python Bytes. 00:30:46.100 --> 00:30:48.560 Follow the show on Twitter at Python Bytes. 00:30:48.560 --> 00:30:51.600 That's Python Bytes as in B-Y-T-E-S. 00:30:51.600 --> 00:30:54.480 And get the full show notes at Python Bytes.fm. 00:30:54.480 --> 00:30:59.560 If you have a news item you want featured, just visit Python Bytes.fm and send it our way. 00:30:59.560 --> 00:31:01.600 We're always on the lookout for sharing something cool. 00:31:01.600 --> 00:31:02.780 This is Brian Okken. 00:31:02.780 --> 00:31:08.080 And on behalf of myself and Michael Kennedy, thank you for listening and sharing this podcast with your friends and colleagues.