WEBVTT 00:00:00.160 --> 00:00:05.440 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.440 --> 00:00:10.960 This is episode 103, recorded November 6, 2018. I'm Michael Kennedy. 00:00:10.960 --> 00:00:11.800 And I'm Brian Okken. 00:00:11.800 --> 00:00:12.600 Hey, Brian. How you doing? 00:00:12.600 --> 00:00:13.940 I am great today. 00:00:13.940 --> 00:00:18.540 Yeah, it's another wonderful day. A lot of cool news in the Python space. 00:00:18.540 --> 00:00:22.280 I have something that I think you all will really like. I'm looking forward to sharing that. 00:00:22.280 --> 00:00:22.660 Cool. 00:00:22.660 --> 00:00:26.200 Yeah, and I know you got some good ones, so I'm looking forward to talking about those as well. 00:00:26.200 --> 00:00:31.400 Before we do, let's say thank you to DigitalOcean. Check them out at pythonbytes.fm/DigitalOcean. 00:00:31.400 --> 00:00:36.300 Get $100 free credit for new users. I'll tell you more about them later. 00:00:36.300 --> 00:00:42.740 Right now, you've got kind of this magical C++ Python combination thing queued up for us, right? 00:00:42.740 --> 00:00:51.160 I do. And I just, we found out about this because a listener, I think it's Sebastian Srebart. 00:00:51.320 --> 00:00:56.540 Wait, Sebastian Srizard. Srizard. Sorry about your last name. 00:00:56.540 --> 00:00:58.940 Thank you, Sebastian, for sending that in. 00:00:58.940 --> 00:00:59.760 Yeah. 00:00:59.760 --> 00:01:05.500 Brian, if anyone else wants to send in some news, you can also butcher their name in honor of them sending that in, right? 00:01:05.500 --> 00:01:06.520 Yeah, it's a tradition. 00:01:06.520 --> 00:01:11.840 As I would as well. I do a lot too. It's becoming a tradition. Thank you, Sebastian. All right, tell us about this thing. 00:01:11.920 --> 00:01:16.840 Well, I think because of the flames in the logo, they actually intended to be pronounced Phoenix. 00:01:16.840 --> 00:01:24.000 It's F-E-N-I-C-S. It's a project. I'm glad that Sebastian sort of translated this for us. 00:01:24.560 --> 00:01:30.260 It's an open source computing platform for solving partial differential equations. 00:01:30.260 --> 00:01:35.100 This is actually really cool. And I'm going to quote right from their site. 00:01:35.100 --> 00:01:41.360 Phoenix enables users to quickly translate scientific models into efficient finite element code 00:01:41.360 --> 00:01:47.540 with the high-level Python and C++ interfaces to help you get started. 00:01:47.540 --> 00:01:52.080 It's got powerful capabilities for experienced programmers, but it's easy to get started. 00:01:52.080 --> 00:01:57.520 And it runs on multiple platforms from laptops to high-performance clusters. 00:01:57.520 --> 00:02:03.440 And it actually looks really pretty cool for anybody dealing with partial differential equations. 00:02:03.440 --> 00:02:06.040 It's a num-focused-backed project. 00:02:06.040 --> 00:02:09.160 So there's money behind it, which is cool. 00:02:09.160 --> 00:02:10.980 Num-focused seems like it's everywhere these days. 00:02:10.980 --> 00:02:12.580 Yeah, well, especially in... 00:02:12.580 --> 00:02:17.940 And it's good to highlight that, to say that this is a backed project, 00:02:17.940 --> 00:02:19.540 because it's a... 00:02:19.540 --> 00:02:23.840 These sorts of things you wouldn't want to depend on and then have them go away. 00:02:23.840 --> 00:02:28.240 Right. Some open source projects, depending upon them, are kind of like getting a puppy, right? 00:02:28.240 --> 00:02:33.440 They're cute and fun, but then you've got to walk them and stuff like that if they get dropped 00:02:33.440 --> 00:02:34.920 or don't work the way you want. 00:02:34.920 --> 00:02:40.600 Yeah. And Sebastian said right off the bat, it looks cool, but there's some features 00:02:40.600 --> 00:02:43.800 features inside that you might not know about right off the bat. 00:02:43.800 --> 00:02:46.240 So I'm going to quote an email that he sent us. 00:02:46.240 --> 00:02:47.160 He said, 00:02:47.160 --> 00:02:51.820 Phoenix is, in fact, a C++ project with a full-featured Python interface. 00:02:51.820 --> 00:02:57.860 The library itself generates C++ code on the fly and be called on the fly from Python. 00:02:57.860 --> 00:02:59.560 It's almost magical. 00:02:59.560 --> 00:03:05.600 Under the hood, it uses SWIG and recently moved to PyBind 11. 00:03:06.200 --> 00:03:11.760 I guess the architecture that was set up to achieve this level of automation might be useful 00:03:11.760 --> 00:03:12.840 for other situations. 00:03:12.840 --> 00:03:14.000 Yeah. So that's crazy. 00:03:14.000 --> 00:03:19.460 You write Python code, this thing writes C++ code, and then calls it all dynamically at runtime. 00:03:19.460 --> 00:03:21.800 Yeah. That's amazing. 00:03:22.480 --> 00:03:40.820 And from the project website, being able to develop the algorithm locally on whatever computer you're on, even a laptop or desktop, and then deploying the same code to run in parallel on thousands of processes, that's just awesome. 00:03:41.040 --> 00:03:43.600 Yeah. I think there's a lot of cool stuff happening here. 00:03:43.600 --> 00:03:48.220 I mean, not everyone is solving using finite element methods to solve PDs, right? 00:03:48.220 --> 00:03:59.520 I understand that's a limited group, but there's a lot of projects that may find what this project is doing interesting from a performance perspective and a dynamic meets compiled language perspective. 00:03:59.720 --> 00:04:12.040 Yeah. And it's also one more example of problems being solved in Python that you wouldn't have thought you could solve with Python because they're just to take too much high performance computing. 00:04:12.040 --> 00:04:13.800 That's a super interesting point. 00:04:13.800 --> 00:04:18.060 So often I hear people who are not that familiar with Python say, well, Python's slow. 00:04:18.060 --> 00:04:18.960 I'm like, hold on. 00:04:19.540 --> 00:04:21.800 What do you mean that Python is slow? 00:04:21.800 --> 00:04:27.200 Like you have to say doing this operation in Python is slow because there's so many variations. 00:04:27.200 --> 00:04:32.720 I mean, it can be, well, Python is slow and CPython, so you can use PyPy, but it could be way more interesting. 00:04:32.720 --> 00:04:44.340 Like, well, yes, but you would actually use a, say some kind of library that has C level compilation elements like SQLAlchemy or NumPy or something. 00:04:44.340 --> 00:04:49.380 And so when you actually talk about that, like you're doing C, you're not doing Python at the hotspots. 00:04:49.380 --> 00:04:53.720 And then you get, you know, way out there with things like Dask and like this and so on. 00:04:53.720 --> 00:04:55.000 And it's pretty awesome. 00:04:55.000 --> 00:04:56.040 Yeah, definitely. 00:04:56.040 --> 00:04:57.120 It's neat to see. 00:04:57.120 --> 00:04:58.900 You're kind of a fan of regular expressions, aren't you? 00:04:58.900 --> 00:04:59.740 Yes. 00:04:59.740 --> 00:05:01.460 I like regular expressions. 00:05:01.460 --> 00:05:05.520 Are you a fan of cursive language, like the fancy calligraphy type stuff? 00:05:05.520 --> 00:05:06.580 Like, do you write that way often? 00:05:06.580 --> 00:05:08.040 Well, I like it. 00:05:08.040 --> 00:05:09.440 I don't write that way, but. 00:05:09.440 --> 00:05:11.480 I don't write it that way either. 00:05:12.480 --> 00:05:24.400 This next project I want to talk about is its goal, I think, primarily is to make regular expressions more easy to indicate their intent and easier to maintain. 00:05:24.400 --> 00:05:29.180 I like to joke that regular expressions are kind of a write-only language. 00:05:29.180 --> 00:05:31.940 You know, you write them, but then you can't read them anymore. 00:05:31.940 --> 00:05:33.600 But they're magic and they do their thing, right? 00:05:33.840 --> 00:05:37.200 So we got this project called cursive underscore RE. 00:05:37.200 --> 00:05:40.580 And RE is, of course, the regular expression module in Python, right? 00:05:40.580 --> 00:05:41.160 Yeah. 00:05:41.160 --> 00:05:44.760 This comes from Chris Patty of Podcasting at Fame. 00:05:44.760 --> 00:05:47.440 And it's actually created by Bogdan Popa. 00:05:47.440 --> 00:05:51.320 My turn to hopefully take a shot at getting the name not too wrong. 00:05:51.320 --> 00:05:57.100 But this is a library for doing regular expressions in modern Python. 00:05:57.100 --> 00:05:58.580 So 3, 6 and above. 00:05:58.580 --> 00:06:03.200 And the idea is instead of writing in Python, 00:06:03.200 --> 00:06:13.100 the string symbols like you normally would, you know, bracket 9, 0-9 dot, you know, close bracket dot, that sort of thing, 00:06:13.100 --> 00:06:18.420 you write in this higher-level language of combinators, they call them. 00:06:18.420 --> 00:06:24.680 And then that overrides the operators in Python to generate a regular expression. 00:06:24.680 --> 00:06:29.800 So this is like all sounds kind of wonky and crazy, but if you've seen an example, it's super clear. 00:06:30.240 --> 00:06:33.240 So you can go and define, say, like a hex color. 00:06:33.240 --> 00:06:44.740 And the way you define it is you say, I would like to create a, you say, beginning of line plus this hash symbol plus group repeated hex digit 00:06:44.740 --> 00:06:49.420 or repeated other hex digit exactly three times plus end of line. 00:06:49.580 --> 00:06:55.300 And you write it in these things that are symbolic of what regular expressions do. 00:06:55.300 --> 00:07:00.580 And they can be, you know, sort of orred and and together and added together and so on. 00:07:00.580 --> 00:07:05.060 And then if you call the string representation of them, you get the actual regular expression. 00:07:05.060 --> 00:07:05.660 Okay. 00:07:05.660 --> 00:07:09.280 So it's a library to build regular expressions. 00:07:09.280 --> 00:07:09.760 Yeah. 00:07:09.760 --> 00:07:11.360 It's a library to build regular expressions. 00:07:11.360 --> 00:07:21.960 The way Bogdan describes it is he says, it's a tiny Python library made up of these combinators to help you write regular expressions that you can read and modify six months down the line. 00:07:21.960 --> 00:07:23.340 Yeah. 00:07:23.340 --> 00:07:24.140 Yeah. 00:07:24.520 --> 00:07:30.780 Definitely one of the problems with regular expressions is they're they're terse and that's good and bad. 00:07:30.780 --> 00:07:32.720 You they're too terse sometimes. 00:07:32.720 --> 00:07:34.380 So, yeah, sometimes. 00:07:34.380 --> 00:07:37.580 And then the other problem is there's a lot of special symbol. 00:07:37.580 --> 00:07:40.600 Like regular expressions are basically nothing but symbols. 00:07:40.600 --> 00:07:40.800 Right. 00:07:40.800 --> 00:07:45.060 They're like a symbol like exploded and its guts came out all over this text. 00:07:45.060 --> 00:07:45.320 Right. 00:07:45.520 --> 00:07:45.920 Yeah. 00:07:45.920 --> 00:07:52.180 But the problem is like some of those symbols are have to be escaped if you actually want to search for them. 00:07:52.180 --> 00:07:53.500 But it's always kind of hard to know. 00:07:53.500 --> 00:07:54.700 Well, which ones do I have to escape? 00:07:54.700 --> 00:08:02.760 So it also does things like if you tell it I'm looking for the text of like square bracket, square bracket, it'll escape properly in regular expression format. 00:08:02.760 --> 00:08:05.600 The the text representation of that. 00:08:05.600 --> 00:08:05.840 Right. 00:08:05.840 --> 00:08:07.560 Because bracket normally means something else. 00:08:07.560 --> 00:08:09.880 Like it's a set of characters or something like that. 00:08:09.880 --> 00:08:10.580 Yeah. 00:08:10.580 --> 00:08:13.940 So I really like that it is sort of a safe way as well. 00:08:14.020 --> 00:08:19.080 Like it's a more you talk about what you want and if it has to be escaped or whatever it does. 00:08:19.080 --> 00:08:20.900 So, yeah, it's I like this. 00:08:20.900 --> 00:08:21.780 This is great. 00:08:21.780 --> 00:08:22.060 Yeah. 00:08:22.060 --> 00:08:22.320 Yeah. 00:08:22.320 --> 00:08:23.420 I do, too. 00:08:23.420 --> 00:08:26.700 I can certainly see myself using this if I'm writing regular expressions. 00:08:26.700 --> 00:08:27.540 It's great. 00:08:27.540 --> 00:08:29.600 Speaking of seeing yourself. 00:08:29.600 --> 00:08:31.160 Seeing yourself. 00:08:31.160 --> 00:08:31.560 Yes. 00:08:31.560 --> 00:08:37.360 Actually, for a long time, I've been following and paying attention to what Adrian Rosebrock. 00:08:37.360 --> 00:08:38.840 This is even an easy one. 00:08:38.840 --> 00:08:40.120 And I even mass record this. 00:08:40.120 --> 00:08:42.040 Adrian Rosebrock. 00:08:42.120 --> 00:08:44.380 He has a site called Py Image Search. 00:08:44.380 --> 00:08:59.580 And essentially, Adrian is teaching people about OpenCV and Python and actually a lot of AI stuff and doing some really cool things with Python and like cameras and webcams and stuff. 00:08:59.580 --> 00:09:03.900 And even on Raspberry Pis and stuff and doing lots of neat things. 00:09:03.900 --> 00:09:06.700 And I don't have a particular article to point to. 00:09:06.700 --> 00:09:08.880 We just haven't covered it before, I don't think. 00:09:08.880 --> 00:09:10.740 And people should know about it. 00:09:10.740 --> 00:09:16.500 So Adrian has both paid and free resources to teach people all about computer vision. 00:09:16.500 --> 00:09:18.060 And I think he's doing a cool job. 00:09:18.180 --> 00:09:19.420 Yeah, he's doing a real cool job. 00:09:19.420 --> 00:09:22.000 And there's so many great examples over there. 00:09:22.000 --> 00:09:23.440 I think OpenCV is great. 00:09:23.440 --> 00:09:27.500 And this is probably the best resource for OpenCV and Python intersected, right? 00:09:27.500 --> 00:09:35.060 I get emails from him every now and then with boxes detecting things running around on videos or something like that. 00:09:35.060 --> 00:09:35.660 It's great. 00:09:35.780 --> 00:09:42.420 So if you've got to do anything with computer vision and you want to use Python to do it and why wouldn't you want that, then this is a cool place, right? 00:09:42.420 --> 00:09:42.800 Yeah. 00:09:42.800 --> 00:09:53.580 And he has some like tons of cool projects that he's done over the years of, you know, hooking up a Raspberry Pi with a camera to detect people coming to your door and stuff like that. 00:09:53.580 --> 00:09:55.220 And it's some cool stuff. 00:09:55.220 --> 00:09:59.580 And then you brought up that he had one of the most successful Kickstarters ever. 00:09:59.820 --> 00:10:03.340 Yeah, he did a Kickstarter called Deep Learning for Computer Vision with Python. 00:10:03.340 --> 00:10:07.520 And yeah, it's definitely one of the most successful Kickstarters ever. 00:10:07.520 --> 00:10:10.380 So if you want to check out that book, that's really good. 00:10:10.380 --> 00:10:14.040 I think he has some videos that are coming along with it, but I linked to the Kickstarter as well. 00:10:14.040 --> 00:10:16.320 Yeah, it did okay. 00:10:16.320 --> 00:10:18.220 Okay. 00:10:18.220 --> 00:10:24.160 Speaking of doing very, very well, I want to tell everyone about DigitalOcean before we move on. 00:10:24.160 --> 00:10:27.220 And I decided it's time to think about this a little bit differently. 00:10:27.400 --> 00:10:29.840 You think about DigitalOcean and you're hosting and stuff a little bit differently. 00:10:29.840 --> 00:10:33.100 So, you know, most of us, I think you brought this up. 00:10:33.100 --> 00:10:34.000 You're not Netflix. 00:10:34.000 --> 00:10:35.100 You're not Google. 00:10:35.100 --> 00:10:36.260 You're not Facebook, right? 00:10:36.260 --> 00:10:36.760 Remember that? 00:10:36.760 --> 00:10:37.300 You're not LinkedIn. 00:10:37.300 --> 00:10:37.800 Yeah. 00:10:37.800 --> 00:10:39.360 You don't need these crazy architectures. 00:10:39.360 --> 00:10:45.640 And yet some of the most popular hosting platforms out there, you know, like AWS or Azure, 00:10:45.640 --> 00:10:56.520 they are built with thousands and thousands of knobs so that you could be Netflix with 50,000 servers running continuous chaos experiments and all that crazy stuff, right? 00:10:57.000 --> 00:11:09.520 But if you are actually just building what 99.9% of us are, more standard applications, then all that stuff is just overhead and complexity and cost. 00:11:09.520 --> 00:11:20.880 So you can join companies like Slack, WeWork, Docker, GitLab, and of course, us over at DigitalOcean and pay like five times less than what you would for AWS or Azure, right? 00:11:20.980 --> 00:11:24.100 So, for example, bandwidth is one cent instead of nine per gigabyte. 00:11:24.100 --> 00:11:28.640 Servers are five bucks instead of, you know, 50 or whatever a reserved instance is over at AWS. 00:11:28.640 --> 00:11:30.940 It's not just about price. 00:11:30.940 --> 00:11:31.820 Keep it simple. 00:11:31.820 --> 00:11:33.380 Use what you need. 00:11:33.380 --> 00:11:40.060 Don't have all these crazy, crazy features that you probably don't actually need because you're not building Netflix or Facebook or LinkedIn. 00:11:40.060 --> 00:11:43.780 Anyway, try them out at pythonbytes.fm/digital ocean. 00:11:43.980 --> 00:11:48.320 Get $100 credit for new users and see why we all love it for their infrastructure. 00:11:48.320 --> 00:11:48.960 Awesome. 00:11:48.960 --> 00:11:49.540 Thanks. 00:11:49.540 --> 00:11:50.080 Indeed. 00:11:50.080 --> 00:11:56.980 So, Brian, this next one, have you watched this video here that I'm about to talk about? 00:11:56.980 --> 00:11:58.940 I don't think I have. 00:11:58.940 --> 00:11:59.600 Maybe I have. 00:11:59.600 --> 00:11:59.960 All right. 00:12:00.300 --> 00:12:02.880 So, while we're talking, click this and open it up. 00:12:02.880 --> 00:12:03.340 Okay. 00:12:03.340 --> 00:12:04.620 And I'll tell people about it. 00:12:04.620 --> 00:12:06.660 Maybe mute your YouTube that it's going. 00:12:06.660 --> 00:12:07.040 Okay. 00:12:07.040 --> 00:12:07.360 Okay. 00:12:07.360 --> 00:12:15.800 So, this thing that I want to talk about is a visualization of Python development from original origin way back in the day up until 2012. 00:12:15.800 --> 00:12:22.380 And it's using this underlying system called GORCE, G-O-U-R-C-E, GORCE, I'm guessing. 00:12:23.020 --> 00:12:29.500 And the idea is GORCE is a visualization library that visualizes trees and stuff like this, like graph trees, not real trees. 00:12:29.500 --> 00:12:35.040 And what you can do is you can point it at a source repository. 00:12:35.040 --> 00:12:36.080 It could be SVN. 00:12:36.080 --> 00:12:39.320 It even supports CVS, but Git and Mercurial. 00:12:39.320 --> 00:12:46.620 And you can point it there, and it will do an animated over time visualization of that source library. 00:12:46.620 --> 00:12:47.800 So, not just Python. 00:12:47.800 --> 00:12:49.960 Any of these repositories you can point it at. 00:12:50.480 --> 00:12:59.900 And it will have the files as they grow, the size of the repository, and these little animated characters come in and start editing files and interacting with it. 00:12:59.900 --> 00:13:06.440 So, if you play this video that we're linking to, this is a GORCE animation of Python development up until 2012. 00:13:06.440 --> 00:13:10.540 And you see it starts out, and Gito is cruising around, adding little bits. 00:13:10.540 --> 00:13:11.620 Then a few more people come. 00:13:11.620 --> 00:13:16.280 And then if you start skipping further and further ahead, it gives just madness at the end. 00:13:16.280 --> 00:13:18.020 People are swirling all over the place. 00:13:18.020 --> 00:13:27.020 And it's just a really great way to, like, see the growth of Python visually through animation, at least in terms of who is participating in building it. 00:13:27.020 --> 00:13:28.060 What do you think? 00:13:28.060 --> 00:13:29.000 Yeah, it is cool. 00:13:29.000 --> 00:13:37.340 And one of the fun things to see is that there's sometimes some people that just sort of sit around one area, which makes sense. 00:13:37.400 --> 00:13:41.160 And then other people that fly around and edit all sorts of stuff. 00:13:41.160 --> 00:13:43.340 Yeah, it's really interesting, right? 00:13:43.340 --> 00:13:45.660 And you can see people appear, and then they'll fade away. 00:13:45.660 --> 00:13:48.680 They'll come and make some contributions, and then they'll, like, leave the scene. 00:13:48.680 --> 00:13:51.480 So, anyway, I think this is really cool. 00:13:51.480 --> 00:13:51.840 Yeah. 00:13:51.840 --> 00:13:55.760 And it's, you know, there's not a lot to take away from it other than it's just nice to appreciate it. 00:13:55.760 --> 00:13:59.440 I would say watch the first minute and then just, like, skip minute by minute and watch a little bit. 00:13:59.500 --> 00:14:00.580 Because it's 14 minutes. 00:14:00.580 --> 00:14:01.960 You don't really want to watch the whole thing. 00:14:01.960 --> 00:14:02.820 I think I do. 00:14:02.820 --> 00:14:06.000 But you could just leave it running for the rest of the show. 00:14:06.000 --> 00:14:06.340 Yeah. 00:14:06.780 --> 00:14:08.500 So, here's what I think. 00:14:08.500 --> 00:14:09.600 The call out to the audience. 00:14:09.600 --> 00:14:16.120 One, who wants to build this for 2012 to present on Python, you know, again? 00:14:16.120 --> 00:14:22.700 And I think this would make an amazing lightning talk if you built that video, and then you, like, went up there and just did a four-minute animation. 00:14:22.700 --> 00:14:23.800 Wouldn't that be cool? 00:14:23.800 --> 00:14:28.680 It'd be cool to just have that going on in the background while you did some other talk. 00:14:28.680 --> 00:14:29.300 Right, yeah. 00:14:29.300 --> 00:14:33.480 Or run this at, like, between sessions at a conference. 00:14:33.480 --> 00:14:34.380 I think it'd be great. 00:14:34.560 --> 00:14:37.240 Anyway, I think people will appreciate checking it out. 00:14:37.240 --> 00:14:37.520 Cool. 00:14:37.520 --> 00:14:46.200 So, one of the things, Brian, that you hear a lot in software development is that there's often a wide range of skills and productivity between developers. 00:14:46.200 --> 00:14:51.580 And I've done a lot of training and interacted with, you know, literally thousands of people in person. 00:14:51.580 --> 00:14:56.580 And I think it's something of a myth, but I think largely there's a lot to this. 00:14:56.580 --> 00:14:58.120 Yeah. 00:14:58.120 --> 00:15:02.920 Some people that just fly and they're just focused and others that just kind of bounce around the keyboard randomly. 00:15:02.920 --> 00:15:04.520 What do you think? 00:15:04.520 --> 00:15:06.580 So, I know that it's a bit controversial. 00:15:06.580 --> 00:15:09.120 There is this notion of a 10x developer. 00:15:09.120 --> 00:15:12.860 And often there's a backlash against it also. 00:15:12.860 --> 00:15:19.960 But I think people think of it as, like, somebody that's really 10 times better than the average good developer. 00:15:19.960 --> 00:15:21.460 And I don't think that's it at all. 00:15:21.460 --> 00:15:30.920 I think that it's just a notion that there is sometimes orders of magnitude between the most effective person in an organization and the least effective. 00:15:31.400 --> 00:15:33.980 And I don't know how you argue against that. 00:15:33.980 --> 00:15:38.260 And if you've ever been at large organizations, it just is. 00:15:38.260 --> 00:15:42.700 At least maybe it's not 10 times, but there's definitely – there's a lot to it. 00:15:42.700 --> 00:15:45.200 It's a multiplicative factor in there, I would say, for sure. 00:15:45.300 --> 00:15:45.640 Yeah. 00:15:45.640 --> 00:15:50.160 And so, this is – regardless of what you want to take away, there's some good advice in this article. 00:15:50.160 --> 00:15:55.600 There's an article I'm linking to that's what any developer can learn from the best. 00:15:55.600 --> 00:15:57.720 And I think these are good things. 00:15:57.720 --> 00:16:08.760 So, one of the things – the idea around it is just this isn't magical and it isn't something that is – it isn't just about skills and hard skills. 00:16:08.860 --> 00:16:10.000 It's other stuff too. 00:16:10.000 --> 00:16:12.520 And there's a clear path to excellence. 00:16:12.520 --> 00:16:14.940 People are not born great developers. 00:16:14.940 --> 00:16:17.420 They get there through focused, deliberate practice. 00:16:17.420 --> 00:16:19.760 And here's a few traits. 00:16:19.760 --> 00:16:25.980 They just listed some traits of things that they see in good developers versus not so great. 00:16:26.500 --> 00:16:30.620 So, great developers are – a few of the traits are problem solving. 00:16:30.620 --> 00:16:32.680 They're skilled at what they're doing. 00:16:32.680 --> 00:16:34.120 They're mentors and teachers. 00:16:34.120 --> 00:16:37.780 They're excellent learners and passionate about stuff. 00:16:37.780 --> 00:16:48.060 So, the problem solver bit I think is really interesting because often the 10x or the multiplier doesn't come in from that they do the same work faster. 00:16:48.720 --> 00:16:57.920 It's sometimes they can just look at things differently because of experience, because of playing around with lots of different things and say, oh, let's just solve this problem differently. 00:16:57.920 --> 00:17:04.060 And it just gets done faster because they take that – take a different approach. 00:17:04.060 --> 00:17:07.700 One of the – I was talking with somebody about databases recently. 00:17:08.380 --> 00:17:17.120 And there's some – for instance, there's some problems that can be solved with graph databases easily that are almost impossible with a relational database. 00:17:17.120 --> 00:17:21.100 It's just using the right tool for the right job sometimes. 00:17:21.100 --> 00:17:21.580 Yeah. 00:17:21.580 --> 00:17:28.320 And I think a lot of that is the – it's not that knowing that means you're 10x smarter or 10x better. 00:17:28.320 --> 00:17:37.640 That means you are curious enough to keep looking and to continuously be on the lookout for, well, okay, I now totally know all about relational databases. 00:17:37.940 --> 00:17:38.720 What else can I learn? 00:17:38.720 --> 00:17:39.860 How do I compare it? 00:17:39.860 --> 00:17:59.720 And I think that's one of the biggest traits that I've noticed among people that have some number of multiplier of efficiency or skills is not that they are necessarily more skilled or have more natural talent, but they're just continuously learning and really passionate about it. 00:17:59.720 --> 00:18:04.420 And they're just – they're just always picking up these little things that help you at each step, right? 00:18:04.420 --> 00:18:04.900 Yeah. 00:18:04.900 --> 00:18:04.940 Yeah. 00:18:04.940 --> 00:18:08.840 And there's an honesty of just like – this isn't just about developers. 00:18:08.840 --> 00:18:10.220 I think it's in every field. 00:18:10.220 --> 00:18:22.440 For instance, somebody that's working with tools and stuff, somebody that needs to hammer in a lot of nails might be smart enough to go, hey, I should go get a nail gun. 00:18:22.440 --> 00:18:24.240 I could do this a lot faster with a nail gun. 00:18:24.340 --> 00:18:25.520 You know what that makes me think of? 00:18:25.520 --> 00:18:34.640 Like you could say I need to have the rings on my engine or my transmission fixed on my car and you take it to a regular place. 00:18:34.640 --> 00:18:35.100 You're like, great. 00:18:35.100 --> 00:18:36.340 We'll have it back in three weeks. 00:18:36.340 --> 00:18:39.300 You go watch something like IndyCar or Formula One. 00:18:39.300 --> 00:18:42.260 They'll pull in and they'll like change the transmission in two laps. 00:18:42.260 --> 00:18:42.740 Yeah. 00:18:42.740 --> 00:18:42.920 Right? 00:18:42.980 --> 00:18:44.360 Those are both mechanics. 00:18:44.360 --> 00:18:45.460 Yeah. 00:18:45.460 --> 00:18:46.420 Those are not the same. 00:18:46.420 --> 00:18:47.140 Right. 00:18:47.140 --> 00:18:47.640 Exactly. 00:18:47.640 --> 00:18:50.060 The deliberate practice is important. 00:18:50.060 --> 00:19:02.060 And I think some people forget that the difference between being an average or a below average developer and being an above average developer is mostly just deciding that you're going to do that. 00:19:02.620 --> 00:19:10.480 And setting aside some of your time in your life to pick something you want to improve and then go and do it. 00:19:10.480 --> 00:19:12.080 Figure out what you want to learn and go do it. 00:19:12.080 --> 00:19:12.580 So. 00:19:12.580 --> 00:19:12.940 Yeah. 00:19:12.940 --> 00:19:13.840 Anyway. 00:19:13.840 --> 00:19:18.140 I guess what I would say is to take away from this article is it's really interesting. 00:19:18.140 --> 00:19:20.040 It's numerically based. 00:19:20.040 --> 00:19:20.240 Right? 00:19:20.240 --> 00:19:24.320 There's a lot of it's based on a survey the guy did with like a thousand folks or something. 00:19:24.320 --> 00:19:27.100 And most importantly, it's about a growth mindset. 00:19:27.100 --> 00:19:27.440 Right? 00:19:27.440 --> 00:19:32.280 It's not to say, well, there's these people and they're just smarter than you or they're smarter than other people or whatever. 00:19:32.960 --> 00:19:37.200 It's here's how those people got that way and you can do it too. 00:19:37.200 --> 00:19:39.360 And I think that's the right message. 00:19:39.360 --> 00:19:39.880 Right. 00:19:39.880 --> 00:19:47.340 And the person that wrote the article said this is from a lot of he's taught a lot of people and there's a lot of people that want to be better, but they don't know how to. 00:19:47.340 --> 00:19:51.380 So this is just sort of some direction on what things to work on. 00:19:51.380 --> 00:19:51.720 So. 00:19:51.720 --> 00:19:52.360 Yeah. 00:19:52.360 --> 00:19:52.980 It's a good article. 00:19:52.980 --> 00:19:54.060 Yep. 00:19:54.060 --> 00:19:54.900 And a nice find. 00:19:54.900 --> 00:19:57.040 Let's close this out with a bit of chaos. 00:19:57.040 --> 00:19:57.920 Sure. 00:19:57.920 --> 00:19:59.400 Why change tradition? 00:19:59.400 --> 00:19:59.720 Right? 00:19:59.720 --> 00:20:00.780 That's right. 00:20:01.500 --> 00:20:05.300 So you've heard of, I'm sure you've heard, I'm pretty sure you've been out on your show. 00:20:05.300 --> 00:20:10.400 If I remember correctly, you've talked about the chaos monkey and things like that, right? 00:20:10.400 --> 00:20:10.700 Yeah. 00:20:10.700 --> 00:20:14.680 Tell people what the, yeah, he's from Netflix, right? 00:20:14.680 --> 00:20:15.640 He used to be. 00:20:15.640 --> 00:20:15.900 Yes. 00:20:15.900 --> 00:20:16.180 Okay. 00:20:16.180 --> 00:20:19.220 So I think the idea of the chaos monkey originated there. 00:20:19.220 --> 00:20:20.640 Tell us what the chaos monkey is. 00:20:20.840 --> 00:20:36.840 Well, I'm probably going to get this wrong, but there's this notion of taking parts of your system and intentionally breaking parts or shutting down, especially in a distributed system, taking some nodes and just killing them every once in a while and seeing how your system recovers from it. 00:20:36.900 --> 00:21:06.880 Yeah, exactly. 00:21:06.880 --> 00:21:10.600 And then in production, it does it. 00:21:10.820 --> 00:21:12.820 And then, you know, you just have to build. 00:21:12.820 --> 00:21:17.360 So, cause you know, the chaos monkey is coming and it means like, well, a standard failure is like nothing. 00:21:17.360 --> 00:21:19.100 Cause the chaos monkey is a madman, right? 00:21:19.100 --> 00:21:20.200 He's running around all the time. 00:21:20.200 --> 00:21:26.080 So this is not a bad philosophy for large organizations or large bits of software. 00:21:26.440 --> 00:21:29.380 However, you're not Netflix, probably people listening. 00:21:29.380 --> 00:21:31.180 So how are you going to build that up? 00:21:31.180 --> 00:21:31.360 Right. 00:21:31.360 --> 00:21:32.460 How are you going to create these things? 00:21:32.460 --> 00:21:36.520 So there's this cool thing I found called chaos toolkit. 00:21:36.520 --> 00:21:45.520 So chaos toolkit is a library built in Python that will help create these chaos monkey like things. 00:21:45.520 --> 00:21:46.580 Cool. 00:21:46.720 --> 00:21:56.400 So chaos engineering is what they call is the discipline of experimenting on distributed systems in order to build confidence in the system's capability to withstand turbulent conditions in production. 00:21:56.400 --> 00:21:57.220 All right. 00:21:57.220 --> 00:22:00.900 So we talked about the chaos monkey and the friends, right? 00:22:00.900 --> 00:22:09.280 There's other types of chaos things that Netflix, but here's a way that you can easily build those types of experiments and systems. 00:22:09.940 --> 00:22:15.640 And it integrates with Kubernetes, AWS, Google cloud, Microsoft Azure, some other, other things like that. 00:22:15.640 --> 00:22:16.160 Right. 00:22:16.160 --> 00:22:29.900 So just to give you a sense of what it can do, like if you look at the AWS API, it'll say, you can do things like go to AWS Lambda and call a delete function concurrency that removes the concurrency limit on any specific Lambda. 00:22:29.900 --> 00:22:34.360 Or you can go just call stop instance on an EC2 instance or, you know, whatever you want. 00:22:34.360 --> 00:22:34.680 Right. 00:22:34.680 --> 00:22:37.140 And presumably it's going to put that back. 00:22:37.140 --> 00:22:37.980 I'm not entirely sure. 00:22:38.700 --> 00:22:46.160 But I guess you probably got to call start instance again on it or set, you know, add function concurrency or set, something like that. 00:22:46.160 --> 00:22:46.560 All right. 00:22:46.560 --> 00:22:54.660 But there's this infrastructure to help you change these types of settings and these types of things around your cloud providers and, you know, make sure your system can take it. 00:22:54.660 --> 00:22:55.040 Nice. 00:22:55.040 --> 00:22:55.780 Yeah. 00:22:55.780 --> 00:22:57.500 You cannot plan for the best. 00:22:57.500 --> 00:22:59.440 So you plan for what you can for. 00:22:59.440 --> 00:23:00.640 That's right. 00:23:00.640 --> 00:23:01.520 Yeah. 00:23:01.520 --> 00:23:03.860 So you build it so that it doesn't have to be perfect. 00:23:03.860 --> 00:23:05.420 And then you're in pretty good shape. 00:23:05.420 --> 00:23:07.940 The stuff that the folks at Netflix are doing is insane, though. 00:23:08.000 --> 00:23:09.220 Like they take it to another level. 00:23:09.220 --> 00:23:10.040 Well, yeah. 00:23:10.040 --> 00:23:19.680 One of the things I remember talking with them about is the reason why they test and do all this in production is because their system is so large. 00:23:19.680 --> 00:23:20.320 You can't. 00:23:20.320 --> 00:23:21.820 It's essentially the world. 00:23:21.820 --> 00:23:22.380 You cannot. 00:23:22.380 --> 00:23:29.460 You can't have a test bed that's similar enough to the real world environment. 00:23:29.740 --> 00:23:31.700 So they don't have that luxury. 00:23:31.700 --> 00:23:32.420 Right. 00:23:32.420 --> 00:23:34.060 So they just test it in production. 00:23:34.060 --> 00:23:36.640 And I've not seen Netflix really go down. 00:23:36.640 --> 00:23:37.920 So I'm saying they're doing it. 00:23:37.920 --> 00:23:38.120 Yeah. 00:23:38.120 --> 00:23:38.440 All right. 00:23:38.440 --> 00:23:39.720 That's it for our main news. 00:23:39.720 --> 00:23:40.760 I have a couple of quick ones. 00:23:40.760 --> 00:23:41.740 You got anything you want to share, Brian? 00:23:41.740 --> 00:23:42.180 Yeah. 00:23:42.300 --> 00:23:45.220 Just on the Python bytes episode 100. 00:23:45.220 --> 00:23:49.480 We one of the things we talked about was pyproject.toml. 00:23:49.480 --> 00:23:51.460 And I wanted to take a deep dive. 00:23:51.460 --> 00:23:58.380 So the last episode of testing code is me talking to Brett Cannon and talking about that for almost an hour or so. 00:23:58.380 --> 00:23:59.340 Oh, nice. 00:23:59.340 --> 00:24:00.480 I definitely want to check that out. 00:24:00.480 --> 00:24:01.120 That's a good one. 00:24:01.120 --> 00:24:02.240 So how about you? 00:24:02.240 --> 00:24:07.460 I have two other things that I don't think justify a whole segment, but I just want to throw them out there. 00:24:07.460 --> 00:24:18.600 Remember a while back we talked about that there was potentially some experiments or something where some college researcher had put some malicious, potentially malicious libraries on PyPI? 00:24:18.600 --> 00:24:19.180 Yeah. 00:24:19.180 --> 00:24:23.400 Well, it turns out recently someone actually put malicious libraries on PyPI. 00:24:23.400 --> 00:24:26.400 So this is October 27, 2018. 00:24:26.400 --> 00:24:31.900 And apparently 12 packages were discovered with various levels of vulnerabilities and stuff. 00:24:32.040 --> 00:24:32.840 So I'm linking to that. 00:24:32.840 --> 00:24:33.960 People should check that out. 00:24:33.960 --> 00:24:38.700 If you don't know how to spell Django, you're going to have a bad time, by the way. 00:24:38.700 --> 00:24:40.060 Yeah. 00:24:40.060 --> 00:24:45.280 So it's a lot of this typo squatting like, oh, I forgot the J, so now I have a virus. 00:24:45.280 --> 00:24:46.140 Something like that. 00:24:46.140 --> 00:24:46.880 Yeah. 00:24:46.880 --> 00:24:48.200 That's sort of lame. 00:24:48.200 --> 00:24:48.460 Yeah. 00:24:48.460 --> 00:24:49.120 It's upsetting. 00:24:49.120 --> 00:24:57.240 At least they've done some work over at PyPI to block properly spelled things that are not actually packages, like RE, for example, right? 00:24:57.240 --> 00:24:58.520 The built-in stuff. 00:24:58.520 --> 00:24:59.000 Well, yeah. 00:24:59.060 --> 00:25:05.520 That's one of the things that's, yeah, was an issue is people can try to pip install things that are in their standard library. 00:25:05.520 --> 00:25:07.080 You don't have to do that. 00:25:07.080 --> 00:25:07.820 Yes, exactly. 00:25:07.820 --> 00:25:09.400 Exactly. 00:25:09.400 --> 00:25:10.120 Don't do that. 00:25:10.120 --> 00:25:15.240 Then the other one was just a quick little Twitter message that someone sent my way. 00:25:15.860 --> 00:25:17.200 And it's pretty cool. 00:25:17.200 --> 00:25:20.300 This guy, not the one who sent it to me. 00:25:20.300 --> 00:25:21.060 I'm sorry. 00:25:21.060 --> 00:25:21.980 I don't remember who sent it. 00:25:21.980 --> 00:25:22.880 I should have written it down. 00:25:22.880 --> 00:25:35.040 But this person called Xtrek, something to that effect, on Twitter, decided to go scanning the standard library source code for interesting things like, what is the longest class name? 00:25:35.040 --> 00:25:36.260 What is the longest function name? 00:25:36.280 --> 00:25:36.760 And so on. 00:25:36.760 --> 00:25:48.760 So they found out that actually in CPython, the longest class name is 200 characters, just the letter A 200 times, which is some kind of test case. 00:25:48.760 --> 00:25:59.180 But for the real ones, there's one called test, mutually exclusive optionals and positions, mixed parent, as the longest class name is there. 00:25:59.180 --> 00:26:11.240 In the longest function name, they believe it's test underscore parser underscore regression underscore special underscore character underscore in parameter column of doctrine first line, which is 84 characters long. 00:26:11.760 --> 00:26:14.040 And then there's other examples. 00:26:14.040 --> 00:26:21.180 Someone says there's actually a test C types that has 33 million characters in it. 00:26:21.180 --> 00:26:26.260 Anyway, it's an interesting thread if you're just wondering what the really long names are. 00:26:26.260 --> 00:26:26.620 Yeah. 00:26:26.620 --> 00:26:28.200 It's almost competitive. 00:26:28.200 --> 00:26:30.480 It almost could compete with Java names. 00:26:30.480 --> 00:26:31.320 Yeah, exactly. 00:26:31.320 --> 00:26:33.300 Standard types right there. 00:26:33.300 --> 00:26:34.560 Awesome. 00:26:34.560 --> 00:26:34.900 All right. 00:26:34.900 --> 00:26:37.900 Well, definitely fun to share all this news with you, Brian. 00:26:37.900 --> 00:26:39.520 As always, thanks for doing it. 00:26:39.520 --> 00:26:39.880 All right. 00:26:39.880 --> 00:26:40.420 Thank you. 00:26:40.420 --> 00:26:40.640 Bye. 00:26:40.640 --> 00:26:40.940 You bet. 00:26:40.940 --> 00:26:41.200 Bye. 00:26:41.200 --> 00:26:41.240 Bye. 00:26:41.240 --> 00:26:43.360 Thank you for listening to Python Bytes. 00:26:43.360 --> 00:26:45.880 Follow the show on Twitter via at Python Bytes. 00:26:45.880 --> 00:26:48.780 That's Python Bytes as in B-Y-T-E-S. 00:26:48.780 --> 00:26:52.180 And get the full show notes at Python Bytes.fm. 00:26:52.180 --> 00:26:56.540 If you have a news item you want featured, just visit Python Bytes.fm and send it our way. 00:26:56.540 --> 00:26:59.240 We're always on the lookout for sharing something cool. 00:26:59.240 --> 00:27:02.620 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:27:02.620 --> 00:27:06.140 Thank you for listening and sharing this podcast with your friends and colleagues.