WEBVTT 00:00:00.000 --> 00:00:05.060 You wake up, brew the coffee, open GitHub, and there it is, another pull request on your open source project. 00:00:05.500 --> 00:00:11.400 13,000 lines added, no issue filed first, no discussion, just here, please review this for me. 00:00:11.960 --> 00:00:23.060 Over the past year, GitHub activity has spiked roughly 12 times in a few short months, and a huge chunk of that signal is landing on the same small group of maintainers who are already stretched very thin. 00:00:23.360 --> 00:00:35.960 The curl bug bounty got buried under AI-generated noise, jazz band, the home of Django classics like pip-tools, and the Django debug toolbar hit what its maintainers called an apocalypse and started sunsetting. 00:00:36.420 --> 00:00:41.200 Even CorePython just shipped fresh guidelines on AI-assisted contributions this week. 00:00:41.700 --> 00:00:45.060 So what does all this actually look like from the receiving end of the pull requests? 00:00:45.440 --> 00:00:50.340 On this episode, Paolo Machare joins us to tell the story from inside the maintainer's chair. 00:00:50.340 --> 00:01:04.360 Paolo is the director of the Django Software Foundation, an organizer of PyCon Italy, a Django's girls coach, and has spent the past year carefully collecting examples of how AI is reshaping open source contributions, the good, the bad, and the extra fingers. 00:01:04.840 --> 00:01:18.600 We dig into his PyCon US talk on AI-assisted contributions and maintainer load, why AI is best understood as an amplifier rather than a new kind of contributor, the widely different policies across 86 open source foundations and projects, 00:01:18.600 --> 00:01:23.820 and we ponder whether projects banning AI today are reacting to last year's models. 00:01:24.420 --> 00:01:29.480 This is Talk Python To Me, episode 551, recorded May 22nd, 2026. 00:01:29.480 --> 00:01:53.080 Welcome to Talk Python To Me, the number one Python podcast for developers and data scientists. 00:01:53.320 --> 00:01:54.920 This is your host, Michael Kennedy. 00:01:55.260 --> 00:01:58.900 I'm a PSF fellow who's been coding for over 25 years. 00:01:58.900 --> 00:02:00.600 Let's connect on social media. 00:02:00.900 --> 00:02:04.080 You'll find me and Talk Python on Mastodon, Bluesky, and X. 00:02:04.240 --> 00:02:06.220 The social links are all in your show notes. 00:02:06.940 --> 00:02:13.980 You can find over 10 years of past episodes at talkpython.fm, and if you want to be part of the show, you can join our recording live streams. 00:02:14.140 --> 00:02:18.200 That's right, we live stream the raw, uncut version of each episode on YouTube. 00:02:18.680 --> 00:02:23.220 Just visit talkpython.fm/youtube to see the schedule of upcoming events. 00:02:23.360 --> 00:02:27.040 Be sure to subscribe there and press the bell so you'll get notified anytime we're recording. 00:02:27.040 --> 00:02:33.880 What if your AI agents worked like FastAPI microservices, typed, autonomous, and discovering each other at runtime? 00:02:34.500 --> 00:02:36.220 That's the world Agent Field is building. 00:02:36.800 --> 00:02:39.720 Join them at talkpython.fm/Agent Field. 00:02:39.920 --> 00:02:45.080 Hey friends, before we dive in, I want to share something I just launched over at Talk Python, a new course. 00:02:45.080 --> 00:02:51.780 If you ship Python to production, you've probably had that quiet thought about what might be lurking in your code. 00:02:52.440 --> 00:02:58.520 My new course turns that worry into a concrete workflow and a reusable AI security agent that's yours forever. 00:02:59.160 --> 00:03:03.020 It's Python Web Security, the OWASP top 10 with agentic AI. 00:03:03.840 --> 00:03:07.540 Check it out at talkpython.fm/AI dash web dash security. 00:03:07.740 --> 00:03:13.640 The link is in your podcast player and on the episode page, or just go to talkpython.fm and click on courses at the top. 00:03:13.640 --> 00:03:14.780 It should be right there. 00:03:15.300 --> 00:03:20.960 So if you're interested in making sure the apps and APIs that you ship are secure, this is the course for you. 00:03:21.580 --> 00:03:22.500 Welcome to Talk Python To Me. 00:03:22.580 --> 00:03:23.240 Great to have you here, man. 00:03:23.880 --> 00:03:24.120 Hi, Michael. 00:03:24.220 --> 00:03:28.220 Thanks for having me and meeting you again after PyCon US last week. 00:03:28.320 --> 00:03:29.700 We had a great time at PyCon. 00:03:30.080 --> 00:03:31.940 I mean, I don't know how you feel about it. 00:03:32.080 --> 00:03:34.740 Given all the work that you do, I suspect you feel similarly. 00:03:35.100 --> 00:03:37.500 But to me, PyCon is like my geek holiday. 00:03:37.500 --> 00:03:38.840 It doesn't feel like work. 00:03:39.060 --> 00:03:42.300 It's like the reward for all the work throughout the year. 00:03:42.300 --> 00:03:42.780 Yeah. 00:03:42.960 --> 00:03:49.360 So for me, it's very nice to meet in person all these people that you talk with during the year. 00:03:49.520 --> 00:03:55.980 And then finally, for a world week, you can chat with them, drink a beer together, or meeting also new people. 00:03:56.340 --> 00:03:56.960 Yeah, absolutely. 00:03:57.140 --> 00:03:58.240 It was tons of fun. 00:03:58.660 --> 00:04:04.140 So we're going to talk about, honestly, I think it's going to be a fad, but we're going to talk about this new fad called AI. 00:04:04.440 --> 00:04:06.100 Probably not going to last. 00:04:06.480 --> 00:04:09.380 But in the meantime, it's kind of a hot topic at the moment. 00:04:09.380 --> 00:04:22.560 So we're going to talk about AI and specifically your talk from PyCon about the effect of AI on open source maintainers, which I think is a perspective that many people don't consider. 00:04:23.040 --> 00:04:23.120 Right? 00:04:23.220 --> 00:04:29.560 There was this graph from GitHub saying, look, we're really sorry that GitHub is down a lot lately. 00:04:29.820 --> 00:04:32.160 But here is the usage graph of GitHub. 00:04:32.160 --> 00:04:38.760 And it did something like 12 times increase in activity over a couple of months. 00:04:38.880 --> 00:04:42.600 I don't know, which I'm not necessarily totally going to give GitHub a pass on that. 00:04:43.460 --> 00:04:53.600 But it is a signal and an indicator of just how much more the contributions, issues, pull requests are to GitHub. 00:04:53.920 --> 00:04:57.740 Some of those are going to private repos or public one person repos. 00:04:57.740 --> 00:05:04.040 But some of that traffic is going towards Django, Flask, Postgres, Curl, whatever. 00:05:04.440 --> 00:05:04.560 Right? 00:05:04.700 --> 00:05:09.100 And there's still one person or a couple of people on the receiving ends of those projects. 00:05:09.460 --> 00:05:10.120 Yeah, it's true. 00:05:10.340 --> 00:05:19.380 As I would say during my talk, I think AI here is a tool that amplified all the contribution, positive and negative effects. 00:05:19.380 --> 00:05:28.640 So also small project or one-man project now have tons and tons of pull requests and issues opened in a very few months of time. 00:05:28.840 --> 00:05:35.440 This is not the best thing to have if you have a very few time as a maintainer or you have also a job in the family. 00:05:35.720 --> 00:05:38.920 So this is what I try to analyze in my talk. 00:05:39.180 --> 00:05:39.560 It's tricky. 00:05:39.820 --> 00:05:40.500 It's very tricky. 00:05:40.840 --> 00:05:45.060 Now, before we get into a bunch of different things, primarily your talk, who are you? 00:05:45.160 --> 00:05:46.180 Quick introduction for everyone. 00:05:46.520 --> 00:05:46.700 Yeah. 00:05:46.940 --> 00:05:47.680 Very quick one. 00:05:47.680 --> 00:05:51.280 I am a software engineer from Italy. 00:05:51.280 --> 00:05:58.180 I studied in Bologna and just after my career in the university, I started working with Python, which I never used at university. 00:05:58.180 --> 00:06:03.660 But I started using Zope, which is a very old web framework from the 2000s. 00:06:03.660 --> 00:06:07.880 And also, I think, we do work in the Zope Corporation back in the time. 00:06:07.880 --> 00:06:15.140 But I liked a lot the backend work with database, relational database in particular. 00:06:15.380 --> 00:06:20.200 So I jumped from the Plone community into the community in the Django one in 2011. 00:06:20.200 --> 00:06:22.700 And I've been more and more involved. 00:06:22.700 --> 00:06:26.700 Until today, I'm a member of the Django Software Foundation. 00:06:26.700 --> 00:06:29.820 I'm a Python Software Foundation fellow. 00:06:30.220 --> 00:06:36.540 And I tried to involve in a buddy initiative like Python Italia organization. 00:06:36.540 --> 00:06:40.940 I founded the local Python Pescal in my city, local meetup. 00:06:41.140 --> 00:06:47.600 And I tried to talk about Python, different conferences in person and remotely. 00:06:48.040 --> 00:06:49.040 Nice. 00:06:49.040 --> 00:06:50.440 Well, that will keep you busy. 00:06:50.440 --> 00:06:52.100 That's a lot going on. 00:06:52.100 --> 00:06:55.060 Let's actually just let me switch over here. 00:06:55.180 --> 00:06:56.320 Let's just jump right into that. 00:06:56.420 --> 00:06:59.700 I think maybe talking about some of your work. 00:06:59.880 --> 00:07:02.240 You mentioned some of these in your introduction here. 00:07:02.380 --> 00:07:05.940 But people are probably familiar with the Python Software Foundation. 00:07:06.540 --> 00:07:13.780 But when you switch from Plone and Zope over to Django, you move to a place that has an incredible community. 00:07:14.560 --> 00:07:19.680 It's probably the biggest community, organized community in all of Python. 00:07:19.680 --> 00:07:21.140 That's not Python itself. 00:07:21.460 --> 00:07:24.240 Maybe something in data science, but I don't think so. 00:07:24.300 --> 00:07:29.120 I think Django has got the most gravity of all of these different organizations. 00:07:29.260 --> 00:07:34.300 So it has its own software foundation with people that work for the Django Software Foundation and so on. 00:07:34.340 --> 00:07:36.920 So you're the director of Django Software Foundation, yes? 00:07:37.180 --> 00:07:37.400 Yeah. 00:07:38.280 --> 00:07:39.160 Tell us about it. 00:07:39.400 --> 00:07:43.240 Django, last year we had the 20th birthday. 00:07:43.520 --> 00:07:44.660 So it's 20 years around. 00:07:44.860 --> 00:07:45.960 Now this year, 21. 00:07:45.960 --> 00:07:50.320 One of the oldest community that now is organized. 00:07:50.700 --> 00:07:53.140 They created the foundation from the beginning. 00:07:53.340 --> 00:07:58.340 So now it's one of the few projects with a big foundation and also a big community. 00:07:59.120 --> 00:08:07.040 And we organized Django Cone since a long time now in different countries, in the US, in Europe, in Africa, in Australia. 00:08:07.040 --> 00:08:10.120 We are starting also some initiatives like that in India. 00:08:10.500 --> 00:08:17.240 And the foundation here has the role to preserve the community and the software. 00:08:17.240 --> 00:08:25.700 I think the Django Software Foundation is one of the most interesting because we experiment a lot of solutions. 00:08:26.120 --> 00:08:31.620 Also before the Python Software Foundation, for example, we started having the Django Fellow a long time ago. 00:08:31.620 --> 00:08:37.640 And now the Python Foundation is the developer in residence, which is the same things. 00:08:37.860 --> 00:08:41.600 We try to be involved in the various initiatives. 00:08:42.120 --> 00:08:47.320 We create, we sustain the conference, the event as a Django Software Foundation. 00:08:47.720 --> 00:08:53.080 For example, we give money for the organization of Django Girls Workshop all around the world. 00:08:53.080 --> 00:09:04.200 Now we created, we are sustaining the Django Naut Space program, which has been the subject of the topic of the last keynote in PyCon US last week. 00:09:04.200 --> 00:09:08.700 And other small initiatives like events or Django on the Mads. 00:09:09.200 --> 00:09:16.980 So the goal of the foundation is to sustain the community, the various aspects of the community, and also have higher. 00:09:16.980 --> 00:09:26.180 Now we have three Django Fellows that can be full-time working in triaging, helping the community to having the best code. 00:09:26.320 --> 00:09:29.920 We can have released the version on Python of Django, sorry. 00:09:30.260 --> 00:09:33.120 And yeah, this is the work we try to do every month. 00:09:33.280 --> 00:09:34.280 It's amazing, really. 00:09:34.560 --> 00:09:35.260 You think about it. 00:09:35.480 --> 00:09:37.340 There's a lot of other Python web frameworks. 00:09:37.660 --> 00:09:38.460 They don't have this. 00:09:38.860 --> 00:09:39.320 You know what I mean? 00:09:39.320 --> 00:09:42.200 There's a lot of data science groups and they have it. 00:09:42.340 --> 00:09:43.240 You know, there's PyData. 00:09:43.240 --> 00:09:46.940 So in a sense, it's somewhat, but that's all the PyData. 00:09:47.100 --> 00:09:49.180 It's not, you know, pandas or whatever, right? 00:09:49.220 --> 00:09:50.360 So this is really impressive. 00:09:50.700 --> 00:09:55.820 And I think it's honestly one of the bits of the secret to the success of Django. 00:09:56.160 --> 00:09:56.800 Yeah, I agree. 00:09:57.080 --> 00:10:03.220 The fact that we had people that can dedicate full-time or volunteers in the boards. 00:10:03.220 --> 00:10:07.340 We all are volunteer and they spend time during the week. 00:10:07.340 --> 00:10:12.440 And then for the meeting to sustain all the working group we are creating now. 00:10:12.760 --> 00:10:21.220 We try to defend the trademark of Django and sustain all the working group that we are now for the security part and for other things. 00:10:21.560 --> 00:10:30.820 So the goalie here is trying to be organized a little to let everyone join the community and stay inside the community of Django. 00:10:30.820 --> 00:10:31.220 Right. 00:10:31.420 --> 00:10:33.420 You're also a, not that one. 00:10:33.580 --> 00:10:36.740 I don't think I have a link for it, but you're a Django Girls coach as well. 00:10:36.900 --> 00:10:37.560 What does that mean? 00:10:37.640 --> 00:10:39.480 I mean, we know what Django Girls is. 00:10:39.540 --> 00:10:46.260 It's like an organization that specifically supports women in tech by helping them get good at Django and providing community there. 00:10:46.360 --> 00:10:48.820 But what does it mean to be a coach for Django Girls? 00:10:49.160 --> 00:10:49.360 Yeah. 00:10:49.520 --> 00:10:57.040 Django Girls is an initiative that started in EuroPython more than 10 years ago, if I remember correctly, or longer than that. 00:10:57.040 --> 00:11:02.460 So they are workshops for one whole day. 00:11:02.900 --> 00:11:10.080 And you invited girls and also low representative people in this workshop for free. 00:11:10.420 --> 00:11:18.460 Being a coach there means only stay there with the people that join and help them through the process of completing the workshop. 00:11:18.940 --> 00:11:20.460 There is a very good guide. 00:11:20.460 --> 00:11:32.460 A lot of people can do at home, but being there for them means starting, creating a human connection with people, demonstrating them that their community is not only code, but also a person. 00:11:32.460 --> 00:11:37.700 And I also organized two of these other than being coached here in Pescara. 00:11:38.080 --> 00:11:40.320 The goal is to organize more of that. 00:11:40.320 --> 00:11:50.460 And if you go on the website, you see that every week, a lot of this workshop happen everywhere in the world, from Australia to US, Europe, Africa, everywhere. 00:11:50.860 --> 00:11:51.380 Yeah, very nice. 00:11:51.620 --> 00:12:03.460 I think that's something that's super important for people to learn earlier, that there's more than just the code and there's more than just the docs and compiler or runtime errors and warnings and stuff like that. 00:12:03.460 --> 00:12:13.300 The stereotype of a software developer, someone who is kind of antisocial, just sits there and, you know, holds themselves up in a basement or whatever. 00:12:13.440 --> 00:12:14.980 And really, it's a team sport. 00:12:15.240 --> 00:12:17.140 And I think that's coming back around to PyCon. 00:12:17.220 --> 00:12:24.520 I think that's why PyCon is so interesting, because you make those, you put a face to the name and you realize, oh, there's all these people doing this and making this possible. 00:12:24.600 --> 00:12:27.840 It's not just this, you know, pip install this package or whatever. 00:12:28.180 --> 00:12:28.780 Totally true. 00:12:28.780 --> 00:12:36.660 Python is a community-driven language, but it means that there is a whole community that sustains everyone. 00:12:36.900 --> 00:12:37.400 100%. 00:12:37.400 --> 00:12:44.440 Now, something else that Django has that's pretty interesting is DjangoNuts, DjangoNuts.space. 00:12:44.600 --> 00:12:51.360 I had Sarah Boyce and Tushar Gupta on a while ago to dive deep into this, but this is a really interesting idea as well. 00:12:51.360 --> 00:12:59.420 And I think this, pulling this up specifically, because I think it relates, it starts to get into the space of where your talk actually lands, right? 00:12:59.580 --> 00:13:03.760 So when I first heard this, I thought, oh, this is to help people get good at Django. 00:13:03.980 --> 00:13:06.380 But really, that's not exactly what it is, is it? 00:13:06.560 --> 00:13:06.800 Yeah. 00:13:06.880 --> 00:13:09.380 I remember seeing the first presentation of the program. 00:13:09.480 --> 00:13:10.980 I was very impressed. 00:13:10.980 --> 00:13:19.780 So if the Django Girls program helps people that do know anything about programming, start doing that. 00:13:19.980 --> 00:13:33.680 This program wants to answer the question of people that already know how to program in Python or in Django, how to start be more involved and start contributing back to the code of Django in particular or other projects. 00:13:33.680 --> 00:13:41.200 So now we added in the footer, you can see Beware, Wigdale, other initiatives, very connected with Python and Django. 00:13:41.200 --> 00:14:00.500 So the goal is to have a small group of people with a navigator that can help them to start contributing, start reading an issue from the issue tracker, understanding how to contribute in the best effective way and to help them do the steps with other people that already did it. 00:14:00.500 --> 00:14:04.120 And it's an accelerator for people that want to contribute back. 00:14:04.400 --> 00:14:09.640 I was part of that once as a navigator, and that's been a very great experience. 00:14:10.320 --> 00:14:15.140 And now I organize more than one per year, more than one session per year. 00:14:15.480 --> 00:14:23.100 And it was great seeing all these people coming for the program and then staying to start contributing effectively. 00:14:23.420 --> 00:14:29.880 You can track them on the code base or the blog of Django or these places that they contribute now. 00:14:29.880 --> 00:14:34.960 And they go in, I forgot what they're called, but like cohorts or whatever, right? 00:14:34.980 --> 00:14:36.500 That goes for a certain period of time. 00:14:36.760 --> 00:14:42.180 And then you apply to be part of the cohort and then the cohort runs and so on. 00:14:42.400 --> 00:14:44.060 So people out there can go apply. 00:14:44.200 --> 00:14:44.780 It's free, right? 00:14:44.820 --> 00:14:46.120 You just have to get accepted. 00:14:46.580 --> 00:14:46.840 Yeah. 00:14:47.200 --> 00:14:48.280 A lot of people apply. 00:14:48.780 --> 00:14:50.880 There is a review of the application. 00:14:50.880 --> 00:14:58.820 So the admins form a small group of four people, of three or four people with a navigator that can add them. 00:14:59.020 --> 00:15:02.120 And then there is a period of six weeks. 00:15:02.380 --> 00:15:07.760 And during the while they meet once in a while, they can work to each other because they have a chat. 00:15:07.760 --> 00:15:14.640 And they have a way to track all the things they learn, the attempt to contribute. 00:15:15.240 --> 00:15:22.740 And they are together as a team for this part of the year, helping them each other and having feedback. 00:15:23.000 --> 00:15:27.600 We have a lot of initiative during this period, like talks and meeting online. 00:15:27.600 --> 00:15:36.280 And a way to have a support also for things that happen in life is a small version of what can happen for real and the whole community. 00:15:36.280 --> 00:15:45.480 When you meet people and you not talk only about the code, sometimes you talk about what happens live, job, other multiple things. 00:15:45.800 --> 00:15:47.820 This is a small version of it. 00:15:48.220 --> 00:15:48.400 Nice. 00:15:48.640 --> 00:15:53.900 You know, there's at least people who are junior developers or earlier stage developers. 00:15:53.900 --> 00:15:57.420 I think there's a pretty real challenge of getting a job right now. 00:15:57.840 --> 00:16:02.900 And contributing to open source is certainly one of those areas that helps you stand above. 00:16:03.080 --> 00:16:10.100 Certainly on a popular project like Django, if you go around and say, yeah, I'm a Django core developer, contributor or whatever, that would make a big difference. 00:16:10.480 --> 00:16:10.680 Right. 00:16:10.780 --> 00:16:15.300 So I think projects like this, you know, people are really super excited. 00:16:15.640 --> 00:16:22.080 Maybe this is a good opportunity to put another feather in the cap or something good on the resume, you know? 00:16:22.080 --> 00:16:22.420 Yeah. 00:16:22.420 --> 00:16:22.560 Yeah. 00:16:22.560 --> 00:16:30.900 Every time I met some new partners in the community, also in my job, every time I pushed them to try to contribute back. 00:16:31.480 --> 00:16:35.380 It's good in your resume, if you can show that you contributed. 00:16:35.620 --> 00:16:43.880 But more than that, you learn so much in contributing to Django, respect of only using Django in your daily job. 00:16:43.880 --> 00:16:48.860 because you are forced to go in the internals of Django with the help of the core contributors. 00:16:49.440 --> 00:17:00.140 You can work also in one of the biggest companies, but it's impossible that you have so many skilled Django contributors or Python contributors when you contribute directly with the code. 00:17:00.140 --> 00:17:02.820 And it's the fastest way to improve as a developer. 00:17:03.540 --> 00:17:07.220 It's more important than having something to put in your CV. 00:17:07.500 --> 00:17:10.680 I think it's an accelerator of your learning. 00:17:10.680 --> 00:17:22.380 So I've seen multiple people who come out of university or other things like that, gotten onto an open source project and it's just skyrocketed their career, which is super cool. 00:17:23.620 --> 00:17:26.560 This portion of Talk Python on Me is brought to you by Agentfield. 00:17:27.160 --> 00:17:34.700 What happens when you give hundreds of AI agents a shared code base and let them write code, review each other's work and ship to production? 00:17:35.360 --> 00:17:38.900 Well, that's exactly what the team behind Agentfield AI built. 00:17:38.900 --> 00:17:43.280 And the wild part, it's not some proprietary system locked behind a paywall. 00:17:43.580 --> 00:17:45.360 It's an open source Python library. 00:17:45.840 --> 00:17:54.840 Now, where most agent frameworks have you wiring up DAGs and workflows, Agentfield lets you build AI agents the way you build FastAPI microservices. 00:17:55.500 --> 00:17:59.040 Think typed Python functions that become autonomous services. 00:17:59.420 --> 00:18:07.360 They discover each other at runtime, call each other like APIs, scale independently, fail independently, and recover on their own. 00:18:07.360 --> 00:18:08.300 And here's the thing. 00:18:08.620 --> 00:18:10.760 You're not just orchestrating LLM calls. 00:18:11.260 --> 00:18:13.540 You can orchestrate entire anonymous tools. 00:18:14.100 --> 00:18:24.920 Spin up multiple Claude Code instances, codex sessions, any coding harness you want, all running as live nodes on the same architecture, collaborating and verifying each other's output. 00:18:25.580 --> 00:18:27.260 That's how they build the factory. 00:18:27.420 --> 00:18:29.160 And it's completely free and open source. 00:18:29.600 --> 00:18:32.200 Check it out at talkpython.fm/agentfield. 00:18:32.340 --> 00:18:34.360 That's talkpython.fm/agentfield. 00:18:34.360 --> 00:18:37.140 The link is in your podcast player's show notes. 00:18:37.460 --> 00:18:39.420 Thank you to Agentfield for supporting the show. 00:18:40.260 --> 00:18:45.520 Okay, last thing before we dive into your main topic is PyCon Italy organizer. 00:18:46.320 --> 00:18:47.240 And this is coming up. 00:18:47.300 --> 00:18:49.520 First of all, I love the website design. 00:18:49.620 --> 00:18:50.260 It looks very fun. 00:18:50.340 --> 00:18:51.740 But it's coming up really soon. 00:18:51.740 --> 00:18:55.700 Basically seven, no, five days from now from recording. 00:18:56.200 --> 00:19:01.540 So people, I think this episode will actually come out after that in terms of on the podcast channels. 00:19:01.640 --> 00:19:08.340 But on the YouTube channel, at least, we can encourage people to attend if they're local, somewhat local, or want to just go to Italy for a few days. 00:19:08.340 --> 00:19:08.900 Yeah, yeah. 00:19:09.200 --> 00:19:17.540 So I started, PyCon Italy is, of course, my preferred conference because it is the first one that I started attending a long time ago. 00:19:17.880 --> 00:19:23.720 And after a while, I decided to be involved and help other organizers to organize it. 00:19:24.200 --> 00:19:25.780 It will happen next year. 00:19:25.780 --> 00:19:32.160 After a long time being organized in Florence, this year we moved, the last year we moved in Bologna. 00:19:32.500 --> 00:19:34.080 And now we have a wider venue. 00:19:34.380 --> 00:19:36.760 So last year we had more than 1,000 people. 00:19:37.180 --> 00:19:38.660 90% of the talk. 00:19:38.860 --> 00:19:39.940 Yeah, it's quite big. 00:19:40.280 --> 00:19:42.080 90% of the talk are in English. 00:19:42.740 --> 00:19:46.000 The keynote, the workshop, we have multiple tracks, I think. 00:19:46.300 --> 00:19:48.820 Six tracks plus the workshop and the keynotes. 00:19:49.160 --> 00:19:55.760 We usually organize social events like drinks and dinner with local food, of course. 00:19:55.780 --> 00:19:56.660 It's from Italy. 00:19:57.100 --> 00:20:04.280 And we have the same approach as other community-driven conferences like PyCon US or Europython. 00:20:04.420 --> 00:20:09.220 But we give a lot of importance to maybe the lunchtime. 00:20:09.220 --> 00:20:18.640 We've spent two hours for having lunch because we think it's a way to network better with other people, with proper food and wine and other things. 00:20:18.980 --> 00:20:21.040 You see, we have very great keynotes. 00:20:21.440 --> 00:20:24.180 The one you are showing from different communities. 00:20:24.180 --> 00:20:27.280 Diego is on the PyCon core team. 00:20:27.600 --> 00:20:29.020 Sarah is one Django fellow. 00:20:29.820 --> 00:20:34.440 And Dawn is involved in community and so many different things, PyLadis and other things. 00:20:34.840 --> 00:20:37.760 And we try to attract a lot of people from Europe. 00:20:37.760 --> 00:20:45.840 Usually, a lot of people from Europe and US attend because they can then spend other days traveling around in Italy, which is not bad. 00:20:46.560 --> 00:20:53.020 And Bologna this year and also last year is a way to be the central part of Italy. 00:20:53.240 --> 00:20:58.980 And traveling very easy to all the other parts of Italy, like you can go in Florence in 30 minutes by train. 00:20:58.980 --> 00:21:00.640 The train system works very well. 00:21:00.640 --> 00:21:05.820 And yeah, we have the first day, which is open and free for everyone. 00:21:05.820 --> 00:21:11.460 So if people listen to you in YouTube, they want to join, they don't have a ticket or they cannot afford it. 00:21:11.460 --> 00:21:14.220 The first day is day for beginners. 00:21:14.720 --> 00:21:16.640 There is workshop for free for everyone. 00:21:16.980 --> 00:21:20.340 You only have to go there and show up without a ticket. 00:21:20.600 --> 00:21:28.480 It's a way to bring people, start attending a conference like that, participate in Jungle Girls workshop or other workshops. 00:21:28.640 --> 00:21:30.560 It can help you be more involved. 00:21:30.560 --> 00:21:44.080 And if you understand only now that you cannot attend because it's too late, you can join us next year because for sure we will have the same enthusiasm, the same great people. 00:21:44.360 --> 00:21:44.860 That sounds great. 00:21:44.960 --> 00:21:48.200 Do you have a mailing list or something people can sign up for? 00:21:48.960 --> 00:21:53.200 Yeah, I think it's in the contact here in the website. 00:21:53.200 --> 00:22:00.700 Yeah, on the footer, you can, you can join there and there is a group on Telegram. 00:22:00.700 --> 00:22:06.080 There are the mail, the mail for be contacted. 00:22:06.960 --> 00:22:10.600 We can share maybe the link in the description of the podcast. 00:22:10.800 --> 00:22:11.220 Perfect. 00:22:11.220 --> 00:22:11.520 Yeah. 00:22:11.520 --> 00:22:13.380 And of course, yeah, yeah. 00:22:13.380 --> 00:22:26.580 And of course, if your company is interested, you can sponsor the comfort because there are a lot of opportunities to, you know, meet people, hire developers and, or connect with other communities. 00:22:26.860 --> 00:22:27.580 So it'd be great. 00:22:27.920 --> 00:22:28.060 Yeah. 00:22:28.100 --> 00:22:28.300 Yeah. 00:22:28.300 --> 00:22:28.780 Very cool. 00:22:28.840 --> 00:22:29.100 All right. 00:22:29.340 --> 00:22:33.160 Let's, let's talk about your talk here. 00:22:34.100 --> 00:22:34.620 Perfect. 00:22:34.880 --> 00:22:35.080 Yeah. 00:22:35.100 --> 00:22:36.060 Let's, let's dive into it. 00:22:36.100 --> 00:22:44.720 So you do, your talk was AI assisted contributions and maintainer load over here at, at PyCon, which there's your, your selfie getting ready. 00:22:44.800 --> 00:22:45.120 That's cool. 00:22:45.380 --> 00:22:45.540 Yeah. 00:22:45.540 --> 00:22:45.760 Yeah. 00:22:45.760 --> 00:22:54.280 I did it at the last moment before closing with, with, with Simon Wilson, was the host of the AI track. 00:22:54.580 --> 00:22:54.900 Yeah. 00:22:55.260 --> 00:22:55.420 Yeah. 00:22:55.480 --> 00:22:57.320 So this was definitely in the, I thought it was cool. 00:22:57.380 --> 00:22:58.520 They had an AI track, right? 00:22:58.800 --> 00:22:59.000 Yeah. 00:22:59.060 --> 00:22:59.240 Yeah. 00:22:59.240 --> 00:23:01.000 It was the first one in the track. 00:23:01.400 --> 00:23:03.560 There was a security track and an AI track. 00:23:03.560 --> 00:23:06.580 And I'll tell you what, the AI track was hacked. 00:23:06.740 --> 00:23:08.840 There was a lot of people in those talks. 00:23:08.840 --> 00:23:10.220 So I thought that was really cool. 00:23:10.520 --> 00:23:10.680 Yeah. 00:23:10.740 --> 00:23:20.380 So tell us a bit about your talk and then we can dive into, you know, you had a bunch of like pull quotes, little examples from different maintainers and luminaries and so on. 00:23:20.460 --> 00:23:26.720 So I thought we could kind of talk through those as a way to cover in the topic, but tell people a bit about high level about your talk. 00:23:27.000 --> 00:23:27.120 Yeah. 00:23:27.160 --> 00:23:38.080 So the idea for the talk came to me when I started seeing more and more contribution in the Django project or Python or connected projects, open source project in general. 00:23:38.080 --> 00:23:49.980 So, since the last year, I started seeing more and more contribution, very strange to be go to wide as a scope and maintainer start complaining about it. 00:23:50.260 --> 00:23:51.960 And I collected the tons of them. 00:23:51.960 --> 00:23:56.300 Then I selected a few to show in the, here in the talk. 00:23:56.520 --> 00:24:00.160 The point was that using AI is a good thing. 00:24:00.340 --> 00:24:02.140 It's a, it can be a good thing. 00:24:02.140 --> 00:24:05.560 It's a tool that can improve your productivity during the day. 00:24:05.560 --> 00:24:16.240 If you know how to use it, but can be also, bad things for maintainer that have to filter and review more pull requests than before, more issue or bigger 00:24:16.240 --> 00:24:25.740 pull requests without having in the other side, someone that know what they are sending to you because they can create a lot of code very easily. 00:24:25.740 --> 00:24:32.220 But maybe if they are not careful, they cannot understand what they are proposing to merge. 00:24:32.220 --> 00:24:38.520 And in the process of this month, the past year, I was seeing that this was accelerating. 00:24:38.520 --> 00:24:41.700 It was accelerating a lot from very few. 00:24:41.700 --> 00:24:41.900 Yeah. 00:24:41.900 --> 00:24:51.220 I found, I've yeah, I found that graph, on GitHub in it's, I mean, you cannot imagine a more vertical increase in usage. 00:24:51.220 --> 00:24:54.900 And I'll put this in a link in the show notes for people, but it's, it's crazy, right? 00:24:54.900 --> 00:24:57.900 And that's what the maintainers are experiencing. 00:24:57.900 --> 00:24:58.400 Exactly. 00:24:58.400 --> 00:25:03.120 So I was collecting all this information, realizing that it was a scalar rating. 00:25:03.120 --> 00:25:11.900 And so after collecting a lot of them also, because in the, also in the jungle community, we were observing this amount of requests. 00:25:11.900 --> 00:25:21.900 Then I, started singing different, project and we started reasoning on how to do, how to face this amount of requests. 00:25:21.900 --> 00:25:26.900 we created a, working group in the jungle community or the AI. 00:25:26.900 --> 00:25:31.900 We started doing some work workshop and I wanted to share what was happening. 00:25:31.900 --> 00:25:40.580 I don't share any solution, how to face it, but I was interesting to, to share what was happening and what different project were doing to face it. 00:25:40.580 --> 00:25:41.580 Yeah. 00:25:41.580 --> 00:25:42.580 It's, it's a challenge. 00:25:42.580 --> 00:25:43.580 It's definitely a challenge. 00:25:43.580 --> 00:25:48.580 so let's go through, I think we can, you're a big fan of movies. 00:25:48.580 --> 00:25:54.580 I can tell you have a lot of movie quotes and the space odyssey and other ones. 00:25:54.580 --> 00:26:03.580 I introduced my daughter to the concept of, how and, and the space odyssey when she first learned about AI and she thought it was the most amazing thing. 00:26:03.580 --> 00:26:04.580 Yeah. 00:26:04.580 --> 00:26:05.580 Yeah. 00:26:05.580 --> 00:26:06.580 True. 00:26:06.580 --> 00:26:07.580 So let's see, here we are. 00:26:07.580 --> 00:26:08.580 All right. 00:26:08.580 --> 00:26:12.580 So the first quote comes from Ken, which, which Ken is this? 00:26:12.580 --> 00:26:13.580 You remember? 00:26:13.580 --> 00:26:17.580 Ken Thompson was the co-creator of, co-creator of Unix. 00:26:17.580 --> 00:26:18.580 Yeah. 00:26:18.580 --> 00:26:19.580 Yeah. 00:26:19.580 --> 00:26:20.580 Cool. 00:26:20.580 --> 00:26:24.580 And so the quote is, you can't trust code that you did not totally create yourself. 00:26:24.580 --> 00:26:32.580 I think this is an interesting way to start this conversation because this comes from a time far before open source, right? 00:26:32.580 --> 00:26:42.580 Unix was created quite a while ago and there's something true to this, but I also think you, you don't, you can't necessarily trust code that you did write yourself. 00:26:42.580 --> 00:26:43.580 Yeah. 00:26:43.580 --> 00:26:44.580 For complicated systems. 00:26:44.580 --> 00:26:52.580 And I think we've seen that you, you can actually trust code in the right systems with the right guardrails. 00:26:52.580 --> 00:26:59.580 Like look at Django, for example, most of the code that you run when you run Django, even if you're a core developer, you yourself didn't write. 00:26:59.580 --> 00:27:00.580 Right. 00:27:00.580 --> 00:27:07.580 Simon Wilson and a few other folks who created it, even they probably didn't write most of the Django code at the way it is today. 00:27:07.580 --> 00:27:08.580 Right. 00:27:08.580 --> 00:27:09.580 Yeah. 00:27:09.580 --> 00:27:13.580 So that you can still find some code that they wrote, but now there are so many contributors. 00:27:13.580 --> 00:27:14.580 Yeah. 00:27:14.580 --> 00:27:30.580 I showed these quotes and also the previous one, not because I think they were totally right in general, but to show people that the fear of new technology has been something in the Howard community that started from the beginning of open source from the late seventies. 00:27:30.580 --> 00:27:42.580 And it is normal that when a new technology like AI show up, the first reaction can be both refusing it totally, or maybe to embrace it because it's something sucking in new. 00:27:42.580 --> 00:27:51.580 And, I wanted to show that some quotes from the past apply also perfectly today from some reaction we are seeing in the community. 00:27:51.580 --> 00:27:52.580 Yeah. 00:27:52.580 --> 00:27:53.580 Yeah. 00:27:53.580 --> 00:27:54.580 It's very, it's a good quote. 00:27:54.580 --> 00:27:56.580 And it's, and I like the idea of you putting it there. 00:27:56.580 --> 00:28:00.580 It's, it's easy to feel like this is a brand new problem in some ways. 00:28:00.580 --> 00:28:03.580 And it is a brand new problem, but not entirely. 00:28:03.580 --> 00:28:04.580 Right. 00:28:04.580 --> 00:28:05.580 These ideas have been there before. 00:28:05.580 --> 00:28:08.580 And open source was revolutionary when it came out. 00:28:08.580 --> 00:28:09.580 Yeah. 00:28:09.580 --> 00:28:10.580 Yeah. 00:28:10.580 --> 00:28:11.580 Totally. 00:28:11.580 --> 00:28:19.580 Not just in the fact that it made a big change, but as it was kind of overthrowing some of the ideas of how software should be built, sold, shared, licensed, all that kind of stuff. 00:28:19.580 --> 00:28:20.580 Yeah. Yeah. 00:28:20.580 --> 00:28:35.580 I think the open source preserved the first, the beginning of, computer science as a science, discipline, because before then the science was sharing then everything with everyone in every part of the world and discussing it. 00:28:35.580 --> 00:28:44.580 And before companies started closing part of the code, they decided to continue that track and continue to sharing as much as possible. 00:28:44.580 --> 00:28:49.580 And this is what bring us today discussing, open source languages or framework. 00:28:49.580 --> 00:28:56.580 There's the obligatory single developer in Nebraska, Lego block block or J, J, J, not Jenga. 00:28:56.580 --> 00:28:57.580 Jenga. 00:28:57.580 --> 00:28:58.580 Jenga. 00:28:58.580 --> 00:28:59.580 What is that? 00:28:59.580 --> 00:29:00.580 Jenga. 00:29:00.580 --> 00:29:01.580 Thank you. 00:29:01.580 --> 00:29:02.580 Jenga. 00:29:02.580 --> 00:29:32.580 And so it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like it seems like 00:29:32.580 --> 00:29:42.520 So I like these two words because you can put there instead of Nebraska, your hometown or your project that you like the most that you contribute to. 00:29:42.680 --> 00:29:44.120 And it's worked the same. 00:29:45.080 --> 00:29:47.360 I think this is the majority of open source projects. 00:29:47.560 --> 00:29:50.160 And it's interesting how some of them are so central. 00:29:50.260 --> 00:29:53.580 We're going to come back to talk about curl a little bit later, I think. 00:29:53.880 --> 00:29:55.300 And that's one of them we had. 00:29:55.500 --> 00:30:00.440 Was it OpenSSL, I think, was one that really felt maybe inspired this diagram? 00:30:00.440 --> 00:30:02.720 Actually, I can't exactly remember, but yeah. 00:30:03.060 --> 00:30:07.800 And that's why I kind of talked so much about Django and the DSF and stuff. 00:30:08.040 --> 00:30:15.460 It's true there's not that many people relative to how many are using it, but it's actually quite a bit more than most projects that do look like this. 00:30:15.580 --> 00:30:25.180 And so when you start swamping that little peg at the end with 15,000 line PRs, it just fixes a minor problem, just optimizes. 00:30:25.560 --> 00:30:25.920 Here you go. 00:30:26.120 --> 00:30:27.220 It's just too much, right? 00:30:27.480 --> 00:30:27.880 Exactly. 00:30:27.880 --> 00:30:33.340 You had this interesting analogy with a picture of Django, the guitar player. 00:30:33.740 --> 00:30:37.860 And there was a lot of funny, and this is the AI generator one, right? 00:30:38.040 --> 00:30:42.500 So there's a lot of interesting things that came up when you said, make me such a picture, right? 00:30:42.680 --> 00:30:44.620 You want to talk us through this analogy real quick? 00:30:44.900 --> 00:30:45.200 Yeah. 00:30:45.200 --> 00:30:51.000 So to show how AI can impact the project, I contribute more, which is Django. 00:30:51.000 --> 00:30:59.980 I said, okay, let's try to ask AI to improve the image of Django Reinhardt, which is the chess guitarist that inspired the name of Django, the framework. 00:30:59.980 --> 00:31:04.600 And asking the AI to improve the picture, it did different things. 00:31:04.600 --> 00:31:08.280 That is very, a metaphor of what it can do to your code too. 00:31:08.280 --> 00:31:15.020 For example, you bring a new, very new guitar, which is something that AI usually do. 00:31:15.200 --> 00:31:21.520 When you try to modernize your code or use AI in your code, it usually brings new packages or new things. 00:31:21.520 --> 00:31:27.640 Maybe your code is very old, but it brings new dependencies, the latest technologies. 00:31:28.220 --> 00:31:33.740 The other things that I changed it was removing some bad parts, like for example, the smoking cigarette. 00:31:33.940 --> 00:31:41.840 It was common at the time when the pictures originally were taken, but nowadays we know that maybe it cannot be so good things. 00:31:41.840 --> 00:31:48.620 And it did also a strange thing that took me a few times to see. 00:31:48.620 --> 00:31:56.020 It was adding an additional finger in the hand of Django Reinhardt, which is strange. 00:31:56.340 --> 00:32:02.940 It's the metaphor of the code you can have generated from the AI that at first look, it seems normal. 00:32:03.360 --> 00:32:10.820 But when you dip that a bit, you understand that it is something that was not possible, having, in this case, an additional finger. 00:32:10.820 --> 00:32:12.300 That's a really good analogy. 00:32:12.620 --> 00:32:18.240 Yeah, you're like, oh, look, this is a great, I can't believe how perfect, look, everything looks, wait a minute. 00:32:18.620 --> 00:32:22.940 And that's also true for AI contributions as well, is it looks fine. 00:32:23.300 --> 00:32:26.860 I found that's also true actually for speech to text. 00:32:27.420 --> 00:32:36.640 It's really, I broke my hand really badly, like in three places up and down my hand in, I don't know, quite a while ago, but seven or eight years. 00:32:36.800 --> 00:32:43.200 But I was still running Talk Python and the business I had, I still get, but I had an insane amount of email and stuff I had to do to keep things going. 00:32:43.200 --> 00:32:45.340 And so I had to use speech to text. 00:32:45.680 --> 00:32:51.600 And I think the reason I bring this up is I think it's a bit of an interesting analogy for AI as well as I would use speech to text. 00:32:51.740 --> 00:32:53.060 It would transcribe it. 00:32:53.220 --> 00:32:55.280 And then I would, I would go to send it. 00:32:55.340 --> 00:32:59.660 And I had the hardest time actually finding the grammar errors. 00:32:59.660 --> 00:33:07.620 But I would say stuff like, we're going to look at their project and figure something, you know, and it would, it would use the different kind of there, right? 00:33:07.660 --> 00:33:10.360 Not the belonging of the person, but the location there. 00:33:10.360 --> 00:33:14.900 But because it was phonetically correct, I would read it back and it would sound okay. 00:33:15.000 --> 00:33:18.200 And there was just something about my brain couldn't find the problem. 00:33:18.300 --> 00:33:23.340 So I had to put a footer in my email, like, sorry, I hurt my hand, have to do voice to text. 00:33:23.580 --> 00:33:25.260 Please just forgive the mistakes. 00:33:25.260 --> 00:33:28.440 I couldn't, I couldn't find a point where I could get it down to zero mistakes. 00:33:28.440 --> 00:33:37.220 And nowadays probably the transcription would be quite a bit better, but it's, there's something about looking at it and it looks right and it sounds right, but it's not right. 00:33:37.220 --> 00:33:41.600 That just makes it very hard to internalize the mistake, you know? 00:33:41.600 --> 00:33:42.380 Totally true. 00:33:42.620 --> 00:33:48.300 AI is very great in generating content that look like perfect when you see at first glance. 00:33:48.300 --> 00:33:58.140 But when you deep dive a bit, especially for code, you can find that one, small characters can change the meaning of the things. 00:33:58.140 --> 00:34:08.540 And a lot of people, when I show this picture, didn't realize at the first glance that this is an additional finger on the, on the picture because it seems very, very nice at the beginning. 00:34:08.540 --> 00:34:08.940 Yeah. 00:34:08.940 --> 00:34:11.700 The most subtle thing for me is the amplifier, honestly. 00:34:11.700 --> 00:34:12.340 Yeah. 00:34:12.340 --> 00:34:12.460 Yeah. 00:34:12.460 --> 00:34:12.740 Yeah. 00:34:12.740 --> 00:34:12.940 Yeah. 00:34:12.940 --> 00:34:15.700 That was interesting. 00:34:15.700 --> 00:34:23.580 I use it as a metaphor because I say that beginning AI can amplify the effect of so many things. 00:34:23.580 --> 00:34:33.380 I use it then in the rest of my talk to say a lot of project already recognize AI as a tool that can amplify a lot of effects. 00:34:33.380 --> 00:34:38.460 You have to be aware that it can amplify positive and negative effects, you know, your code. 00:34:38.460 --> 00:34:38.900 Yeah. 00:34:38.900 --> 00:34:39.260 Yeah. 00:34:39.260 --> 00:34:42.140 It's an amplifier, but what is it amplifying, right? 00:34:42.140 --> 00:34:43.500 This is an amazing analogy. 00:34:43.500 --> 00:34:51.500 Now people who know me know that I certainly am on the positive side of, of working with AI, especially the newer agentic ones. 00:34:51.500 --> 00:34:59.900 I think you can do really incredible stuff, not necessarily discussing the environmental and, and copyright aspects, but just as a tool, I think it's absolutely incredible. 00:34:59.900 --> 00:35:06.100 But I think it's also really important to acknowledge its weaknesses where you have to put in guardrails, how you should use it. 00:35:06.100 --> 00:35:06.340 Right. 00:35:06.340 --> 00:35:08.820 And, and I think that's a good analogy for it. 00:35:08.820 --> 00:35:09.700 okay. 00:35:10.040 --> 00:35:10.260 Yeah. 00:35:10.340 --> 00:35:10.700 Amplifier. 00:35:10.700 --> 00:35:16.820 So if you got low quality code, maybe it just really like makes way more of that. 00:35:16.820 --> 00:35:17.020 Right? 00:35:17.020 --> 00:35:18.300 So yeah. 00:35:18.300 --> 00:35:26.700 So recognizing the fact that AI is a very good tool that can amplify positive and negative think is something real. 00:35:26.700 --> 00:35:34.700 I think I use AI every day for my daily job, but I can recognize the fact that people can use it in a bad way. 00:35:34.700 --> 00:35:39.180 And so open tons of poor requests that are bad in your project. 00:35:39.180 --> 00:35:44.620 So we have to be realistic here and to see that maybe it can use correctly. 00:35:44.740 --> 00:35:48.820 Everyone want to do that, but it's not every time the case. 00:35:48.820 --> 00:35:55.860 So, yeah, we're going to get into the different projects that do and some of their policies or whatever, and some of them are just no. 00:35:56.180 --> 00:35:59.180 I think that's a little bit short sighted, but we're going to get to we'll get to that. 00:35:59.180 --> 00:36:00.340 We'll get to that. 00:36:00.340 --> 00:36:04.100 So you put out there that AI does not change the nature of open source. 00:36:04.100 --> 00:36:05.460 It changes the scale. 00:36:05.580 --> 00:36:05.940 Yeah. 00:36:05.940 --> 00:36:12.500 This is what my thoughts when I, for the first time, work on the slides for what I was observing. 00:36:13.180 --> 00:36:25.100 Then I changed a bit this idea because when I spoke with people, I realized that was a slightly different because I, exactly, I presented this talk 00:36:25.260 --> 00:36:32.700 DjangoCon Europe in Athens in April, one month ago, and I received this very interesting feedback. 00:36:32.700 --> 00:36:45.540 one of them was the fact that maybe changing the scale, the scale of the contribution of the amplifying things, AI was a tool that was able to change the nature of open source. 00:36:45.540 --> 00:36:54.180 Because if you cannot trust people that open up requests, if you lose your trust in other members 00:36:54.180 --> 00:37:00.540 that are involved because they have too much, so they contribute in a bad way, open source will change. 00:37:00.540 --> 00:37:03.340 So we have to be aware of that. 00:37:03.340 --> 00:37:03.740 I think. 00:37:03.740 --> 00:37:14.660 A rule of thumbs or whatever, like sort of AI ethics, I guess, is it shouldn't, it shouldn't take you less time to create a thing that it takes someone else to review that thing. 00:37:14.660 --> 00:37:19.140 It shouldn't take you less time to just read a thing. 00:37:19.140 --> 00:37:19.540 Right. 00:37:19.540 --> 00:37:26.020 But I imagine there's a lot of people out there who are just, oh, I've got this great idea for Django or whatever, you know, open source project. 00:37:26.020 --> 00:37:29.500 Make it for me and then I'll submit it as a PR and did they test it? 00:37:29.500 --> 00:37:30.500 Did they verify it? 00:37:30.500 --> 00:37:34.500 Did they work on dry principles of refactoring everything just right? 00:37:34.500 --> 00:37:36.500 You know, some people absolutely. 00:37:36.500 --> 00:37:38.340 And that's, that's the challenge. 00:37:38.340 --> 00:37:45.020 You don't want to throw that away necessarily, but a lot of times I think they just see it works and test pass maybe and send it. 00:37:45.020 --> 00:37:56.500 You know, one of the projects I showed later in the talk stated in the guideline exactly what you just said, that if your pull request required less effort and has reviewing it. 00:37:56.500 --> 00:38:02.020 So we throw away that the contribution because it can be a waste of time. 00:38:02.020 --> 00:38:05.460 It's good that we can use this tool to vibe code things. 00:38:05.460 --> 00:38:13.020 So to create them locally for ourselves, but, contributing and deploying it in production is a different thing. 00:38:13.020 --> 00:38:25.260 Now you put out an example from OCaml that says there was a, somebody did a PR with a 13,323 lines, added 30 or changed and 36 lines removed. 00:38:25.580 --> 00:38:26.620 No issue. 00:38:26.820 --> 00:38:28.580 Just drop the pull request. 00:38:28.580 --> 00:38:28.820 Right? 00:38:28.820 --> 00:38:30.220 Like that's problematic. 00:38:30.420 --> 00:38:36.860 I think this is, I tried to show some examples starting from the beginning to show how it escalated. 00:38:36.860 --> 00:38:41.260 So this is one of the first I saw in November, a few months ago. 00:38:41.260 --> 00:38:45.980 I was one of the first with a proper discussion that, people talk about. 00:38:45.980 --> 00:38:52.940 So this pull request was created without any discussion from someone that was involved in the project and they closed it. 00:38:53.180 --> 00:38:55.580 Of course, which was impossible to review. 00:38:55.700 --> 00:38:57.060 It doesn't have to be this way. 00:38:57.180 --> 00:39:00.300 You know, you could go to your AI and say, I want to add this feature. 00:39:00.300 --> 00:39:08.680 Your job is to minimally add the absolute minimum amount of changes to the code such that this feature works or whatever. 00:39:08.680 --> 00:39:09.180 Right? 00:39:09.240 --> 00:39:16.140 Like just as if you were a developer, you know, I'm going to reformat the entire project and you're, you're a different formatter. 00:39:16.140 --> 00:39:18.840 And then just change a bunch of other things. 00:39:18.840 --> 00:39:19.080 Right. 00:39:19.160 --> 00:39:24.340 It's, it's, but again, I think it's the amplification, the amplifier that you talked about. 00:39:24.340 --> 00:39:27.580 The person who did this PR doesn't understand contributing to open source. 00:39:27.700 --> 00:39:32.420 They may understand software, but not, not working on a team sort of thing. 00:39:32.420 --> 00:39:32.740 Right? 00:39:32.900 --> 00:39:33.500 Exactly. 00:39:33.620 --> 00:39:37.580 In fact, they were forced to close the discussion of this because it was. 00:39:37.820 --> 00:39:38.380 That's right. 00:39:38.380 --> 00:39:43.260 The, the, it says something like, I have the impression that this conversation is becoming less productive. 00:39:43.260 --> 00:39:45.140 So I'll move ahead and lock the topic. 00:39:45.380 --> 00:39:46.780 Cause it's just out of control. 00:39:46.780 --> 00:39:47.020 Right? 00:39:47.100 --> 00:39:48.060 Yeah, absolutely. 00:39:48.220 --> 00:39:49.420 Go dot was another. 00:39:49.420 --> 00:39:55.180 Yeah, these things escalated from one single request to a lot of them. 00:39:55.180 --> 00:40:08.540 So, each in only three months from the previous example to this one, I saw in the months that there was, escalated and this case, they created so many slow pull requests 00:40:08.540 --> 00:40:15.780 for this project that they were exhausted and they put out in the social, this, this emergency. 00:40:15.780 --> 00:40:16.300 Yeah. 00:40:16.300 --> 00:40:17.780 I'll throw out another example. 00:40:17.780 --> 00:40:24.820 Cause I want to come back to it as a counter example, but this is does not just, disprove or devalue it. 00:40:24.820 --> 00:40:39.180 the maintainer of curl, Daniel Stenberg, I believe he's become sort of a poster child for, maintainer abuse, I think, cause he's written a lot about it and curls so popular, but they had a bug bounty program, which is slightly different than accepting PRs, but 00:40:39.180 --> 00:40:41.560 it's still accepting contributions from people. 00:40:41.680 --> 00:40:47.800 And this bug bounty program was out there to help find bugs in curl, which is really important and you get paid for it. 00:40:47.800 --> 00:40:55.560 And the motivation to, come contribute to go dot or OCaml is like, Hey, I get my name listed as a contributor. 00:40:55.560 --> 00:40:55.960 That's great. 00:40:55.960 --> 00:40:58.800 Or I just get a feature I want, but for the bug bounty, you got money. 00:40:59.360 --> 00:41:04.720 So that thing just got absolutely swamped to the point where they canceled. 00:41:04.720 --> 00:41:08.960 I don't know if they canceled the bug bounty, but they certainly took away the financial reward for it. 00:41:08.960 --> 00:41:09.280 Right. 00:41:09.280 --> 00:41:10.040 That's pretty bad. 00:41:10.040 --> 00:41:10.320 Yeah. 00:41:10.320 --> 00:41:13.440 I, I thought that the project was facing this problem. 00:41:13.440 --> 00:41:19.640 I, I can say that also Django used this type of program and we starting seeing more and 00:41:19.640 --> 00:41:28.960 more, issue when they tried to modify things or the environment of some very old bug, trying 00:41:28.960 --> 00:41:36.800 to find edge cases for the main goal to have a rewarding from that, but the issue were not real. 00:41:36.880 --> 00:41:42.720 We're only trying to force some strange cases when the code behaved like that. 00:41:42.960 --> 00:41:56.000 And we saw something like that also when there was some initiative like, October 1st or similar, where people in the past tried to open us very small pull requests with a typo only to have 00:41:56.000 --> 00:41:58.080 some rewards from, from that. 00:41:58.080 --> 00:42:00.240 I, I really disliked. 00:42:00.240 --> 00:42:01.440 October fest. 00:42:01.440 --> 00:42:04.240 I mean, I run, I have a bunch of GitHub repositories. 00:42:04.240 --> 00:42:04.960 I'd looked the other day. 00:42:04.960 --> 00:42:13.760 I have some like 390 GitHub repositories, but a much smaller, much, much smaller portion are actual projects that people can use mostly web stuff. 00:42:13.760 --> 00:42:19.600 But even for these little tiny projects that have a couple hundred stars, I would get PRs. 00:42:19.600 --> 00:42:22.240 Like I've, there was a problem with your read me. 00:42:22.240 --> 00:42:27.120 I fixed it and they'll like change an adjective from like quickly to fastly or something. 00:42:27.120 --> 00:42:32.800 I'm like, no, I am not playing your, oh, I contributed and got a PR accepted game. 00:42:32.800 --> 00:42:35.200 And I was just like, why do, why do we need this? 00:42:35.200 --> 00:42:39.920 This is, this is not in the spirit of it and it's not, it's just not right. 00:42:39.920 --> 00:42:43.040 So I can only imagine with AI now what it's like. 00:42:43.040 --> 00:42:43.440 Yeah. 00:42:43.440 --> 00:42:56.800 And this is the main motivation because we, as I said before, we started that project like Django not space to help people to properly contribute to a project, not only fix a typo to have a reward, it can be a t-shirt or money. 00:42:56.800 --> 00:42:58.640 It's not our open source work. 00:42:58.640 --> 00:43:00.160 Yeah, for sure. 00:43:00.160 --> 00:43:01.200 Jazz band. 00:43:01.200 --> 00:43:07.680 This is another project that I don't know if this is what directly caused such a problem, but it might've been the final straw. 00:43:07.680 --> 00:43:09.280 So tell us about jazz band. 00:43:09.280 --> 00:43:09.520 Yeah. 00:43:09.520 --> 00:43:18.400 so Janice was the creator of jazz band was also now in the board of PSF contributed in the Django ecosystem for so long time. 00:43:18.400 --> 00:43:28.000 And they created this very good project where people can go and self-maintain projects in a direct and democratic way. 00:43:28.000 --> 00:43:29.440 And was very effective. 00:43:29.440 --> 00:43:43.920 There is a lot of projects that are very well used, like the Django debug toolbar, but also not specific for Django, like pip-tools that I personally use for long times was very effective to generate your log file. 00:43:44.400 --> 00:43:47.280 You can do today with uv lock, for example. 00:43:47.280 --> 00:43:47.440 Yeah. 00:43:47.440 --> 00:43:50.080 I was on team, I'm on team pip-tools as well. 00:43:50.080 --> 00:43:51.040 I love that thing. 00:43:51.040 --> 00:43:51.520 Perfect. 00:43:51.520 --> 00:43:53.440 I used for so many times. 00:43:53.440 --> 00:44:02.800 I liked the fact that you were able to create a log file locally and don't have any additional dependencies on production, only installing it with pips. 00:44:02.800 --> 00:44:12.080 So in the, this context, Jetsband was the containers, that, hosted all these projects, 00:44:12.080 --> 00:44:22.720 maintained, but more and more, he has issues with the lack of maintainers, lack of time of people, having more and more, contribution open it. 00:44:22.720 --> 00:44:36.160 So in this case, the, the, some issues were already there in the project, maintaining, maybe move to the other things in the meantime, but the AI accelerated, amplified the process 00:44:36.160 --> 00:44:42.400 suddenly having the slope of pull requests, you know, this project accelerated the process to sunset. 00:44:42.400 --> 00:44:57.280 And in fact, then it's wrote directly here in the article, I extracted only that parts that was useful for the slide, but at a certain point he writes clearly that there is, was the slope apocalypse as he wrote here. 00:44:58.240 --> 00:45:00.800 And then they decided to set the project. 00:45:00.800 --> 00:45:01.280 Yeah. 00:45:01.280 --> 00:45:02.240 That's too bad. 00:45:02.240 --> 00:45:08.800 You know, I think it's really, it's really interesting, the timing of this, because you said use AI at work. 00:45:08.800 --> 00:45:09.920 I definitely do as well. 00:45:09.920 --> 00:45:13.600 And I think recently it has gotten so incredibly good. 00:45:13.600 --> 00:45:21.840 I, I would encourage people to absolutely use the very best model that you can afford to, to do your work. 00:45:21.840 --> 00:45:27.280 And especially if you're going to do a PR or something, I think a lot of the challenges is the AIs were not as good. 00:45:27.280 --> 00:45:32.000 And I think people would say, oh, I, I'll just use, I don't want to wait. 00:45:32.000 --> 00:45:34.640 So I'll use this fast one, or I don't want to spend that much money on it. 00:45:34.640 --> 00:45:38.480 So I'll use haiku rather than opus or something like that. 00:45:38.480 --> 00:45:50.240 And coming back around to curl, Daniel Stenberg recently wrote a new post called high quality chaos, saying there's, there's not really much more AI slop actually said, I complained and complained 00:45:50.240 --> 00:45:55.440 about the high frequency junk submissions of the, to the curl bug bounty program in March. 00:45:55.440 --> 00:46:02.000 Once again, something like said, the slop, the slop situation is not a problem anymore. 00:46:02.000 --> 00:46:12.320 Actually has graphs of like percent of slop, which is a really interesting thing, but it's just the amount of contributions, even though they're finding real issues are, it's like out of 00:46:12.320 --> 00:46:22.560 control. So even, even if you have, if you have all of this stuff coming in, how are you going to deal with it? So I think partly this, this is just speculation on my part. I'd love to hear what you 00:46:22.560 --> 00:46:35.280 think as well, but I think this slop apocalypse is a little bit fading, but it's just left such a sour taste in the mouth of so many maintainers that now they see AI. They're just like, no, that puts aside 00:46:35.280 --> 00:46:46.880 the thing of like 13,000 lines of PR, which is just also no. But what is your thought on, on the kind of kind of legacy of older AI versus newer AI? Do you, do you agree? Do you disagree? I thought it was 00:46:46.880 --> 00:46:51.360 interesting that Daniel Stenberg kind of like was so against it. And I was like, I'm against it, but for a new reason now. 00:46:51.360 --> 00:47:04.800 Yeah. it, it remembers me, reading the hackers for Steven Levy, the book from the eighties when he wrote about the beginning of, occurs in the original meaning and the open source 00:47:05.440 --> 00:47:12.960 context, the story was that at the beginning at the initial, teams or university, they 00:47:12.960 --> 00:47:26.960 started creating code and sharing with, with each other without any license at all, or very freely. And then someone show up and started applying some license on it and started to sell this code. 00:47:26.960 --> 00:47:33.200 So they face in that moment, they realized that some type of mutings was needed, like open source 00:47:33.200 --> 00:47:42.320 license and started using it. We are now in a similar situation when until yesterday was enough to have 00:47:42.320 --> 00:47:49.760 the process of having pull requests and trust each other. Now with this amount of pull requests, it's not 00:47:50.640 --> 00:47:58.240 enough. this mechanism, we have to find something new to try to filter better the amount of contribution. 00:47:58.240 --> 00:48:13.120 The, the tool is not bad per se is the way people are using it. And maybe it's changing because, when you have your new toys, you want to try with everything, you start creating things after a while you realize 00:48:13.120 --> 00:48:26.800 the best way to use it. So maybe now people are more aware of, or to use properly AI or LLM. So people are improving, but still we need some way to collect or to handle better this new. 00:48:26.800 --> 00:48:40.960 Yeah, I agree. I agree. I agree. In your research, did you find something I haven't seen a lot from, and I'm wondering if you found it is, did you find people using AI as a first level analysis before 00:48:40.960 --> 00:48:54.960 they even looked at it? So were people setting up AI bots on like the GitHub or other, other work and say a CI flow to analyze the PR for certain criteria before they even have to look at it? I feel like, 00:48:54.960 --> 00:49:01.360 I mean, there's like code rabbit and some other things. I saw many different way to use it. 00:49:01.360 --> 00:49:12.960 I don't, I, I'm not, this is not a recommendation by the way. I just, it's just, something that I've heard of, that's sort of in that space, you know? Yeah. I remember seeing multiple 00:49:12.960 --> 00:49:19.600 way to, to use AI. Someone who of course was to use AI to analyze AI contribution. 00:49:19.600 --> 00:49:32.480 Like turn it against themselves. Like if there's AI slop coming in, like get an AI guardian though, just battles it back. You know? Yeah. Yeah. It can work. It can also work very bad for, for us if it's not 00:49:32.480 --> 00:49:45.280 working properly. more and more, I've seen project experimenting, trying to have different approach of this, this problem. For example, I remember seeing a project from Armin Ronacher, 00:49:45.280 --> 00:49:55.840 the creator of original creator of Flask. They remember exactly the project was, experiencing heat, but the thing was to close every pull request. That's the first thing. 00:49:55.840 --> 00:50:07.520 They close every pull request. And they ask the creator to open and trying to explain, you know, in some way, why, it's worth it to reopening hits. For some reason, the AI was not good at doing 00:50:07.520 --> 00:50:20.240 this stuff. And so in this way they recognized that people were effectively interacting, you know, not a bot, for example, and like a GitHub recapture. Exactly. Exactly. It was an experiment. 00:50:20.240 --> 00:50:27.360 Another interesting one is, do you know, Granyan, the server, they're writing in Rust for Whiskey. 00:50:27.360 --> 00:50:36.240 Yeah. Yeah, absolutely. I've had Giovanni on the show to talk about it. And it's also powers talk Python and the courses and stuff. So yeah, I definitely know it. And the maintainer, 00:50:36.240 --> 00:50:44.160 uh, was listening in, in, in, in some interview and he said that he was using AI after they merged 00:50:44.160 --> 00:50:57.520 the commit, he went back in time to the previous pointed of the code and asked AI to, create the same feature for them. And they, they analyzed the difference between what was the quality of code 00:50:57.520 --> 00:51:10.160 created by AI and was, was merged to have some sort of comparison, to see how, how good was the work of core contributed that not very well that they called. So I had so many different 00:51:10.160 --> 00:51:20.640 experiments and I'm not sure one of these is the best of the others, but it's interesting that people are experimenting with it. Yeah. I think we're still finding our way. Yeah. I think we're still 00:51:20.640 --> 00:51:32.960 finding our way. Speaking of finding our way, let's, I want to talk about this next. So Mariota just, announced, a new guidelines for using AI tools when contributing to CPython. Thank you, Paolo, 00:51:32.960 --> 00:51:37.520 for pointing me at this morning. So this came out, I don't know, what was the time on this? 00:51:37.520 --> 00:51:48.320 Day and a half ago, something like that. Yeah. Exactly. And it's, it's interesting. Is this the link? No, this says the person submitting an issue or PR is responsible for its content, 00:51:48.320 --> 00:51:58.240 regardless of whether AI tools were used in its creation. Generative AI tools can produce output quickly, but discretion, good judgment, critical thinking are the foundation of all good contributions. 00:51:58.240 --> 00:52:05.120 We value good code, concise, accurate documentation, and well-scoped PRs without the unneeded code churn. 00:52:05.120 --> 00:52:19.200 It goes on with details about acceptable use and other things. What's I think noteworthy here is that it's not just, no, we will not, we will not accept AI generated PRs. It's, it's, you have to use good 00:52:19.200 --> 00:52:31.280 engineering regardless of the tools, right? Yeah. I showed multiple examples of, guidelines or API and AI policy in different projects. And the Python one was the first one, the first level. 00:52:31.280 --> 00:52:43.840 when I used it, they, they was slightly different, but the meaning is exactly the same. You are responsible for what you contribute. you can use different tools and AI is a tool as another one. 00:52:43.840 --> 00:52:55.360 So, but still the responsibility is yours when you contribute. And this is one of the first level of strictness or guidelines I found that it is important that they state the fact that they 00:52:55.360 --> 00:53:01.040 consider AI as another tool and not a person. and you can use it, but you have to do. 00:53:01.040 --> 00:53:02.560 Yeah. Consciously. 00:53:02.560 --> 00:53:14.080 Yeah. I think it's reasonable. Another one is over here. There's this article on Redmonk by Kate Holterhoff called the generative AI policy landscape and open source. And this is much, much broader. It says, 00:53:14.080 --> 00:53:25.200 um, she compiled and analyzed the generative AI policies of 86 open source foundations, including, organizations, including foundations, like the Linux foundation, Apache eclipse, 00:53:25.200 --> 00:53:31.520 kernel curl, matplotlib, and so on. And then there's this bunch of graphs and stuff, about 60, 00:53:31.520 --> 00:53:43.120 55% said permissive. We allow it. 25% said banned instantly, somewhere undecided. There's what you've seen this too, right? What are your thoughts on this? Pretty interesting. 00:53:43.120 --> 00:53:51.920 Yeah. I saw it. The article is from, four, five months ago. So I was, was observing was the 00:53:51.920 --> 00:54:04.800 fact that it was, changing every week. This is every week there are new project or like we just saw that Python project changed a bit, the guidelines about AI and so are doing all the other, yeah, 00:54:04.800 --> 00:54:18.560 extrapolated from this, some of, project that analyze, other than that here, you see, there are a majority of projects that are permissive. What I did is to analyze how permissive they were in the 00:54:18.560 --> 00:54:27.920 same context. So from Python to Arch or FastAPI or Matplotlib, they are, different shades of 00:54:27.920 --> 00:54:40.880 permission level they guaranteed to the contributor. only one project that I found was no contribution, no AI contribution at all. the other one were slightly different to each other. And they tried 00:54:40.880 --> 00:54:46.720 to, to show how can be slightly different between every, every small, every different project. 00:54:46.720 --> 00:54:57.200 I think a really interesting part of this report is there's a different reason. They asked the different projects. What reasons do you have for either having a policy or having a ban? And they 00:54:57.200 --> 00:55:09.600 broke it down by quality, copyright, and ethics. And certainly it looks like the biggest area is just quality. Like there is ethics is some of them are high and some of them are low, right? There's some 00:55:09.600 --> 00:55:18.960 debate there. Copyright is maybe in the middle, but certainly quality is where people seem to like, well, you know, ethics aside, just the peer, just like, it doesn't give me good stuff is what 00:55:18.960 --> 00:55:32.960 people said they were worried about. Yeah. I also think they are connected. a lot of time, what we do here is to have high qualities in maintaining good connection between projects. So 00:55:32.960 --> 00:55:40.800 ethics and copyright is also a way to maintain high quality. It's only not only how fast can be your code 00:55:40.800 --> 00:55:55.280 in executing is also of us can exchange with other projects we are using. You usually use a stack of layers in your project when you deploy it. If you can interact better with others, it's the better. 00:55:55.280 --> 00:56:04.240 I do. I do wonder how much of this is old opinion versus new opinion. The reports, when is the report published? Although it's been updated, like published and updated. It's 00:56:04.240 --> 00:56:18.240 February 26th, 2026. That's not super, super old, but the report was written now, but the people's opinion from say, Ashi Linux may have been formed two years ago, right? Like I, in terms of the quality 00:56:18.240 --> 00:56:26.960 bar, like, I don't think ethics or copyright really changes meaningfully over that time period, but I think the quality may, so it's going to be interesting to see how this evolves. 00:56:26.960 --> 00:56:28.240 Yeah. Yeah, for sure. 00:56:28.240 --> 00:56:38.400 All right. We're getting short on time. I think maybe let's wrap this up with two things. I'm going to include, some levity, a joke at the end for people, that they can appreciate because I think 00:56:38.400 --> 00:56:50.880 it's good, but let's sort of have you close things out. Let's just give, what advice you have for maintainers and you know, all this research you've, you've studied a lot of it. You've lived in the space a lot. So what do you think? What do you say to folks out there? 00:56:50.880 --> 00:57:04.880 Yeah. Well, I say that exactly at the, at the end of the, my talk is that, we have to consider this AI, as a tools, new tools, very powerful that we can use and we have to use properly. It can 00:57:04.880 --> 00:57:18.560 change the scale of your project and the things we need more is having more human, the loop to face all these things, like be connected more and more in community, like with it's PyCon US more and more 00:57:18.560 --> 00:57:24.720 when I met people in PyCon US people asked to be in a channel together to discuss this type of things. 00:57:24.720 --> 00:57:38.800 Another example I show was to start collaborating more closely between person of the community. And for example, we are just starting now with cartoon ribs on the experiment to have small Django sprints. 00:57:38.800 --> 00:57:51.600 We named it Django on the Mad that we had an experiment additional last year in Spain. In September, we will have another one here where I live in Pescara, on the Arctic Arctic sea. And we, but we want to 00:57:51.600 --> 00:58:05.680 create a format that people can replicate elsewhere, to meet in person, to, share their personal thoughts and the, about how to use properly these new tools and how to manage it. So I think the secret 00:58:05.680 --> 00:58:19.680 here is to, to be more connected with your community, which is the most important value of your project. AI can produce code. The things that AI cannot produce is connection between people. It's 00:58:19.680 --> 00:58:32.240 impossible to generate that. You only have to create in person or directly or online, but directly with other people that like we are doing now here, speaking with each other. And this is the best thing we can 00:58:32.240 --> 00:58:43.840 continue doing, creating connection, speaking, because it's the only way to find a solution, a better solution, experimenting together and evolving as a community, as a project. This is the point I think. 00:58:43.840 --> 00:58:55.360 Yeah. Very nice. I certainly want to encourage people out there to think, think before they just fire off a AI based ER, you know, maybe have, maybe open an issue and have a conversation about it. Is this 00:58:55.360 --> 00:59:10.320 what you want, what exactly you want with it? And then, you know, kind of like the Python and CPython guidelines recommended. All right. I want to leave some, I want to leave the audience with something fun here for this, this conversation, just, just as a bit of a joke. If you're feeling, you're 00:59:10.320 --> 00:59:22.080 feeling a bit overwhelmed, too much AI, I believe Glyph turned me onto this. this German guy does incredible videos and this one is called Senior Engineer Tries Vibe Coding. Have you seen this, Paulo? 00:59:22.080 --> 00:59:27.760 No, no, it's first time. Oh, you have to watch it. It's, you know, it's, it's absolutely golden. 00:59:27.760 --> 00:59:38.400 It's, it's, this guy is just like, got a towel, like he's not working out too much. And we've tried this for four and a half days now. I'm not even getting paid. We're going to do the UI over 00:59:38.400 --> 00:59:43.920 again, but it's just, you'll really appreciate it if you've worked with AI and it's been. 00:59:43.920 --> 00:59:54.080 Yeah. I need to check it for sure. Yeah. So I'll, I'll leave that in the show notes for everyone as a a bit of a way to lighten, lighten the mood and enjoy it. But I don't know. Let's also, 00:59:54.080 --> 01:00:06.480 let's get your final thoughts on AI going forward and in the world of open source, which is how do you see it shaping up? Yeah. My final thought is, you have to track it as a, as a tool and, 01:00:07.200 --> 01:00:21.360 be more connected with other people to exchange as many interesting things you can do with this tool without, leaving, high heads, other people. The only way you can improve us, this field is, 01:00:21.360 --> 01:00:30.720 we, we together with other, with other people. So experiment it, share your thoughts, the more you can, and it's the best way you can improve. 01:00:30.720 --> 01:00:41.520 Yeah. A hundred percent. I feel like this is, there is an engineering skill that is different than the engineering skills we had before. And a lot of people just see it as a chat box and without 01:00:42.080 --> 01:00:51.600 looking at it and applying it as a, a practice of engineering, you end up with a lot of the junk and you end up with this like joke video we got here. So I just encourage people to look into, 01:00:51.600 --> 01:01:02.240 treat this as a serious skill, not just a chat box that's supposed to understand every nuanced thing you ask for. Yeah. And don't trust something or someone that say that you're 01:01:02.240 --> 01:01:12.640 absolutely right every, every time. Oh my gosh. You're absolutely right. This has absolutely been a great episode. Thank you, Paulo. Thank you for having me and goodbye for everyone. 01:01:12.640 --> 01:01:13.920 Yeah. 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