#496 A lake house in Seattle-এর সম্পূর্ণ প্রতিলিপি

in that size. 00:03:33.200 --> 00:03:35.000 And so the next thing they reach for 00:03:35.320 --> 00:03:37.640 is typically a commercial tool like Databricks, 00:03:37.920 --> 00:03:38.400 Snowflake, 00:03:39.020 --> 00:03:40.100 Dask, or some of these other things 00:03:40.220 --> 00:03:41.040 that are like Spark. 00:03:41.280 --> 00:03:43.819 So you're distributing the memory 00:03:43.840 --> 00:03:45.560 of that data set across many machines 00:03:45.960 --> 00:03:48.340 or maybe even across one very large machine, 00:03:48.540 --> 00:03:49.580 but doing it in a distributed manner. 00:03:50.160 --> 00:03:52.160 Most people probably don't need to go that far. 00:03:52.400 --> 00:03:54.520 Like most people probably are still sitting in the range 00:03:54.560 --> 00:03:56.500 where you can see on this chart that Polars handles. 00:03:56.840 --> 00:04:01.040 Polars can handle straight up into 100 gigabytes of data 00:04:01.340 --> 00:04:02.260 easily on a single machine. 00:04:03.340 --> 00:04:05.460 And then DuckDB takes it to the next step, 00:04:05.660 --> 00:04:08.360 which actually kind of fitting for this episode. 00:04:08.420 --> 00:04:09.980 I think this will be an interesting episode 00:04:10.060 --> 00:04:11.259 because there's a lot of information here 00:04:11.280 --> 00:04:13.420 about DuckDB later on in the show. 00:04:13.940 --> 00:04:17.140 But he kind of goes on again and shows that basically 00:04:17.390 --> 00:04:20.220 the average size of a row in Redshift is about a kilobyte. 00:04:20.500 --> 00:04:22.680 Every Redshift cluster has like 10 machines in it. 00:04:22.940 --> 00:04:25.820 They're capable of guzzling 8 gigabytes per second 00:04:25.960 --> 00:04:26.360 from S3, 00:04:26.770 --> 00:04:27.940 but really in actuality, 00:04:28.520 --> 00:04:31.280 almost 95% of the tables in Redshift 00:04:31.560 --> 00:04:33.500 contain fewer than 100 gigabytes of data. 00:04:34.120 --> 00:04:36.040 Most people are still in the range 00:04:36.040 --> 00:04:37.900 of just using a single machine with DuckDB 00:04:38.220 --> 00:04:39.260 or even just Polars, 00:04:39.490 --> 00:04:40.260 which is probably similar, 00:04:40.820 --> 00:04:41.940 simpler to maintain and manage, 00:04:42.320 --> 00:04:43.860 but it's above the reach of pandas. 00:04:44.040 --> 00:04:45.300 That's why the post is kind of 00:04:45.360 --> 00:04:47.120 going on about pandas needing to go extinct. 00:04:47.780 --> 00:04:48.580 Another interesting bit, 00:04:48.920 --> 00:04:49.040 again, 00:04:49.360 --> 00:04:50.000 kind of good code 00:04:50.160 --> 00:04:50.720 examples in here. 00:04:51.220 --> 00:04:55.720 When we get down into some of the tables for the performance, 00:04:56.100 --> 00:04:57.260 what strikes you 00:04:57.380 --> 00:04:57.460 here, 00:04:57.620 --> 00:04:57.740 Michael, 00:04:57.940 --> 00:04:58.820 on their memory usage? 00:04:59.300 --> 00:05:00.500 The pandas library, 00:05:00.800 --> 00:05:02.480 we're talking about 30, this is one of

00:05:02.520 --> 00:05:03.800 those examples, I can't remember which one, 00:05:04.300 --> 00:05:06.520 but four minutes basically for the duration of the 00:05:06.480 --> 00:05:06.760 processing, 00:05:07.380 --> 00:05:08.320 38 gigabytes of RAM. 00:05:08.680 --> 00:05:11.580 If you get into pullers, that gets halved, 18 gig of RAM. 00:05:11.890 --> 00:05:14.360 And if you go into DuckDB to do the same operation, 00:05:14.920 --> 00:05:18.160 five seconds at 1.93 gigabytes of RAM. 00:05:18.920 --> 00:05:20.700 So even the- 00:05:20.700 --> 00:05:23.780 It's like nine to 20 times as much, 00:05:23.980 --> 00:05:24.120 yeah. 00:05:24.400 --> 00:05:24.940 Yeah, I mean, 00:05:25.150 --> 00:05:27.040 an iPhone could do this operation 00:05:27.500 --> 00:05:28.880 against 100 gigabytes of data. 00:05:29.680 --> 00:05:30.420 It's fine though, 00:05:30.450 --> 00:05:31.340 'cause you can just get more memory, 00:05:31.460 --> 00:05:32.260 memory's cheap these days. 00:05:32.820 --> 00:05:33.120 Memory's cheap. 00:05:33.280 --> 00:05:34.680 Totally cheap, totally, totally cheap. 00:05:35.880 --> 00:05:38.280 So I just think folks need to put this one to bed. 00:05:39.220 --> 00:05:40.740 Pandas was probably a good way to start, 00:05:41.180 --> 00:05:42.520 but you'll notice in here, 00:05:42.600 --> 00:05:45.180 like the Polars notation or syntax, 00:05:45.600 --> 00:05:46.200 really, really similar. 00:05:46.620 --> 00:05:48.420 I like the DuckDB syntax. 00:05:49.280 --> 00:05:50.560 I think he's got some examples in here 00:05:50.700 --> 00:05:53.120 where he reads in and does some more operations. 00:05:53.540 --> 00:05:55.420 He does a couple against some larger machines, 00:05:55.800 --> 00:05:57.480 then falls back into an older laptop, 00:05:57.740 --> 00:05:58.640 like a framework 13, 00:05:59.240 --> 00:06:02.260 to show that this is still useful as a developer tool. 00:06:03.340 --> 00:06:06.140 So I think here, Pollard versus DuckDB kind of comes down 00:06:06.280 --> 00:06:07.140 to your workload, 00:06:07.300 --> 00:06:08.380 your experience, and your preference. 00:06:09.040 --> 00:06:09.540 It's a good post. 00:06:09.620 --> 00:06:12.200 I really liked all the code snippets he links over 00:06:13.280 --> 00:06:15.380 into the GitHub where you can actually try it yourself. 00:06:16.160 --> 00:06:19.320 It goes against the New York City taxi data set. 00:06:19.640 --> 00:06:20.700 It's got a ton of data in there, 00:06:21.020 --> 00:06:21.980 so it's fun to play with. 00:06:22.300 --> 00:06:24.120 And you can see that we want to wait minutes 00:06:24.300 --> 00:06:25.140 or do you want to wait seconds? 00:06:25.640 --> 00:06:27.360 And would you want to use all your memory for this? 00:06:28.200 --> 00:06:28.560 And actually, 00:06:28.760 --> 00:06:30.200 another thing that Pollard 00:06:30.220 --> 00:06:32.920 and DuckDB did much better was utilizing the CPU. 00:06:33.300 --> 00:06:36.480 PurePandas in this case was using like a multi-core

machine, 00:06:37.020 --> 00:06:38.200 146% CPU, 00:06:38.720 --> 00:06:39.960 where if you go to PureDuckDB, 00:06:40.400 --> 00:06:42.380 there were over 800% CPU. 00:06:42.580 --> 00:06:47.020 So obviously eight to 10 cores are being fully utilized as opposed to 00:06:47.280 --> 00:06:48.900 basically one and one and a half cores. 00:06:49.180 --> 00:06:49.460 Yeah, 00:06:49.660 --> 00:06:49.940 that's awesome. 00:06:50.520 --> 00:06:51.720 I feel like this is a pretty 00:06:52.440 --> 00:06:57.400 data heavy episode for the data science crew out there. It wasn't on purpose, but we ended up that 00:06:57.560 --> 00:06:57.620 way. 00:06:57.840 --> 00:07:00.500 I have a little bit of a real-time follow-up for you, Calvin, 00:07:00.920 --> 00:07:02.059 for people who... 00:07:02.140 --> 00:07:02.160 Yeah, 00:07:02.860 --> 00:07:04.160 I was gonna ask the one last bit in here. 00:07:04.240 --> 00:07:05.960 He does mention Apache Arrow, 00:07:06.070 --> 00:07:06.980 and if you've not played with it, 00:07:07.270 --> 00:07:08.840 it allowed him to switch back and forth 00:07:08.990 --> 00:07:09.800 between pandas, 00:07:09.980 --> 00:07:10.360 Polars, 00:07:10.580 --> 00:07:11.420 and DuckDB 00:07:12.350 --> 00:07:14.540 without having to reload or copy the data into RAM. 00:07:14.860 --> 00:07:16.660 So you could actually do the same operation 00:07:16.940 --> 00:07:17.660 with each of the libraries 00:07:17.870 --> 00:07:19.620 without actually having to take the data back out of RAM. 00:07:19.800 --> 00:07:22.060 So check out Arrow, 00:07:22.580 --> 00:07:23.120 which is really cool. 00:07:23.700 --> 00:07:25.540 It's kind of a little bonus side bit 00:07:25.550 --> 00:07:27.000 that was in the blog post. 00:07:27.800 --> 00:07:30.320 So data folks who got medium-sized data, 00:07:31.020 --> 00:07:32.800 This is going to be a godsend for you. 00:07:33.200 --> 00:07:33.280 Yeah. 00:07:33.490 --> 00:07:35.480 I think Arrow is the foundation of pandas too, 00:07:35.660 --> 00:07:36.340 if I remember correctly, 00:07:36.580 --> 00:07:37.720 and also a polar. 00:07:37.960 --> 00:07:38.700 So that's pretty sweet. 00:07:39.140 --> 00:07:39.300 Yeah. 00:07:39.580 --> 00:07:40.660 My real-time follow-up here. 00:07:40.680 --> 00:07:41.680 It was a real-time follow-up here. 00:07:42.070 --> 00:07:42.180 Oh. 00:07:43.240 --> 00:07:47.720 If you were working with one of these and you want to switch to the other, 00:07:47.980 --> 00:07:48.680 I had Marco 00:07:48.820 --> 00:07:49.320 Garale on, 00:07:49.680 --> 00:07:53.020 really on Talk Python a while ago to talk about narwhals. 00:07:53.440 --> 00:07:58.540 And narwhals is a facade adaptive layer that speaks native polars, 00:07:58.810 --> 00:08:00.920 but also talks pandas. 00:08:01.340 --> 00:08:03.060 So if you want to try like, 00:08:03.280 --> 00:08:03.380 oh, 00:08:03.540 --> 00:08:04.260 let's see what we're doing. 00:08:04.290 --> 00:08:07.420 You know, you could use this as a intermediate layer to

kind of swap that out a little more 00:08:07.700 --> 00:08:08.760 easily than rewrite and everything. 00:08:09.060 --> 00:08:09.260 Yeah. 00:08:09.530 --> 00:08:11.760 I think people just need to drop pandas. 00:08:11.810 --> 00:08:12.100 I mean, 00:08:12.470 --> 00:08:13.300 it was great. 00:08:13.370 --> 00:08:14.620 It was great 10 years ago. 00:08:15.760 --> 00:08:16.120 Yeah. 00:08:17.460 --> 00:08:17.560 Yeah. 00:08:18.100 --> 00:08:19.380 I have some funny jokes, 00:08:19.540 --> 00:08:20.860 but let's carry it. 00:08:20.960 --> 00:08:21.720 Let's move on. 00:08:22.040 --> 00:08:24.240 Let's move on to Pydantic Pint. 00:08:24.680 --> 00:08:27.180 So Pydantic Pint is really interesting. 00:08:27.640 --> 00:08:28.340 Do you know Pint? 00:08:28.720 --> 00:08:29.500 Are you familiar with Pint? 00:08:29.520 --> 00:08:34.780 actually i've never i've never used pint so pint we've covered that on the show back in the day 00:08:34.780 --> 00:08:40.599 and pint is interesting because if you're i mean all you got to do is say mars lander sample return 00:08:40.880 --> 00:08:46.200 whatever and it's like the 100 million dollar plus fail because somebody used feet and somebody used 00:08:46.580 --> 00:08:52.040 neighbors or something like that right and so pint lets you do math in python with units attached 00:08:52.480 --> 00:08:58.540 which is pretty cool right so i can say instead of just having a distance i have 42 i have 42 00:08:58.560 --> 00:09:04.280 kilometers and you can say like two miles to whatever and so on so it it basically mean forces 00:09:04.390 --> 00:09:09.880 you to work in units right a lot of times we don't do this as regular programmers but if you do 00:09:10.040 --> 00:09:15.280 anything scientific well there you go right so that's the background on pint but what i want to 00:09:15.280 --> 00:09:21.520 talk about is actually not pint it's called pidantic pint because pidantic is an awesome 00:09:21.720 --> 00:09:27.640 library that lets you validate the inputs and parse them and everything whenever you read some sort of 00:09:27.520 --> 00:09:31.340 JSON, right? Like if it's FastAPI or just a JSON file or whatever, 00:09:31.740 --> 00:09:32.340 you need database, 00:09:33.260 --> 00:09:34.440 SQL model, 00:09:34.680 --> 00:09:35.320 all those things. Yeah. 00:09:35.550 --> 00:09:41.800 So Pydantic Pint takes this idea and adds units to your data validation 00:09:41.980 --> 00:09:42.160 libraries. 00:09:42.760 --> 00:09:45.800 So instead of saying I have a box that has a length and a width, 00:09:46.050 --> 00:09:46.960 I could say I have a box 00:09:46.960 --> 00:09:53.360 that has a length and a width that is a Pint quantity and the validation is to convert it to 00:09:53.300 --> 00:09:53.420 meters. 00:09:53.870 --> 00:09:57.000 So even if you parse something that says feet or centimeters or whatever, 00:09:57.260 --> 00:09:58.200 it will show up 00:09:58.500 --> 00:09:58.660 correctly. 00:09:59.020 --> 00:09:59.620 What do

you think? 00:10:00.380 --> 00:10:01.300 That's definitely handy. 00:10:01.460 --> 00:10:01.720 Yeah. 00:10:02.040 --> 00:10:03.200 It's the kind of thing that's like, 00:10:03.370 --> 00:10:03.860 you don't have to, 00:10:04.240 --> 00:10:05.860 you're not going to use it a lot unless you're really in, 00:10:06.120 --> 00:10:06.260 you know, 00:10:06.310 --> 00:10:07.180 some kind of engineering or something. 00:10:07.980 --> 00:10:09.560 Oh my God, this is so good. 00:10:09.720 --> 00:10:10.540 It's like so perfect. 00:10:10.730 --> 00:10:12.940 This has got to solve so many, like you said, 00:10:13.140 --> 00:10:15.560 small mistakes that end up in huge damage. 00:10:16.120 --> 00:10:17.300 A hundred percent. So yeah. 00:10:17.540 --> 00:10:18.780 And to be able to validate with it too. 00:10:19.130 --> 00:10:19.940 Yeah. Just automatically, 00:10:20.140 --> 00:10:23.260 right. Just all the Pydantic validations have a Pydantic base model 00:10:23.280 --> 00:10:25.260 And it parses over to whatever it is. 00:10:25.420 --> 00:10:26.660 And if you put, I don't know, 00:10:27.060 --> 00:10:28.880 liters into the length, 00:10:29.450 --> 00:10:30.180 well, liters, 00:10:30.390 --> 00:10:31.780 you can't convert liters to meters. 00:10:32.420 --> 00:10:33.780 So I don't know. 00:10:34.260 --> 00:10:37.360 And it kind of fits perfectly under the Pydantic scope 00:10:37.720 --> 00:10:40.120 of the data validation and serialization. 00:10:40.560 --> 00:10:41.400 Like it just, 00:10:41.660 --> 00:10:42.180 there's natural, 00:10:42.470 --> 00:10:44.820 like this should exist and they made it exist. 00:10:45.200 --> 00:10:45.700 Yeah, it's really cool. 00:10:45.700 --> 00:10:47.140 So you can have say a FastAPI endpoint 00:10:47.290 --> 00:10:49.040 that just automatically just takes units 00:10:49.240 --> 00:10:50.440 and automatically converts units. 00:10:50.550 --> 00:10:52.380 And yeah, it's a really nice one there. 00:10:52.720 --> 00:10:53.720 Speaking of really nice, 00:10:53.920 --> 00:11:00.240 now this transition here has nothing to do with the sponsorship, 00:11:00.400 --> 00:11:01.000 the previous one. 00:11:01.140 --> 00:11:03.820 They just happen to do great stuff in open source code too. 00:11:04.480 --> 00:11:09.320 But Pydantic also happens to create Logfire, which I told you about at the beginning. 00:11:09.720 --> 00:11:13.020 So let me go ahead and tell you about our sponsorship offer, 00:11:13.280 --> 00:11:13.620 Logfire, 00:11:13.820 --> 00:11:14.560 not Pydantic 00:11:14.700 --> 00:11:16.780 Pint, which is not even from them, but it's based on Pydantic. 00:11:17.140 --> 00:11:18.000 So here's the deal. 00:11:18.600 --> 00:11:19.620 It's 2 a.m. 00:11:19.780 --> 00:11:20.940 Your AI agent failed. 00:11:21.280 --> 00:11:21.900 Was it the model? 00:11:22.040 --> 00:11:22.620 A tool call? 00:11:22.660 --> 00:11:23.120 the database, 00:11:23.570 --> 00:11:25.960 just the general unreliability of, 00:11:26.320 --> 00:11:28.180 hey, I think a new model is coming. So the current 00:11:28.190 --> 00:11:29.560 one starts breaking periodically. 00:11:29.890 --> 00:11:31.420 So most observability tools, 00:11:31.650 --> 00:11:32.700 they can't tell you because 00:11:33.070 --> 00:11:34.200 they only see part of your stack. 00:11:34.560 --> 00:11:35.760 PyDentaglogfire sees all of it. 00:11:36.080 --> 00:11:37.160 One trace across your agents, 00:11:37.380 --> 00:11:39.700 LMs, APIs, and databases down to the infrastructure, 00:11:40.160 --> 00:11:40.420 services, 00:11:40.760 --> 00:11:41.100 Kubernetes, 00:11:41.300 --> 00:11:41.520 hosts. 00:11:41.980 --> 00:11:42.540 It's built on open 00:11:42.760 --> 00:11:44.820 telemetry with SDKs for Python, 00:11:45.380 --> 00:11:45.760 TypeScript, 00:11:45.960 --> 00:11:46.440 and Rust. 00:11:47.160 --> 00:11:49.359 And it works with any OTEL compatible 00:11:49.380 --> 00:11:49.640 language. 00:11:50.140 --> 00:11:50.540 Every prompt, 00:11:50.880 --> 00:11:51.300 token count, 00:11:51.500 --> 00:11:54.620 and cost right next to your vector searches and API calls. 00:11:54.940 --> 00:11:59.420 You query everything with Postgres compatible SQL to understand what your app is doing, 00:12:00.400 --> 00:12:02.420 and so can your coding agent. 00:12:02.610 --> 00:12:05.100 It can use the same way because it talks SQL really well. 00:12:05.260 --> 00:12:05.500 If you 00:12:05.720 --> 00:12:06.720 connect it to the MCP server, 00:12:07.050 --> 00:12:09.940 your agent can also figure out what is going on. So stop guessing, 00:12:10.250 --> 00:12:10.720 read the trace, 00:12:10.940 --> 00:12:11.660 Pydenic, Logfire. 00:12:11.960 --> 00:12:12.400 AI, 00:12:12.670 --> 00:12:14.140 it is still just engineering, 00:12:14.750 --> 00:12:16.039 even if it's weird engineering 00:12:16.060 --> 00:12:16.400 these days. 00:12:16.690 --> 00:12:20.260 So visit pythonbytes.fm/logfire today and sign up. 00:12:20.540 --> 00:12:21.540 Get 10 million records free 00:12:21.810 --> 00:12:22.220 every month, 00:12:22.440 --> 00:12:23.340 no credit card required. 00:12:23.710 --> 00:12:24.700 You can even click, 00:12:24.870 --> 00:12:26.260 and I really like this, there's a little 00:12:26.480 --> 00:12:27.340 copy of this text, 00:12:27.560 --> 00:12:29.860 onboard you with your agent. 00:12:30.340 --> 00:12:32.180 Click that and it gives you a prompt you can drop 00:12:32.320 --> 00:12:36.460 in to Claude Code or Codex or whatever and it automatically knows what to do to set up Logfire 00:12:36.520 --> 00:12:38.660 in your app. So thank you to Pydantic for supporting the show. 00:12:39.000 --> 00:12:41.359 Calvin, I know you're a big fan of the 00:12:41.420 --> 00:12:42.580 visibility into the token. 00:12:43.080 --> 00:12:43.180 Yeah. 00:12:43.820 --> 00:12:47.280 I was just curious now if copy the setup prompt is the new 00:12:47.740 --> 00:12:48.720 pipe to bash, 00:12:49.130 --> 00:12:50.920 like pipe some curl to bash. 00:12:51.380 --> 00:12:51.540 Yes.

00:12:51.790 --> 00:12:53.860 This is the replacing that. I think it is. 00:12:54.260 --> 00:12:54.960 That's why I was getting it. Yeah. No, 00:12:55.120 --> 00:12:56.780 no. I think it is. And it's amazing. 00:12:57.780 --> 00:12:58.860 I have some stuff that 00:12:58.870 --> 00:13:00.120 I'm working on. I'm like, oh, this is like, 00:13:01.020 --> 00:13:02.000 this idea is perfect. 00:13:02.150 --> 00:13:02.920 I love it so much. 00:13:03.210 --> 00:13:03.740 So yeah, 00:13:04.210 --> 00:13:05.980 pretty cool. Thanks to Pydantic for sponsoring the show. 00:13:06.370 --> 00:13:06.680 Thank you. 00:13:07.000 --> 00:13:09.399 And let's jump over to 00:13:09.820 --> 00:13:13.460 your topic next. You've got in the wrong order in the other. So what's next? 00:13:13.620 --> 00:13:13.960 Well, 00:13:14.980 --> 00:13:15.440 speaking of 00:13:15.800 --> 00:13:16.120 Pydantic, 00:13:16.920 --> 00:13:22.000 this comes from Bob Builderboss, a friend of the show. I know you've had him on numerous 00:13:22.240 --> 00:13:24.780 times for other events and things, 00:13:25.240 --> 00:13:27.520 but this one is about how to run, 00:13:27.980 --> 00:13:29.240 how Rust code becomes something 00:13:29.280 --> 00:13:29.920 you can import. 00:13:30.340 --> 00:13:32.840 I think it's interesting that we can, 00:13:33.080 --> 00:13:34.980 if people are complaining about performance, 00:13:35.260 --> 00:13:41.440 the first news article I had about getting rid of pandas and bringing back in with polars and 00:13:41.660 --> 00:13:43.300 DuckTV was about performance. 00:13:43.560 --> 00:13:45.080 This is similarly veined. 00:13:45.360 --> 00:13:48.400 Like if I've got a very computationally 00:13:48.900 --> 00:13:53.660 intense data structure or function that's happening in my program, 00:13:53.900 --> 00:13:55.360 it'd be sure be nice if I could 00:13:55.440 --> 00:14:00.240 maybe replace it out with a Rust version of that, but have it act natively inside of my Python code. 00:14:00.580 --> 00:14:06.780 So this blog post from Bob goes over basically what Pydantic V2 does, which is a data validation 00:14:06.940 --> 00:14:09.480 library that most Python apps are using these days. 00:14:09.660 --> 00:14:11.480 It's actually a Rust extension under the 00:14:11.560 --> 00:14:12.360 covers that does the work. 00:14:12.620 --> 00:14:13.180 Its core, 00:14:13.360 --> 00:14:15.860 Pydantic core, is all built with Py03, 00:14:16.080 --> 00:14:17.340 the same tool chain we're 00:14:17.340 --> 00:14:18.340 going to use here in this example. 00:14:18.800 --> 00:14:23.120 So it goes over some examples of basically you write a normal Rust 00:14:23.240 --> 00:14:23.520 module, 00:14:23.820 --> 00:14:26.200 you annotate it with some specific macros, 00:14:26.560 --> 00:14:28.719 and then you'll actually be able to import that 00:14:28.780 --> 00:14:32.360 into your Python code fairly naturally. 00:14:32.770 --> 00:14:37.040 I think there's basically the Rust parser is incredibly fast. 00:14:37.640 --> 00:14:41.060 Python never touches anything until the boundary crossing. 00:14:41.130 --> 00:14:42.680 So if you call for some data, 00:14:43.210 --> 00:14:44.440 that all happens over in Rust. 00:14:44.860 --> 00:14:48.220 The thing that I think the article covers that's really important is that 00:14:48.520 --> 00:14:51.100 if you pull back that data across the boundary, 00:14:51.760 --> 00:14:55.380 those Rust results get turned into Python objects. 00:14:55.780 --> 00:14:59.640 And so you're going to want to think carefully about how you bring back parts of that, because 00:14:59.800 --> 00:15:03.480 maybe you're only interested in a small piece of what is coming back and you don't need to 00:15:03.720 --> 00:15:03.900 populate. 00:15:04.100 --> 00:15:04.580 Say, for example, 00:15:05.220 --> 00:15:09.120 100,000 JSON values means that you're going to get 100,000 Python dictionary 00:15:09.320 --> 00:15:12.280 objects after the parsing is all done and everything gets passed back. 00:15:12.500 --> 00:15:16.360 You don't incur the penalty until you cross that threshold back into Python land. 00:15:16.880 --> 00:15:18.580 Maybe you don't need all 100,000, 00:15:18.800 --> 00:15:21.340 but there's some other operation you can do to get down 00:15:21.500 --> 00:15:22.860 to just the pieces you need. 00:15:22.920 --> 00:15:25.880 So what's nice is errors cross a boundary too. 00:15:26.140 --> 00:15:28.040 So if Rust runs into errors, 00:15:28.440 --> 00:15:30.040 those come back as Python exceptions. 00:15:30.500 --> 00:15:33.360 So it makes it easy to debug and figure out what's going on. 00:15:33.370 --> 00:15:37.360 So you get clean Python semantics while still leveraging Rust. 00:15:38.140 --> 00:15:41.080 So basically for anyone porting Rust, if you're returning a scalar, 00:15:41.240 --> 00:15:41.660 don't sweat it. 00:15:42.080 --> 00:15:43.260 If you're returning a big structure, 00:15:43.460 --> 00:15:44.400 profile the boundary. 00:15:44.870 --> 00:15:51.540 And if that's a real cost, you want to switch over and maybe do more of the algorithm on the Rust side. 00:15:51.860 --> 00:15:56.660 So much like you can use C or other languages in, 00:15:57.000 --> 00:15:59.200 I don't know if you can, can you use Ruby to do this kind of thing? 00:15:59.620 --> 00:16:00.320 I don't know if you can or not, 00:16:00.560 --> 00:16:03.000 but you definitely use Rust. 00:16:03.270 --> 00:16:04.380 And I'm kind of excited about that. 00:16:04.450 --> 00:16:05.920 I know you've been doing some coursework on it. 00:16:06.020 --> 00:16:10.480 And it sounds like Bob has also made some learning materials to lead folks through. 00:16:10.910 --> 00:16:16.519 It just feels like there's a really nice friendship between the Rust communities and the Python communities 00:16:16.980 --> 00:16:19.520 and all the niceties that have been put in place 00:16:19.700 --> 00:16:22.200 to allow us to use Rust almost natively 00:16:22.430 --> 00:16:23.080 over

in the Python world. 00:16:23.400 --> 00:16:24.000 So thanks, 00:16:24.200 --> 00:16:26.380 Bob, for the awesome post about that. 00:16:26.680 --> 00:16:26.860 He's, 00:16:27.040 --> 00:16:28.240 again, code examples in here, 00:16:28.740 --> 00:16:30.720 kind of explains to the Python folks 00:16:30.860 --> 00:16:31.880 who have never touched Rust 00:16:32.080 --> 00:16:33.760 what the function signatures look like, 00:16:33.790 --> 00:16:36.060 which I appreciate because breaking it down 00:16:36.150 --> 00:16:37.660 and telling me what each of those pieces means 00:16:38.080 --> 00:16:40.760 that I can now pretty easily read some Rust code 00:16:40.810 --> 00:16:41.720 and understand what's going on 00:16:41.790 --> 00:16:44.640 because it doesn't look terribly foreign to me, 00:16:44.780 --> 00:16:45.960 but it's just different enough, 00:16:46.080 --> 00:16:50.160 but this goes over a good usage of what each of those pieces mean for you. 00:16:50.360 --> 00:16:52.180 Yeah, it's pretty surprisingly similar to Python, 00:16:52.490 --> 00:16:52.580 honestly. 00:16:52.840 --> 00:16:52.960 Yeah, 00:16:53.250 --> 00:16:53.360 yeah, 00:16:53.520 --> 00:16:54.980 and incredibly fast, 00:16:55.140 --> 00:16:56.780 but you get to think about things a little differently 00:16:56.920 --> 00:16:58.540 because of the way it manages memory, 00:16:59.000 --> 00:17:01.480 and I think that's the big differentiator. 00:17:01.660 --> 00:17:03.440 What was that cartoon with the guys? 00:17:03.570 --> 00:17:05.459 Like, I'd gladly pay you on Tuesday. 00:17:05.839 --> 00:17:06.380 It was Popeye. 00:17:07.339 --> 00:17:08.300 Popeye, that was Popeye, 00:17:08.480 --> 00:17:08.540 right? 00:17:08.699 --> 00:17:08.939 Yeah, it was. 00:17:08.980 --> 00:17:10.319 I mean, I was thinking borrow checkers. 00:17:10.520 --> 00:17:15.339 It was Wimpy, who will glad you pay you Tuesday for a hamburger today. 00:17:15.540 --> 00:17:16.020 Yeah, 00:17:16.240 --> 00:17:19.280 that's the difference of Rust is you've got the borrow checker. 00:17:20.130 --> 00:17:20.600 Always checking. 00:17:20.910 --> 00:17:21.160 Yep, yep. 00:17:21.500 --> 00:17:21.839 Always checking. 00:17:22.620 --> 00:17:22.860 So yeah, 00:17:23.089 --> 00:17:23.400 I don't know. 00:17:23.900 --> 00:17:28.199 You've been doing a little more with Rust and Python and teaching some folks these things. 00:17:28.459 --> 00:17:28.680 Yeah, 00:17:28.880 --> 00:17:29.020 yeah, 00:17:29.220 --> 00:17:30.220 a little bit, a little bit. 00:17:30.480 --> 00:17:33.180 I have two follow-ups here. 00:17:33.530 --> 00:17:36.080 So you talked about DuckDB, 00:17:36.530 --> 00:17:39.020 but the question is, do you have a lake house? 00:17:39.400 --> 00:17:39.940 Do you have a lake? 00:17:40.070 --> 00:17:40.360 I mean, 00:17:40.570 --> 00:17:41.020 a lake house. 00:17:41.100 --> 00:17:43.360 So we've heard of data lakes, 00:17:44.320 --> 00:17:47.720 which is a place you just kind of dump a ton of like an insane. 00:17:47.860 --> 00:17:49.940 This is like back to your big data thing, DuckDB thing. 00:17:50.410 --> 00:17:53.280 You just dump a bunch of data into this data lake and you figure it out. 00:17:53.540 --> 00:17:53.640 Well, 00:17:53.960 --> 00:17:55.320 that's grown up a little bit. 00:17:55.680 --> 00:17:58.920 And now there's this thing called DuckDB, 00:17:59.240 --> 00:18:00.860 but it's an implementation, 00:18:01.190 --> 00:18:03.820 an example of what's called an open lake format. 00:18:04.380 --> 00:18:04.600 Who knew? 00:18:04.730 --> 00:18:05.060 Do you know? 00:18:05.380 --> 00:18:06.980 I didn't know that. 00:18:07.080 --> 00:18:08.840 We've done lake house implementations. 00:18:08.870 --> 00:18:10.820 I didn't know there was an open lake format now. 00:18:10.920 --> 00:18:12.260 So the story is, 00:18:12.920 --> 00:18:16.860 what if we could use S3 to scale our data access? 00:18:17.780 --> 00:18:19.180 S3 scale is pretty large. 00:18:19.540 --> 00:18:21.680 If you can read stuff off the file system instead of out of memory, 00:18:21.920 --> 00:18:23.640 you can scale that tremendously large. 00:18:23.940 --> 00:18:29.540 In the open lake story is, well, if you put file formats in S3 00:18:29.920 --> 00:18:32.100 that everything could read, like maybe JSON files 00:18:32.260 --> 00:18:34.720 that tell you what the files mean, you could read them first. 00:18:35.080 --> 00:18:36.360 Here's where the data lives in each piece. 00:18:36.720 --> 00:18:37.600 And then parquet files. 00:18:38.040 --> 00:18:38.120 Yeah. 00:18:38.320 --> 00:18:40.020 Or zipped CSV. 00:18:40.400 --> 00:18:45.440 don't know take your pick right it could be whatever so i just had the folks from duck lake on which is 00:18:45.540 --> 00:18:54.200 a duck db implementation story of uh this open lake format well then i get this message here saying 00:18:54.480 --> 00:18:59.620 yep guess what aws i'm sure you know this is a i did i did see this one come by yeah yeah and 00:18:59.920 --> 00:19:06.599 in duck labs just offered basically the h1 is bad let me read let me read the first sentence today 00:19:06.620 --> 00:19:11.480 we are announcing that Amazon has signed an agreement to acquire Duck Labs, 00:19:11.890 --> 00:19:12.520 the Amsterdam 00:19:12.810 --> 00:19:15.960 based company behind the open source analytical database DuckDB. 00:19:16.240 --> 00:19:18.160 I'll put a link to the announcement 00:19:18.210 --> 00:19:18.840 and I thought, well, 00:19:19.240 --> 00:19:19.320 hmm, 00:19:19.760 --> 00:19:20.480 what does this even mean? 00:19:21.180 --> 00:19:22.700 And I didn't, I wasn't entirely sure. 00:19:22.870 --> 00:19:23.480 So I, 00:19:23.890 --> 00:19:24.520 I like, 00:19:24.770 --> 00:19:26.380 I went and did some looking here. I'm like, 00:19:26.540 --> 00:19:26.720 there's, 00:19:27.100 --> 00:19:28.920 there's actually a lot of pieces in play. 00:19:29.680 --> 00:19:32.400 So let me lay it out and I'll tell you what part AWS acquired, 00:19:32.820

--> 00:19:34.220 what part didn't. Okay. So first of 00:19:34.200 --> 00:19:34.240 Well, 00:19:34.400 --> 00:19:35.920 thanks to Dylan McConnell who sent this in. 00:19:36.200 --> 00:19:38.260 What does this mean for the DuckDB ecosystem? 00:19:38.540 --> 00:19:39.400 So first of all, 00:19:39.560 --> 00:19:39.860 DuckDB, 00:19:40.160 --> 00:19:41.280 which you gave a shout out before, 00:19:41.660 --> 00:19:42.020 twice really, 00:19:43.340 --> 00:19:46.660 is the open source in-pros analytical SQL engine, 00:19:47.300 --> 00:19:48.020 MIT licensed. 00:19:48.400 --> 00:19:49.440 It's like SQLite, 00:19:49.840 --> 00:19:50.960 but for columnar data, 00:19:51.410 --> 00:19:52.240 which if you're And 00:19:52.240 --> 00:19:53.360 way more, and way, way more. 00:19:53.940 --> 00:19:54.180 Yeah. 00:19:54.420 --> 00:19:54.860 Anything you 00:19:54.860 --> 00:19:56.740 pointed at becomes SQL queryable. It's 00:19:56.740 --> 00:19:57.020 amazing. 00:19:57.520 --> 00:19:57.620 Yeah, 00:19:57.750 --> 00:19:58.340 yeah, yeah. So you 00:19:58.340 --> 00:20:00.680 can say pointed at a pandas data frame and then 00:20:00.680 --> 00:20:02.800 do SQL queries against your Panda. Like there's 00:20:02.800 --> 00:20:03.700 a bunch of plugins. 00:20:03.940 --> 00:20:05.940 It's far beyond just a database, 00:20:06.580 --> 00:20:09.700 but it's an in-process sort of data processing engine, 00:20:10.180 --> 00:20:11.620 much like that. 00:20:11.920 --> 00:20:14.300 So the IP of this is not owned by any company. 00:20:14.500 --> 00:20:16.860 It's held in a DuckDB foundation. 00:20:17.240 --> 00:20:17.360 Oh, 00:20:17.520 --> 00:20:18.140 good. Thank goodness. 00:20:18.180 --> 00:20:18.820 That sounds good. 00:20:19.100 --> 00:20:19.600 That is good. 00:20:19.600 --> 00:20:21.580 It was spun out of CWI Amsterdam 00:20:21.810 --> 00:20:24.500 from the folks who mentioned that article. 00:20:25.000 --> 00:20:25.100 Good, 00:20:25.540 --> 00:20:26.940 but we'll come back to it. 00:20:27.180 --> 00:20:28.280 Then there's Duck Labs. 00:20:28.720 --> 00:20:32.960 And the story is Amazon AWS has acquired Duck Labs. 00:20:33.360 --> 00:20:36.480 This is the company formerly branded DuckDB Labs, 00:20:36.700 --> 00:20:39.460 founded over five years ago by Hannes 00:20:39.880 --> 00:20:44.280 Mullison and Mark Roosevelt to give DuckDB a stable home, 00:20:44.720 --> 00:20:45.260 bootstrapped, 00:20:45.500 --> 00:20:46.220 grew to 30 people. 00:20:46.760 --> 00:20:48.300 Now they can go chill on their island, 00:20:48.480 --> 00:20:49.300 which congrats to them. 00:20:49.400 --> 00:20:49.620 That's awesome. 00:20:50.360 --> 00:20:52.000 Because DuckDB really has taken over, right? 00:20:52.600 --> 00:20:53.920 Then there's DuckLake, 00:20:54.280 --> 00:20:55.920 which I mentioned earlier. 00:20:56.560 --> 00:20:58.980 It's one of three projects by DuckLab. 00:20:59.380 --> 00:21:01.200 And there's DuckDB, 00:21:01.460 --> 00:21:01.960 there's DuckLake. 00:21:01.990 --> 00:21:05.440 And then there's an API for working with this called Quack. 00:21:05.780 --> 00:21:06.960 Check out the Talk Python episode. 00:21:07.390 --> 00:21:10.420 But it's an open lake, lake house format. 00:21:10.450 --> 00:21:13.200 All the waterfowl puns are great. 00:21:13.480 --> 00:21:13.880 It is. 00:21:14.020 --> 00:21:16.820 And we actually on the podcast had a fun conversation about like, 00:21:17.140 --> 00:21:18.380 do you need a more serious name? 00:21:18.810 --> 00:21:20.980 Like RPC for your data lake. 00:21:21.140 --> 00:21:21.220 Like, 00:21:21.380 --> 00:21:23.000 no, they're like, we're calling it Quack. 00:21:23.220 --> 00:21:23.580 Come on now. 00:21:23.900 --> 00:21:26.000 And then also we have MotherDuck, 00:21:26.320 --> 00:21:31.340 which I think I thought Mother Duck was online version of DuckDB. 00:21:31.730 --> 00:21:32.180 But no, 00:21:32.480 --> 00:21:36.300 that is a separate Seattle company selling serverless hosted DuckDB. 00:21:36.860 --> 00:21:39.400 And originally it was started in partnership with DuckDB Labs 00:21:39.410 --> 00:21:42.080 and they worked closely with Hannes and Mark for years 00:21:42.680 --> 00:21:45.540 and even contracted Duck Labs for some of the engineering. 00:21:45.980 --> 00:21:46.640 So now, 00:21:46.820 --> 00:21:48.060 what does this all mean? 00:21:48.310 --> 00:21:50.740 So the foundation owning DuckDB is awesome, 00:21:51.220 --> 00:21:52.200 but it has three directors, 00:21:52.710 --> 00:21:53.940 the people who own Duck Labs. 00:21:55.720 --> 00:22:00.180 So there's a bit of a, how much independence is it really going to have? 00:22:00.480 --> 00:22:03.020 There are some other folks, 00:22:03.360 --> 00:22:05.580 other governance and so on there. 00:22:05.880 --> 00:22:06.680 But, you know, it's cool. 00:22:06.760 --> 00:22:07.220 There's a foundation. 00:22:07.680 --> 00:22:09.780 It's not super independent of DuckDB at the moment. 00:22:09.980 --> 00:22:10.780 Maybe it will be, though, 00:22:11.020 --> 00:22:11.460 after this. 00:22:11.800 --> 00:22:15.040 Mother Duck immediately moved into the business that Duck Labs vacated. 00:22:15.320 --> 00:22:18.220 They now sell enterprise support for DuckDB and so on. 00:22:19.200 --> 00:22:22.040 Everyone expects an AWS DuckDB service, 00:22:22.400 --> 00:22:24.060 probably a Duck Lake as well. 00:22:24.180 --> 00:22:30.180 already used s3 right but maybe just a little more formal yeah there's s3 query and some adjacent 00:22:30.620 --> 00:22:35.520 like technologies that sound like this may maybe this will augment or replace what's really nice 00:22:35.570 --> 00:22:42.400 about ducklapse is it runs a local duckdb or a local postgres server and a lot of the chatty api 00:22:42.400 --> 00:22:46.600 that would come from an open table format and the metadata now all happen in the database and then 00:22:46.600 --> 00:22:51.500 it just fetches and reads the files yeah that's pretty cool yeah we can't beat physics if we can

00:22:51.400 --> 00:22:55.680 keep the data, the bits where they're at physically and bring the compute to it. 00:22:56.020 --> 00:22:56.440 That's the win. 00:22:56.660 --> 00:22:56.740 Yeah. 00:22:57.040 --> 00:23:02.060 So the duck lake angle actually is probably the most interesting one because AWS heavily 00:23:02.440 --> 00:23:04.180 committed to iceberg through S3 tables, 00:23:04.500 --> 00:23:06.780 which is a competitor, 00:23:07.220 --> 00:23:08.820 at least a competitor competing 00:23:09.040 --> 00:23:10.960 concept to a duck lake. 00:23:11.390 --> 00:23:12.500 So yeah, check it out. 00:23:12.770 --> 00:23:15.140 I think AWS just got better. 00:23:15.360 --> 00:23:15.760 We'll see what that 00:23:15.880 --> 00:23:16.600 means for the rest of the world. 00:23:16.890 --> 00:23:18.860 What do you think? I mean, you're on the inside of this a little bit. 00:23:19.180 --> 00:23:19.240 Yeah, 00:23:19.420 --> 00:23:20.760 a little bit, but you know, they've, 00:23:21.020 --> 00:23:23.240 we've had mixed reviews on their handling of open source, 00:23:23.620 --> 00:23:30.720 but luckily they don't have any control over the open source other than they've just bought out the founders who are on the board of the open source foundation. 00:23:31.220 --> 00:23:33.060 It's still separate enough of an entity. 00:23:33.170 --> 00:23:34.880 I don't think there's a conflict here. 00:23:35.210 --> 00:23:36.200 I mean, it's going to be good for the project. 00:23:36.390 --> 00:23:37.380 The project is already incredible. 00:23:37.540 --> 00:23:38.680 Like the DuckDB stuff is, 00:23:38.960 --> 00:23:39.420 it's just like, 00:23:39.740 --> 00:23:48.860 if someone had thought about SQLite and said, I need to grow all these other features that handle all kinds of crazy data and do give me native like JSON access and functions. 00:23:49.000 --> 00:23:54.640 And it's a really great platform for building cool little utilities or talking to giant chunks of data, 00:23:55.040 --> 00:23:56.600 as we saw in the first article. 00:23:56.700 --> 00:23:56.920 Yeah, 00:23:57.120 --> 00:23:57.700 yeah, yeah, absolutely. 00:23:57.760 --> 00:23:58.740 And use barely any memory. 00:23:59.220 --> 00:23:59.360 I mean, 00:23:59.560 --> 00:24:00.960 that's why this will run and work. 00:24:01.280 --> 00:24:03.340 Yeah, the DuckDB part is really interesting on that. 00:24:03.360 --> 00:24:04.999 And then the Duck Lake is like insane. 00:24:05.840 --> 00:24:09.240 You know, you could have terabytes of Parquet files all broken in little bits. 00:24:09.240 --> 00:24:09.740 No big deal. 00:24:10.180 --> 00:24:10.920 No big deal. 00:24:11.140 --> 00:24:11.620 NBD, 00:24:12.100 --> 00:24:12.460 NBD. 00:24:12.800 --> 00:24:13.040 Yeah, 00:24:13.240 --> 00:24:13.640 I love it. 00:24:13.640 --> 00:24:14.900 Well, how about some extras? 00:24:15.320 --> 00:24:20.620 Well, I will continue on my beating the uv drum. 00:24:21.090 --> 00:24:22.240 The latest release, 00:24:22.480 --> 00:24:25.120 every week we got something new, the latest release from the uv folks, 00:24:25.490 --> 00:24:28.700 we get code signing on Mac and Windows. 00:24:29.490 --> 00:24:32.080 So the binaries are now officially signed, 00:24:32.480 --> 00:24:32.940 code signed. 00:24:33.700 --> 00:24:33.820 Again, 00:24:34.070 --> 00:24:36.160 I think this is all coming together, 00:24:37.080 --> 00:24:39.500 ensuring we can secure the software supply 00:24:39.780 --> 00:24:40.500 chain part of this. 00:24:40.530 --> 00:24:42.660 So I'm excited to see that release too. 00:24:42.740 --> 00:24:46.920 I was hoping I wouldn't see a uv thing this week, but sure enough, it popped up in my feed. 00:24:47.290 --> 00:24:52.580 And I was like, I have to mention it because they just keep making everything better and better and better. 00:24:52.800 --> 00:24:54.640 So uv is now code signed. 00:24:55.040 --> 00:25:00.600 So you can trust that it came from the right source on your own machine if you're on Mac and Linux. 00:25:01.040 --> 00:25:02.160 Yeah, that's excellent. 00:25:02.620 --> 00:25:02.900 Excellent, excellent. 00:25:03.060 --> 00:25:03.240 Yeah. 00:25:03.620 --> 00:25:04.460 I see it's still going. 00:25:04.650 --> 00:25:07.300 Oh my gosh, the code signing is such a pain these days. 00:25:07.520 --> 00:25:11.920 It used to be you could just build an EXE or a.app and you could just go here, try my app. 00:25:12.540 --> 00:25:13.540 How dangerous is that? 00:25:15.140 --> 00:25:15.500 I know. 00:25:15.640 --> 00:25:16.820 That's how the world used to be, though. 00:25:17.440 --> 00:25:18.240 Well, we used to have, what, 00:25:18.400 --> 00:25:22.040 R login with no password to remote machines? 00:25:22.540 --> 00:25:22.980 What could go wrong? 00:25:23.420 --> 00:25:23.740 It's fine. 00:25:24.020 --> 00:25:24.340 It's fine. 00:25:24.520 --> 00:25:24.780 Trust. 00:25:25.200 --> 00:25:26.200 You've got to have a lot of trust. 00:25:27.040 --> 00:25:28.740 Why would somebody do something mean to computers? 00:25:29.140 --> 00:25:29.540 I don't know. 00:25:29.660 --> 00:25:29.980 I don't know. 00:25:30.220 --> 00:25:32.040 I remember in Windows 95, 00:25:32.640 --> 00:25:35.460 we had a bunch of them at a university I worked at. 00:25:35.860 --> 00:25:38.300 Plugged straight in to Ethernet. 00:25:38.640 --> 00:25:40.800 And the Ethernet, everyone got its own IP address. 00:25:41.080 --> 00:25:41.640 And guess what? 00:25:41.680 --> 00:25:43.580 That thing got taken over pretty quickly. 00:25:44.160 --> 00:25:45.380 The university I was at, 00:25:45.630 --> 00:25:47.720 they all had public IP addresses in the labs too. 00:25:48.080 --> 00:25:48.800 Yeah, it didn't go well. 00:25:49.080 --> 00:25:49.240 No. 00:25:49.740 --> 00:25:50.600 It did not go well. 00:25:51.040 --> 00:25:53.620 Speaking of things that need to be patched and updated, check this out. 00:25:54.040 --> 00:25:56.460 So there's two things that involve restarts. 00:25:56.840 --> 00:25:58.100 But

I have a MacBook Pro, 00:25:58.600 --> 00:25:59.180 M5 Pro. 00:25:59.500 --> 00:26:00.000 Very nice. 00:26:00.120 --> 00:26:00.360 Love it. 00:26:00.360 --> 00:26:02.640 I got it this summer or earlier, maybe spring. 00:26:02.690 --> 00:26:03.480 I don't know, whenever I got it. 00:26:03.800 --> 00:26:05.620 And it came with a power supply. 00:26:05.960 --> 00:26:06.500 One of those power bricks. 00:26:06.940 --> 00:26:12.960 the power brick had to reboot the other day to to update itself i was sitting there working and i 00:26:12.960 --> 00:26:19.000 saw this article come up come by say apple releases a firmware update for the 140 watt usbc power 00:26:19.240 --> 00:26:23.620 adapter which is the one that runs the macbook and it's almost that that's almost actual size 00:26:23.690 --> 00:26:29.180 right there it's so big yeah it's it's a beefy boy it is uh yeah i think this is even a little 00:26:29.310 --> 00:26:34.040 small this big old picture of it it's it's it's heavy but i was i read this article and i was 00:26:33.960 --> 00:26:35.820 sitting there working and my Mac, 00:26:36.070 --> 00:26:37.320 when it comes off of power, 00:26:37.620 --> 00:26:38.780 it dims the monitor. 00:26:39.070 --> 00:26:39.200 Yeah. 00:26:39.550 --> 00:26:40.080 So I'm, 00:26:40.200 --> 00:26:43.820 I'm just, you know, and I was an hour or two later, I was sitting there and everything goes dim for a 00:26:43.950 --> 00:26:44.040 second. 00:26:44.350 --> 00:26:46.560 The little power just disconnects. 00:26:46.810 --> 00:26:47.240 And then two, 00:26:47.570 --> 00:26:47.660 two, 00:26:47.820 --> 00:26:47.900 three, 00:26:48.280 --> 00:26:49.140 five seconds later, something 00:26:49.240 --> 00:26:49.500 like that, 00:26:49.780 --> 00:26:50.280 power comes back, 00:26:50.500 --> 00:26:52.860 brightness comes back. I'm like, I just, 00:26:53.380 --> 00:26:54.920 my power break just rebooted. 00:26:55.080 --> 00:26:56.300 What in the world is going on here? 00:26:56.480 --> 00:26:57.080 That's crazy. 00:26:57.680 --> 00:27:00.560 I've, I've, I've replaced all my Mac power books. 00:27:00.940 --> 00:27:01.480 I've got, 00:27:01.950 --> 00:27:03.580 not that I'm trying to be an ad for Anchor 00:27:03.580 --> 00:27:04.980 or anything like that, but the Anchor Prime 00:27:05.540 --> 00:27:09.000 has 160 watt, like itty bitty little power brick 00:27:09.160 --> 00:27:09.280 that, 00:27:09.520 --> 00:27:10.440 because I travel quite a bit, 00:27:10.780 --> 00:27:13.500 it has four USB-C ports on it 00:27:13.690 --> 00:27:18.220 and they can all deliver a combined sum of 160 watts. 00:27:18.610 --> 00:27:22.220 So I can full bore charge my MacBook Pro M4 Max 00:27:22.820 --> 00:27:25.440 and my iPad and my phone all at the same time. 00:27:25.680 --> 00:27:26.180 That's beautiful. 00:27:26.640 --> 00:27:28.340 And it's smaller than that brick. 00:27:28.980 --> 00:27:30.680 Yeah, I'm also a fan of the Anchor stuff. 00:27:30.960 --> 00:27:33.780 This one, since I had it anyway, I just plugged it into the wall part of the house. 00:27:33.880 --> 00:27:36.780 And just if I'm in that part of the house, I just grabbed that cord. 00:27:37.120 --> 00:27:41.320 But yeah, normally if I travel, I have an Anker that's actually a power brick, a little battery. 00:27:41.760 --> 00:27:45.000 And it has two USB things and it'll do not quite as high as yours, 00:27:45.280 --> 00:27:46.020 but pretty high. 00:27:46.360 --> 00:27:49.000 And it's super nice because it's also a power brick, 00:27:49.380 --> 00:27:49.460 right? 00:27:49.640 --> 00:27:50.540 So if I need to charge up, 00:27:50.700 --> 00:27:52.900 it'll even charge the MacBook, 00:27:53.180 --> 00:27:54.900 but then just get it and just plug it into the wall. 00:27:54.980 --> 00:27:56.420 And then it just becomes a power thing. 00:27:56.740 --> 00:27:58.160 I'm also a fan of this Anker stuff. 00:27:58.500 --> 00:28:00.280 Okay, a couple more extras really quick here. 00:28:00.880 --> 00:28:07.700 we've got do not play yeah so the story of vs code the official documentary is out have you 00:28:07.800 --> 00:28:12.360 watched this no i have not watched this it's an hour and 38 minutes and i'm here for it okay 00:28:12.880 --> 00:28:17.720 all right it's got a lot of people that uh maybe you didn't see coming like Eric gamma for example 00:28:17.900 --> 00:28:21.800 you know thinking back to the gang of four patterns and all that kind of stuff because 00:28:22.000 --> 00:28:27.839 he was apparently involved in the early days um so yeah cool we had uh cult repo do the python 00:28:27.860 --> 00:28:33.500 documentary we had them do the jet brains document or intellij documentary and here's the vs code one 00:28:33.660 --> 00:28:37.680 i'm really loving these like high quality production i mean these are these are nice 00:28:38.120 --> 00:28:43.360 little nice videos they yeah a lot of people behind it um i mean there's such there's an 00:28:43.440 --> 00:28:48.920 audience for all these things i'm here for it too i love the fact that like the underdogs can feel 00:28:49.660 --> 00:28:54.700 they are important for us and we can now hear more of the story about how some of these things came 00:28:54.660 --> 00:29:00.120 about yeah it's really interesting i mean vs code has taken over so much and then yeah anyway and the 00:29:00.160 --> 00:29:05.940 origins are way way more uh less ambitious let's say it's cool to check out so it also has over 00:29:06.020 --> 00:29:09.580 half a million views so there is an audience for this apparently there's absolutely audience okay 00:29:09.720 --> 00:29:14.320 think speaking of things that make you reboot yesterday last night yesterday mac os golden 00:29:14.500 --> 00:29:21.279 gate ios golden gate watch os 27 golden gate all those things came out so uh-oh did you upgrade i 00:29:21.300 --> 00:29:22.300 I did. Why wouldn't I mean, 00:29:22.460 --> 00:29:23.440 I'm like, oh, let's go. 00:29:23.980 --> 00:29:25.040 I'm not yet upgraded

on this. 00:29:25.080 --> 00:29:26.040 I'm usually of that opinion, 00:29:26.400 --> 00:29:31.020 but lately I may wait a month or until a.1 or.1 to come out. 00:29:32.460 --> 00:29:34.840 I spent one day working with it and so far it's okay. 00:29:35.280 --> 00:29:35.480 Okay. 00:29:35.760 --> 00:29:37.960 I'm going to upgrade then on your full recommendation. 00:29:38.420 --> 00:29:38.780 Well, 00:29:39.260 --> 00:29:41.820 I've not upgraded my MacBook or my streaming computer. 00:29:41.870 --> 00:29:43.600 I only recorded my main desktop. 00:29:44.380 --> 00:29:45.280 So we'll see. 00:29:45.640 --> 00:29:47.300 Ask me next week. Ask me how I feel about it. 00:29:47.360 --> 00:29:47.460 You're right. 00:29:48.360 --> 00:29:48.580 Honestly, 00:29:48.770 --> 00:29:52.040 the one thing to be a little careful about as developers is the Rosetta 2. 00:29:52.580 --> 00:29:52.660 Yeah. 00:29:53.080 --> 00:29:53.720 That's going away. 00:29:54.260 --> 00:29:56.920 So your ability to run Intel compiled stuff, 00:29:57.270 --> 00:29:59.280 you might think, Michael, why would I run Intel 00:29:59.600 --> 00:29:59.900 compiled stuff? 00:30:00.070 --> 00:30:00.360 Like, ooh, 00:30:00.560 --> 00:30:01.000 Docker. 00:30:01.500 --> 00:30:03.480 Certain Docker things only have Intel versions. 00:30:03.900 --> 00:30:04.620 So that's going to be a mega hassle. 00:30:04.620 --> 00:30:05.380 I mean, that's pretty rare. 00:30:06.280 --> 00:30:07.940 People have cross-compiled most of this stuff. 00:30:07.990 --> 00:30:09.840 Because when was the last time an Intel Mac was released? 00:30:10.560 --> 00:30:14.960 Yeah, but let's suppose I'm deploying to an x86 server. 00:30:15.380 --> 00:30:15.500 Yeah. 00:30:15.940 --> 00:30:16.880 And I want to test something. 00:30:17.440 --> 00:30:18.720 I think it's gotten a lot better. 00:30:19.240 --> 00:30:20.120 It definitely has gotten better. 00:30:20.600 --> 00:30:21.260 But it used to be, 00:30:21.480 --> 00:30:22.460 the thing is, I don't know. 00:30:22.520 --> 00:30:26.420 It used to be certain stuff would only work in an x86 version of Linux. 00:30:26.420 --> 00:30:27.040 Oh, I remember this. 00:30:27.200 --> 00:30:27.260 Yeah. 00:30:27.380 --> 00:30:30.760 But that was like three, four, five years ago when I was really dealing with that. 00:30:31.000 --> 00:30:35.780 Actually, it was when I was dealing with like Databricks and trying to coordinate that stuff. 00:30:36.120 --> 00:30:36.320 Nice. 00:30:36.660 --> 00:30:38.400 So this one still has it, but the one after it, 00:30:38.560 --> 00:30:39.040 whatever that's called, 00:30:39.200 --> 00:30:39.480 won't. 00:30:39.720 --> 00:30:43.260 So this is like your last safe upgrade if you're worried about the other thing. 00:30:43.560 --> 00:30:46.020 So did Apple actually deliver some AI features this time? 00:30:46.360 --> 00:30:46.980 Well, I'll tell you what. 00:30:47.060 --> 00:30:48.680 the new Siri caught me off guard. 00:30:48.700 --> 00:30:49.300 I'm like, oh yeah, 00:30:49.460 --> 00:30:49.860 I did. 00:30:50.180 --> 00:30:51.360 I did actually upgrade the phone. 00:30:51.480 --> 00:30:52.560 And I guess it has the new Siri 00:30:52.640 --> 00:30:53.200 because it sounded, 00:30:53.740 --> 00:30:55.080 I asked it something like set a timer 00:30:55.120 --> 00:30:56.400 and it said something completely different 00:30:56.460 --> 00:30:56.920 that I'm used to. 00:30:57.020 --> 00:30:57.920 And it sounded better. 00:30:58.000 --> 00:30:58.180 I'm like, 00:30:58.400 --> 00:30:58.800 oh wait, 00:30:59.360 --> 00:31:00.600 I haven't had a chance to test it though. 00:31:01.080 --> 00:31:01.380 All right. 00:31:01.540 --> 00:31:03.040 It did set the timer like a champ. 00:31:03.160 --> 00:31:03.620 Let me tell you. 00:31:04.260 --> 00:31:04.620 Well done. 00:31:05.280 --> 00:31:05.600 Way to go. 00:31:07.100 --> 00:31:07.380 All right. 00:31:07.580 --> 00:31:08.660 Let's talk a joke. 00:31:09.040 --> 00:31:09.200 Okay. 00:31:09.380 --> 00:31:09.760 Speaking of, 00:31:10.500 --> 00:31:10.680 you know, 00:31:10.780 --> 00:31:12.360 the new Siri is supposed to be agentic. 00:31:12.660 --> 00:31:14.760 So the joke is we're agentic now. 00:31:14.940 --> 00:31:15.780 We're an agentic startup. 00:31:15.860 --> 00:31:16.080 You ready? 00:31:16.460 --> 00:31:17.120 This is how you, 00:31:17.680 --> 00:31:19.720 there's certain things you've got to position yourself. 00:31:19.890 --> 00:31:23.420 I was just watching an ad because I started watching football yesterday. 00:31:24.160 --> 00:31:28.220 And normally ads are excluded from my life, but apparently not in football, 00:31:28.420 --> 00:31:29.100 American football. 00:31:29.440 --> 00:31:32.940 And there's some ad for Zoom that Zoom is an AI company. 00:31:33.100 --> 00:31:34.160 They're not about meetings anymore. 00:31:34.520 --> 00:31:34.800 Nope. 00:31:35.060 --> 00:31:39.160 They can book the thing so that your dry cleaning gets picked up. 00:31:39.320 --> 00:31:40.840 They can do a slideshow. 00:31:40.910 --> 00:31:41.200 Like what? 00:31:41.660 --> 00:31:41.840 Okay. 00:31:42.300 --> 00:31:44.760 So everyone's got to be some kind of AI thing now. 00:31:44.960 --> 00:31:45.740 So here's the joke. 00:31:48.960 --> 00:31:54.880 i changed all of our loading dot dot dot states to thinking dot dot dot we're an agentic startup now 00:31:55.420 --> 00:31:59.600 perfect i'm gonna get right on that yeah get right in there like you can there's so much vc 00:31:59.730 --> 00:32:06.620 money to be had from this go for it discombobulating i'm thinking oh oh i hate that so much about 00:32:06.730 --> 00:32:10.740 Claude Code it drives me crazy that it's got all these random little words yeah the reason i don't 00:32:10.760 --> 00:32:11.340 like it is I don't, 00:32:12.000 --> 00:32:16.720 it feels like it's made for someone with ADHD who just can't possibly let it 00:32:16.860 --> 00:32:18.120 just be for like five seconds. 00:32:18.640 --> 00:32:21.240 And so if I'm like doing something else, I'd look over and like word 00:32:21.340 --> 00:32:24.040 starts. I'm like, Oh, maybe it's no, it's not done. It's like, Oh, maybe it's done. 00:32:24.380 --> 00:32:24.680 Oh no, 00:32:24.780 --> 00:32:26.040 no. It's just still like randomly. 00:32:26.800 --> 00:32:29.080 Like, could it just have the little icon go? 00:32:29.380 --> 00:32:29.440 No, 00:32:29.920 --> 00:32:30.160 no, no, 00:32:30.240 --> 00:32:30.280 no. 00:32:30.520 --> 00:32:31.780 It's thinking is combobulating. 00:32:32.080 --> 00:32:33.020 It's wording. 00:32:33.360 --> 00:32:34.180 I don't know. What is it doing? 00:32:34.350 --> 00:32:35.840 That's why you need her to exactly. 00:32:36.440 --> 00:32:36.620 That's, 00:32:36.960 --> 00:32:38.440 that's, that's the story for another episode. 00:32:38.820 --> 00:32:39.520 All right. Sounds good. 00:32:39.940 --> 00:32:40.200 All right. Well, 00:32:40.860 --> 00:32:42.100 thanks as always for being here, Calvin. 00:32:42.270 --> 00:32:43.100 And thank you everyone for listening. 00:32:43.360 --> 00:32:43.980 We'll talk to you soon. 00:32:44.360 --> 00:32:44.560 Yeah. Bye.