Meeting Title: [Pool-Parts-to-Go]-Weekly-Sprint-Review Date: 2024-03-29 Meeting participants: Patrick Trainer, Jack Tomei, Ryan Luke Daque, Agustin, Uttam Kumaran


WEBVTT

1 00:00:22.174 00:00:22.610 Ryan Luke Daque: Hello!

2 00:00:23.310 00:00:24.320 Ryan Luke Daque: I.

3 00:00:24.320 00:00:25.020 Uttam Kumaran: Wow!

4 00:00:28.030 00:00:30.160 Uttam Kumaran: You’d be logging off today early.

5 00:00:31.814 00:00:33.389 Ryan Luke Daque: But maybe we’ll see.

6 00:00:35.980 00:00:39.490 Ryan Luke Daque: Have a lot to to fix or to do to finish.

7 00:00:40.460 00:00:42.922 Ryan Luke Daque: and we still have the meeting later with.

8 00:00:43.540 00:00:44.389 Ryan Luke Daque: But what’s that?

9 00:00:44.800 00:00:45.209 Uttam Kumaran: Guy.

10 00:00:45.650 00:00:46.700 Ryan Luke Daque: Guy, yeah.

11 00:00:50.590 00:00:51.229 Patrick Trainer: So up.

12 00:00:52.200 00:00:53.590 Uttam Kumaran: And hey!

13 00:00:54.340 00:00:56.590 Ryan Luke Daque: You saw the sneak peek you.

14 00:00:56.730 00:00:57.220 Ryan Luke Daque: hey?

15 00:00:57.710 00:01:00.430 Uttam Kumaran: Sick. Let’s go.

16 00:01:00.430 00:01:00.800 Ryan Luke Daque: Hey? What.

17 00:01:01.200 00:01:05.949 Patrick Trainer: It’s it’s pretty cool, like, I I mean, it’s definitely

18 00:01:06.190 00:01:08.669 Patrick Trainer: there’s some parts of evidence that are

19 00:01:09.070 00:01:13.660 Patrick Trainer: a little cumbersome just because it like it is a mix of like

20 00:01:15.610 00:01:25.200 Patrick Trainer: I I I the like the mix of markdown and code, as like the snippets, is a little awkward, but like I think I’ve got. I think I’ve got it figured out.

21 00:01:26.610 00:01:31.859 Uttam Kumaran: Do you? Wanna just yeah, do you wanna just take like 2 min to just share that. And then we could just like.

22 00:01:31.860 00:01:32.679 Patrick Trainer: Yeah. Yeah. Oh, yeah.

23 00:01:32.680 00:01:35.270 Uttam Kumaran: About how how the process was.

24 00:01:35.760 00:01:36.310 Ryan Luke Daque: Yeah, that would be.

25 00:01:36.310 00:01:38.740 Patrick Trainer: Yeah, so I can actually.

26 00:01:39.980 00:01:43.289 Patrick Trainer: where are we? So one sec.

27 00:01:46.040 00:01:48.070 Patrick Trainer: Okay, so

28 00:01:51.930 00:01:53.360 Patrick Trainer: alright, y’all are seeing this.

29 00:01:55.280 00:02:06.740 Patrick Trainer: So this, this is what we got. Basically, Jack recreated your warranty analysis all in evidence. It’s actually pretty sweet. Everything’s pretty dynamic.

30 00:02:08.280 00:02:14.720 Patrick Trainer: most most everything on here is is actually dynamic. And like, we’ll update with

31 00:02:15.160 00:02:17.619 Patrick Trainer: Like our 7 days, or like

32 00:02:18.010 00:02:19.690 Patrick Trainer: all these different filters.

33 00:02:20.230 00:02:22.170 Patrick Trainer: And then.

34 00:02:23.270 00:02:41.641 Patrick Trainer: oh, it goes into. We have, like some some heat bars that are mapped into buckets. And then it basically just follows like, it’s just text. It’s following this the same layout Jack that you already had? But then we also have this cool kind of little commit graph whatever and then

35 00:02:41.970 00:02:45.139 Uttam Kumaran: Wait. So what is this? This one is showing by day

36 00:02:45.240 00:02:48.550 Uttam Kumaran: the concentration of okay return goods.

37 00:02:48.550 00:02:54.560 Patrick Trainer: Yeah, concentration of returns. So it’s like, you can see when people like people are ordering

38 00:02:55.005 00:02:59.270 Patrick Trainer: like they’ll they’ll buy parts in the. It looks in the spring

39 00:02:59.979 00:03:04.529 Patrick Trainer: in the spring months, and then like right before then, and then

40 00:03:05.345 00:03:08.029 Patrick Trainer: and then they return them when it’s cold

41 00:03:08.629 00:03:14.220 Patrick Trainer: as well, this is pretty funny, and like on the on the nose

42 00:03:14.891 00:03:19.519 Patrick Trainer: but the way this looks in code

43 00:03:21.660 00:03:26.280 Patrick Trainer: is kinda like this, please? Then?

44 00:03:29.597 00:03:31.349 Patrick Trainer: what is it called?

45 00:03:33.190 00:03:34.010 Patrick Trainer: Sorry?

46 00:03:37.920 00:03:39.390 Patrick Trainer: What is that mode?

47 00:03:40.720 00:03:42.880 Ryan Luke Daque: So everything’s in the in the Md.

48 00:03:43.820 00:03:48.489 Patrick Trainer: Yeah. So what we have over here, we have like, so this is the.

49 00:03:48.670 00:03:52.040 Patrick Trainer: this is the main directory, right?

50 00:03:52.050 00:03:56.870 Patrick Trainer: And so it’s got like some some config stuff. And you basically, you

51 00:03:56.940 00:04:00.349 Patrick Trainer: you define your connection here.

52 00:04:01.441 00:04:06.130 Patrick Trainer: Like, just your query string and then it’s like you provide

53 00:04:06.590 00:04:11.747 Patrick Trainer: username pass that gets hashed and then you

54 00:04:12.520 00:04:14.010 Patrick Trainer: provide. Are you able.

55 00:04:14.415 00:04:14.820 Ryan Luke Daque: Ceiling!

56 00:04:14.820 00:04:18.760 Patrick Trainer: The text, yeah? And then you. So then you provide like a source query

57 00:04:18.910 00:04:23.269 Patrick Trainer: in which a source query can is basically like any

58 00:04:23.650 00:04:34.769 Patrick Trainer: tabular data set. So like, just very similar to how real is thinking about like a source it’s it’s just kind of like the the base model that you’re working off of

59 00:04:35.310 00:04:46.040 Patrick Trainer: and then you’re so you’re able to have like a bunch of those alert as many sources as you as you need like. If you want to go from Snowflake to Csv to duct dB,

60 00:04:46.100 00:04:48.569 Patrick Trainer: you can have all of those different sources.

61 00:04:49.330 00:04:51.290 Patrick Trainer: and then you can

62 00:04:51.850 00:04:54.740 Patrick Trainer: import. You can also add like

63 00:04:55.600 00:04:59.390 Patrick Trainer: queries here, like where you’re just like

64 00:05:00.090 00:05:01.669 Patrick Trainer: doing this. And then

65 00:05:02.240 00:05:04.310 Patrick Trainer: when you come into the index.

66 00:05:04.380 00:05:06.510 Patrick Trainer: you’re then able to

67 00:05:07.120 00:05:08.220 Patrick Trainer: labs

68 00:05:08.940 00:05:12.009 Patrick Trainer: like, import it essentially like

69 00:05:12.410 00:05:14.230 Patrick Trainer: this. And so

70 00:05:14.820 00:05:17.580 Patrick Trainer: this then, allows you to.

71 00:05:18.252 00:05:19.789 Patrick Trainer: You can like

72 00:05:20.030 00:05:23.109 Patrick Trainer: create one of these like SQL

73 00:05:24.382 00:05:27.580 Patrick Trainer: code blocks, and then you can

74 00:05:27.740 00:05:28.640 Patrick Trainer: sweat

75 00:05:29.360 00:05:33.409 Patrick Trainer: star from, and then you can reference it with like

76 00:05:35.030 00:05:37.960 Patrick Trainer: early brackets essentially like that.

77 00:05:37.980 00:05:49.689 Patrick Trainer: But the idea for this is so, we’re define it. Oh, so let me actually go back the the source table that you

78 00:05:50.160 00:05:51.560 Patrick Trainer: defined here

79 00:05:51.950 00:05:55.420 Patrick Trainer: in your in your sources. Those get

80 00:05:57.260 00:05:58.650 Patrick Trainer: materialized

81 00:05:58.940 00:06:00.489 Patrick Trainer: in duct dB,

82 00:06:01.200 00:06:05.759 Patrick Trainer: and there. Like, you can view that here. It’s just that it’s

83 00:06:06.772 00:06:14.190 Patrick Trainer: yeah, they’re just getting materialized as tables and duct Tb, and then everything in the code blocks

84 00:06:14.390 00:06:17.579 Patrick Trainer: and whatnot. Those are all

85 00:06:17.820 00:06:24.359 Patrick Trainer: views built on top of those materializations. So that’s how that’s kind of how they’re working.

86 00:06:24.470 00:06:26.790 Patrick Trainer: And so what these are.

87 00:06:26.790 00:06:27.840 Uttam Kumaran: Wow!

88 00:06:28.570 00:06:29.250 Uttam Kumaran: Poof!

89 00:06:31.710 00:06:32.770 Patrick Trainer: we’re

90 00:06:34.190 00:06:45.049 Patrick Trainer: we’ve defined we can call like this warranty claims source, and it can call it directly because we’re it’s just shooting a query over to duct dB.

91 00:06:45.050 00:06:45.820 Uttam Kumaran: People, school.

92 00:06:45.820 00:06:49.309 Patrick Trainer: And then we’re creating like an an alias

93 00:06:49.400 00:06:52.460 Patrick Trainer: up here that you can then reference

94 00:06:52.470 00:06:53.660 Patrick Trainer: in

95 00:06:54.100 00:06:59.720 Patrick Trainer: later down and so basically, you’re just writing text

96 00:07:00.211 00:07:07.269 Patrick Trainer: and then you have these like props. It’s really similar to like react props.

97 00:07:08.145 00:07:21.829 Patrick Trainer: It’s pretty. Yeah, it’s pretty much the same thing it’s there, and it’s all like all of the charting, the chat charting library that they use E charts, which is like it. I they use that and react a lot. So it’s like, it’s the same

98 00:07:22.080 00:07:25.429 Patrick Trainer: Api and params and everything which is pretty nice.

99 00:07:26.422 00:07:30.040 Patrick Trainer: And then so you create like these different.

100 00:07:30.040 00:07:30.370 Ryan Luke Daque: And turn.

101 00:07:30.370 00:07:36.710 Patrick Trainer: Parts. You provide like data to the to to the prop itself.

102 00:07:36.800 00:07:40.330 Patrick Trainer: So we have like this daily claims, which is like

103 00:07:40.560 00:07:42.849 Patrick Trainer: somewhat. Yeah, appear.

104 00:07:43.910 00:07:47.210 Patrick Trainer: And then you can also provide like

105 00:07:47.310 00:07:48.840 Patrick Trainer: these templates

106 00:07:50.143 00:07:55.239 Patrick Trainer: where users can put in information. And then there’s also, like

107 00:07:55.350 00:08:10.019 Patrick Trainer: certain props as well, that have kind of like an interactive clicking uniface, very similar to real that you can chain together to like build more

108 00:08:11.190 00:08:18.580 Patrick Trainer: like complicated filters, and then filters that also extend across like the entire dashboard.

109 00:08:19.571 00:08:22.880 Patrick Trainer: And so you basically, you’re writing

110 00:08:23.470 00:08:25.490 Patrick Trainer: kind of like a

111 00:08:27.250 00:08:29.179 Patrick Trainer: like, a a

112 00:08:30.010 00:08:35.150 Patrick Trainer: very minorly transform SQL. On top of like this base model

113 00:08:35.179 00:08:37.899 Patrick Trainer: to just power, your dashboard

114 00:08:37.960 00:08:41.639 Patrick Trainer: and or your your page your report.

115 00:08:42.029 00:08:46.579 Patrick Trainer: and then and then, really, all you’re doing is just adding in

116 00:08:48.570 00:08:52.820 Patrick Trainer: the like, filling in the the the values of of where they go

117 00:08:53.426 00:08:54.199 Patrick Trainer: and then.

118 00:08:54.200 00:09:01.019 Uttam Kumaran: I think, basically, like, a good way is like, you get as far as you can, just like on sequel and analysis.

119 00:09:01.460 00:09:08.549 Uttam Kumaran: And then it’s like, okay, this is like, okay, where we’re just gonna put into a Google doc. And then basically, the nice thing here is that

120 00:09:08.710 00:09:09.929 Uttam Kumaran: it’s it

121 00:09:10.250 00:09:13.350 Uttam Kumaran: can query, the same data like throughout

122 00:09:13.390 00:09:17.610 Uttam Kumaran: time. I think the one thing that may get tough is like, if we have to do

123 00:09:18.140 00:09:19.870 Uttam Kumaran: database changes.

124 00:09:20.400 00:09:22.929 Uttam Kumaran: or if we change table structures.

125 00:09:23.660 00:09:24.140 Patrick Trainer: Right.

126 00:09:24.140 00:09:26.190 Uttam Kumaran: Keeping, knowing that this

127 00:09:27.130 00:09:33.390 Uttam Kumaran: is in sync with that is like the only thing I thought of where I’m like. Damn if we like rename a column or.

128 00:09:33.390 00:09:33.950 Patrick Trainer: Right.

129 00:09:33.950 00:09:36.349 Uttam Kumaran: Change that that’s not happening too often, but.

130 00:09:37.540 00:09:38.390 Patrick Trainer: Right.

131 00:09:38.830 00:09:39.940 Patrick Trainer: Yeah, I’m

132 00:09:40.890 00:09:43.170 Patrick Trainer: I’m not quite sure how.

133 00:09:43.170 00:09:51.820 Uttam Kumaran: It’s mainly just knowing that like no, it’s just mainly knowing that this is in the lineage of like. This is a consumer. That table like, I just want to make. Maybe that’s just like

134 00:09:52.580 00:09:55.470 Uttam Kumaran: something we have to do. Or again, maybe.

135 00:09:58.019 00:10:04.519 Uttam Kumaran: yeah, I don’t know to think about it. But and ha! And is it issuing queries from your local credentials?

136 00:10:04.520 00:10:06.139 Patrick Trainer: Yeah, it’s it’s

137 00:10:06.160 00:10:07.459 Patrick Trainer: it’s all

138 00:10:08.520 00:10:14.290 Patrick Trainer: like, it uses the connection string from here. And what it’s doing. It

139 00:10:14.700 00:10:17.789 Patrick Trainer: is like it. It downloads

140 00:10:17.920 00:10:21.590 Patrick Trainer: these source queries and creates Parkay files.

141 00:10:21.670 00:10:28.860 Patrick Trainer: and then duct dB, duct dB, queries those park files directly, and then everything

142 00:10:28.950 00:10:30.270 Patrick Trainer: in here

143 00:10:31.020 00:10:35.209 Patrick Trainer: all these push queries get issued in the browser.

144 00:10:35.290 00:10:37.120 Patrick Trainer: and so like

145 00:10:37.130 00:10:41.399 Patrick Trainer: it’s it. What I found really helpful was like. If you open up

146 00:10:41.470 00:10:42.960 Patrick Trainer: devtools.

147 00:10:43.110 00:10:47.380 Patrick Trainer: you can see like the requests

148 00:10:47.520 00:10:48.889 Patrick Trainer: that are being made

149 00:10:50.130 00:10:50.990 Patrick Trainer: of.

150 00:10:51.670 00:10:52.930 Patrick Trainer: I don’t need that.

151 00:10:53.160 00:10:56.160 Patrick Trainer: So like, if we go here and like.

152 00:10:56.300 00:10:57.750 Patrick Trainer: update this.

153 00:10:58.000 00:11:00.300 Patrick Trainer: Actually here, let’s

154 00:11:00.990 00:11:02.779 Patrick Trainer: get on. Move this over.

155 00:11:03.150 00:11:05.170 Patrick Trainer: And we’re going to

156 00:11:05.680 00:11:07.950 Patrick Trainer: think, I, yeah, okay, I have it running

157 00:11:08.620 00:11:13.020 Patrick Trainer: so what we’re going to do is we’re going to.

158 00:11:14.740 00:11:16.970 Patrick Trainer: Let’s just pick one

159 00:11:18.990 00:11:21.080 Patrick Trainer: gets replaced or

160 00:11:24.920 00:11:26.150 Patrick Trainer: daily claims.

161 00:11:26.770 00:11:29.190 Patrick Trainer: Okay? And so we’ll

162 00:11:29.460 00:11:30.679 Patrick Trainer: come here

163 00:11:31.820 00:11:33.530 Patrick Trainer: and we can write

164 00:11:34.270 00:11:35.860 Patrick Trainer: and let me actually

165 00:11:36.200 00:11:38.260 Patrick Trainer: get this over as well.

166 00:11:39.090 00:11:43.690 Patrick Trainer: And so if we have this like SQL.

167 00:11:43.890 00:11:46.110 Patrick Trainer: Box, and we call it Foo.

168 00:11:46.785 00:11:49.899 Patrick Trainer: We can see like what’s coming in

169 00:11:51.270 00:11:53.390 Patrick Trainer: and say, if you have like

170 00:11:55.465 00:12:00.640 Patrick Trainer: syntax error like Asdf like that’s that’s a nothing.

171 00:12:01.270 00:12:03.229 Patrick Trainer: It’ll it’ll show

172 00:12:04.390 00:12:07.599 Patrick Trainer: like the actual error from Duct. dB,

173 00:12:08.030 00:12:13.559 Patrick Trainer: and so like this, it’s what it’s running Duct dB, in in wasom.

174 00:12:13.700 00:12:16.960 Patrick Trainer: And so it’s like everything is running in the browser.

175 00:12:17.990 00:12:18.580 Patrick Trainer: And

176 00:12:18.700 00:12:23.079 Patrick Trainer: it just issues like like this is, this is how

177 00:12:23.400 00:12:41.580 Patrick Trainer: you debug like. It doesn’t say that in the do in the docs, like their docs, are actually pretty lacking. But this this is the best way I found to debug is just to is just watch the the Javascript console go, and then, like once that

178 00:12:41.980 00:12:43.490 Patrick Trainer: comes on.

179 00:12:45.422 00:12:47.750 Patrick Trainer: like Star, it’ll go.

180 00:12:47.950 00:12:51.349 Patrick Trainer: and I’ll show like we’ve got like a cursor, and it’ll

181 00:12:51.770 00:12:53.909 Patrick Trainer: figure that out. And

182 00:12:54.060 00:12:57.870 Patrick Trainer: and then it just like shows us you’re creating these props.

183 00:13:00.110 00:13:05.790 Patrick Trainer: then what else we can like if we come not into the console. But we like, go into network.

184 00:13:06.270 00:13:08.009 Patrick Trainer: Let’s turn that off.

185 00:13:08.140 00:13:09.050 Patrick Trainer: And we’ll

186 00:13:10.050 00:13:11.240 Patrick Trainer: reload

187 00:13:11.270 00:13:12.490 Patrick Trainer: this.

188 00:13:15.670 00:13:16.580 Patrick Trainer: Yeah.

189 00:13:20.440 00:13:21.190 Patrick Trainer: So

190 00:13:21.350 00:13:24.110 Patrick Trainer: like, it’s creating these.

191 00:13:24.420 00:13:26.580 Patrick Trainer: issuing these queries to these

192 00:13:27.540 00:13:29.570 Patrick Trainer: like part K files.

193 00:13:29.730 00:13:34.250 Patrick Trainer: and then it just has like like a web socket

194 00:13:35.550 00:13:39.869 Patrick Trainer: going back and forth, talking to duck dB and in Webassembly.

195 00:13:40.550 00:13:42.130 Patrick Trainer: And

196 00:13:42.670 00:13:46.100 Patrick Trainer: and then some of these things. It’s it’s nice, too, because if you

197 00:13:46.280 00:13:47.700 Patrick Trainer: dump into the

198 00:13:48.090 00:13:53.760 Patrick Trainer: into here you can. I mean we? We can see all this, but we can also

199 00:13:54.162 00:13:55.410 Patrick Trainer: kind of get like

200 00:13:55.780 00:13:58.580 Patrick Trainer: a picture of what’s happening

201 00:13:59.770 00:14:01.179 Patrick Trainer: underneath everything.

202 00:14:01.780 00:14:02.349 Patrick Trainer: But

203 00:14:02.640 00:14:05.420 Patrick Trainer: yeah. So this is that this is definitely the best way to

204 00:14:05.680 00:14:06.460 Patrick Trainer: rob.

205 00:14:06.570 00:14:12.529 Patrick Trainer: to debug and whatnot, because before it was, it was very like all you get when

206 00:14:13.180 00:14:14.830 Patrick Trainer: evidence is running

207 00:14:14.910 00:14:15.950 Patrick Trainer: is

208 00:14:16.450 00:14:17.980 Patrick Trainer: like a very

209 00:14:19.950 00:14:22.189 Patrick Trainer: like it just tells you that it updated

210 00:14:22.340 00:14:26.920 Patrick Trainer: or it didn’t. And like, sometimes you get like unexpected tokens. But

211 00:14:28.196 00:14:29.489 Patrick Trainer: not not

212 00:14:29.510 00:14:31.774 Patrick Trainer: all the time. So

213 00:14:32.840 00:14:37.610 Patrick Trainer: yeah, when you’re when you’re working with it, it’s definitely good to have, like both of these, side by side.

214 00:14:37.680 00:14:39.920 Patrick Trainer: and with the the console open.

215 00:14:42.880 00:14:44.240 Patrick Trainer: if you’ve used

216 00:14:44.300 00:14:56.419 Patrick Trainer: sniff streamlit like, it’s very similar, like, if not the it’s it’s the exact same to streamlit, except for it doesn’t support python. Which I’m

217 00:14:56.700 00:15:00.389 Patrick Trainer: comparing like the 2 of like evidence versus

218 00:15:00.400 00:15:01.690 Patrick Trainer: versus streamlit.

219 00:15:01.840 00:15:06.040 Patrick Trainer: I think, like I think streamlit is.

220 00:15:07.990 00:15:14.029 Patrick Trainer: I don’t. It might be a little more ergonomic to write about. I don’t know just because you can

221 00:15:14.470 00:15:19.770 Patrick Trainer: like you like you can write python functions directly, and that’s just that’s super handy.

222 00:15:22.200 00:15:23.220 Patrick Trainer: this, though.

223 00:15:23.220 00:15:25.949 Uttam Kumaran: The ui elements here, I think like, look.

224 00:15:25.950 00:15:34.269 Patrick Trainer: Yeah, yeah, these look really good. And yeah, it it’s and streamlets issuing queries directly to snowflake, too. And so.

225 00:15:34.543 00:15:35.089 Uttam Kumaran: There’s a.

226 00:15:35.090 00:15:46.460 Patrick Trainer: On a latency and all that. So it’s like the the quickness of this is like that that feels really nice. And there they do have like templating

227 00:15:46.520 00:15:49.178 Patrick Trainer: abilities, or or or like

228 00:15:50.600 00:15:56.999 Patrick Trainer: basically. So the like, their library or the the package is written enclosure. And so

229 00:15:57.010 00:15:59.069 Patrick Trainer: the closure templating is

230 00:15:59.370 00:16:04.610 Patrick Trainer: not that great? From how I understand? But you can

231 00:16:04.840 00:16:06.959 Patrick Trainer: more or less do

232 00:16:07.010 00:16:18.593 Patrick Trainer: what you need to do if you if you’re like looping through values. The only thing that I haven’t quite figured out is like I found myself wanting to

233 00:16:19.630 00:16:23.103 Patrick Trainer: like create variables, essentially to to reference back to.

234 00:16:23.590 00:16:26.249 Patrick Trainer: just because there is like a lot of

235 00:16:27.720 00:16:33.699 Patrick Trainer: kind of like back and forth, going up and down the page, trying to make everything line up

236 00:16:34.543 00:16:38.226 Patrick Trainer: and then the other part was

237 00:16:44.100 00:16:44.890 Patrick Trainer: but was it?

238 00:16:45.880 00:16:48.459 Patrick Trainer: Oh, then there’s then there’s just some.

239 00:16:48.470 00:16:50.329 Patrick Trainer: This not being like a

240 00:16:50.620 00:17:02.415 Patrick Trainer: of a fully polished like it, you can definitely tell. It’s like a open source work in pro progress of like. Sometimes it says like dates, ask the key, and sometimes it’s

241 00:17:03.130 00:17:03.820 Uttam Kumaran: Like a.

242 00:17:03.820 00:17:06.390 Patrick Trainer: And why. And then sometimes it’s like

243 00:17:07.190 00:17:10.450 Patrick Trainer: act or like here when you like, you can format

244 00:17:11.218 00:17:18.690 Patrick Trainer: values into like dollars. Sometimes it’s just format. Sometimes it’s value format, sometimes it’s

245 00:17:19.349 00:17:20.909 Patrick Trainer: FMT.

246 00:17:21.079 00:17:24.340 Patrick Trainer: Which is like that’s annoying.

247 00:17:24.349 00:17:24.839 Ryan Luke Daque: Yeah.

248 00:17:24.839 00:17:31.419 Patrick Trainer: But it it’s not. It’s not the end of the world, but there’s there’s there’s boards in there.

249 00:17:33.459 00:17:35.589 Patrick Trainer: And then

250 00:17:35.869 00:17:38.069 Patrick Trainer: oh, and then you can also.

251 00:17:38.129 00:17:42.559 Patrick Trainer: What’s also helpful is when you’re like in here.

252 00:17:42.859 00:17:44.319 Patrick Trainer: you can

253 00:17:45.699 00:17:46.729 Patrick Trainer: of

254 00:17:47.329 00:17:49.629 Patrick Trainer: you can like show the queries

255 00:17:50.059 00:17:50.769 Patrick Trainer: that

256 00:17:51.219 00:17:58.899 Patrick Trainer: you’ve defined at the the top of your file, or and at the top of like the prop, or whatever that you’re using.

257 00:17:59.491 00:18:02.609 Patrick Trainer: I just went ahead and did it at the top

258 00:18:02.966 00:18:05.183 Patrick Trainer: but you get a pretty nice like

259 00:18:05.709 00:18:06.659 Patrick Trainer: of

260 00:18:07.379 00:18:08.459 Patrick Trainer: little table here. There.

261 00:18:08.460 00:18:09.630 Uttam Kumaran: Oh, nice!

262 00:18:09.630 00:18:12.739 Patrick Trainer: That you can scroll through and kind of like, see what’s going on.

263 00:18:12.870 00:18:15.449 Patrick Trainer: which is, which is nice and and

264 00:18:15.560 00:18:16.719 Patrick Trainer: pretty helpful.

265 00:18:16.880 00:18:19.509 Patrick Trainer: and then it also has, like

266 00:18:19.640 00:18:21.499 Patrick Trainer: this little SQL, console

267 00:18:22.640 00:18:24.440 Patrick Trainer: where you can like

268 00:18:25.530 00:18:26.780 Patrick Trainer: Dad.

269 00:18:30.830 00:18:34.640 Patrick Trainer: 3 series. I think this is the command, yeah.

270 00:18:35.030 00:18:38.329 Patrick Trainer: where you can like. Yeah, where you can like issue

271 00:18:38.770 00:18:41.530 Patrick Trainer: queries directly in there.

272 00:18:42.080 00:18:44.029 Patrick Trainer: And then it also has

273 00:18:45.205 00:18:46.840 Patrick Trainer: actually, really good.

274 00:18:47.110 00:18:49.910 Patrick Trainer: actually pretty solid auto, complete, too.

275 00:18:51.880 00:18:53.609 Patrick Trainer: like one.

276 00:18:54.960 00:18:56.409 Patrick Trainer: and which you can

277 00:18:57.610 00:18:58.559 Patrick Trainer: do you like that.

278 00:18:58.560 00:18:59.235 Uttam Kumaran: Hmm.

279 00:18:59.910 00:19:01.880 Patrick Trainer: And like these are the

280 00:19:03.510 00:19:07.570 Patrick Trainer: like materialized tables. There, I guess it’s just if you’re wanting to

281 00:19:07.870 00:19:14.130 Patrick Trainer: the query directly in in this and then it has, like

282 00:19:14.940 00:19:17.120 Patrick Trainer: some settings options

283 00:19:17.340 00:19:18.620 Patrick Trainer: where

284 00:19:19.780 00:19:22.739 Patrick Trainer: you can like. If you want to add.

285 00:19:22.940 00:19:28.620 Patrick Trainer: like it, new sources or something like that, you can just do it directly in here

286 00:19:29.100 00:19:31.760 Patrick Trainer: which is pretty nice, and it will

287 00:19:34.000 00:19:38.429 Patrick Trainer: pop up like a little like a a directory nested under sources.

288 00:19:39.141 00:19:42.498 Patrick Trainer: And then you can also

289 00:19:44.560 00:19:47.759 Patrick Trainer: you can template out like different pages.

290 00:19:47.880 00:19:49.290 Patrick Trainer: You can add

291 00:19:49.330 00:19:52.079 Patrick Trainer: kind of like like partials and stuff

292 00:19:52.340 00:19:54.669 Patrick Trainer: if you want to

293 00:19:55.380 00:19:57.010 Patrick Trainer: templatized text.

294 00:19:57.290 00:19:59.150 Patrick Trainer: and then

295 00:19:59.640 00:20:00.460 Patrick Trainer: the

296 00:20:01.500 00:20:06.220 Patrick Trainer: and then going from there like, once you get the basic structure.

297 00:20:06.380 00:20:07.720 Patrick Trainer: you can

298 00:20:08.060 00:20:10.310 Patrick Trainer: baked logic into

299 00:20:10.520 00:20:13.209 Patrick Trainer: really the page directly of like

300 00:20:14.677 00:20:27.479 Patrick Trainer: like changing parameters based on like if a value is over X or below y, and that changes like color stuff like that, you can make. You can make things pretty dynamic.

301 00:20:30.600 00:20:32.060 Patrick Trainer: And then

302 00:20:32.952 00:20:35.320 Patrick Trainer: apart from that, everything.

303 00:20:35.610 00:20:38.870 Patrick Trainer: What is nice is everything is in

304 00:20:39.800 00:20:42.699 Patrick Trainer: like duct. dB, SQL. Dialect.

305 00:20:42.770 00:20:45.109 Patrick Trainer: And so you’re you’re not having to

306 00:20:45.150 00:20:46.969 Patrick Trainer: switch between dialects

307 00:20:47.080 00:20:48.470 Patrick Trainer: between sources.

308 00:20:48.550 00:20:51.899 Patrick Trainer: so you can just like keep it all in one, which

309 00:20:52.060 00:20:53.310 Patrick Trainer: I think is pretty

310 00:20:53.610 00:20:54.840 Patrick Trainer: pretty candy.

311 00:20:58.420 00:21:16.129 Uttam Kumaran: Okay, cool. I mean, I think what I’m gonna do is I? I have a meeting with evidence team on Monday. And I added to that, I’m basically gonna wanna call them and ask them, how like, what’s best way to structure it? Because we have, I wanna use this for clients and for some internal stuff.

312 00:21:17.040 00:21:18.620 Uttam Kumaran: Second thing.

313 00:21:18.640 00:21:21.600 Uttam Kumaran: And like, I wanna get the cost of the teams plan, basically.

314 00:21:21.660 00:21:30.130 Uttam Kumaran: because again, this could go into like we either we could offer it for free. And we could just say, like, it’s part of like our cost, or

315 00:21:30.300 00:21:34.749 Uttam Kumaran: we have people buy like, buy it and host it.

316 00:21:35.060 00:21:40.230 Uttam Kumaran: It’s if it’s like really cheap. I might just be like Yo, this is just comes in as part of like

317 00:21:40.710 00:21:42.370 Uttam Kumaran: our platform cost.

318 00:21:42.500 00:21:47.470 Uttam Kumaran: And then again, we either charge a platform fee or we raise our prices. Basically so.

319 00:21:47.530 00:21:54.859 Uttam Kumaran: But I think this looks really good. I. So the one thing I also want to see is like, by maybe by Monday we can decide on getting this. Either

320 00:21:54.900 00:22:03.270 Uttam Kumaran: we have. We have a self hosted version. Evidence. Cloud is free. And we can basically also do this

321 00:22:05.600 00:22:18.470 Uttam Kumaran: in the teams thing. But maybe we we could just publish it and kind of get this thing on Monday, or I’m gonna send them probably a screenshot. I’m just sending them like a long update about some other stuff. So yeah.

322 00:22:19.540 00:22:20.160 Patrick Trainer: Boop.

323 00:22:20.630 00:22:26.310 Patrick Trainer: Yeah. Give some instructions of how to deploy here. I didn’t. I hadn’t tried, but

324 00:22:27.280 00:22:28.420 Patrick Trainer: sure it’s fine.

325 00:22:30.610 00:22:34.617 Uttam Kumaran: Yeah. Maybe if you wanna make the Pr and then we can take a look probably next week.

326 00:22:35.820 00:22:36.960 Uttam Kumaran: Yeah, cool.

327 00:22:38.740 00:22:41.119 Uttam Kumaran: No, it’s it’s great. It’s pretty great.

328 00:22:41.120 00:22:41.919 Ryan Luke Daque: So far, though.

329 00:22:41.920 00:22:44.400 Uttam Kumaran: I guess it is all it is all Jack’s work. Dustin.

330 00:22:45.950 00:22:53.940 Jack Tomei: The only the only number that stands out to me, Patrick, is the extended warranty sales at 10,000.

331 00:22:54.390 00:22:56.220 Patrick Trainer: Yeah, that’s that that awesome?

332 00:22:57.040 00:23:04.479 Patrick Trainer: Oh, it it! It wasn’t a dynamic value, but I copied it directly from

333 00:23:07.300 00:23:10.650 Patrick Trainer: your document there, where they go.

334 00:23:14.940 00:23:18.070 Jack Tomei: Maybe I was looking at the wrong thing, and then on the what was I looking at?

335 00:23:18.070 00:23:19.090 Uttam Kumaran: So we also.

336 00:23:19.090 00:23:23.069 Patrick Trainer: You’re right. It does say, 207. Yeah, I’m not sure what that’s about, but.

337 00:23:23.870 00:23:26.639 Uttam Kumaran: We also, Jack included all their.

338 00:23:26.770 00:23:30.670 Uttam Kumaran: They said they had a second shopify instance for

339 00:23:31.043 00:23:35.900 Uttam Kumaran: parts of warranties. And so that’s all coming in now. So the analysis actually may

340 00:23:35.910 00:23:39.039 Uttam Kumaran: have changed because it includes a lot of that. But basically.

341 00:23:39.340 00:23:39.640 Jack Tomei: Laura.

342 00:23:39.640 00:23:42.310 Uttam Kumaran: This, it should be more so, I wonder.

343 00:23:42.310 00:23:45.940 Jack Tomei: Table. That is my! Does. My my warranty claims table.

344 00:23:46.700 00:23:48.819 Uttam Kumaran: I think it should all feed in. Yeah.

345 00:23:48.820 00:23:49.410 Jack Tomei: Time.

346 00:23:50.160 00:23:53.010 Uttam Kumaran: So we can take a look. But I also want to send this, and then

347 00:23:53.320 00:23:57.560 Uttam Kumaran: send this also to Cody to be like, what’s the status on those changes.

348 00:23:59.200 00:24:05.989 Uttam Kumaran: but yeah, I know we only have like 6 min left. So maybe, Jack, you want to go, and then I’ll go on that one.

349 00:24:07.167 00:24:10.639 Jack Tomei: I haven’t really done much. I’m waiting on Brian to

350 00:24:10.780 00:24:14.529 Jack Tomei: we have. We don’t have any conversion data. For marketing.

351 00:24:14.530 00:24:14.885 Uttam Kumaran: Okay.

352 00:24:15.240 00:24:17.579 Jack Tomei: Limits my ability, I can’t say like.

353 00:24:18.630 00:24:25.360 Jack Tomei: well, I can’t say anything about revenue, and I can’t say anything about like marketing. Spend per item which were like the 2 big things.

354 00:24:25.990 00:24:30.610 Jack Tomei: so I met with Brian on like Tuesday, I think, and we like digged into the 5 Tran stuff.

355 00:24:30.970 00:24:42.819 Jack Tomei: And it looks like there’s like a little report builder kind of thing where we created like a custom report or something, cause I I met with Kim, too, and I saw like what Kim can see, and she can see like so much stuff that we don’t have.

356 00:24:42.820 00:24:43.990 Uttam Kumaran: Oh, shit. Okay.

357 00:24:43.990 00:24:45.680 Jack Tomei: In our tables. So it’s like

358 00:24:46.160 00:24:48.999 Jack Tomei: she’s not. Gonna she’s not gonna stop using her stuff until our stuff.

359 00:24:49.280 00:24:49.840 Uttam Kumaran: Okay. Like.

360 00:24:49.840 00:24:57.820 Jack Tomei: Good cause. It’s her so I’m trying to kind of get it there. Brian, I think, was 6, so I I’m not exactly sure where that went. But

361 00:24:58.090 00:24:59.149 Jack Tomei: are he submitted to help.

362 00:24:59.150 00:25:03.929 Uttam Kumaran: He said. He submitted a ticket. So we’ll just wait for that, I guess. Okay, okay, cool.

363 00:25:04.253 00:25:10.067 Jack Tomei: Yeah. And then, cleavo, I’m just trying to wrap my head around it. It’s very. It’s a lot

364 00:25:10.820 00:25:14.049 Jack Tomei: the just I need to figure out like what it all means, because

365 00:25:15.250 00:25:29.059 Jack Tomei: I was hoping it would just be like clicks, and like not like that. There’s like so many redundant names of things. So I’m trying to figure out like, what’s actually going on. So yeah, that’s kind of my plan for today.

366 00:25:29.820 00:25:30.450 Uttam Kumaran: Okay.

367 00:25:31.390 00:25:37.769 Uttam Kumaran: yeah, if anything on the modeling side there. Brian’s also worked on Clavio stuff. And then I’m gonna be

368 00:25:37.960 00:25:42.630 Uttam Kumaran: in and out of pocket today. But I’ll also just be tapping in. So

369 00:25:43.362 00:25:49.189 Uttam Kumaran: there are event types. But yeah, I have to look. I haven’t looked at the events table. The new events table, yet either so.

370 00:25:49.190 00:25:54.159 Jack Tomei: It’s strange how the events types can be like product viewed viewed product

371 00:25:54.330 00:25:55.430 Jack Tomei: product.

372 00:25:55.630 00:25:57.569 Jack Tomei: all viewed like, there’s 3.

373 00:25:57.570 00:25:59.550 Uttam Kumaran: Oh, okay. Okay.

374 00:25:59.550 00:26:03.029 Jack Tomei: You know. So I’m trying to understand like, Ha! Are these duplicates

375 00:26:03.460 00:26:07.720 Jack Tomei: what’s going on? And that the same for like clicked product clicked.

376 00:26:08.090 00:26:13.150 Jack Tomei: link clicked. It’s like, okay. So I need to understand, like, what’s the flow in my mind? It’s like

377 00:26:13.780 00:26:16.550 Jack Tomei: you open the email, you click the link

378 00:26:17.110 00:26:19.509 Jack Tomei: you purchase like that.

379 00:26:19.510 00:26:20.190 Uttam Kumaran: There’s like a set.

380 00:26:20.190 00:26:20.810 Jack Tomei: And.

381 00:26:20.810 00:26:21.580 Uttam Kumaran: There’s like a set.

382 00:26:21.580 00:26:22.340 Jack Tomei: There’s like, Yeah.

383 00:26:22.635 00:26:24.110 Uttam Kumaran: And then you get it.

384 00:26:24.110 00:26:30.560 Jack Tomei: It depends like the biggest portion it should be, but then received is smaller than opened. So I’m like, okay, then what does opened mean?

385 00:26:31.350 00:26:32.563 Uttam Kumaran: Oh, huh!

386 00:26:33.355 00:26:35.019 Jack Tomei: You know like, then what does it need to.

387 00:26:35.020 00:26:39.350 Uttam Kumaran: Okay, I mean Dude, I would say. I would say, I think, Augustine, I think we have a

388 00:26:39.980 00:26:49.109 Uttam Kumaran: I think we have another thread with their support about stuff. But just hammer. There’s I think we just hammer their support because they’re they’ve been pretty responsive. And

389 00:26:49.230 00:26:53.679 Uttam Kumaran: I think we’re just gonna like it’s we can just use them for that.

390 00:26:53.900 00:26:58.320 Jack Tomei: That’s that’s what I should do. So where do I? I just go to like Cleavia’s website.

391 00:26:59.130 00:27:02.670 Uttam Kumaran: Yeah, I have a Clavio login that’s in one passor.

392 00:27:03.080 00:27:06.329 Uttam Kumaran: You can just use that. And then just imagine.

393 00:27:06.605 00:27:20.090 Jack Tomei: Ask them all these event questions. Cause I I it’s a pretty simple funnel. We’re trying to build where it’s like you open it. You click the link specific steps, but they really broke it down to like 60 different events. So I’m like just trying to understand.

394 00:27:20.990 00:27:25.273 Patrick Trainer: I think the events they have there’s like a higher level

395 00:27:25.720 00:27:30.939 Patrick Trainer: like metric associated with it. And I I think that is the.

396 00:27:32.240 00:27:33.340 Jack Tomei: That’s what I’m joining, too.

397 00:27:33.340 00:27:34.860 Patrick Trainer: That that you’re looking for. Yeah.

398 00:27:34.860 00:27:43.159 Jack Tomei: So that’s what I joined to the metrics to get the names which these are the names that I’m finding. They’re all like product viewed viewed product viewed all products, product.

399 00:27:43.160 00:27:43.870 Patrick Trainer: Like via.

400 00:27:43.870 00:27:45.330 Jack Tomei: Like, fuck. Okay.

401 00:27:45.330 00:27:45.890 Patrick Trainer: Yeah.

402 00:27:46.235 00:27:51.070 Jack Tomei: Then maybe it’s a date thing like, maybe it’s because our events table is.

403 00:27:51.860 00:28:11.270 Jack Tomei: it starts today. So it’s I can’t do a much of time, and that like can’t look back, really, which I it’s fine because we’re gonna do this mostly for weekly reporting going forward. But maybe that join is joining to like old metric names that don’t exist anymore. It’s tough for me to understand it.

404 00:28:11.270 00:28:13.678 Patrick Trainer: It could be also, like

405 00:28:14.750 00:28:19.239 Patrick Trainer: triggers or events that are unique to different pages.

406 00:28:19.260 00:28:21.050 Patrick Trainer: or unique, or like different campaigns.

407 00:28:21.050 00:28:22.930 Jack Tomei: Emails. Yeah, exactly.

408 00:28:23.160 00:28:31.840 Jack Tomei: So that’s like, I gotta really dig in and be like, okay, where do I see product? Viewed? And where do I see view products, you know. I just haven’t done that yet.

409 00:28:34.340 00:28:35.631 Patrick Trainer: Going down the rabbit hole.

410 00:28:36.235 00:28:36.580 Jack Tomei: Yeah.

411 00:28:36.860 00:28:38.440 Agustin: That’s great.

412 00:28:39.260 00:28:40.480 Agustin: Can I go next?

413 00:28:40.760 00:28:41.780 Agustin: Yeah.

414 00:28:42.400 00:28:48.719 Agustin: okay. Today is supposed to be my off day. But I wanted to be here to keep up with the rest of the team.

415 00:28:49.417 00:28:55.500 Agustin: So yeah, this week, I focus on the Clavio integration, the events integration.

416 00:28:55.530 00:29:01.239 Agustin: I found an issue in a snowflake right now, which is a bit alarming, but not too serious.

417 00:29:01.510 00:29:02.630 Agustin: which is

418 00:29:03.010 00:29:09.649 Agustin: that we have in the Snowflake. We have integrations to connect with external Apis. Right?

419 00:29:10.010 00:29:16.439 Agustin: So we have an integration with Slack to send the messages, the other messages.

420 00:29:17.460 00:29:18.150 Agustin: and

421 00:29:18.610 00:29:23.229 Agustin: that integration is not working for every procedure. Right?

422 00:29:23.330 00:29:25.469 Agustin: It only works for webinar right now.

423 00:29:25.610 00:29:31.189 Agustin: So every day I try to recreate that integration in every

424 00:29:31.300 00:29:36.610 Agustin: schema of each procedure and the work. But the following day it doesn’t work. So

425 00:29:36.800 00:29:40.839 Agustin: my first. My guess now is that it’s not on the documentation.

426 00:29:40.890 00:29:48.769 Agustin: but we should create different slack integration for each procedure, even though it’s the same that makes sense.

427 00:29:49.670 00:29:57.120 Uttam Kumaran: Yeah, I wonder if we should just have a a fun like a Udf, that’s for slack bot. And then we can call that function

428 00:29:57.430 00:29:59.350 Uttam Kumaran: when we want to send an alert.

429 00:30:00.060 00:30:03.020 Uttam Kumaran: and then that can get used across other functions.

430 00:30:03.970 00:30:12.609 Agustin: Yeah, I try to call it the same because it’s kind of global inside the database. But it’s not working for every procedure. So I will try using different.

431 00:30:12.610 00:30:13.520 Uttam Kumaran: Are

432 00:30:14.380 00:30:17.020 Uttam Kumaran: okay. Oh, I see. Okay.

433 00:30:18.040 00:30:18.710 Agustin: Yeah.

434 00:30:19.190 00:30:20.710 Agustin: so yeah, I think that’s

435 00:30:21.040 00:30:22.480 Agustin: everything I have.

436 00:30:24.440 00:30:29.580 Uttam Kumaran: Yeah, I’m consulting all the alerts that one alerts channel 2. So I’m adding, some like work flow alerts.

437 00:30:29.979 00:30:37.390 Uttam Kumaran: But we’ve been getting a lot better on like the alerting and monitoring stuff. I guess, Ryan, if you want to go next and just talk a little bit about that.

438 00:30:37.390 00:30:37.940 Ryan Luke Daque: Yeah, sure.

439 00:30:37.940 00:30:43.889 Agustin: Just a quick comment. I will, for try to fix this today to make sure we are getting the data.

440 00:30:46.210 00:30:46.730 Uttam Kumaran: Yes.

441 00:30:47.000 00:30:57.789 Ryan Luke Daque: Yeah, from on my end. I I did work on a couple of bugs that we noticed like first was the shipping category. And, like Unis shipments, are not showing in the

442 00:30:57.800 00:31:01.209 Ryan Luke Daque: Vital Science dashboard, or like one of the dashboards that we have.

443 00:31:01.470 00:31:04.390 Ryan Luke Daque: fix that fixed also the

444 00:31:04.640 00:31:05.940 Ryan Luke Daque: like to

445 00:31:06.280 00:31:28.956 Ryan Luke Daque: source tables from one from Amazon and one from shopify that we’re failing the source. Freshness tests like refund order items from shopify is one of them, and we know I I noticed that like not every day, there are refunds. So basically, the the 24 h source freshness was too stringent for that, because there there might be like 2 or 3 days where we don’t have any.

446 00:31:29.926 00:31:32.909 Ryan Luke Daque: We’re not getting any refunds from shopify.

447 00:31:33.560 00:31:37.370 Ryan Luke Daque: So yeah, I bumped up the sort the freshness period to like

448 00:31:37.440 00:31:39.531 Ryan Luke Daque: 3 days for that one.

449 00:31:39.990 00:31:45.609 Ryan Luke Daque: yeah, hopefully, we don’t get more than that. But yeah, we’ll see we could. We’ll keep on like a

450 00:31:47.030 00:31:50.080 Ryan Luke Daque: improving that as soon as we see anything else.

451 00:31:50.090 00:31:51.099 Ryan Luke Daque: it comes up

452 00:31:51.760 00:31:59.250 Ryan Luke Daque: other than that I did. Change the asset link repository as as well as the models to to the standard.

453 00:31:59.907 00:32:04.169 Ryan Luke Daque: Yeah, that that worked well so far. So I might start working on

454 00:32:04.220 00:32:12.581 Ryan Luke Daque: pool parts to go in in terms of like standardizing the model structures and stuff. But pool parts has like a tons models. It’s gonna take a while.

455 00:32:14.130 00:32:15.810 Ryan Luke Daque: yeah, I also added,

456 00:32:16.260 00:32:22.810 Ryan Luke Daque: tests for asset link as well source precious tests and like unit tests for the Mars models.

457 00:32:23.170 00:32:27.319 Ryan Luke Daque: And I did start working on changing the date.

458 00:32:27.500 00:32:32.290 Ryan Luke Daque: Use that, Jack, like mentioned last week, because

459 00:32:32.920 00:32:34.270 Ryan Luke Daque: like data

460 00:32:34.310 00:32:38.444 Ryan Luke Daque: would be date day or like month would be date month and stuff like that.

461 00:32:39.076 00:32:41.009 Ryan Luke Daque: I’m still working on that one. It’s

462 00:32:41.460 00:32:44.830 Ryan Luke Daque: getting into a lot of errors, especially since we are.

463 00:32:44.970 00:32:51.050 Ryan Luke Daque: There’s a lot of dashboards already. Tied up to our models in light dash. So we’ll have to like

464 00:32:51.880 00:32:55.670 Ryan Luke Daque: change everything if you need to change the the date names and stuff. So

465 00:32:55.900 00:33:04.800 Ryan Luke Daque: I might work on this on the weekend, where, like not, nobody’s like watching the dashboards, especially like from ep pool part side.

466 00:33:06.060 00:33:07.239 Ryan Luke Daque: Just so

467 00:33:07.380 00:33:08.180 Ryan Luke Daque: we don’t

468 00:33:08.410 00:33:10.449 Ryan Luke Daque: destroy the dashboards and stuff.

469 00:33:11.290 00:33:11.925 Ryan Luke Daque: Yeah.

470 00:33:12.950 00:33:19.639 Ryan Luke Daque: I I also wanted to work on every day evidence like a normally detection. I know not against elementary.

471 00:33:19.930 00:33:27.168 Ryan Luke Daque: I haven’t really started on that. But maybe after this call I’ll start like looking into it. So at least I get like

472 00:33:27.980 00:33:34.190 Ryan Luke Daque: a few sample stuff that we can compare with a big eye in the meeting later. So, yeah.

473 00:33:34.738 00:33:36.629 Ryan Luke Daque: that’s about it for me.

474 00:33:38.190 00:33:43.643 Uttam Kumaran: Yeah, I think the only other thing on my end is, yeah, it’s like super productive week, I think.

475 00:33:44.120 00:33:52.660 Uttam Kumaran: well, I think we’re one like overall, like alerts and stuff have been good. I think our data quality is getting a lot better, especially on the pool part side.

476 00:33:52.660 00:34:16.029 Uttam Kumaran: I think, Ryan. I’ll have a couple of things on the asset link side. I’m about to jump on a call and talk to them. I kinda made some moves on real this week. And I really like the product I met with. One of their sales guys was in town. For data council, and again, I basically was like, we wanna test it out. So I’m actually going to be demoing it to have something folks right after this

477 00:34:16.139 00:34:17.870 Uttam Kumaran: and kind of see what they think.

478 00:34:18.288 00:34:26.401 Uttam Kumaran: I like how easy it is for them to drive and do analysis in real, so kind of excited to see that paired with

479 00:34:27.250 00:34:30.670 Uttam Kumaran: with evidence. And I think.

480 00:34:31.159 00:34:43.139 Uttam Kumaran: yeah, there’s probably like 50,000 other things on zoom. But that’s the gist, the website almost done. I’ll send you guys a little video like a little loom video of it kind of

481 00:34:43.230 00:34:44.869 Uttam Kumaran: kind of almost finish

482 00:34:45.201 00:35:05.699 Uttam Kumaran: and we’re we’re actually starting to make some good progress on one of the AI clients that we have. And I know, Pat, you’ve been involved in that. There’s another Brian also been involved in that. But I’ll actually should start looping into other stuff. But probably more to share on like some of the AI work we’ve been doing next week. So

483 00:35:07.980 00:35:11.095 Uttam Kumaran: cool. I think that’s it for me.

484 00:35:12.240 00:35:24.760 Uttam Kumaran: let me know if everybody has anything. If everybody can also submit invoices and stuff today that way. I can have some closed out going into the weekend. So money can come in.

485 00:35:25.820 00:35:27.109 Uttam Kumaran: yeah, I think that’s it.

486 00:35:31.674 00:35:33.295 Uttam Kumaran: Okay. Alright, thank you. Guys.

487 00:35:33.620 00:35:34.160 Agustin: Everyone.

488 00:35:34.160 00:35:34.880 Ryan Luke Daque: Sounds good.

489 00:35:35.240 00:35:37.869 Jack Tomei: Yeah, it’s great work, Patrick. That evidence stuff is sick.

490 00:35:38.230 00:35:38.869 Patrick Trainer: Hey? Thank you.

491 00:35:39.210 00:35:39.890 Uttam Kumaran: Thanks, Matt.

492 00:35:39.890 00:35:40.900 Ryan Luke Daque: Nice.

493 00:35:41.140 00:35:41.626 Patrick Trainer: See? Y’all.

494 00:35:41.870 00:35:42.450 Ryan Luke Daque: Thanks, bye.