Meeting Title: Weekly-Sprint-Review Date: 2024-02-08 Meeting participants: Ryan Luke Daque, Uttam Kumaran


WEBVTT

1 00:02:12.490 00:02:13.390 Uttam Kumaran: Hey, Ryan?

2 00:02:13.980 00:02:15.630 Ryan Luke Daque: Hi, Adam, how’s it going?

3 00:02:15.720 00:02:19.019 Ryan Luke Daque: Good! How are you doing? Great as well?

4 00:02:19.820 00:02:21.139 Uttam Kumaran: How’s the week, Ben?

5 00:02:21.420 00:02:24.180 Ryan Luke Daque: Yeah. Pretty pretty good so far.

6 00:02:24.940 00:02:26.079 Uttam Kumaran: Okay? Great.

7 00:02:26.760 00:02:33.120 Uttam Kumaran: Yeah. It’s been really good, too. It’s been a little bit busy, but getting a little bit lighter.

8 00:02:33.910 00:02:35.100 Ryan Luke Daque: Nice.

9 00:02:37.270 00:02:44.489 Uttam Kumaran: Great. So do you wanna drive, and maybe we could look at the current sprint.

10 00:02:45.530 00:02:48.000 Ryan Luke Daque: Yeah, sure. Let me share my screen.

11 00:02:51.370 00:02:53.070 Ryan Luke Daque: Can you see my screen?

12 00:02:53.240 00:02:54.230 Uttam Kumaran: Yes.

13 00:02:57.290 00:03:01.059 Ryan Luke Daque: So let’s go to the current sprint here.

14 00:03:01.220 00:03:08.299 Ryan Luke Daque: Have a couple of tickets and review. Well, these haven’t merged already, so I guess we can move these

15 00:03:08.400 00:03:12.480 Ryan Luke Daque: to done so. First of all is modeling the Zendesk data.

16 00:03:12.820 00:03:19.469 Ryan Luke Daque: So I use the 5 grand package from Dbt to to do this, and then created them

17 00:03:20.330 00:03:23.150 Ryan Luke Daque: Zendesk customers.

18 00:03:23.850 00:03:28.910 Ryan Luke Daque: Model. And I believe you also created another one yesterday. Right?

19 00:03:28.980 00:03:31.439 Ryan Luke Daque: which is just the Zendesk tickets.

20 00:03:31.460 00:03:35.039 Ryan Luke Daque: Just for each ticket and stuff. And

21 00:03:35.400 00:03:41.110 Ryan Luke Daque: so yeah. And then I basically joined this to the

22 00:03:42.220 00:03:43.280 Ryan Luke Daque: a.

23 00:03:43.360 00:03:45.430 Ryan Luke Daque: all orders

24 00:03:46.170 00:03:49.249 Ryan Luke Daque: shopify customer site, I believe. So. Yep.

25 00:03:50.530 00:03:52.340 Ryan Luke Daque: yeah.

26 00:03:52.470 00:03:54.500 Ryan Luke Daque: So I guess the only

27 00:03:55.220 00:04:01.239 Ryan Luke Daque: the the more the little thing that we need to do still, for this is

28 00:04:01.340 00:04:05.119 Ryan Luke Daque: joining, finding a way to join Zendesk

29 00:04:05.420 00:04:07.030 Ryan Luke Daque: tickets to the orders.

30 00:04:07.240 00:04:14.660 Ryan Luke Daque: We don’t have a straightforward way to do it. So we have 1 one thing that I mentioned in the chat that

31 00:04:14.730 00:04:24.009 Ryan Luke Daque: is a possibility to do is using the create date of the order. So if that ticket, if the Zendesk ticket is created between the

32 00:04:25.010 00:04:29.129 Ryan Luke Daque: the order created and yeah, you mentioned the next order.

33 00:04:29.160 00:04:31.989 Ryan Luke Daque: great date. And that could be a possible

34 00:04:33.270 00:04:47.000 Ryan Luke Daque: way to join them along with the customer email. Yeah, we can. I’m gonna test. I’m doing some analysis today on discounts. So I’m going to

35 00:04:47.010 00:04:51.649 Uttam Kumaran: just give a look at like what I can see. And then I’m gonna propose some stuff to them tomorrow.

36 00:04:51.710 00:04:57.310 Uttam Kumaran: so that’s probably we’re probably okay on that.

37 00:04:59.170 00:05:00.610 Ryan Luke Daque: Yeah, sounds good.

38 00:05:02.700 00:05:04.990 Ryan Luke Daque: Yeah. So on that case.

39 00:05:05.150 00:05:11.300 Ryan Luke Daque: I guess we can complete this. And then we’ll just create a different ticket for, like joining Zendesk to orders.

40 00:05:11.440 00:05:12.480 Ryan Luke Daque: That’ll yep.

41 00:05:12.690 00:05:13.800 Uttam Kumaran: yeah, let’s do that.

42 00:05:16.920 00:05:17.880 Ryan Luke Daque: Paris.

43 00:05:24.290 00:05:31.469 Ryan Luke Daque: Next we have the adding the metrics to shopify customers. So this is just adding the metrics to the yeah Mo file. I already

44 00:05:32.360 00:05:35.589 Ryan Luke Daque: did that. But this is

45 00:05:35.920 00:05:38.200 Ryan Luke Daque: so yeah, we can just sit down as well.

46 00:05:41.570 00:05:46.259 Ryan Luke Daque: Next we have, we’re moving. PP, 2, G employees.

47 00:05:46.270 00:05:52.300 Ryan Luke Daque: So yeah, I created a macro that would just filter out emails that end in this

48 00:05:53.200 00:05:57.779 Ryan Luke Daque: email address. But since it’s a macro, we can like add other

49 00:05:57.940 00:06:02.559 Ryan Luke Daque: email addresses, if if there’s any other identifier that we can

50 00:06:02.870 00:06:05.300 Ryan Luke Daque: think of. Okay.

51 00:06:06.700 00:06:10.939 Ryan Luke Daque: yep. And it’s already in the shopify order items.

52 00:06:10.950 00:06:14.220 Ryan Luke Daque: It’s been used in this. That’s

53 00:06:14.430 00:06:22.749 Ryan Luke Daque: model. So it’s also like flowing down all order items in all orders.

54 00:06:25.250 00:06:28.859 Ryan Luke Daque: Last thing that I did so was the shipment stable.

55 00:06:29.660 00:06:33.680 Ryan Luke Daque: I did merge it yesterday, basically

56 00:06:33.840 00:06:39.870 Ryan Luke Daque: create the shipments table, which is basically a union of this

57 00:06:39.940 00:06:43.779 Ryan Luke Daque: shopify ship ship station

58 00:06:44.580 00:06:47.380 Ryan Luke Daque: shipments and Ltl shipments

59 00:06:47.990 00:06:55.529 Ryan Luke Daque: and then just used live dash joins to shopify order items, Amazon order items

60 00:06:56.020 00:06:57.780 Ryan Luke Daque: and all order items.

61 00:06:58.270 00:07:00.450 Uttam Kumaran: Okay, how does the table look?

62 00:07:00.580 00:07:02.829 Ryan Luke Daque: Yeah, we can look at it,

63 00:07:04.350 00:07:07.849 Ryan Luke Daque: And then I kind of want to go through and replace

64 00:07:09.330 00:07:15.100 anytime where we’re just looking at shipments. I kind of want to replace it with this table. Maybe.

65 00:07:16.460 00:07:17.590 Ryan Luke Daque: Yeah. Sure.

66 00:07:19.010 00:07:22.199 Ryan Luke Daque: Why is it not showing here?

67 00:07:33.910 00:07:36.240 Ryan Luke Daque: Yeah, let me open up.

68 00:07:37.340 00:07:38.630 Ryan Luke Daque: Yes, go.

69 00:07:40.400 00:07:42.080 Uttam Kumaran: Maybe it’s not in prod.

70 00:07:43.750 00:07:46.500 Ryan Luke Daque: I emerged, and I believe.

71 00:07:47.330 00:07:48.380 check.

72 00:07:53.700 00:07:55.730 Ryan Luke Daque: Yeah, it should be charged.

73 00:08:26.690 00:08:28.050 Ryan Luke Daque: You better shipping it.

74 00:08:28.670 00:08:31.590 Ryan Luke Daque: This this is already in broad.

75 00:08:32.470 00:08:34.579 Ryan Luke Daque: I’m not sure why it’s not showing here.

76 00:08:35.679 00:08:43.589 Uttam Kumaran: what what is it called? It’s called shipments.

77 00:08:43.730 00:08:46.270 Ryan Luke Daque: Yeah, it’s called shipments.

78 00:08:46.310 00:08:48.009 Uttam Kumaran: Could maybe, did you?

79 00:08:49.460 00:08:51.989 Uttam Kumaran: If you check Dbt projects

80 00:08:54.290 00:08:55.560 Ryan Luke Daque: well

81 00:08:56.850 00:09:02.160 Uttam Kumaran: in here, is there? Is there a did you add this to prod like

82 00:09:02.630 00:09:08.310 Uttam Kumaran: meaning? If you look at any of the other like, look@allorders.yaml.

83 00:09:09.940 00:09:11.320 Uttam Kumaran: Oh, I think I

84 00:09:11.440 00:09:12.850 Ryan Luke Daque: yeah, I didn’t.

85 00:09:13.200 00:09:15.609 Uttam Kumaran: Yeah, it should be like.

86 00:09:16.760 00:09:21.939 Uttam Kumaran: you can run. You can run the preview locally, though, maybe we can look at that. And then you can make an update.

87 00:09:32.120 00:09:35.790 Ryan Luke Daque: So I should add, basically

88 00:09:36.320 00:09:39.929 Ryan Luke Daque: tags here. This looks like I missed that. But

89 00:09:44.000 00:09:46.010 Ryan Luke Daque: yeah, it’s gonna kind of feed.

90 00:09:47.070 00:09:48.240 Ryan Luke Daque: Wait a minute.

91 00:09:54.280 00:09:55.519 Ryan Luke Daque: This is where I

92 00:10:01.390 00:10:05.949 Ryan Luke Daque: added the joints. as you can see, it’s joining on

93 00:10:06.020 00:10:16.620 Ryan Luke Daque: to Amazon orders and all order items. Amazon orders is a bit tricky for Ltl to test. Like all of these

94 00:10:17.190 00:10:21.659 Ryan Luke Daque: conditions which I basically just copied from

95 00:10:21.900 00:10:23.720 Ryan Luke Daque: Amazon order items.

96 00:10:25.360 00:10:26.520 Ryan Luke Daque: Okay, that’s it.

97 00:10:26.570 00:10:28.760 Ryan Luke Daque: Yeah, let’s wait for the preview.

98 00:10:44.300 00:10:52.890 Uttam Kumaran: Okay, that’s fine. Yeah. Maybe what one thing we could do is just look at whether we can start replacing things with this staples where there’s duplicates.

99 00:10:53.210 00:10:54.070 Ryan Luke Daque: Hmm.

100 00:10:57.120 00:11:02.289 Ryan Luke Daque: you go shipments. And then these would be like the shipment

101 00:11:03.560 00:11:07.269 information to basically from Shh shopify.

102 00:11:07.750 00:11:09.669 Ryan Luke Daque: And then there’s a couple of

103 00:11:10.380 00:11:13.600 Ryan Luke Daque: stuff in here from Ltl, like, PO,

104 00:11:14.530 00:11:17.280 Ryan Luke Daque: yeah. And then.

105 00:11:17.970 00:11:21.289 Ryan Luke Daque: so let’s say, for shopify orders. We can.

106 00:11:22.530 00:11:23.270 Ryan Luke Daque: But

107 00:11:24.910 00:11:28.129 let’s say we get the ordered item Id from

108 00:11:28.550 00:11:30.709 Ryan Luke Daque: and shipments, and then

109 00:11:32.520 00:11:33.470 Ryan Luke Daque: I don’t know.

110 00:11:36.470 00:11:38.300 Ryan Luke Daque: Make the customer email

111 00:11:42.190 00:11:43.000 Ryan Luke Daque: crazy.

112 00:11:46.820 00:11:53.360 Ryan Luke Daque: Yeah. So if the order Id is coming from shopify, we should be able to see the customer

113 00:11:53.940 00:11:58.780 Ryan Luke Daque: email from shopify file order items. So I guess the swarms aren’t

114 00:12:05.830 00:12:09.049 Uttam Kumaran: yeah. So the one thing maybe we can also start doing is.

115 00:12:09.370 00:12:26.190 Uttam Kumaran: And II started adding this to the Prs is, we should just do start doing some light testing of the of the new models. Once we push I found that to be really helpful. So maybe we can start doing that. Because, yeah, I think there’s probably just like one small issue. But

116 00:12:26.800 00:12:33.369 Uttam Kumaran: take a look. I wanted to cover one more thing, but maybe we can leave this ticket open and figure out what’s going on.

117 00:12:33.600 00:12:34.390 Ryan Luke Daque: Sure.

118 00:12:39.940 00:12:48.590 Ryan Luke Daque: You’re leaving this open because of weekly. Yeah, I just wanna do like a review, a review of those

119 00:12:48.630 00:12:56.949 Uttam Kumaran: like, just make sure that the model works and the joins work, and we’re able to see all the data. And then also, you know, small things like.

120 00:12:57.200 00:13:01.550 Uttam Kumaran: the order item, Id looks like it’s coming in as a number should be

121 00:13:02.470 00:13:05.060 Ryan Luke Daque: and things like that. Yeah.

122 00:13:06.230 00:13:09.050 Uttam Kumaran: it should only, but probably only take like, 10 min.

123 00:13:09.090 00:13:10.320 Ryan Luke Daque: Right? Yeah.

124 00:13:12.050 00:13:14.420 Ryan Luke Daque: Yeah. And this is screen.

125 00:13:14.720 00:13:19.850 Ryan Luke Daque: Yeah, whatever I’ll I’ll check. I guess maybe it’s my.

126 00:13:21.210 00:13:22.929 Ryan Luke Daque: yeah, that’s quite beautiful.

127 00:13:24.210 00:13:26.280 Ryan Luke Daque: Okay, I’ll take a look at this.

128 00:13:28.270 00:13:29.040 Uttam Kumaran: Okay.

129 00:13:29.580 00:13:34.670 Ryan Luke Daque: yeah. So what I’m currently working on is the

130 00:13:37.360 00:13:40.869 Ryan Luke Daque: this one creating links. This is basically just

131 00:13:42.190 00:13:46.790 Ryan Luke Daque: of vital signs like, dash update. Just add in

132 00:13:47.650 00:13:50.109 Ryan Luke Daque: Markdown pass for this building.

133 00:13:50.820 00:13:56.429 Ryan Luke Daque: Do other reports like should be efficient with the monthly and refund discount snashboards?

134 00:13:58.630 00:14:02.439 Ryan Luke Daque: Yeah. And yeah, I’ll I’ll look into.

135 00:14:02.770 00:14:08.100 Ryan Luke Daque: Go back, look into the shipments people as well like, why, this is not showing us in

136 00:14:08.550 00:14:11.410 Ryan Luke Daque: string. It’s supposed to be shown as string here, even

137 00:14:13.570 00:14:14.250 Ryan Luke Daque: yeah.

138 00:14:15.550 00:14:26.030 Ryan Luke Daque: And also these like why these are not showing up. This could be an order from the Sha shoot station side. It’s not showing in

139 00:14:26.340 00:14:29.499 Ryan Luke Daque: shopify, but it looks like a shopify already.

140 00:14:30.280 00:14:31.470 Ryan Luke Daque: I think I look at that.

141 00:14:34.590 00:14:36.820 Uttam Kumaran: Okay. And then.

142 00:14:36.880 00:14:39.510 Uttam Kumaran: is there are there any other tickets in the backlog

143 00:14:40.250 00:14:45.609 Ryan Luke Daque: check here? We do have couple

144 00:14:51.420 00:14:57.280 Ryan Luke Daque: on my side. The modify local Dbt.

145 00:14:57.800 00:15:07.010 Uttam Kumaran: and then the other ones. Those are both in review. So I actually have a Pr open that that starts the preview within the Pr.

146 00:15:07.410 00:15:17.030 Uttam Kumaran: And then also. you’re you should be able to. Now. everything should be able. When you run locally it will run into your own in a dead environment.

147 00:15:17.790 00:15:19.770 Ryan Luke Daque: Yeah, that’ll be great.

148 00:15:19.920 00:15:26.629 Uttam Kumaran: So both of those I’m gonna push. Maybe if you wanna take a look, I think those Pr’s are open right now, so you could probably take a look.

149 00:15:26.880 00:15:28.260 Ryan Luke Daque: Sure.

150 00:15:28.880 00:15:33.030 Uttam Kumaran: not sure if there, I think. There, I kind of consolidated into one

151 00:15:34.020 00:15:35.990 Uttam Kumaran: thing.

152 00:15:37.440 00:15:38.750 Uttam Kumaran: And then

153 00:15:41.100 00:15:44.049 Uttam Kumaran: I think to

154 00:15:44.540 00:15:52.160 Uttam Kumaran: 2 things that I one thing I’d like to add, maybe you could see if you could have time to work on it today. I wanna add.

155 00:15:52.450 00:15:57.100 Uttam Kumaran: maybe you can create this ticket. I wanna add.

156 00:15:57.180 00:15:59.800 Uttam Kumaran: 7 day, 30 day

157 00:16:00.630 00:16:12.169 Uttam Kumaran: like this year. I wanna add those types of like con, those like aggregated metrics to the kpi tables, I think.

158 00:16:12.420 00:16:27.459 Uttam Kumaran: or or actually maybe to be so. The the the question I got from them was, they want to see products queue. But they also want to see metrics that are like sales this 7 days, this 30 days

159 00:16:28.400 00:16:33.729 Uttam Kumaran: this year. Last year. They want to see those metrics pretty frequently.

160 00:16:34.000 00:16:37.750 Uttam Kumaran: and so I wonder what’s the best way of handling that

161 00:16:41.010 00:16:48.259 Uttam Kumaran: like, I think. I wonder whether the best way of handling that is. in all orders. We need to create

162 00:16:48.350 00:16:52.249 Uttam Kumaran: we need to create like

163 00:16:52.900 00:16:54.400 Ryan Luke Daque: metric.

164 00:16:54.900 00:16:55.890 Uttam Kumaran: That’s

165 00:16:57.770 00:17:03.319 Uttam Kumaran: just like sales 30 days. And then it’s like total sales, except there’s a date filter.

166 00:17:06.250 00:17:07.020 Ryan Luke Daque: Hmm.

167 00:17:07.440 00:17:08.670 Uttam Kumaran: you see what I mean?

168 00:17:09.609 00:17:14.589 Ryan Luke Daque: Yeah, I’m trying to wrap my head around it. So we have a metric that’s going to be.

169 00:17:15.640 00:17:18.790 Ryan Luke Daque: So we we have a metric for 7 days sales.

170 00:17:19.230 00:17:28.259 Uttam Kumaran: which let’s just take sales, for example. Yeah. So we want to show sales by the previous 7 days

171 00:17:28.450 00:17:34.229 Uttam Kumaran: the previous 30 days. I also want to look at

172 00:17:40.850 00:17:42.789 Uttam Kumaran: this year and last year.

173 00:17:43.990 00:17:44.760 Ryan Luke Daque: Hmm!

174 00:17:48.590 00:17:58.309 Ryan Luke Daque: So that would be like 4 metrics right? Like one for 7 days, one for 30 days, one for last last year, one for this year, something like that for sales.

175 00:17:58.890 00:17:59.950 Uttam Kumaran: Yeah.

176 00:18:04.760 00:18:10.060 Ryan Luke Daque: yeah, I think that think that’s doable. I’ll test it out.

177 00:18:10.560 00:18:16.259 Uttam Kumaran: I will. Let me send you. I’ll send you what the email I got from them was

178 00:18:16.460 00:18:17.280 Ryan Luke Daque: sure.

179 00:18:17.930 00:18:19.510 Uttam Kumaran: And you can take a look

180 00:18:23.540 00:18:29.980 Ryan Luke Daque: so recorded in all orders. For example, we have a sales sales.

181 00:18:35.270 00:18:36.979 Uttam Kumaran: so this will be good to get done.

182 00:18:37.890 00:18:42.989 Uttam Kumaran: So they wanna share that with them. But here’s what they said. Just send in slack

183 00:18:53.250 00:18:54.879 thought products sold.

184 00:18:56.730 00:18:59.969 Ryan Luke Daque: Can you make a drop down toggle options for that? Said

185 00:19:03.830 00:19:06.899 Ryan Luke Daque: to the top product sold as in vital science.

186 00:19:07.140 00:19:08.140 Uttam Kumaran: Yeah.

187 00:19:35.110 00:19:39.140 think it’s not by those.

188 00:19:45.500 00:19:48.510 Ryan Luke Daque: I guess this is in the weekly metrics.

189 00:19:49.940 00:19:50.740 Ryan Luke Daque: something.

190 00:20:03.380 00:20:06.610 Ryan Luke Daque: Yep. So I guess top products. So

191 00:20:08.340 00:20:09.810 Ryan Luke Daque: upcoming.

192 00:20:26.180 00:20:32.550 Ryan Luke Daque: So I guess something like this. But then they would have somewhere to toggle it between

193 00:20:34.410 00:20:37.920 Ryan Luke Daque: between those dates or like time frames.

194 00:20:38.300 00:20:39.160 Uttam Kumaran: Okay.

195 00:20:44.100 00:20:44.880 Ryan Luke Daque: okay.

196 00:20:48.560 00:20:50.170 Ryan Luke Daque: I’ll add this to the ticket.

197 00:20:51.650 00:20:59.219 Uttam Kumaran: Okay, great. And then I think that let me, I’m I’m gonna be doing some analysis today. So I’ll let you know, if there’s

198 00:20:59.540 00:21:04.950 Uttam Kumaran: like small things. But I think we actually may be able to move on to

199 00:21:06.310 00:21:11.809 Uttam Kumaran: I’m gonna add this to.

200 00:21:14.560 00:21:16.969 Uttam Kumaran: I’ll add this to the current sprint.

201 00:21:21.220 00:21:24.620 Ryan Luke Daque: I mean this high priority.

202 00:21:24.670 00:21:29.910 Uttam Kumaran: Yeah, we can make a high priority so we could have it done. And then

203 00:21:31.200 00:21:33.160 Uttam Kumaran: I’m going to look at.

204 00:21:46.980 00:21:47.830 Uttam Kumaran: Hmm.

205 00:21:49.700 00:21:52.320 Uttam Kumaran: One sec. I’m just gonna look at the backlog.

206 00:22:20.330 00:22:28.070 Uttam Kumaran: The only other thing is this ticket. Add customer acquisition cost by attribution source

207 00:22:33.520 00:22:35.210 Ryan Luke Daque: mission source.

208 00:22:38.250 00:22:44.219 Ryan Luke Daque: So the main thing I wanted to do is you now have that shopify customers? Thing

209 00:22:44.400 00:22:45.270 Ryan Luke Daque: right?

210 00:22:45.540 00:22:47.299 Uttam Kumaran: I wanted to.

211 00:22:48.140 00:22:55.110 Uttam Kumaran: begin to show weak.

212 00:22:55.460 00:23:03.150 Uttam Kumaran: So number of customers by source number, amount of spend on that channel. and then the customer acquisition cost.

213 00:23:08.700 00:23:11.590 Uttam Kumaran: And then you can do this. Yeah, by week.

214 00:23:13.620 00:23:16.750 Uttam Kumaran: And that would be great because I was gonna try to handle that.

215 00:23:17.150 00:23:18.480 Ryan Luke Daque: Okay, okay.

216 00:23:19.120 00:23:31.830 Uttam Kumaran: And I have a bunch of ideas for next week. But next week, maybe more we may do. We may spend some more time on like analysis of some things, but I will list all that. So that’s this. Probably like the last thing. I think

217 00:23:32.020 00:23:33.070 Uttam Kumaran: those 2

218 00:23:33.510 00:23:36.380 Ryan Luke Daque: sounds good. Okay, I’ll work on this

219 00:23:36.450 00:23:39.700 Ryan Luke Daque: the rest of their meeting. Week.

220 00:23:44.870 00:23:52.039 Ryan Luke Daque: Between the 7 day, 30 day last year I created metrics and the customer acquisition costs which ones

221 00:23:52.780 00:23:55.729 Ryan Luke Daque: or a higher priority. I guess

222 00:23:55.790 00:23:57.529 Uttam Kumaran: this one is higher.

223 00:23:57.580 00:24:02.639 Uttam Kumaran: But see if you can get, let me know by the end of the day today, which one’s your

224 00:24:02.810 00:24:09.840 Uttam Kumaran: you’re able to. Yeah, I’ll I’ll start working on this one the 7 day, 30 day

225 00:24:09.950 00:24:11.640 Ryan Luke Daque: this year, last year

226 00:24:12.490 00:24:18.179 Ryan Luke Daque: aggregate in the fix and see if I can do this quickly. If not, I’ll I’ll actually

227 00:24:18.430 00:24:19.300 Uttam Kumaran: okay.

228 00:24:23.110 00:24:24.370 Ryan Luke Daque: Sounds good.

229 00:24:24.930 00:24:28.570 Uttam Kumaran: Okay. Alright. Yeah. Message me on slack, if anything.

230 00:24:29.090 00:24:29.990 Thanks.

231 00:24:30.420 00:24:32.980 Uttam Kumaran: Okay, thanks for bye, bye.

232 00:24:33.090 00:24:33.820 Uttam Kumaran: bye.