Meeting Title: Weekly-Sprint-Review Date: 2024-02-05 Meeting participants: Ryan Luke Daque, Uttam Kumaran
WEBVTT
1 00:00:16.450 ⇒ 00:00:17.270 Ryan Luke Daque: Hello!
2 00:04:10.620 ⇒ 00:04:11.440 Ryan Luke Daque: Hello!
3 00:04:12.610 ⇒ 00:04:19.549 Uttam Kumaran: Hey, Ryan! Hey? With them? How’s it going? Good! How are you? I’m doing? Great! How’s the weekend?
4 00:04:19.950 ⇒ 00:04:22.089 Ryan Luke Daque: Yeah, it was pretty fun.
5 00:04:22.320 ⇒ 00:04:24.910 Ryan Luke Daque: who went
6 00:04:25.310 ⇒ 00:04:26.090 Ryan Luke Daque: the
7 00:04:26.870 ⇒ 00:04:34.500 Ryan Luke Daque: yeah. Where did we go? We were. We just went out out on the house last week. Yeah.
8 00:04:35.660 ⇒ 00:04:48.489 Ryan Luke Daque: how was the weather like there. It’s just starting to get a little warmer here in Texas. Oh, hey, there’s only 2 seasons here in the Philippines, basically like just the wet and the dry season.
9 00:04:48.720 ⇒ 00:04:57.190 Uttam Kumaran: Okay? So yeah, it’s it’s pretty much, almost almost summer here. So it’s pretty warm. Oh, nice. Okay, good.
10 00:04:59.080 ⇒ 00:05:04.160 Ryan Luke Daque: It’s pretty humid, though, like all the time.
11 00:05:04.230 ⇒ 00:05:15.299 Ryan Luke Daque: Yeah. So what do you do in your house? Do you just run a fan? Yeah, fan, or like air conditioning unit, especially during like noon times where it’s very hot.
12 00:05:16.310 ⇒ 00:05:22.980 Uttam Kumaran: goes up to like 33°C, or something 35
13 00:05:23.270 ⇒ 00:05:26.929 Ryan Luke Daque: pretty warm about you. I was weekend.
14 00:05:27.340 ⇒ 00:05:34.520 Uttam Kumaran: Yeah, weekend was good. I went to. I went. There’s a I went to a Museum of Art on Saturday here in Austin.
15 00:05:34.770 ⇒ 00:05:40.239 Uttam Kumaran: And just look at some interesting art, and they had some really interesting installations.
16 00:05:40.370 ⇒ 00:05:41.830 Uttam Kumaran: And then.
17 00:05:42.030 ⇒ 00:05:50.250 Uttam Kumaran: yeah, I went to I went to dinner on Friday at this like French restaurant. Have you ever had have you ever had like Escargo?
18 00:05:50.780 ⇒ 00:05:56.629 Ryan Luke Daque: I don’t believe so. It doesn’t sound familiar. It’s like it’s like, it’s like snail
19 00:05:57.080 ⇒ 00:06:08.550 Uttam Kumaran: It’s like a pretty classic like French dish, where they bake like the snails, and with a lot of butter. Very, very good, if you ever try it.
20 00:06:08.750 ⇒ 00:06:13.159 Uttam Kumaran: But yeah. Went to the French went to a French restaurant
21 00:06:13.210 ⇒ 00:06:14.779 Uttam Kumaran: Friday, and then,
22 00:06:15.910 ⇒ 00:06:19.060 Uttam Kumaran: yeah, yesterday just hung out. Didn’t do much.
23 00:06:19.130 ⇒ 00:06:27.199 Uttam Kumaran: I started to. I’ve been starting to run again. I don’t know if you run or go to the gym or anything, but I I used to run
24 00:06:27.250 ⇒ 00:06:36.650 Uttam Kumaran: a lot, so I’m just slowly getting back into it. Nice good for you. I I’m also planning to do the same. Hopefully, this year I can start, maybe like.
25 00:06:37.220 ⇒ 00:06:40.110 Ryan Luke Daque: go back, be active because I’ve
26 00:06:40.350 ⇒ 00:06:50.660 Ryan Luke Daque: ever since the pandemic I’ve been just in the house sitting. No activity, I know. Like II get some
27 00:06:50.970 ⇒ 00:07:04.159 Ryan Luke Daque: like pain back pain because of sitting for too long, and stuff like that. II didn’t have this before. I was pretty active before as well.
28 00:07:05.080 ⇒ 00:07:10.220 Ryan Luke Daque: but I used to go to the gym, and I used to like play table tennis
29 00:07:10.410 ⇒ 00:07:14.730 Ryan Luke Daque: like competitive table table tennis even.
30 00:07:14.960 ⇒ 00:07:20.980 Ryan Luke Daque: But yeah, I haven’t been doing that for like quite some time now, ever since, like
31 00:07:21.640 ⇒ 00:07:23.580 Ryan Luke Daque: the pandemic. So yeah.
32 00:07:24.510 ⇒ 00:07:29.039 Ryan Luke Daque: I’ve been a lot of sitting. So yeah.
33 00:07:29.220 ⇒ 00:07:50.019 Uttam Kumaran: and like, lost some muscle already and stuff like that. Yeah, you should. You should go. III it was hard for me like in the pandemic. I felt the same way. I gained a lot of weight and actually didn’t gain. I didn’t gain any weight, because I’m I’m I’m pretty. If I don’t do anything. I’m pretty skinny. I don’t know. Maybe that’s just like the Indian genetics, but I wasn’t like had no muscle.
34 00:07:50.260 ⇒ 00:08:01.000 Uttam Kumaran: And this is like last April, and then I decided to start going back to the gym, and then I gained some good muscle weight back, and then you know what the one thing I tried to do, and
35 00:08:01.170 ⇒ 00:08:14.630 Uttam Kumaran: tough cause like like December, I was working a lot like now, now I’m gonna try and get back into it. But II try to in the morning, go for a walk before I work, just to get the energy up as well. Right?
36 00:08:14.720 ⇒ 00:08:26.229 Uttam Kumaran: Yeah. Go. For like a 2, 3 mile walk. And you know they recommend trying to get like 10,000 steps per day. And then II also like, you know, I go to the gym, and I can lift weights. But
37 00:08:26.670 ⇒ 00:08:33.689 Uttam Kumaran: It’s tough. It’s tough to keep the habit up, cause it’s pretty boring for me like II find I like playing tennis
38 00:08:33.850 ⇒ 00:08:34.909 Uttam Kumaran: nice.
39 00:08:35.429 ⇒ 00:08:42.659 Uttam Kumaran: so if we play tennis I can play all day, but if it’s like walking or gym, it’s so boring
40 00:08:42.750 ⇒ 00:08:48.409 Uttam Kumaran: so that it’s like tough for me, cause I have to just keep my mind occupied.
41 00:08:48.520 ⇒ 00:08:49.300 Uttam Kumaran: But
42 00:08:49.740 ⇒ 00:08:56.869 Uttam Kumaran: I think you’ll find that it helps. I don’t know. You could just start with walking or something, you know, every like couple of hours. Go for a little walk.
43 00:08:57.190 ⇒ 00:09:09.470 Ryan Luke Daque: Yeah, maybe when this currently, we’re like living in like, just near the street, basically like a a public road. So it’s pretty much like during daytime. It’s also, it’s pretty like.
44 00:09:09.630 ⇒ 00:09:12.989 Ryan Luke Daque: busy. Yeah. So like this.
45 00:09:13.490 ⇒ 00:09:15.129 Ryan Luke Daque: it’s not great
46 00:09:15.260 ⇒ 00:09:18.360 Ryan Luke Daque: much place to walk or something like that.
47 00:09:18.480 ⇒ 00:09:19.950 Ryan Luke Daque: But yeah.
48 00:09:20.290 ⇒ 00:09:24.899 Ryan Luke Daque: but yeah, that’s yeah. I need to go back to some physical activity somehow.
49 00:09:25.320 ⇒ 00:09:29.940 Uttam Kumaran: Yeah, it’s good. It’ll be good. It’s good for your brain, too.
50 00:09:30.270 ⇒ 00:09:31.180 Uttam Kumaran: Yeah.
51 00:09:34.310 ⇒ 00:09:42.780 Uttam Kumaran: Okay, great do you want to take a look at some stuff? Yeah, sure. Do. Do you want to drive?
52 00:09:52.780 ⇒ 00:09:53.569 Ryan Luke Daque: I mean.
53 00:09:57.790 ⇒ 00:10:02.400 Ryan Luke Daque: let’s go to the current spring. I guess we can check all the
54 00:10:03.410 ⇒ 00:10:04.710 Ryan Luke Daque: tasks here.
55 00:10:05.130 ⇒ 00:10:06.350 Uttam Kumaran: Yep, right
56 00:10:07.020 ⇒ 00:10:11.359 Ryan Luke Daque: that are in review. Let’s let’s start with the things that are in review.
57 00:10:11.710 ⇒ 00:10:22.669 Ryan Luke Daque: So I did put the refunds and discounts dashboard interview. But of course this is like an ongoing like continuous improvement stuff. We can always like
58 00:10:23.070 ⇒ 00:10:27.139 Ryan Luke Daque: find ways to improve the refunds and discounts dashboard.
59 00:10:27.820 ⇒ 00:10:34.990 Ryan Luke Daque: yeah, I did add a couple of things last Friday, like the
60 00:10:36.270 ⇒ 00:10:37.829 Ryan Luke Daque: year on year
61 00:10:38.070 ⇒ 00:10:41.450 Uttam Kumaran: stuff as well as like adding that
62 00:10:41.540 ⇒ 00:10:47.230 Ryan Luke Daque: trend line for yeah, refunds over time. But like.
63 00:10:48.260 ⇒ 00:10:53.810 Ryan Luke Daque: so that’s like conditional form mapping for this table refunds as percent of sales.
64 00:10:54.110 ⇒ 00:10:56.739 Uttam Kumaran: So I added the discounts as well
65 00:10:56.930 ⇒ 00:11:00.320 Ryan Luke Daque: over time as percent sales.
66 00:11:00.860 ⇒ 00:11:08.380 Ryan Luke Daque: But yeah, there’s still a lot to like work on like we can find a way to include these total discounts by code. This doesn’t look quick.
67 00:11:08.810 ⇒ 00:11:10.640 Ryan Luke Daque: pretty, I guess
68 00:11:10.900 ⇒ 00:11:19.870 Uttam Kumaran: just a lot of codes. I don’t know if we can like. Yeah. So I ended up so so on that topic I did. I send the meeting from Friday.
69 00:11:20.340 ⇒ 00:11:32.789 Uttam Kumaran: The one thing that we talked about was trying to get like a consolidation of these discount codes and so I’ll I think I’ll I’m gonna take a look and just try to make some updates to this
70 00:11:33.080 ⇒ 00:11:44.820 Uttam Kumaran: just kind of get a better sending of discounts. Yeah, like, we can group these to like warranty or or everything that has just warranty and like coupons. And
71 00:11:45.050 ⇒ 00:11:50.619 Ryan Luke Daque: like Fedex, would be another one. Well, we can see, we’ll have to look into this.
72 00:11:51.090 ⇒ 00:11:51.980 Ryan Luke Daque: But yeah.
73 00:11:53.200 ⇒ 00:11:55.330 Ryan Luke Daque: yeah, invent even, yeah.
74 00:11:56.850 ⇒ 00:11:58.909 Ryan Luke Daque: Yeah. So
75 00:12:00.500 ⇒ 00:12:04.230 Ryan Luke Daque: going back to the past is this. since we’re like
76 00:12:04.530 ⇒ 00:12:11.269 Ryan Luke Daque: we already created the the like Mvp. For the dashboard. So can we like complete this?
77 00:12:13.740 ⇒ 00:12:16.639 Ryan Luke Daque: I can’t post it here right? I guess
78 00:12:21.270 ⇒ 00:12:22.120 Ryan Luke Daque: done
79 00:12:29.900 ⇒ 00:12:38.879 Ryan Luke Daque: next is just this one that’s this is just adding the label. This was fixed. I added, so this already done
80 00:12:41.340 ⇒ 00:12:42.320 Ryan Luke Daque: as well.
81 00:12:44.780 ⇒ 00:12:45.530 Uttam Kumaran: Okay.
82 00:12:47.400 ⇒ 00:12:57.750 Ryan Luke Daque: this was like, II named both shopify customer attribution and shopify customers the same. So it’s not showing the shopify customer attribution wasn’t showing
83 00:12:58.210 ⇒ 00:13:00.010 Ryan Luke Daque: and light dash.
84 00:13:00.830 ⇒ 00:13:15.429 Ryan Luke Daque: yeah, in progress. We have Amazon fees. This one I already have the model in place. But when I did, yeah, like we would discuss last Thursday, I believe, like there’s still a couple of
85 00:13:19.190 ⇒ 00:13:21.529 Ryan Luke Daque: fees that aren’t matching between
86 00:13:21.860 ⇒ 00:13:25.159 Ryan Luke Daque: our model and the exported sheet.
87 00:13:25.710 ⇒ 00:13:27.979 Ryan Luke Daque: basically, which was this one.
88 00:13:29.070 ⇒ 00:13:31.269 And I can’t like pinpoint.
89 00:13:33.550 ⇒ 00:13:36.910 Ryan Luke Daque: What like? Which one? Which ones are
90 00:13:37.220 ⇒ 00:13:42.189 Ryan Luke Daque: we are supposed to like, filter out, or maybe cause they. I’ve seen like
91 00:13:42.600 ⇒ 00:13:44.130 Ryan Luke Daque: a fee
92 00:13:44.410 ⇒ 00:13:49.100 Ryan Luke Daque: feed names that are refunds. But then they’re also
93 00:13:49.490 ⇒ 00:14:00.510 Ryan Luke Daque: I important drag that they also exist here. Not all of them do so. It’s it’s pretty weird. But yeah.
94 00:14:01.020 ⇒ 00:14:04.389 Ryan Luke Daque: I didn’t push that as well. The code
95 00:14:04.640 ⇒ 00:14:05.940 Ryan Luke Daque: it’s still in.
96 00:14:06.570 ⇒ 00:14:15.819 Uttam Kumaran: Yeah, let’s move it to me. And then I’m gonna take one more look, and if I can’t figure it out. I’m gonna get support. So maybe we can.
97 00:14:16.110 ⇒ 00:14:19.120 Uttam Kumaran: You could just add it to my sprint.
98 00:14:20.580 ⇒ 00:14:24.359 Ryan Luke Daque: Okay, so soon we change this?
99 00:14:25.000 ⇒ 00:14:25.840 Uttam Kumaran: Umhm.
100 00:14:26.760 ⇒ 00:14:29.390 Ryan Luke Daque: Okay, yes. And then I assign it to you.
101 00:14:29.790 ⇒ 00:14:30.760 Uttam Kumaran: Yeah.
102 00:14:33.120 ⇒ 00:14:36.430 Uttam Kumaran: I think maybe he had the gear. Yeah.
103 00:14:42.330 ⇒ 00:14:50.379 Ryan Luke Daque: Next we have this blocked ticket, which was the yeah, this is the bug that we were seeing him.
104 00:14:52.770 ⇒ 00:14:58.920 Ryan Luke Daque: And this was in in shoppy file, where it’s like showing multiple shipments
105 00:14:59.130 ⇒ 00:15:02.019 Ryan Luke Daque: for a single order. Item.
106 00:15:02.490 ⇒ 00:15:04.020 Uttam Kumaran: okay, okay.
107 00:15:05.990 ⇒ 00:15:11.700 Ryan Luke Daque: yeah. And then we have 2 in the back back log. But I guess we can move this to this right?
108 00:15:13.550 ⇒ 00:15:19.129 Ryan Luke Daque: This one first one would be just creating links. This could be fast one, I believe.
109 00:15:21.370 ⇒ 00:15:26.170 Ryan Luke Daque: Yeah, just adding this to the bioscience dashboard.
110 00:15:26.330 ⇒ 00:15:27.220 Uttam Kumaran: Okay?
111 00:15:28.370 ⇒ 00:15:32.289 Ryan Luke Daque: And we also have this model zoom desk data.
112 00:15:33.870 ⇒ 00:15:39.740 Uttam Kumaran: Yeah. So let’s look at Let me just take a look, too. So
113 00:15:40.970 ⇒ 00:15:44.309 Uttam Kumaran: this one looks like it should be in review.
114 00:15:45.500 ⇒ 00:15:49.620 Uttam Kumaran: So the one thing we’re gonna do is let’s move everything
115 00:15:50.090 ⇒ 00:15:52.250 Uttam Kumaran: that
116 00:15:52.600 ⇒ 00:15:57.100 Uttam Kumaran: we can move everything that’s currently in
117 00:15:57.590 ⇒ 00:15:59.530 Uttam Kumaran: this print to next sprint.
118 00:16:00.290 ⇒ 00:16:01.110 Ryan Luke Daque: Okay.
119 00:16:04.900 ⇒ 00:16:22.239 Uttam Kumaran: I’m actually gonna just move this one to the backlog. So there’s a bunch on my plate. So I’m gonna let me. I’m gonna move the 5 train light connector to the back log, because I don’t know when they’re going to really get back to us on that.
120 00:16:26.300 ⇒ 00:16:28.060 Uttam Kumaran: Let me do that.
121 00:16:30.280 ⇒ 00:16:35.249 Uttam Kumaran: I got some more clarity on the light dash issues. So I can take that on
122 00:16:35.260 ⇒ 00:16:45.280 Uttam Kumaran: And then there’s one ticket I worked at like yesterday and like on Friday, to work a few like hours. There’s a ship station issue that I’m helping
123 00:16:45.320 ⇒ 00:16:46.939 Uttam Kumaran: their team with.
124 00:16:47.140 ⇒ 00:17:00.400 Uttam Kumaran: So I was helping them build automation roles within within shipstation. So that’s that’s in review right now. I’m just gonna mark it as done because I don’t think I should have anything else for that.
125 00:17:00.900 ⇒ 00:17:12.560 Uttam Kumaran: And then, yeah, because of that, I just didn’t get to this in progress. the Stafso will have to move this to the sprint.
126 00:17:12.930 ⇒ 00:17:16.980 Uttam Kumaran: But let’s move. Let’s do that.
127 00:17:36.640 ⇒ 00:17:37.490 Uttam Kumaran: Okay?
128 00:17:38.600 ⇒ 00:17:39.870 Uttam Kumaran: And then
129 00:17:43.280 ⇒ 00:17:49.090 Uttam Kumaran: we can talk about what we want to do for the multi-quantity. Same product.
130 00:17:49.420 ⇒ 00:17:50.450 Ryan Luke Daque: Yeah.
131 00:18:00.560 ⇒ 00:18:07.710 Ryan Luke Daque: multi quantity, same product. So II think we would be. We’re thinking about like aggregating all the
132 00:18:10.160 ⇒ 00:18:12.520 Ryan Luke Daque: ship station borders
133 00:18:15.140 ⇒ 00:18:19.780 Ryan Luke Daque: before, like joining them into the borderline items
134 00:18:21.000 ⇒ 00:18:23.150 Ryan Luke Daque: like from here. So
135 00:18:26.150 ⇒ 00:18:29.459 Ryan Luke Daque: so the the thing that’s tough is.
136 00:18:29.590 ⇒ 00:18:31.220 Uttam Kumaran: yeah. Right now we want to.
137 00:18:32.480 ⇒ 00:18:36.579 Uttam Kumaran: I almost want to create like a a shipments table.
138 00:18:38.030 ⇒ 00:18:42.570 Uttam Kumaran: And then, instead of the only thing that comes in to order items
139 00:18:42.820 ⇒ 00:18:45.250 Uttam Kumaran: is the shipping price.
140 00:18:47.070 ⇒ 00:18:49.840 Uttam Kumaran: because what fields are causing the duplication?
141 00:18:53.270 ⇒ 00:18:58.820 Ryan Luke Daque: think there’s like ship date would be would be a.
142 00:18:59.490 ⇒ 00:19:02.510 Uttam Kumaran: Oh, okay, okay, yeah.
143 00:19:02.520 ⇒ 00:19:10.109 Ryan Luke Daque: The the 2 like, if there are like 2 shipments in one was shipped on a different day, then that would cause like duplication as well.
144 00:19:11.420 ⇒ 00:19:14.419 Ryan Luke Daque: Yeah, what else did I see there
145 00:19:15.120 ⇒ 00:19:16.370 Ryan Luke Daque: like there was
146 00:19:16.470 ⇒ 00:19:22.210 Ryan Luke Daque: this weird thing where it’s the quantity like the I mean the dimension that was different.
147 00:19:22.720 ⇒ 00:19:28.139 Ryan Luke Daque: It doesn’t make sense that it shouldn’t be different, because it’s the same item, just
148 00:19:29.400 ⇒ 00:19:35.480 Ryan Luke Daque: a different quantity. But yeah, it was shown as a different dimension.
149 00:19:36.040 ⇒ 00:19:41.389 Uttam Kumaran: Wait a second. And so so I’m looking at Amazon order items. So this happens
150 00:19:41.600 ⇒ 00:19:46.170 Uttam Kumaran: on the join between ship station order items on line 65, right?
151 00:19:48.730 ⇒ 00:19:49.630 Ryan Luke Daque: That
152 00:19:53.640 ⇒ 00:19:55.459 Ryan Luke Daque: Amazon order items.
153 00:19:56.330 ⇒ 00:19:57.200 Uttam Kumaran: Yeah.
154 00:20:07.210 ⇒ 00:20:08.660 Ryan Luke Daque: Line 65
155 00:20:18.730 ⇒ 00:20:21.509 Ryan Luke Daque: in this one has
156 00:20:22.520 ⇒ 00:20:27.379 Ryan Luke Daque: shipping amount. Yeah, this would. Yeah, this would definitely get doubled. Here.
157 00:20:28.860 ⇒ 00:20:31.159 Uttam Kumaran: Wait, are we looking the same thing.
158 00:20:31.220 ⇒ 00:20:35.780 Uttam Kumaran: Oh, this is in archive.
159 00:20:36.950 ⇒ 00:20:38.570 Ryan Luke Daque: Oh, yeah, I think this is
160 00:20:39.670 ⇒ 00:20:41.489 Ryan Luke Daque: this is Amazon orders.
161 00:20:43.630 ⇒ 00:20:46.130 Uttam Kumaran: Yeah, so you go. Line 65,
162 00:20:46.180 ⇒ 00:20:50.080 Uttam Kumaran: you should see. Oh, wait. I don’t. Why am I? Why is mine like.
163 00:20:52.900 ⇒ 00:21:10.020 Uttam Kumaran: Okay, yeah, you’re right. So I’m in line 79. So yeah, where it says ship.
164 00:21:11.860 ⇒ 00:21:20.610 Uttam Kumaran: So so the thing that’s happening is we have multiple line items
165 00:21:21.520 ⇒ 00:21:27.589 Uttam Kumaran: for the same product that are calving multiple shipments.
166 00:21:28.690 ⇒ 00:21:31.349 Uttam Kumaran: And then what is the issue that it’s causing?
167 00:21:32.140 ⇒ 00:21:34.140 Ryan Luke Daque: It’s duplicating.
168 00:21:36.730 ⇒ 00:21:38.990 Ryan Luke Daque: It’s which field engine?
169 00:21:39.040 ⇒ 00:21:41.110 Uttam Kumaran: Yeah. Which field in particular.
170 00:21:42.620 ⇒ 00:21:48.750 Ryan Luke Daque: like there would be 2 ship dates Well, the amount would be double as well.
171 00:21:51.320 ⇒ 00:22:00.369 Uttam Kumaran: But isn’t it okay? Right? Isn’t it fine? If we have 2 items per order that are the same product with 2 ship dates.
172 00:22:01.030 ⇒ 00:22:05.230 Uttam Kumaran: or are, does does the issue occur in Amazon orders.
173 00:22:05.290 ⇒ 00:22:07.370 Uttam Kumaran: Is that where like the issue comes up?
174 00:22:10.380 ⇒ 00:22:11.510 Ryan Luke Daque: Hmm.
175 00:22:11.930 ⇒ 00:22:13.259 Uttam Kumaran: do you see what I mean?
176 00:22:14.090 ⇒ 00:22:15.110 Ryan Luke Daque: Yeah.
177 00:22:17.290 ⇒ 00:22:20.800 Ryan Luke Daque: Depends on. Like, for example, we’re looking at.
178 00:22:23.050 ⇒ 00:22:28.859 Uttam Kumaran: Oh, like, well, the thing is, if you look at Amazon orders, we’re joining on the line item.
179 00:22:29.320 ⇒ 00:22:33.620 Uttam Kumaran: then there’s gonna be an issue, because one order is gonna have multiple shipments.
180 00:22:33.700 ⇒ 00:22:35.210 Ryan Luke Daque: Yeah? And then.
181 00:22:35.370 ⇒ 00:22:42.450 Ryan Luke Daque: yeah, cause we also have it, doesn’t. It doesn’t matter too much on the item level. But at the order level, it matters.
182 00:22:43.400 ⇒ 00:22:48.169 Ryan Luke Daque: yeah. And then the quantity might be doubled here, too. Because, like.
183 00:22:49.440 ⇒ 00:22:52.970 Ryan Luke Daque: since we’re joining in the order item, level.
184 00:22:53.480 ⇒ 00:22:58.029 Ryan Luke Daque: yeah, we’re joining. Yeah, we didn’t split it here.
185 00:22:58.950 ⇒ 00:23:03.110 Ryan Luke Daque: So quantities like here it would be like the home.
186 00:23:04.720 ⇒ 00:23:12.749 Uttam Kumaran: I wonder what we should do here? So the the first assumption is like, okay, we, we can have multiple shipments for order.
187 00:23:15.070 ⇒ 00:23:20.349 Uttam Kumaran: So anything in Amazon orders, we need to make sure that gets duped.
188 00:23:20.700 ⇒ 00:23:23.330 Ryan Luke Daque: Yeah, which is tough, because
189 00:23:26.720 ⇒ 00:23:28.240 Uttam Kumaran: quarter weight. So
190 00:23:30.020 ⇒ 00:23:35.320 Uttam Kumaran: service code shipment length with height, package order status all this.
191 00:23:48.130 ⇒ 00:23:48.960 Uttam Kumaran: Hmm.
192 00:23:56.650 ⇒ 00:24:00.740 Uttam Kumaran: okay. So maybe, can you add to the notes on the ticket?
193 00:24:01.850 ⇒ 00:24:06.000 Uttam Kumaran: So let’s add a couple of like facts that we know. So one is.
194 00:24:06.740 ⇒ 00:24:10.330 Uttam Kumaran: there are some orders with multiple
195 00:24:10.650 ⇒ 00:24:15.130 Uttam Kumaran: items. The second thing is that
196 00:24:15.350 ⇒ 00:24:21.939 Uttam Kumaran: we don’t split same product items in order items
197 00:24:22.100 ⇒ 00:24:24.509 Uttam Kumaran: before joining to ship station.
198 00:24:25.120 ⇒ 00:24:28.459 Uttam Kumaran: causing duplication in fields like quantity.
199 00:24:31.870 ⇒ 00:24:35.900 Uttam Kumaran: The third thing is. Amazon orders
200 00:24:36.910 ⇒ 00:24:39.840 Uttam Kumaran: doesn’t add. So Amazon orders
201 00:24:40.230 ⇒ 00:24:43.929 Uttam Kumaran: is also includes adjoin to ship station order items.
202 00:24:45.080 ⇒ 00:24:48.660 Uttam Kumaran: And if there are multiple order items
203 00:24:49.290 ⇒ 00:24:54.879 Uttam Kumaran: for through multiple shipments per order per order item.
204 00:24:56.070 ⇒ 00:24:58.600 Uttam Kumaran: there are multiple shipments for order, then we’re going to get duplication there.
205 00:25:04.470 ⇒ 00:25:09.489 Uttam Kumaran: So then, let’s if you list below like potential solutions.
206 00:25:09.920 ⇒ 00:25:11.780 Uttam Kumaran: let’s think out loud. So
207 00:25:11.850 ⇒ 00:25:16.620 Uttam Kumaran: one is we should create a shipments table
208 00:25:20.210 ⇒ 00:25:24.650 Uttam Kumaran: where we will source any information about shipping.
209 00:25:25.800 ⇒ 00:25:31.410 Uttam Kumaran: as it relates to orders. Any non-aggregated information relate to shipping.
210 00:25:35.940 ⇒ 00:25:38.210 Uttam Kumaran: The second thing is.
211 00:25:49.020 ⇒ 00:25:50.440 Uttam Kumaran: And
212 00:25:52.550 ⇒ 00:25:59.579 Uttam Kumaran: okay. Second thing is, we can remove all non aggregated shipping information from Amazon orders.
213 00:26:00.630 ⇒ 00:26:01.430 Ryan Luke Daque: Hmm.
214 00:26:03.210 ⇒ 00:26:06.400 Uttam Kumaran: and this actually may be the better route
215 00:26:08.790 ⇒ 00:26:10.510 Ryan Luke Daque: ambitions work.
216 00:26:11.020 ⇒ 00:26:17.699 Ryan Luke Daque: So what we do is remove everything. And we just do, Max, ship data like or Min.
217 00:26:18.580 ⇒ 00:26:19.430 Ryan Luke Daque: yeah.
218 00:26:19.760 ⇒ 00:26:23.560 Uttam Kumaran: And then we have number of shipments. things like that
219 00:26:23.980 ⇒ 00:26:28.959 Ryan Luke Daque: cause. But then I, the only thing I want to check is like, where if these are being used anywhere
220 00:26:30.950 ⇒ 00:26:32.610 Uttam Kumaran: because I do think we are.
221 00:26:32.820 ⇒ 00:26:34.610 Ryan Luke Daque: we’re using it in.
222 00:26:34.760 ⇒ 00:26:38.350 Uttam Kumaran: Well, I think it’s getting used in all orders.
223 00:26:39.300 ⇒ 00:26:40.230 Ryan Luke Daque: Yeah.
224 00:26:41.370 ⇒ 00:26:42.860 Uttam Kumaran: which is brutal.
225 00:26:45.390 ⇒ 00:26:46.770 Uttam Kumaran: That’s gonna be tough.
226 00:26:48.910 ⇒ 00:26:56.249 Uttam Kumaran: Cause we have to remove from everything, because in all orders. We have ship date, shipping platform order, status, package
227 00:26:56.570 ⇒ 00:27:01.560 Uttam Kumaran: pipe length with service code weight amount. Yeah, everything’s basically.
228 00:27:05.870 ⇒ 00:27:11.830 Ryan Luke Daque: I guess this isn’t just for Amazon orders as well. It should be in shopify as well. Right?
229 00:27:12.270 ⇒ 00:27:15.210 Ryan Luke Daque: Because we’re also yeah. I’m gonna have to look at
230 00:27:15.540 ⇒ 00:27:17.099 Ryan Luke Daque: Minnesota. Oh, were you?
231 00:27:17.310 ⇒ 00:27:23.820 Uttam Kumaran: Oh, man there are. So them I mean. The main thing is, look, I wanna be able to see the total shipping cost.
232 00:27:23.940 ⇒ 00:27:28.639 Ryan Luke Daque: That’s the big. I want to see the shipping cost. I want to see who the ship in was with.
233 00:27:29.330 ⇒ 00:27:37.149 Uttam Kumaran: I want to see the sum of the weight. and I want to see honestly like that’s
234 00:27:37.270 ⇒ 00:27:46.069 Uttam Kumaran: that’s pretty much it. So I actually think we could remove the went with length, hit the wink length, height pretty easily.
235 00:27:46.670 ⇒ 00:27:54.930 Uttam Kumaran: and then move all the all the calculate, anything that references that to a shipment. So I think the the first thing we have to do is create a shipment stable.
236 00:27:55.940 ⇒ 00:27:56.700 Ryan Luke Daque: Right?
237 00:27:57.460 ⇒ 00:28:04.250 Uttam Kumaran: That’s number one. The second thing is, we’ll need to look at how much we can remove problem.
238 00:28:05.450 ⇒ 00:28:07.360 Uttam Kumaran: All the order tables
239 00:28:08.630 ⇒ 00:28:13.649 Ryan Luke Daque: that we’re not using that we’re not using. Yeah.
240 00:28:13.710 ⇒ 00:28:20.799 Ryan Luke Daque: like you mentioned the height with, the thing is, I don’t. So can you, on a note? Can you add another thing. Can you say
241 00:28:21.210 ⇒ 00:28:28.829 Uttam Kumaran: we? We need to check whether there are orders that get shipped by multiple providers?
242 00:28:30.550 ⇒ 00:28:31.640 Ryan Luke Daque: Yeah, that’s
243 00:28:36.290 ⇒ 00:28:46.150 Ryan Luke Daque: walk back something like that, or like like the first or last. Yeah. Doesn’t need to be in separate.
244 00:28:46.830 ⇒ 00:28:59.409 Ryan Luke Daque: well, I guess we we don’t have to add that in all orders, because we can just use the shipment table if we need to know that information right
245 00:29:04.110 ⇒ 00:29:07.090 Ryan Luke Daque: like here, we don’t have a shipment
246 00:29:09.140 ⇒ 00:29:10.470 Ryan Luke Daque: related
247 00:29:15.100 ⇒ 00:29:15.970 Ryan Luke Daque: table
248 00:29:17.610 ⇒ 00:29:18.710 Ryan Luke Daque: or visual.
249 00:29:20.920 ⇒ 00:29:26.710 Ryan Luke Daque: like we’re not even in in the visual, in the vital signs. We’re not even showing which one
250 00:29:27.960 ⇒ 00:29:30.680 Ryan Luke Daque: of who shipped which order, I guess
251 00:29:37.800 ⇒ 00:29:38.660 Ryan Luke Daque: the
252 00:29:39.140 ⇒ 00:29:41.980 Uttam Kumaran: yeah, I’m just thinking, hold on 1 s.
253 00:29:46.280 ⇒ 00:29:54.230 Uttam Kumaran: Okay, let’s let’s just let’s keep it as that. For now I think the main task that I think, let’s save this
254 00:29:54.740 ⇒ 00:29:56.710 Uttam Kumaran: and say this, keep us open.
255 00:29:57.080 ⇒ 00:30:03.859 Uttam Kumaran: I wanted. Can you create another ticket called crate shipments table? And maybe you can take that on this week?
256 00:30:03.880 ⇒ 00:30:04.909 Ryan Luke Daque: Yeah, Sharon.
257 00:30:12.210 ⇒ 00:30:16.180 Uttam Kumaran: And then if you just wanna add some notes to there.
258 00:30:18.960 ⇒ 00:30:20.560 Ryan Luke Daque: meantime.
259 00:30:23.030 ⇒ 00:30:23.720 Ryan Luke Daque: it’s just
260 00:30:31.010 ⇒ 00:30:31.750 Ryan Luke Daque: model.
261 00:30:34.900 ⇒ 00:30:40.170 Uttam Kumaran: Okay? So then, yeah, the main, the main notes are, I wanna I want to see every single shipment
262 00:30:41.940 ⇒ 00:30:44.559 Uttam Kumaran: and all attributes about them.
263 00:30:47.640 ⇒ 00:30:50.869 Uttam Kumaran: And then we want to join in order items.
264 00:31:00.580 ⇒ 00:31:02.810 Ryan Luke Daque: We’re joining all order items here.
265 00:31:03.450 ⇒ 00:31:15.890 Uttam Kumaran: Yeah. so you could probably end up joining. You’ll probably end up doing a light dash drawing for all order items. Right?
266 00:31:16.900 ⇒ 00:31:17.830 Ryan Luke Daque: Yeah.
267 00:31:20.000 ⇒ 00:31:24.099 Ryan Luke Daque: yeah, we can. We can. We can just do like that to join
268 00:31:24.990 ⇒ 00:31:27.630 Ryan Luke Daque: don’t need it in the model.
269 00:31:33.550 ⇒ 00:31:34.390 Ryan Luke Daque: And what?
270 00:31:35.410 ⇒ 00:31:38.689 Ryan Luke Daque: Well, I guess we can join everything from all organis
271 00:31:39.320 ⇒ 00:31:41.560 Ryan Luke Daque: based on the order Id
272 00:31:41.870 ⇒ 00:31:43.260 Ryan Luke Daque: or the item I need.
273 00:31:46.680 ⇒ 00:31:49.979 Ryan Luke Daque: and this shipment stable would have the
274 00:31:52.410 ⇒ 00:31:54.209 Ryan Luke Daque: aggregates of everything.
275 00:31:56.250 ⇒ 00:31:58.489 Ryan Luke Daque: or like, would this?
276 00:31:58.740 ⇒ 00:32:01.210 Uttam Kumaran: No, this would be at the actual shipment level.
277 00:32:01.670 ⇒ 00:32:05.229 Ryan Luke Daque: right? So we’ll see the duplicate
278 00:32:06.330 ⇒ 00:32:12.019 Ryan Luke Daque: like multiple shipments here, or a single order. Id
279 00:32:12.240 ⇒ 00:32:13.080 Ryan Luke Daque: right?
280 00:32:14.760 ⇒ 00:32:21.240 Ryan Luke Daque: Got it so you could bring in the order id, and then aggregate if you want. But that’ll all happen in light dash. Yeah.
281 00:32:22.100 ⇒ 00:32:23.160 Uttam Kumaran: see what I mean.
282 00:32:23.370 ⇒ 00:32:24.969 Ryan Luke Daque: Yeah. Makes sense.
283 00:32:29.680 ⇒ 00:32:30.510 Ryan Luke Daque: Okay.
284 00:32:34.320 ⇒ 00:32:35.070 this.
285 00:32:35.180 ⇒ 00:32:38.209 Ryan Luke Daque: I guess high like the first priority for this week.
286 00:32:45.880 ⇒ 00:32:48.150 Uttam Kumaran: Yeah. So I would say,
287 00:32:48.380 ⇒ 00:32:52.730 Uttam Kumaran: zendes, data is like number one.
288 00:32:52.930 ⇒ 00:32:53.960 Ryan Luke Daque: Okay.
289 00:32:58.790 ⇒ 00:33:05.189 Uttam Kumaran: so we can even go back if you want to go even back further to the backlog view. We can even look at everything that’s there
290 00:33:05.400 ⇒ 00:33:06.420 Ryan Luke Daque: makes sense.
291 00:33:22.770 ⇒ 00:33:25.560 Uttam Kumaran: Okay? So yeah, I would say,
292 00:33:26.710 ⇒ 00:33:28.770 Uttam Kumaran: Xander’s data is number one.
293 00:33:32.470 ⇒ 00:33:34.989 Uttam Kumaran: and then the shipments is number 2.
294 00:33:40.870 ⇒ 00:33:53.290 Uttam Kumaran: And then also there’s one ticket at the top called removing. So there, there’s a lot of there’s some orders in like dash that are coming from email addresses that are like at pool parts to go.
295 00:33:53.530 ⇒ 00:33:54.550 Ryan Luke Daque: Hmm!
296 00:33:55.020 ⇒ 00:33:57.629 Uttam Kumaran: So I wanted to get rid of those from the sales data.
297 00:33:58.870 ⇒ 00:34:00.709 Ryan Luke Daque: Which model is it coming from?
298 00:34:01.730 ⇒ 00:34:07.949 Uttam Kumaran: So I would say, it’s I mean, it’s gonna come from Amazon, or or it’s gonna come from shopify.
299 00:34:09.350 ⇒ 00:34:16.340 Uttam Kumaran: So what I want to do is I want to write a macro that removes certain
300 00:34:17.370 ⇒ 00:34:19.210 Uttam Kumaran: email addresses
301 00:34:20.440 ⇒ 00:34:23.690 Uttam Kumaran: from Amazon from shopify orders.
302 00:34:27.510 ⇒ 00:34:29.630 Uttam Kumaran: So if you go to shopify
303 00:34:30.639 ⇒ 00:34:34.440 Uttam Kumaran: your best. yeah.
304 00:34:36.090 ⇒ 00:34:41.180 Ryan Luke Daque: or even or even it’s like, if you go to. Yeah, even at the, we should remove it from the
305 00:34:41.330 ⇒ 00:34:44.479 Uttam Kumaran: order item level.
306 00:34:48.320 ⇒ 00:34:52.579 Uttam Kumaran: So I like, I want to get rid of it from.
307 00:34:55.440 ⇒ 00:34:58.179 Uttam Kumaran: yeah, I wanna I wanna
308 00:34:59.630 ⇒ 00:35:02.330 Uttam Kumaran: get rid of it from that final cte.
309 00:35:04.970 ⇒ 00:35:06.200 Ryan Luke Daque: okay.
310 00:35:06.290 ⇒ 00:35:16.019 Uttam Kumaran: basically, there should be a where clause that says or or we can actually remove it from select star from final. We can remove
311 00:35:16.280 ⇒ 00:35:21.100 Uttam Kumaran: any orders associated where the email address includes
312 00:35:21.240 ⇒ 00:35:23.389 Uttam Kumaran: poolparts to go.com.
313 00:35:23.530 ⇒ 00:35:24.400 Ryan Luke Daque: Yeah.
314 00:35:26.260 ⇒ 00:35:29.309 Uttam Kumaran: that’s that’s pretty much the update. So it should be a quick one.
315 00:35:30.070 ⇒ 00:35:31.050 Ryan Luke Daque: Gotcha.
316 00:35:35.430 ⇒ 00:35:39.599 Ryan Luke Daque: So now this is. So this would be add products.
317 00:35:39.830 ⇒ 00:35:48.300 Uttam Kumaran: yeah. And the macro is so that we can. You know, I think there’s they’re using also some other personal email addresses. So I just want to be able to add
318 00:35:49.370 ⇒ 00:35:55.480 Uttam Kumaran: a couple of those, and th. This may come in handy in a couple of other models where we need to get rid of their data. So
319 00:35:57.050 ⇒ 00:36:04.150 Uttam Kumaran: it should be. I don’t know if it’s it’s pretty should be easy to write a macro that’s just like a where clause. Right? Okay?
320 00:36:08.360 ⇒ 00:36:12.200 Uttam Kumaran: cool. I think
321 00:36:13.540 ⇒ 00:36:18.649 Uttam Kumaran: that should be it the the only other thing that maybe lower priority. Then
322 00:36:18.690 ⇒ 00:36:24.860 Ryan Luke Daque: so this is the yeah, this is a, this is like a lower power. So we could. I mean again. But this one’s very quick. So
323 00:36:25.060 ⇒ 00:36:26.940 Ryan Luke Daque: yeah.
324 00:36:27.080 ⇒ 00:36:27.900 Ryan Luke Daque: yes.
325 00:36:37.380 ⇒ 00:36:41.800 Uttam Kumaran: And then there’s this one, the add, add metrics.
326 00:36:42.690 ⇒ 00:36:45.240 Ryan Luke Daque: So shopify.
327 00:36:45.640 ⇒ 00:36:47.860 Uttam Kumaran: I think it’s at the top of that. So
328 00:36:48.020 ⇒ 00:36:49.969 Ryan Luke Daque: quick point as well.
329 00:36:50.330 ⇒ 00:36:52.300 Uttam Kumaran: Okay, cool should be even.
330 00:36:52.840 ⇒ 00:36:58.560 Ryan Luke Daque: I guess in time. because this is just adding veterans, metrics to
331 00:37:00.380 ⇒ 00:37:03.449 Uttam Kumaran: manage it quick.
332 00:37:04.940 ⇒ 00:37:09.770 Ryan Luke Daque: Yeah, let’s just make it small in case you have anything. Choose.
333 00:37:44.780 ⇒ 00:37:49.619 Ryan Luke Daque: what about this clean up all the shipping dimensions. And
334 00:37:53.470 ⇒ 00:37:55.590 Ryan Luke Daque: this is shipping. you know.
335 00:37:56.460 ⇒ 00:38:01.479 Uttam Kumaran: yeah, actually, this would be great to do as well.
336 00:38:03.260 ⇒ 00:38:05.660 Uttam Kumaran: This is a very low priority, though.
337 00:38:07.160 ⇒ 00:38:10.589 Ryan Luke Daque: So let’s like, let’s leave this off for now.
338 00:38:11.520 ⇒ 00:38:17.960 Uttam Kumaran: Because I think there’s gonna be some, probably some bugs and stuff that come up. So let’s look at the current sprint.
339 00:38:18.480 ⇒ 00:38:19.550 Uttam Kumaran: Tab
340 00:38:31.980 ⇒ 00:38:34.180 Uttam Kumaran: is everything listed in there?
341 00:38:35.130 ⇒ 00:38:38.890 Ryan Luke Daque: Yeah, looks like it. This one. I guess this is
342 00:38:40.940 ⇒ 00:38:43.890 Ryan Luke Daque: no just creating links.
343 00:38:46.640 ⇒ 00:38:56.260 Uttam Kumaran: Yeah, the can you add one ticket on my side is, I want to. I want to create a
344 00:38:59.240 ⇒ 00:39:05.730 Uttam Kumaran: on the Weekly monthly dashboard. I want to add
345 00:39:06.330 ⇒ 00:39:08.309 Uttam Kumaran: customer acquisition cost
346 00:39:13.820 ⇒ 00:39:15.030 Uttam Kumaran: by
347 00:39:15.200 ⇒ 00:39:19.550 Uttam Kumaran: that customer source that the attribution source that you that you created
348 00:39:42.200 ⇒ 00:39:44.059 Ryan Luke Daque: think this is medium or like
349 00:39:45.410 ⇒ 00:39:49.229 Ryan Luke Daque: should do click one. I guess. Yeah, medium should be fine.
350 00:39:52.530 ⇒ 00:39:53.220 Uttam Kumaran: Umhm
351 00:40:04.680 ⇒ 00:40:10.640 Ryan Luke Daque: sounds good to just question regarding this Zendesk ticket.
352 00:40:10.770 ⇒ 00:40:18.929 Ryan Luke Daque: Currently we don’t. We don’t have it yet in 5 tram. So I need to create the integration.
353 00:40:19.360 ⇒ 00:40:24.450 Ryan Luke Daque: And it’s all in snowflakes. Me on that. So it’s just modeling them.
354 00:40:27.470 ⇒ 00:40:28.350 Ryan Luke Daque: Yeah.
355 00:40:29.350 ⇒ 00:40:30.100 Ryan Luke Daque: okay?
356 00:40:33.270 ⇒ 00:40:34.230 Ryan Luke Daque: Sounds good.
357 00:40:39.520 ⇒ 00:40:48.490 Uttam Kumaran: Okay, great. So yeah, I would say, Zendesk, getting done is super big priority. And that’s again, probably be the one thing. Biggest thing that I try to share with them.
358 00:40:48.820 ⇒ 00:40:56.929 Uttam Kumaran: the pull parts of the employees hopefully, is like very small. And then, yeah,
359 00:40:57.310 ⇒ 00:41:12.360 Uttam Kumaran: I think I think if we can get all this done, I think there’s gonna be a couple of smaller things that we can talk about. Probably tomorrow. Wednesday. We can add more stuff to the sprint again. So let’s just try to let’s try to like nail this stuff. And then, yeah, I have time this week.
360 00:41:12.720 ⇒ 00:41:25.299 Uttam Kumaran: I’m I’m glad you’re taking on a lot of the heavier task, because I’m gonna try to do some just light dash stuff. I was doing a lot for another client last week. So I’m messing around with a lot of light dash settings. So I’ll be able to finish that up.
361 00:41:25.740 ⇒ 00:41:26.620 Ryan Luke Daque: Nice.
362 00:41:28.940 ⇒ 00:41:31.780 Uttam Kumaran: Okay, cool anything else.
363 00:41:31.820 ⇒ 00:41:35.199 One more thing, though, I noticed in like Ltl.
364 00:41:35.820 ⇒ 00:41:39.579 Ryan Luke Daque: we didn’t have any shipments since Friday.
365 00:41:39.840 ⇒ 00:41:42.950 Uttam Kumaran: Oh, that’s okay. Yeah, they don’t. So they don’t ship anything over the weekend.
366 00:41:43.480 ⇒ 00:41:45.080 Ryan Luke Daque: even on Friday.
367 00:41:45.600 ⇒ 00:41:58.499 Uttam Kumaran: sometimes on Friday. But maybe not always. So whatever the data you’re seeing, there is gonna be accurate. Okay? So basically, if you don’t have anything. I just don’t add anything as well.
368 00:41:59.500 ⇒ 00:42:04.740 Ryan Luke Daque: It’s just weird, though, like the last few days were just like one rule
369 00:42:04.880 ⇒ 00:42:09.870 Ryan Luke Daque: per day, where it’s like multiple roles. Right?
370 00:42:10.120 ⇒ 00:42:14.590 Ryan Luke Daque: But yeah, I guess that’s if it is what it is. I guess.
371 00:42:18.220 ⇒ 00:42:23.360 Uttam Kumaran: Yeah, I think as long as the data matches, it’s fine. And then, yeah, I’m also. I’ll also.
372 00:42:23.440 ⇒ 00:42:30.309 Uttam Kumaran: I probably need to take a couple of hours to look at the fees, so I’ll take a look at the Amazon fee stuff. It seems like that sounds good.
373 00:42:31.950 ⇒ 00:42:34.500 Ryan Luke Daque: Do we need to create a ticket for that one?
374 00:42:34.920 ⇒ 00:42:36.240 Uttam Kumaran: No, that’s already there.
375 00:42:36.500 ⇒ 00:42:37.400 Ryan Luke Daque: Oh, it is
376 00:42:38.650 ⇒ 00:42:43.240 Ryan Luke Daque: so it will. You’ll be the same ticket then. Oh, yeah. I to your name. Gotcha.
377 00:42:45.980 ⇒ 00:42:46.730 Ryan Luke Daque: Cool?
378 00:42:49.290 ⇒ 00:42:57.389 Uttam Kumaran: Okay. Alright. Well, message me on slack. If anything sure sounds good. Okay. Talk soon. You, too.