Meeting Title: US x BF | Standup Date: 2025-11-05 Meeting participants: Uttam Kumaran, Awaish Kumar, Emily Giant, Amber Lin, Demilade Agboola
WEBVTT
1 00:00:36.890 ⇒ 00:00:38.599 Uttam Kumaran: Hmm, why is this not working?
2 00:00:45.700 ⇒ 00:00:51.670 Awaish Kumar: like, urban stems, like, where are they located? Like, they work in… Eastern time zone, or…
3 00:00:51.830 ⇒ 00:00:52.890 Awaish Kumar: Pacific.
4 00:00:53.300 ⇒ 00:00:55.060 Uttam Kumaran: A little bit all over.
5 00:00:57.060 ⇒ 00:01:01.270 Awaish Kumar: Oh, I was trying to set up this, north Dream Export.
6 00:01:02.760 ⇒ 00:01:05.620 Uttam Kumaran: Oh, I know, they’re in U.S. West, I think.
7 00:01:06.240 ⇒ 00:01:07.250 Awaish Kumar: Oh, okay.
8 00:01:07.570 ⇒ 00:01:09.190 Uttam Kumaran: They’re US West, too.
9 00:01:11.980 ⇒ 00:01:12.630 Awaish Kumar: Okay.
10 00:01:12.900 ⇒ 00:01:17.729 Uttam Kumaran: Hi, Emily. Sorry, I don’t know why my webcam is just not showing up on my side.
11 00:01:19.030 ⇒ 00:01:19.750 Emily Giant: Hmm.
12 00:01:19.900 ⇒ 00:01:22.460 Uttam Kumaran: It’s very weird. Can you guys see me, or no?
13 00:01:23.120 ⇒ 00:01:25.130 Emily Giant: No.
14 00:01:25.330 ⇒ 00:01:31.009 Uttam Kumaran: Oh, alright, well, I don’t know what just happened. I will unplug this guy.
15 00:01:33.820 ⇒ 00:01:47.680 Uttam Kumaran: Okay, cool, I guess we can get started. I think initial first question is, Emily, I wanted to kind of… we’re starting to use Metaplane to set, like, pretty good SLAs on tables. Okay. And so I just wanted to…
16 00:01:47.900 ⇒ 00:01:58.979 Uttam Kumaran: kind of get a sense of what you think is fair for… oh, there I am… for, XF tables and for marts. Like, is for our SLA to…
17 00:01:59.710 ⇒ 00:02:00.710 Uttam Kumaran: Wide.
18 00:02:01.240 ⇒ 00:02:02.060 Emily Giant: Yeah.
19 00:02:02.370 ⇒ 00:02:03.030 Uttam Kumaran: Okay.
20 00:02:03.150 ⇒ 00:02:04.719 Uttam Kumaran: Should be, like, 2 hours?
21 00:02:04.970 ⇒ 00:02:06.570 Emily Giant: Yep. Sounds better.
22 00:02:06.770 ⇒ 00:02:12.389 Uttam Kumaran: Okay, alright. You could say one hour, I don’t mind. We will just get there.
23 00:02:13.290 ⇒ 00:02:16.279 Emily Giant: For a full refresh?
24 00:02:16.370 ⇒ 00:02:20.300 Uttam Kumaran: Well, give me, like, give me, like, the minimum, and then give me, like, the…
25 00:02:20.770 ⇒ 00:02:25.360 Uttam Kumaran: This would be a dream if we could have one hour refresh on everything.
26 00:02:25.360 ⇒ 00:02:43.430 Emily Giant: Yeah, it would be a dream to have a one-hour research on everything, but I think it’s a crazy dream, maybe fueled by drugs. Like, I don’t know if that’s a thing, so, I do think it is, once we’re able to slash and burn, Tableau Items XF.
27 00:02:43.930 ⇒ 00:02:44.500 Uttam Kumaran: Yeah, yeah.
28 00:02:44.500 ⇒ 00:02:55.369 Emily Giant: I don’t see why we would not be able to do it in under an hour with the new tables, especially with, like, some of the optimizations that you were doing with sort keys and stuff. I think there’s, like, a lot of…
29 00:02:55.790 ⇒ 00:03:04.630 Emily Giant: room there for improvement in the inventory models. I know you did a bunch of them, but, like, that could get a lot speedier,
30 00:03:04.790 ⇒ 00:03:10.459 Emily Giant: But I would say, like, 2 is… a good goal.
31 00:03:11.820 ⇒ 00:03:12.420 Uttam Kumaran: Okay.
32 00:03:14.340 ⇒ 00:03:17.160 Uttam Kumaran: Okay, so… I wish you heard it here first.
33 00:03:17.470 ⇒ 00:03:23.109 Uttam Kumaran: I feel like it’s actually totally fine, like, we’re gonna get the jobs all the way down to running in, like.
34 00:03:23.360 ⇒ 00:03:29.180 Uttam Kumaran: 15-30 minutes, and I think what we’ll figure out is, like,
35 00:03:29.650 ⇒ 00:03:37.090 Uttam Kumaran: how we want to stagger things, like off hours and things like that, if we need to optimize, but that’s fine. Okay, and the second… yeah, and the second question…
36 00:03:37.090 ⇒ 00:03:48.240 Emily Giant: Just, I think that there’s been some deprecations, in dbt with tagging, and so our tagging has gotten a little misaligned again with, like.
37 00:03:48.240 ⇒ 00:03:59.630 Emily Giant: exclusions, inclusions, and stuff with jobs, and if we do another pass-through after the new revenue mart is, like, signed off on, we could probably really optimize with, like.
38 00:03:59.730 ⇒ 00:04:05.670 Emily Giant: We don’t need to run the caretags final tables every 30-minute interval. We can do that.
39 00:04:06.000 ⇒ 00:04:17.789 Emily Giant: every 4 hours. Like, we need to re… rework that, with the new marked tables, because right now it’s still set up for running both the old version and the new version.
40 00:04:17.790 ⇒ 00:04:18.290 Uttam Kumaran: Yeah.
41 00:04:18.290 ⇒ 00:04:28.140 Emily Giant: So we… I don’t know if we need to create a ticket for that, or, like, that’s something that Awash could probably, like, crush that job, especially if we’re not ready for,
42 00:04:28.300 ⇒ 00:04:30.210 Emily Giant: Looker deprecations yet?
43 00:04:30.580 ⇒ 00:04:35.880 Emily Giant: that would be in, like, legacy tables. There’s still some legacy tables that are,
44 00:04:36.290 ⇒ 00:04:41.859 Emily Giant: running every 30-minute refresh, and, like, those never need to refresh.
45 00:04:42.280 ⇒ 00:04:49.819 Emily Giant: So, just, like, all those little things in the project.yaml file, I think, could be some big wins with the jobs.
46 00:04:50.300 ⇒ 00:04:54.260 Emily Giant: Okay. Especially after New Revenue Mart is deployed.
47 00:04:54.590 ⇒ 00:04:55.130 Uttam Kumaran: Okay.
48 00:04:56.040 ⇒ 00:05:07.619 Demilade Agboola: Yeah, something I’ve been looking at was selected.yaml, where you create, like, custom collections or nodes, and we can create that and use that as a selector.yaml.
49 00:05:07.790 ⇒ 00:05:10.340 Demilade Agboola: We can exclude much more easily.
50 00:05:10.700 ⇒ 00:05:13.259 Demilade Agboola: Oh, so that’s something I was looking at, yeah.
51 00:05:13.260 ⇒ 00:05:13.850 Emily Giant: Yeah.
52 00:05:15.210 ⇒ 00:05:15.790 Uttam Kumaran: Okay.
53 00:05:17.650 ⇒ 00:05:23.299 Uttam Kumaran: Okay, great. And then, so we’re also… we… Oasia’s working on the North Beam exports.
54 00:05:23.300 ⇒ 00:05:23.740 Emily Giant: Cool.
55 00:05:24.150 ⇒ 00:05:28.549 Uttam Kumaran: And then the second piece we just talked about is…
56 00:05:28.710 ⇒ 00:05:44.630 Uttam Kumaran: We’re gonna have the staging sort of process solved, probably this week. Awash is taking another pass. I… we proposed, like, one solution that should work, and so ideally, what we’re gonna… right now, because dbt’s staging.
57 00:05:45.430 ⇒ 00:05:59.660 Uttam Kumaran: Is… the way they do it is kind of stupid. They create a schema for every staging run, I get it, but it doesn’t support things like incremental models, so they have to run every single time. Very stupid, like, they’re a very…
58 00:05:59.690 ⇒ 00:06:06.869 Uttam Kumaran: well-funded company, they should have thought about this, and so instead, what I’m… what I’ve done in the past is, like, we should just create
59 00:06:07.170 ⇒ 00:06:18.739 Uttam Kumaran: staging. It has an exact replica of everything in production, and whenever we push a PR, it runs there. That way, also, what we can do, Emily, is point a bunch of Looker Explorers to staging.
60 00:06:18.990 ⇒ 00:06:20.670 Emily Giant: So…
61 00:06:20.670 ⇒ 00:06:21.920 Uttam Kumaran: We can test end-to-end.
62 00:06:22.700 ⇒ 00:06:26.290 Uttam Kumaran: Right? We don’t necessarily need to do,
63 00:06:26.960 ⇒ 00:06:30.079 Uttam Kumaran: When we can, we don’t necessarily need to create, like, staging dashboards.
64 00:06:30.080 ⇒ 00:06:32.120 Emily Giant: But at least we have…
65 00:06:32.180 ⇒ 00:06:42.369 Uttam Kumaran: staging versions of the tables. The reason being is we can also then put Metaplane onto staging, and so if there is, like.
66 00:06:42.860 ⇒ 00:06:48.279 Uttam Kumaran: Wide variances in things and staging, then we know that we can block them at that point.
67 00:06:48.280 ⇒ 00:06:49.899 Emily Giant: Okay. Before they get into production.
68 00:06:50.030 ⇒ 00:06:50.950 Uttam Kumaran: So…
69 00:06:51.570 ⇒ 00:06:58.739 Uttam Kumaran: kind of like a long time coming thing I wanted to make, so yeah. Awish, go ahead, I don’t know if you had… if you had something to say there.
70 00:06:59.180 ⇒ 00:07:05.590 Awaish Kumar: I was just asking, like, are we using a lot of, this operator in incremental models?
71 00:07:11.210 ⇒ 00:07:12.130 Uttam Kumaran: Yes.
72 00:07:12.710 ⇒ 00:07:16.780 Awaish Kumar: Okay, then it might fail, but otherwise, like, if we just.
73 00:07:16.780 ⇒ 00:07:17.130 Uttam Kumaran: Alright.
74 00:07:17.130 ⇒ 00:07:19.880 Awaish Kumar: We could use environment.
75 00:07:20.020 ⇒ 00:07:28.469 Awaish Kumar: as a filter with incremental. So, if staging, then it will add date filters in the source table.
76 00:07:28.470 ⇒ 00:07:30.919 Uttam Kumaran: Oh, yeah, yeah, totally, totally. Yeah.
77 00:07:30.920 ⇒ 00:07:31.580 Awaish Kumar: But if…
78 00:07:31.580 ⇒ 00:07:35.280 Uttam Kumaran: No, definitely, so if you move… if you end up moving this to Git of Actions, you could…
79 00:07:35.790 ⇒ 00:07:38.349 Uttam Kumaran: The environment parameters.
80 00:07:39.170 ⇒ 00:07:42.710 Uttam Kumaran: And then… Yeah, just do it there.
81 00:07:43.940 ⇒ 00:07:48.559 Uttam Kumaran: I wasn’t able to do the parameters in dbt Cloud.
82 00:07:48.970 ⇒ 00:07:53.539 Uttam Kumaran: Otherwise, I would have created a production job with staging, but it didn’t let me,
83 00:07:54.080 ⇒ 00:07:55.670 Uttam Kumaran: Or at least I couldn’t figure it out.
84 00:07:56.440 ⇒ 00:08:08.309 Emily Giant: Yeah, it’s, I mean, it’s way better than it used to be with the staging, but, like, I know that when, you had the PR a couple days ago, that those were, like, not real errors.
85 00:08:08.310 ⇒ 00:08:10.030 Uttam Kumaran: Yeah, and that’s…
86 00:08:10.030 ⇒ 00:08:10.710 Emily Giant: Yeah.
87 00:08:11.840 ⇒ 00:08:14.149 Emily Giant: And it, like, if we ever were to grow…
88 00:08:14.350 ⇒ 00:08:23.780 Emily Giant: the team of me to teach people what are and aren’t real errors is a lot. But yeah, that sounds good.
89 00:08:29.170 ⇒ 00:08:29.790 Uttam Kumaran: Okay.
90 00:08:32.440 ⇒ 00:08:36.139 Uttam Kumaran: Okay Alright, great, and then let’s talk about…
91 00:08:36.280 ⇒ 00:08:37.400 Uttam Kumaran: Yeah, go ahead, go ahead, Milada.
92 00:08:37.400 ⇒ 00:08:39.659 Demilade Agboola: On the revenue side,
93 00:08:40.370 ⇒ 00:08:47.550 Demilade Agboola: I have been able to push a PR for, like, the revenue for Legacy, as well as the Shopify.
94 00:08:48.450 ⇒ 00:08:50.950 Demilade Agboola: And so, Emily and I have, like,
95 00:08:51.110 ⇒ 00:08:54.580 Demilade Agboola: QA session today to just ensure that numbers
96 00:08:55.240 ⇒ 00:08:59.139 Demilade Agboola: look as good as they should, but I have tested and they look pretty good.
97 00:08:59.310 ⇒ 00:09:02.769 Demilade Agboola: So this is, like, revenue plus shipping and all of that.
98 00:09:03.060 ⇒ 00:09:09.049 Demilade Agboola: We should be… Like, literally by no stages for this revenue.
99 00:09:09.530 ⇒ 00:09:28.009 Emily Giant: I commented in the ticket, but, we can clear this up now. I had created the model for overriding the forced upgrade, or redistributing forced upgrade prices to the items that was sent for the, like, fulfilled revenue column. Were you able to work that in to the revenue model, or do we still need to do that piece?
100 00:09:29.160 ⇒ 00:09:30.299 Demilade Agboola: None of those work then.
101 00:09:30.750 ⇒ 00:09:37.850 Emily Giant: Oh my gosh, you’re amazing, okay. I was gonna work on that, like, most of the day, so… yay, thank you.
102 00:09:41.860 ⇒ 00:09:48.219 Emily Giant: Alright, cool, I’ll close that one out, or I’ll assign the ticket to you, since it was, like, a piece, and then close it.
103 00:09:50.580 ⇒ 00:10:04.210 Uttam Kumaran: Okay, great. And then, so let’s say we… kind of, like, maybe I can… we can go around the horn in terms of priorities for this week. So one is I’m gonna push this optimization PR, and I’m gonna go kind of crank through a bunch of Looker stuff today.
104 00:10:06.210 ⇒ 00:10:20.090 Uttam Kumaran: And that’s, like, kind of the main stuff. Emily, in terms of feedback we got from Zach, so next week we’re gonna do a little bit of a deep dive into those two areas, like forecasting and shipping. So, I may…
105 00:10:20.960 ⇒ 00:10:25.019 Uttam Kumaran: Do a little bit of a canvas, and then grab time with you.
106 00:10:25.250 ⇒ 00:10:25.630 Emily Giant: Okay.
107 00:10:25.630 ⇒ 00:10:27.790 Uttam Kumaran: Like, here’s what we found, like.
108 00:10:28.140 ⇒ 00:10:30.270 Uttam Kumaran: What can we kind of do discovery on?
109 00:10:30.620 ⇒ 00:10:33.170 Uttam Kumaran: Who should we go talk to, etc.
110 00:10:36.990 ⇒ 00:10:46.679 Uttam Kumaran: And then, yeah, I mean, I think still core drivers for me, probably what I would like to hear this week, is about how we are getting things into Looker, and what help we can do for…
111 00:10:47.400 ⇒ 00:10:56.760 Uttam Kumaran: planning that, you know. Additionally, like, look, if it’s too tense to do these things during the week, and then we can do them on the weekend. But, yeah.
112 00:10:57.940 ⇒ 00:11:02.699 Emily Giant: I think that enough people are frightened of liquor at this point that, as long as we don’t
113 00:11:02.850 ⇒ 00:11:03.850 Emily Giant: break.
114 00:11:04.040 ⇒ 00:11:19.679 Emily Giant: the revenue tables, that we know Menakshi looks at, it’ll be okay to do it during the week. And we can also have, like… their staging’s actually really good. So, unlike DBT and some others, where, like, you don’t know what’s gonna happen when you deploy it, like, Looker’s pretty decent.
115 00:11:19.680 ⇒ 00:11:26.439 Emily Giant: I’m meeting with Oish later today. Let’s plan a time that works for you, just so we know that that’s, like, blocked off.
116 00:11:26.570 ⇒ 00:11:36.700 Emily Giant: But, yeah. Looker, and Finishing Revenue,
117 00:11:37.170 ⇒ 00:11:41.740 Emily Giant: figuring out where we’re at with North Beeman Marketing,
118 00:11:43.480 ⇒ 00:11:49.700 Emily Giant: But, yeah, I think we’re in an okay spot. I’ll feel a lot better once revenue is, like, signed, sealed, delivered.
119 00:11:50.000 ⇒ 00:11:53.149 Emily Giant: I think another big thing for Zach.
120 00:11:53.550 ⇒ 00:11:59.120 Emily Giant: And the team would be getting the subscription forecasting back up and running, because that’s actually, like.
121 00:11:59.450 ⇒ 00:12:08.880 Emily Giant: not just, like, AI-generated forecasting, we can use what we know is getting generated in the future to a degree, instead of just, like, building
122 00:12:09.420 ⇒ 00:12:16.439 Emily Giant: like, forecasting models that are only using hypothetical information.
123 00:12:17.030 ⇒ 00:12:24.939 Emily Giant: So that would be a big one for next week. But yeah, I think… that making Looker usable.
124 00:12:25.550 ⇒ 00:12:28.909 Emily Giant: Is number one priority after revenue.
125 00:12:29.380 ⇒ 00:12:29.890 Uttam Kumaran: Fair.
126 00:12:31.780 ⇒ 00:12:32.450 Uttam Kumaran: Okay.
127 00:12:33.340 ⇒ 00:12:38.749 Emily Giant: I do have a question about, so, I’m working on some ad hoc tickets since we’re, like.
128 00:12:39.050 ⇒ 00:12:41.609 Emily Giant: Getting to the end of revenue,
129 00:12:42.030 ⇒ 00:12:44.880 Emily Giant: One of the questions is,
130 00:12:45.480 ⇒ 00:12:49.710 Emily Giant: if I’m seeing, like, a column, that’s
131 00:12:49.980 ⇒ 00:13:01.450 Emily Giant: sometimes populated and sometimes not. For instance, there’s a… there’s a need… one of my tickets is, building further up in the order process, flags for uncommitted orders.
132 00:13:01.880 ⇒ 00:13:06.249 Emily Giant: And, I have samples of orders that look the way I need
133 00:13:06.870 ⇒ 00:13:14.300 Emily Giant: them to look to identify in the polyatomic tables, but, like, I cannot figure out… where…
134 00:13:14.670 ⇒ 00:13:29.149 Emily Giant: this information is supposed to be ingested into the Polytomic tables, or if it’s being, like, successfully put through on our end? Is that a question for my dev team, or for Polytomic? Like, quantity committed, for example, like.
135 00:13:29.450 ⇒ 00:13:32.500 Emily Giant: Sometimes it will have values.
136 00:13:32.580 ⇒ 00:13:41.330 Emily Giant: for the orders, and sometimes it won’t. But that seems crucial for me in this one transaction line table.
137 00:13:41.410 ⇒ 00:13:54.110 Emily Giant: to be able to flag whether or not an order was successfully committed from the moment of ordering, as opposed to what we evaluate now is… let me just show you real quick, I don’t know if I can…
138 00:13:54.110 ⇒ 00:13:57.740 Uttam Kumaran: I would probably verify as upstream as possible.
139 00:13:58.090 ⇒ 00:14:02.570 Uttam Kumaran: So, with… I would probably verify internally first, given, like, a transaction.
140 00:14:02.570 ⇒ 00:14:04.189 Emily Giant: Okay. And then have them…
141 00:14:04.580 ⇒ 00:14:10.949 Uttam Kumaran: basically say, are you guys getting this information? If they are, then it’s a… it’s probably a polyatomic thing.
142 00:14:11.180 ⇒ 00:14:15.589 Emily Giant: Okay. Yeah, it’s this, like, when it says not committed.
143 00:14:15.800 ⇒ 00:14:23.480 Emily Giant: And I know, like, there are different versions of this. This is what it’s supposed to look like once it’s in the facility.
144 00:14:23.840 ⇒ 00:14:27.359 Emily Giant: If it is delivered and not committed.
145 00:14:27.540 ⇒ 00:14:33.069 Emily Giant: I need to find that indication in the tables, and… where I thought it was.
146 00:14:33.390 ⇒ 00:14:35.960 Emily Giant: It seems like this is the max state where they’re…
147 00:14:36.100 ⇒ 00:14:44.870 Emily Giant: our values, but then there’s a ton of orders that don’t have values at all. So I, yeah, I’ll validate with, Alex.
148 00:14:45.310 ⇒ 00:14:46.320 Emily Giant: First.
149 00:14:46.590 ⇒ 00:14:52.640 Emily Giant: And then C… what’s up from there, but sometimes I don’t know, like.
150 00:14:52.760 ⇒ 00:14:58.319 Emily Giant: where to start with my line of questioning when it comes to polyatomic data, so… Okay.
151 00:14:58.480 ⇒ 00:15:02.349 Emily Giant: just making sure that I’m going to the right stakeholder.
152 00:15:02.550 ⇒ 00:15:05.190 Emily Giant: Or the right contributing party.
153 00:15:05.950 ⇒ 00:15:06.500 Uttam Kumaran: Okay.
154 00:15:08.350 ⇒ 00:15:09.100 Uttam Kumaran: Great.
155 00:15:10.470 ⇒ 00:15:11.050 Emily Giant: Okay.
156 00:15:12.520 ⇒ 00:15:13.920 Uttam Kumaran: Anything else this week?
157 00:15:14.600 ⇒ 00:15:19.390 Emily Giant: Let me think… okay, revenue, get that, like, firmly across the finish line.
158 00:15:20.960 ⇒ 00:15:23.290 Emily Giant: It’s Wednesday.
159 00:15:23.850 ⇒ 00:15:26.610 Uttam Kumaran: Yeah. I think a really solid…
160 00:15:26.720 ⇒ 00:15:34.250 Emily Giant: write up, or… Bullet point list of what is going on with Northbeam now.
161 00:15:34.540 ⇒ 00:15:37.040 Emily Giant: And what the…
162 00:15:37.300 ⇒ 00:15:44.170 Emily Giant: next steps are. I think we’re all just a little confused. Okay. Were you able to talk to them about the $500 a month situation?
163 00:15:44.170 ⇒ 00:15:46.109 Uttam Kumaran: Yeah, yeah, we… so that got cleared.
164 00:15:47.010 ⇒ 00:15:51.050 Uttam Kumaran: And then there, we’re, we’re late… They’re not charging us.
165 00:15:51.050 ⇒ 00:15:53.089 Emily Giant: Oh, dandy.
166 00:15:53.090 ⇒ 00:15:55.539 Uttam Kumaran: And then, Oasis now landing that.
167 00:15:55.730 ⇒ 00:15:57.040 Emily Giant: Into S3.
168 00:15:57.200 ⇒ 00:15:58.050 Uttam Kumaran: Right now.
169 00:15:58.050 ⇒ 00:16:00.329 Emily Giant: Sweet. So, we should be able to move it.
170 00:16:01.250 ⇒ 00:16:05.720 Uttam Kumaran: into… Redshift, as soon as that’s done.
171 00:16:06.080 ⇒ 00:16:08.139 Emily Giant: Sweet, okay, great.
172 00:16:08.370 ⇒ 00:16:11.579 Emily Giant: Yeah, those are my big things.
173 00:16:12.320 ⇒ 00:16:17.799 Emily Giant: The subscription forecasting, that was something that I mentioned in addition to the shipping stuff.
174 00:16:17.840 ⇒ 00:16:31.490 Emily Giant: that I might need support, making sure that it’s set up correctly in dbt, as opposed to just in Looker, because it looks like in the past it was heavily reliant on Looker, and that will make things go slow.
175 00:16:31.490 ⇒ 00:16:39.939 Emily Giant: So, I’ll write up a ticket with more details on that, so that there’s a better understanding with the group of, like, what I’m talking about, but I think we’re in…
176 00:16:40.400 ⇒ 00:16:48.700 Emily Giant: A good spot after revenue is thumbs up to start slashing and burning Looker big time.
177 00:16:48.930 ⇒ 00:16:49.540 Uttam Kumaran: Okay.
178 00:16:49.950 ⇒ 00:16:50.620 Uttam Kumaran: Okay.
179 00:16:50.810 ⇒ 00:16:54.490 Emily Giant: And then the retagging thing. Whatever Demolade mentioned about, like, the…
180 00:16:54.490 ⇒ 00:16:55.230 Uttam Kumaran: Yeah.
181 00:16:55.410 ⇒ 00:17:05.009 Emily Giant: specifying lineage instead of just excluding certain models, because that does get dicey. Like, I’ve tried to do that by adding, like, legacy tags, but then…
182 00:17:05.160 ⇒ 00:17:12.540 Emily Giant: It will skip one that is essential, and then won’t run the downstream stuff, so… Yeah.
183 00:17:12.980 ⇒ 00:17:13.569 Uttam Kumaran: Okay.
184 00:17:13.579 ⇒ 00:17:17.779 Emily Giant: If there’s a better way, that would be… Yeah, that’d be great.
185 00:17:19.569 ⇒ 00:17:20.319 Uttam Kumaran: Cool.
186 00:17:20.810 ⇒ 00:17:27.040 Uttam Kumaran: Okay, so yeah, big day today. I should be, like, cruising and stuff, so I’ll just send a note if we need help.
187 00:17:27.700 ⇒ 00:17:28.410 Emily Giant: Sweet.
188 00:17:28.810 ⇒ 00:17:31.779 Emily Giant: Okay. Oh wait, do you want to get a time on the calendar?
189 00:17:32.380 ⇒ 00:17:36.660 Awaish Kumar: No, I just added, my immediate calendar.
190 00:17:40.420 ⇒ 00:17:43.460 Emily Giant: Let me see… Yep.
191 00:17:44.330 ⇒ 00:17:48.320 Emily Giant: Wait, 12.15 to 1. As long as I’m open then, then we’re good.
192 00:17:49.090 ⇒ 00:17:52.080 Emily Giant: Yep, sounds great.
193 00:17:53.040 ⇒ 00:17:54.130 Emily Giant: It’s a yes.
194 00:17:54.980 ⇒ 00:17:56.539 Awaish Kumar: Perfect. Alright. Thank you.
195 00:17:56.540 ⇒ 00:17:57.659 Emily Giant: I’ll see you soon.
196 00:17:57.660 ⇒ 00:17:58.400 Uttam Kumaran: Thank you.
197 00:17:58.400 ⇒ 00:17:58.950 Emily Giant: Alright, bye.
198 00:17:58.950 ⇒ 00:17:59.680 Demilade Agboola: Thank you.