Meeting Title: [Javvy] Daily Standup Date: 2025-04-16 Meeting participants: Annie Yu, Caio Velasco, Aakash Tandel
WEBVTT
1 00:01:19.460 ⇒ 00:01:21.409 Annie Yu: Hello! Akash! Hello, Kyle!
2 00:01:22.860 ⇒ 00:01:23.619 Aakash Tandel: How are you all.
3 00:01:27.650 ⇒ 00:01:28.620 Caio Velasco: I don’t know how to.
4 00:01:29.010 ⇒ 00:01:29.760 Aakash Tandel: How are you? Good?
5 00:01:31.616 ⇒ 00:01:38.123 Aakash Tandel: Alright! I know oashes out of office today, so we will go ahead and get started.
6 00:01:39.070 ⇒ 00:01:51.873 Aakash Tandel: just to talk from a high level. Real quick. And Robert is still out on the the conferencing we have couple of things in progress. I know this is also in progress.
7 00:01:53.350 ⇒ 00:01:59.479 Aakash Tandel: we want to try to get all the things to the finish line this week.
8 00:02:00.850 ⇒ 00:02:08.773 Aakash Tandel: There is one new thing that Annie. I wanted you to work on ahead of the klaviyo stuff. So
9 00:02:09.470 ⇒ 00:02:15.654 Aakash Tandel: if you see this. So this is a request coming from Justin.
10 00:02:17.369 ⇒ 00:02:27.509 Aakash Tandel: You want to know if the customer addresses if we have the orders for fulfilled by merchant and to pull a list of those
11 00:02:27.820 ⇒ 00:02:32.019 Aakash Tandel: users, and their addresses, basically.
12 00:02:33.540 ⇒ 00:02:39.510 Aakash Tandel: So I’ve added a little bit of context here, but that he had a let me just get link to his
13 00:02:39.680 ⇒ 00:02:46.040 Aakash Tandel: question. And this is, gonna take precedent over.
14 00:02:47.480 ⇒ 00:02:49.899 Aakash Tandel: are you in this channel? Yes, you’re in the channel. Okay, cool.
15 00:02:53.950 ⇒ 00:02:56.669 Aakash Tandel: This can take precedence over the
16 00:02:57.540 ⇒ 00:02:59.740 Aakash Tandel: Clavia work. Does that make sense.
17 00:03:00.450 ⇒ 00:03:08.640 Annie Yu: Yeah, so the goal is to get those Amazon order. Id, who
18 00:03:09.500 ⇒ 00:03:12.460 Annie Yu: who match that Fbm and then
19 00:03:13.040 ⇒ 00:03:21.280 Annie Yu: find those people in the facultures is that? Or or we just need a list of those customer. Id.
20 00:03:21.578 ⇒ 00:03:23.969 Annie Yu: We just need a list of their addresses.
21 00:03:23.970 ⇒ 00:03:24.740 Annie Yu: Oh, okay.
22 00:03:25.043 ⇒ 00:03:35.660 Aakash Tandel: So customer. Id, I’m assuming will be kind of tied to to that, we’ll probably need both of those. But the main thing he’s asking for is the customer addresses for fulfilled by merchant.
23 00:03:36.400 ⇒ 00:03:39.320 Annie Yu: Okay? So that means that would.
24 00:03:39.700 ⇒ 00:03:47.410 Annie Yu: I’m looking at the promotion id column. So that would mean anything that start with Fba.
25 00:03:47.650 ⇒ 00:03:48.620 Annie Yu: Right?
26 00:03:49.746 ⇒ 00:03:51.620 Aakash Tandel: Fbm, so.
27 00:03:52.910 ⇒ 00:03:54.800 Annie Yu: Bam. Okay.
28 00:03:59.000 ⇒ 00:04:10.870 Aakash Tandel: So yeah, there’s there’s gonna be a little bit of investigation to see if we have that type of thing. I don’t know what’s it’s Gonna say, that would indicate. But it’s just the opposite of Fba.
29 00:04:11.360 ⇒ 00:04:16.369 Annie Yu: Okay, okay, Gotcha. So I’ll I’ll look into the promotion id.
30 00:04:16.579 ⇒ 00:04:16.969 Aakash Tandel: Sorry.
31 00:04:16.970 ⇒ 00:04:17.560 Annie Yu: Here.
32 00:04:18.420 ⇒ 00:04:18.970 Aakash Tandel: Awesome
33 00:04:19.551 ⇒ 00:04:26.980 Aakash Tandel: cool, so that’ll be that let me just go through your thing. Now, now that I set that
34 00:04:28.170 ⇒ 00:04:28.930 Aakash Tandel: okay.
35 00:04:31.240 ⇒ 00:04:33.650 Aakash Tandel: So these looked good to me. I’m gonna ship.
36 00:04:34.550 ⇒ 00:04:45.300 Aakash Tandel: I think I already shipped one of these. No, I’m gonna ship both these. And we say, Hey, these are ready for client feedback. It sounds like from your end. They are ready for client feedback, both north beam and attentive. Is that correct?
37 00:04:46.350 ⇒ 00:04:53.410 Annie Yu: Yes, and I’m just gonna double check. So I should remove those work in progress. Or did you.
38 00:04:53.840 ⇒ 00:05:09.630 Aakash Tandel: Yeah, let’s remove that in the title. Just just call it yep, and yeah, we can. I’ll ship the I’ll send a message over slack to send those over. And yeah, those will be good to go. I think we’ve shipped the 1st basic bits of those which is good.
39 00:05:09.760 ⇒ 00:05:10.539 Annie Yu: Okay. Awesome.
40 00:05:11.470 ⇒ 00:05:25.574 Aakash Tandel: Cool. I know Klaviyo is in progress. And there was a lot of we were kind of all 3 of us were talking in the Channel about that. I think the one thing I’m noticing is that this is not
41 00:05:25.990 ⇒ 00:05:43.110 Aakash Tandel: kind of formatted in the way that you’d readily be able to analyze the data. So what I did is I created a ticket for Kyle, and that one is right here. Kyle, it’s to pull all of the unique metric names. Oh, did you already do this?
42 00:05:44.340 ⇒ 00:05:45.406 Caio Velasco: Yes, no.
43 00:05:45.940 ⇒ 00:05:47.969 Aakash Tandel: Oh, awesome. Okay. Cool.
44 00:05:52.340 ⇒ 00:05:57.679 Caio Velasco: Which at the end of the day was this, basically selecting distinct from products.
45 00:05:58.153 ⇒ 00:06:01.790 Caio Velasco: put some more things just to just to get a
46 00:06:02.380 ⇒ 00:06:18.749 Caio Velasco: just to confirm that anything that came from the Api that got into the raw layer and snowflake, and then well, was brought into prod. That’s more or less like the flow. And at the end of the day whatever was brought. It’s also in prod, because it was just a select
47 00:06:19.638 ⇒ 00:06:24.269 Caio Velasco: so yeah, you have both sheets over there, but they must contain the same thing.
48 00:06:24.740 ⇒ 00:06:30.610 Aakash Tandel: Okay, cool. I’ll take a look at this. This is helpful. Basically, I’m trying to get
49 00:06:31.960 ⇒ 00:06:47.629 Aakash Tandel: so I can’t. I can’t tell, it doesn’t seem like this was so. I’m looking at this product list. It doesn’t seem like these are created by the system itself. It seems like someone created these is that does that match with what you’re seeing
50 00:06:48.990 ⇒ 00:06:50.790 Aakash Tandel: here? Let me share my whole screen.
51 00:06:51.800 ⇒ 00:07:18.925 Aakash Tandel: Okay? So I pulled up the thingy right? And I’m saying, hey? If a system created it, it would have been more consistent with things like view. Content is like, there’s no space in between subscription underscore queued like this is also inconsistent. There’s a there’s a little churn Buster at the front of this thing like this doesn’t look like something that was created by default by a system. It seems like someone created this. So that’s why I asked
52 00:07:19.780 ⇒ 00:07:26.060 Aakash Tandel: Aman and Vlad in the Channel, or in one of the slack channels if
53 00:07:26.320 ⇒ 00:07:31.029 Aakash Tandel: they have, like some sort of data dictionary, or whoever created these events had some sort of
54 00:07:34.350 ⇒ 00:07:38.510 Caio Velasco: If you go into my my answer, the comment
55 00:07:39.350 ⇒ 00:07:43.610 Caio Velasco: over there in the 1st number one
56 00:07:45.990 ⇒ 00:07:48.870 Caio Velasco: did that all the all- all at the bottom. Yeah.
57 00:07:53.230 ⇒ 00:07:54.420 Aakash Tandel: Oh, in your ticket.
58 00:07:54.978 ⇒ 00:08:13.470 Caio Velasco: Okay. So it was in the other. Okay, yes, the 1st one. So that’s why I started from the Api just to make sure we knew what was happening. So they, the Api, the metrics Api, have 2, has 2 things like you can either have the metrics that are default from cloud view, or you can create custom ones.
59 00:08:13.600 ⇒ 00:08:15.290 Caio Velasco: So maybe that’s what’s happened.
60 00:08:15.720 ⇒ 00:08:18.569 Aakash Tandel: Yeah, that’s what that’s what I would assume, too. So.
61 00:08:18.950 ⇒ 00:08:19.620 Caio Velasco: Okay.
62 00:08:19.940 ⇒ 00:08:22.485 Aakash Tandel: That makes sense. Okay, cool.
63 00:08:23.220 ⇒ 00:08:25.291 Aakash Tandel: we’re going down the right path with these.
64 00:08:25.770 ⇒ 00:08:31.070 Aakash Tandel: want to get more information from the client. I will kind of
65 00:08:31.630 ⇒ 00:08:37.149 Aakash Tandel: move this along. It’s slightly blocked right now. And it’s also not taking precedent. So that’s okay. I think for them.
66 00:08:37.159 ⇒ 00:08:37.624 Caio Velasco: Okay.
67 00:08:38.360 ⇒ 00:08:44.859 Annie Yu: Okay? And can I actually follow up on the email? Send is I. I saw that you
68 00:08:45.110 ⇒ 00:08:53.862 Annie Yu: duck into something there. Even though I can’t even use that information and database. So I’m not sure.
69 00:08:54.910 ⇒ 00:09:00.280 Annie Yu: is that is that like something? Also, we consider block or.
70 00:09:00.760 ⇒ 00:09:04.339 Aakash Tandel: Yeah, this is your question right here. When I was writing about the email sent.
71 00:09:05.520 ⇒ 00:09:09.600 Annie Yu: Yeah, yeah, yeah. Basically, I’m trying to see what
72 00:09:09.600 ⇒ 00:09:12.610 Annie Yu: flag email send using metric name is that it.
73 00:09:12.610 ⇒ 00:09:13.899 Aakash Tandel: Yeah. Exactly. Yep.
74 00:09:13.900 ⇒ 00:09:14.690 Annie Yu: Okay. Okay.
75 00:09:14.988 ⇒ 00:09:16.479 Aakash Tandel: I just don’t know what.
76 00:09:16.960 ⇒ 00:09:31.649 Aakash Tandel: because because they also are using the word subscription as a like product, because you can subscribe to the product. Basically, right? You can have a subscription for protein or concentrate, and it can get sent to your posted business on a recurring basis. So
77 00:09:31.810 ⇒ 00:09:48.540 Aakash Tandel: basically, the text filtering here is going to be a little tricky, but I’m sure they whoever set this up should have some sort of documentation, saying, Hey, this is how the business is treating XY. Or Z, and then we can go ahead and model it the same way in our dashboard. That’s kind of what I’m looking for from the client.
78 00:09:49.127 ⇒ 00:09:51.479 Annie Yu: okay, that makes sense. Thank you so much.
79 00:09:51.480 ⇒ 00:10:16.950 Aakash Tandel: Yep, okay, cool. Yeah. And then, yeah, I know you talked about segments. It just seems like the way that they’re creating a campaign is. 1st you create a segment, and then that’s tied to a campaign. So you can say, Hey, the segment, for this campaign is all users who are like in California, for example, that’s the segment and then the campaign goes out to those users based off of a specific time and stuff like that, so
80 00:10:17.010 ⇒ 00:10:25.539 Aakash Tandel: we can pull that off. But that’s not something that’s immediately important. So that’s good to flag. Thanks for flagging that in the loom. And then.
81 00:10:25.540 ⇒ 00:10:26.860 Annie Yu: Yeah, that makes sense.
82 00:10:27.050 ⇒ 00:10:39.309 Annie Yu: Okay? And and I think one more thing I mentioned was that I’m also not sure the relationship between campaign and message id, even though I’m not sure at this point, if Message Id is important at all.
83 00:10:39.620 ⇒ 00:10:40.380 Annie Yu: But.
84 00:10:41.300 ⇒ 00:11:02.170 Aakash Tandel: Yes, I don’t know yet. Let’s see what the client says. I’m the way this normally would work is like a campaign has either a set of messages or a single message. So you know, you basically would set a the Ca, if the campaigns just like for like one day. It’s like, Hey, by or like Valentine’s Day. Right?
85 00:11:02.820 ⇒ 00:11:11.769 Aakash Tandel: Take buy this, you know. Use this 20% off coupon for, and all your Valentine’s Day purchases only valid through February 14, th or whatever.
86 00:11:12.610 ⇒ 00:11:36.079 Aakash Tandel: Sometimes their campaigns are a little bit longer, you know. If they have like a win back strategy, they’re like, Hey, you’ve or you canceled. Your subscription like the 1st email, is like, Hey, we miss you, whatever blah blah! And then the next one’s like, Hey, if you come back, here’s a 10% off coupon. And the next one is like, Hey, you know, these are all new coffee flavors or whatever. So that type of thing we.
87 00:11:36.560 ⇒ 00:11:46.860 Aakash Tandel: that type of logic. I don’t know how important that is in the visualization of the data here, so we’ll kind of wait on the client to see how they want to move forward in that.
88 00:11:47.160 ⇒ 00:11:47.730 Annie Yu: Okay. Cool.
89 00:11:47.730 ⇒ 00:11:48.820 Aakash Tandel: The thing I like
90 00:11:49.660 ⇒ 00:12:17.999 Aakash Tandel: universally like. I don’t just want to recreate the tools. Metrics in our metrics like that’s not the value of this. The value in my mind is tying this to other pieces of data, primarily revenue and order data. So I want to get at. Hey, how does these things directly tied to revenue and orders, and just do that part first.st The other parts are nice. But again, if you want to see, like what messages are tied to what campaign? Just go into Klaviyo, and that that’ll show you that. So yeah.
91 00:12:18.000 ⇒ 00:12:20.640 Annie Yu: Yeah, okay, no. That makes sense.
92 00:12:21.480 ⇒ 00:12:37.490 Aakash Tandel: Cool. Okay? Yeah. So the main thing is this Fbm customer address data, and I link to the slack thread that you have access to. So hopefully, you can read that and figure out what they’re trying to ask. Yeah, okay, anything else. Any.
93 00:12:38.134 ⇒ 00:12:39.270 Annie Yu: I think that’s it.
94 00:12:39.500 ⇒ 00:12:45.220 Aakash Tandel: Awesome, Kyle, I saw you.
95 00:12:49.050 ⇒ 00:12:55.245 Aakash Tandel: Oh, yeah, you you did that. The metric name thing. So thanks for knocking that out early.
96 00:12:56.360 ⇒ 00:13:09.639 Aakash Tandel: The. I guess this is the one that I think you and Robert were talking about, and it sounds like Robert’s trying to get you, added to Amazon to do some investigations into
97 00:13:10.090 ⇒ 00:13:12.810 Aakash Tandel: kind of some of these assumptions. Is that correct?
98 00:13:13.830 ⇒ 00:13:17.376 Caio Velasco: Yeah. So what’s happened in this one? Is that
99 00:13:18.340 ⇒ 00:13:23.410 Caio Velasco: When I started with this well, this was done from any
100 00:13:23.510 ⇒ 00:13:30.010 Caio Velasco: months ago. So the the all the models are very big and and a bit complex, especially the factor, this one.
101 00:13:30.170 ⇒ 00:13:42.339 Caio Velasco: And then I understood that we were building a dim Products table which we we already have a 1st version. But then I started to to ask myself what kind of cogs
102 00:13:42.790 ⇒ 00:13:48.489 Caio Velasco: or Amazon cox they want me to bring into this them products, because
103 00:13:49.140 ⇒ 00:13:52.160 Caio Velasco: cogs are based on on the order
104 00:13:52.360 ⇒ 00:14:03.700 Caio Velasco: and we are the products is not an order level table. It’s it’s a skew, orderly table. And then, at the same time, we have this spreadsheet. That is exactly skew
105 00:14:04.400 ⇒ 00:14:08.059 Caio Velasco: data, and it has the product cost of a skew.
106 00:14:08.200 ⇒ 00:14:21.020 Caio Velasco: So then, at the end of the day, I understood that now we we just want to bring that spreadsheet into them products. That’s it. But before we had only shopify stuff on the, on the on that tab
107 00:14:21.873 ⇒ 00:14:50.439 Caio Velasco: and now I have brought the the Amazon ones. So I just basically copied and paste, and added this new column to identify what is shopify skews and what are Amazon skews? And that’s it. We are bringing that into the problem. So that’s that’s 1 thing I already talked to a wish, and and we have to understand like, now that there is a new column. If anything is gonna break when portable tries to, you know, bring new things, because I don’t know if it runs every day.
108 00:14:50.896 ⇒ 00:14:52.929 Caio Velasco: So this is one thing.
109 00:14:53.210 ⇒ 00:15:05.820 Caio Velasco: and and then, since. Now I also noticed that there are some skews from Amazon that are the same as the shopify one, but with different numbers, different things. Then we need to somehow
110 00:15:06.340 ⇒ 00:15:20.219 Caio Velasco: look at the all the models coming from this source this source, and make sure that whatever was looking into shopify skews is gonna be still looking at shopify schools and same for Amazon.
111 00:15:20.340 ⇒ 00:15:39.720 Caio Velasco: So from this we still have other things to do. And it’s not gonna be something like extremely quick, especially because I was not the one who started this. But at least now I think we have some kind of progress, and I have a more understanding of what has happened, and then the other side, we have the.
112 00:15:40.170 ⇒ 00:15:49.939 Caio Velasco: the, the assumptions, or that that equation that that we we know for for Amazon. But this is outside of dim products. So
113 00:15:50.310 ⇒ 00:16:00.560 Caio Velasco: it’s a new. It would be a new ticket at the end of the day. And now I see that you know they were in the same ticket, but they’re they was a bit confused. So this would be something else.
114 00:16:01.810 ⇒ 00:16:12.770 Aakash Tandel: Okay, can you? Write the general outline of that ticket, or just like the general idea of that ticket? And then I can kind of fill in the details. And you know.
115 00:16:13.290 ⇒ 00:16:13.810 Caio Velasco: Yes.
116 00:16:13.810 ⇒ 00:16:21.819 Aakash Tandel: Add some context there, because I think you have more understanding of kind of the basic level. I think I followed. But I’d need to like, look into it.
117 00:16:22.590 ⇒ 00:16:26.439 Caio Velasco: Perfect, perfect. I will do that. I will do that in this one. Here, in the last comment.
118 00:16:26.440 ⇒ 00:16:39.750 Aakash Tandel: Okay, awesome. Thanks. Yeah. The the real answer is like, this is not the way. This is not a scalable way that they’re doing their data. Big companies do not run off of
119 00:16:39.930 ⇒ 00:16:50.500 Aakash Tandel: like the way that they’re doing this. Typically, there’s I don’t know if you guys have worked with pims, product information management systems which are the skew level information. And then your merchant
120 00:16:50.600 ⇒ 00:16:58.760 Aakash Tandel: applications. So things like shopify and Amazon are separate, and then you would combine that data. So it’d be a lot cleaner, I think.
121 00:16:59.390 ⇒ 00:17:21.470 Aakash Tandel: annoying because they have, I understand, because it’s a small company. And they, you know, they’ve they’ve bootlegged it. But yeah, this is where this like is like a little weird, because they’re using a Amazon and shopify as both their pim and their merchant level information. So we do have to like decouple, those good, those ideas, if that makes sense.
122 00:17:23.180 ⇒ 00:17:25.049 Caio Velasco: Okay, okay, no, it does make sense.
123 00:17:25.480 ⇒ 00:17:31.609 Caio Velasco: But and yeah, so BA-, basically, that’s that’s it. For though for this one
124 00:17:32.058 ⇒ 00:17:34.850 Caio Velasco: and I already did the metric name one
125 00:17:35.387 ⇒ 00:17:40.940 Caio Velasco: I know, I think now we I have that that other one that you mentioned that was like lower priority.
126 00:17:42.000 ⇒ 00:17:48.230 Caio Velasco: I can also start with that. However, then I must say that the the 10 h we had
127 00:17:48.350 ⇒ 00:17:50.099 Caio Velasco: it’s kind of like gone.
128 00:17:51.010 ⇒ 00:17:51.470 Aakash Tandel: Yeah.
129 00:17:51.780 ⇒ 00:18:00.469 Caio Velasco: Because I spent quite a lot of time the last 3 days, and I can still do it, depending on how you wanna pursue this, and I’m totally open. I have time on my end.
130 00:18:01.355 ⇒ 00:18:01.840 Caio Velasco: Yeah.
131 00:18:01.840 ⇒ 00:18:29.070 Aakash Tandel: So the thing with it is like the contracts ending at the end of this week. So and we don’t have next week. Because, Robert and I’m on our meeting to kind of talk about the future work. So I would say, focus on this and see, as far as you can get on this guy, and then don’t worry about. This is a nice to have, if you if you like. If for some reason this is very quick. And you finish this like, you know, whatever. And you have like 2 h left over.
132 00:18:29.770 ⇒ 00:18:36.879 Aakash Tandel: This is the priority. And then we’re basically pausing after this week for all of us.
133 00:18:36.880 ⇒ 00:18:43.110 Caio Velasco: Okay, perfect. And then question about this is, are we continue with Javi, or, or is it.
134 00:18:43.110 ⇒ 00:18:49.200 Aakash Tandel: I I have a feeling we will but next week will be a break week for Javi. Yeah.
135 00:18:49.740 ⇒ 00:18:51.150 Caio Velasco: Okay. Cool. Perfect.
136 00:18:51.280 ⇒ 00:18:56.829 Aakash Tandel: Yup, and then, yeah, we’ll definitely keep you guys up to date with like,
137 00:18:57.550 ⇒ 00:19:02.032 Aakash Tandel: contracts and stuff like that for future work on this client.
138 00:19:03.250 ⇒ 00:19:21.020 Aakash Tandel: I Robert is out today as well. But the idea is that once we have information we’ll definitely share it with the team. Right now. Aman, and Robert are kind of working on like scope of work, and like what it would look like. Cost wise and stuff like that. So that’s all kind of up in negotiations at the moment.
139 00:19:21.680 ⇒ 00:19:23.739 Caio Velasco: Okay. Cool. Thank you.
140 00:19:24.110 ⇒ 00:19:40.769 Aakash Tandel: Cool. Alright. Let me know if you guys have any questions feel free to slack me. I can help try to push on things. I’m getting more of an understanding of some of the pieces that Robert has had under his belt the whole time. So yeah, hopefully, I can help out a little bit more.
141 00:19:40.770 ⇒ 00:19:44.820 Annie Yu: Okay? And oh, sorry. One more thing about Kyle’s ticket, I think.
142 00:19:46.335 ⇒ 00:19:50.500 Annie Yu: That column days to order. I don’t think we.
143 00:19:51.000 ⇒ 00:19:56.240 Annie Yu: I don’t think adding, that will help anymore. So.
144 00:19:56.240 ⇒ 00:19:57.000 Aakash Tandel: Oh, okay.
145 00:19:58.656 ⇒ 00:19:59.210 Caio Velasco: Which one.
146 00:19:59.210 ⇒ 00:20:01.260 Annie Yu: Just wanna bring that up.
147 00:20:01.640 ⇒ 00:20:03.560 Aakash Tandel: The tentative ticket.
148 00:20:04.740 ⇒ 00:20:06.810 Aakash Tandel: The days to order one.
149 00:20:06.940 ⇒ 00:20:08.350 Caio Velasco: Okay. Thank you.
150 00:20:08.350 ⇒ 00:20:09.949 Aakash Tandel: Do you want me to pull that off, Annie?
151 00:20:10.903 ⇒ 00:20:12.109 Annie Yu: Yeah. Sure.
152 00:20:12.110 ⇒ 00:20:22.680 Aakash Tandel: Okay, I will put it in the backlog. I’ll put it in requirements started, and then I’ll put it in would need. I’ll add a comment.
153 00:20:22.890 ⇒ 00:20:28.659 Aakash Tandel: We need to refine ticket to move forward.
154 00:20:29.960 ⇒ 00:20:43.090 Annie Yu: Yeah, cause I realized to have those metrics. I have to do custom expression, anyway, in the Meta base. So I don’t think having that will give us the ability to that. People filter
155 00:20:43.330 ⇒ 00:20:46.420 Annie Yu: the the days window. They want.
156 00:20:47.010 ⇒ 00:20:47.660 Aakash Tandel: Okay.
157 00:20:48.511 ⇒ 00:20:50.220 Aakash Tandel: That makes sense. Okay, cool.
158 00:20:50.975 ⇒ 00:20:51.590 Aakash Tandel: Alright.
159 00:20:52.000 ⇒ 00:20:58.540 Aakash Tandel: Let me know. If you guys need anything, I will ship out those 2 dashboards, Annie, that you finished up for North Beam, and attentive.
160 00:20:58.540 ⇒ 00:21:01.779 Annie Yu: Yeah. The all done the the title, too.
161 00:21:02.050 ⇒ 00:21:03.680 Aakash Tandel: Sweet. Thank you. Okay.
162 00:21:03.990 ⇒ 00:21:05.370 Aakash Tandel: Alright. Y’all have a good day.
163 00:21:05.370 ⇒ 00:21:05.860 Annie Yu: Yeah.
164 00:21:05.860 ⇒ 00:21:06.179 Aakash Tandel: Talk to you later.
165 00:21:06.180 ⇒ 00:21:06.720 Annie Yu: Bye, bye.
166 00:21:06.720 ⇒ 00:21:07.260 Caio Velasco: Good day.