Meeting Title: PP2G | Weekly Kickoff_2025_03_17 Date: 2025-03-17 Meeting participants: Aakash Tandel, Luke Daque, Amber Lin, Yoon, Uttam, Caio Velasco
WEBVTT
1 00:00:16.070 ⇒ 00:00:17.630 Amber Lin: Good morning.
2 00:00:21.430 ⇒ 00:00:23.083 Amber Lin: Hi! Akaj!
3 00:00:25.000 ⇒ 00:00:32.890 Amber Lin: Hello! We have. We’re waiting for Pius, I believe. Let me check if he accepted or declined this meeting
4 00:00:35.700 ⇒ 00:00:36.669 Uttam: Yes, good morning.
5 00:00:37.480 ⇒ 00:00:37.930 Amber Lin: Good morning!
6 00:00:37.930 ⇒ 00:00:38.610 Luke Daque: Cleverly.
7 00:00:42.210 ⇒ 00:00:42.800 Amber Lin: Hmm.
8 00:00:43.170 ⇒ 00:00:44.200 Luke Daque: How’s everyone?
9 00:00:45.910 ⇒ 00:00:47.210 Amber Lin: How is your weekend
10 00:00:47.450 ⇒ 00:00:48.410 Uttam: Good.
11 00:00:50.320 ⇒ 00:00:51.799 Uttam: How about you, Ryan? How was it?
12 00:00:53.424 ⇒ 00:00:57.900 Luke Daque: Pretty good as well. Yesterday we went swimming with my kids.
13 00:00:59.010 ⇒ 00:01:02.370 Luke Daque: And they they wanted to go swimming. So yeah, we went to.
14 00:01:02.960 ⇒ 00:01:09.890 Luke Daque: Yeah, basically just stayed in a place with a swimming pool like half of the day, and just ate out
15 00:01:10.080 ⇒ 00:01:10.690 Caio Velasco: Okay.
16 00:01:10.900 ⇒ 00:01:12.549 Luke Daque: Yeah, cool. Pretty cool.
17 00:01:15.620 ⇒ 00:01:21.910 Amber Lin: Yeah, this weekend, my partner, because she’s a she’s a professional swimmel college
18 00:01:22.060 ⇒ 00:01:35.380 Amber Lin: athlete swimmer. So she was pretty good, and she was teaching me how to, and I realized how shitty I swam because I did not move forward in the water. And she’s like, Oh, do this! And then I just, and I just went forward. It was crazy
19 00:01:35.990 ⇒ 00:01:38.500 Uttam: Oh, I miss swimming! I love swimming
20 00:01:39.730 ⇒ 00:01:42.720 Luke Daque: Yeah, I was a swimmer before as well.
21 00:01:42.940 ⇒ 00:01:43.440 Amber Lin: Oh, wow!
22 00:01:43.440 ⇒ 00:01:44.800 Uttam: No way. Okay.
23 00:01:45.470 ⇒ 00:01:46.070 Luke Daque: What is it?
24 00:01:46.070 ⇒ 00:01:52.730 Luke Daque: I I stopped basically after I graduated. But yeah, I was an athlete before. Sooner
25 00:01:52.730 ⇒ 00:01:58.610 Amber Lin: Wow! Wait! What? Kinda what did you specialize in? My partner does fly mostly
26 00:01:59.120 ⇒ 00:02:02.747 Luke Daque: I was like, mostly all around, like, yeah, everything.
27 00:02:03.450 ⇒ 00:02:04.480 Luke Daque: But yeah.
28 00:02:05.470 ⇒ 00:02:06.450 Amber Lin: Cool.
29 00:02:07.040 ⇒ 00:02:08.350 Amber Lin: Hi Kyle.
30 00:02:09.820 ⇒ 00:02:10.620 Caio Velasco: Hello!
31 00:02:10.620 ⇒ 00:02:11.760 Amber Lin: Pronounce your name.
32 00:02:12.040 ⇒ 00:02:13.680 Caio Velasco: Yes, Kyle, perfect.
33 00:02:14.090 ⇒ 00:02:14.920 Amber Lin: Great
34 00:02:16.064 ⇒ 00:02:31.550 Amber Lin: oh, I also did a photo shoot this weekend. So I got paid again for another photo shoot. I got to know this person at a random business brunch, and they were like, Oh, I have this farmers market that I need photos of. So
35 00:02:31.750 ⇒ 00:02:33.330 Amber Lin: that was very exciting.
36 00:02:35.320 ⇒ 00:02:36.469 Luke Daque: That’s cool
37 00:02:36.930 ⇒ 00:02:44.349 Amber Lin: Hmm, let me share with you guys a figma jam that we can just all look at
38 00:02:45.840 ⇒ 00:02:47.559 Amber Lin: put that in our chat
39 00:03:00.770 ⇒ 00:03:02.920 Luke Daque: Can you share access? I don’t think I
40 00:03:02.920 ⇒ 00:03:08.840 Amber Lin: Yes, I let me let you guys edit edit.
41 00:03:09.040 ⇒ 00:03:09.920 Amber Lin: Yes.
42 00:03:13.270 ⇒ 00:03:15.960 Amber Lin: and edit. Great
43 00:03:24.620 ⇒ 00:03:26.269 Amber Lin: is everybody on there?
44 00:03:27.060 ⇒ 00:03:27.970 Amber Lin: Great
45 00:03:28.761 ⇒ 00:03:41.359 Amber Lin: last, right now we’re in mid March 17 to 21.st I think I check with everyone. If you guys are, gonna be out of office this week, I don’t think anyone is right.
46 00:03:44.000 ⇒ 00:03:49.930 Amber Lin: If you are, just put it on the calendar just like, put a little block or put a stamp on there so that I know.
47 00:03:59.070 ⇒ 00:04:19.030 Amber Lin: Let’s move on to the action items from last week last week we talked about a few things we want to do forward for this client. I know we’re still working on a few things. How is that going? How’s the I know there’s the skew, and then there’s the warranty.
48 00:04:20.040 ⇒ 00:04:22.930 Amber Lin: How are those 2 items going?
49 00:04:28.130 ⇒ 00:04:36.330 Amber Lin: Oh, I think that’s for pious, and he is not here. Bo, do you know anything about how how those 2
50 00:04:36.650 ⇒ 00:04:38.379 Amber Lin: projects are going
51 00:04:39.296 ⇒ 00:04:49.820 Yoon: Yeah, I mean the warranty. I have no idea I was working with pious. I haven’t heard from him
52 00:04:50.740 ⇒ 00:04:52.309 Yoon: back yet.
53 00:04:55.080 ⇒ 00:05:03.050 Amber Lin: Okay, sounds good. I will check in with him about that. And I know this week we wanted to do some forecasting.
54 00:05:03.230 ⇒ 00:05:12.130 Amber Lin: especially for this client. They wanted something flashy, and I wanted to talk a little bit more about what you guys think should go into that?
55 00:05:12.260 ⇒ 00:05:15.600 Amber Lin: How’s that? Gonna go? etc?
56 00:05:17.640 ⇒ 00:05:23.440 Yoon: So yeah, I’ll have to talk with pious about that. But there’s
57 00:05:23.540 ⇒ 00:05:26.679 Yoon: basically gonna be 2 ways of forecasting demand
58 00:05:26.910 ⇒ 00:05:29.609 Yoon: which 1st is going to be
59 00:05:29.840 ⇒ 00:05:36.490 Yoon: on a perspective of full parse to go itself. And another way.
60 00:05:37.300 ⇒ 00:05:43.189 Yoon: Another forecasting way will be on the Asia Connection
61 00:05:43.340 ⇒ 00:05:47.490 Yoon: perspective, which is going to be bye
62 00:05:47.770 ⇒ 00:05:59.270 Yoon: getting forecasting the demand, forecasting the the demand for all of the vendors, so getting the
63 00:06:02.540 ⇒ 00:06:06.040 Yoon: ordering the the parts from China itself.
64 00:06:06.450 ⇒ 00:06:07.070 Amber Lin: Okay.
65 00:06:07.070 ⇒ 00:06:09.580 Yoon: Because Asia connection is the company where
66 00:06:09.970 ⇒ 00:06:12.680 Yoon: is importing all, the all the parts
67 00:06:13.140 ⇒ 00:06:13.740 Amber Lin: Okay.
68 00:06:13.740 ⇒ 00:06:15.359 Yoon: And Paul Corsico is
69 00:06:15.360 ⇒ 00:06:18.120 Amber Lin: Oh, they’re selling it
70 00:06:20.190 ⇒ 00:06:30.550 Amber Lin: cool. Let’s see, cause I do want to get some action on this week, either on the skews which I need bias to talk about that.
71 00:06:30.700 ⇒ 00:06:31.770 Amber Lin: And
72 00:06:31.840 ⇒ 00:07:01.710 Amber Lin: last week, when we talked about this, we’re talking, we’re saying, oh, this the forecasting wouldn’t take too long to have a basic model going. Do you think you would be able to have some progress on this one this week, or maybe just for Monday and Tuesday? Go on a spike and research what you need. And maybe the time commitments, the different tools that we need. Would that be a good thing to do? Say, maybe today or tomorrow.
73 00:07:01.950 ⇒ 00:07:08.190 Uttam: Yeah, I just wanna double down on that as well. If we’re able to just have something to show
74 00:07:08.539 ⇒ 00:07:16.010 Uttam: cause I can get a meeting booked, but I kind of want to try to put as much in front of them as possible to sell the next set of
75 00:07:16.512 ⇒ 00:07:32.220 Uttam: priorities that we’re gonna propose. One of which is this forecasting? I know that’s on the top of the list. If we can have anything to show for that in that meeting, in addition to sort of putting together what we propose, we’re gonna work on. That would be amazing.
76 00:07:32.730 ⇒ 00:07:33.720 Amber Lin: Yeah.
77 00:07:34.760 ⇒ 00:07:39.670 Amber Lin: So I think this week the 2 main things this is already.
78 00:07:40.780 ⇒ 00:07:46.090 Amber Lin: This is already to start.
79 00:07:46.420 ⇒ 00:07:48.329 Amber Lin: So we need to
80 00:07:48.760 ⇒ 00:07:57.499 Amber Lin: start. So why don’t go backwards? When we present to the client, I think we should present some sort of forecasting ability.
81 00:07:57.890 ⇒ 00:08:02.140 Amber Lin: maybe in a dashboard, if that’s possible. Something visual would be nice.
82 00:08:02.550 ⇒ 00:08:11.429 Amber Lin: and I will pitch the weekly monthly business reviews. I will do this
83 00:08:11.950 ⇒ 00:08:16.739 Amber Lin: and anything else that comes to mind that we should also present
84 00:08:18.720 ⇒ 00:08:31.150 Aakash Tandel: Can you give a quick, high level overview, for? I don’t know if Kyle and Luke are as familiar with the forecasting stuff. But I’m definitely not. So. Can you give a high level overview of what we’re what that looks like or what we’re trying to do? There
85 00:08:31.870 ⇒ 00:08:39.069 Amber Lin: Sure. So please add on to this from my understanding, the forecasting is for them to
86 00:08:39.789 ⇒ 00:08:49.979 Amber Lin: no sales or profits down the line based on their previous transactions. And the reason why they want this is that
87 00:08:50.130 ⇒ 00:09:05.059 Amber Lin: they’re going through M. And A and the CEO, which is our main point of contact needs something more flashy for his credibility and for his show. So we need to support him on that mission. And that’s why we’re gonna do some forecasting for him.
88 00:09:05.440 ⇒ 00:09:14.559 Aakash Tandel: And is that forecasting? What does the deliverable look like? Is that a like a model they have? Or is it like going to be integrated into a dashboard that’s existing
89 00:09:14.700 ⇒ 00:09:21.529 Amber Lin: Yeah, that’s what I wanted us to investigate. So that’s what we’re talking about right now. And
90 00:09:21.830 ⇒ 00:09:30.629 Amber Lin: I think it should be something visual. But I am not the expert, and not the one doing it. So, please. But please tell me what is plausible
91 00:09:31.090 ⇒ 00:09:42.169 Yoon: So the output of the model. It will be a a forecasting model where it outputs the the next end months, for example, of the of the demand.
92 00:09:42.310 ⇒ 00:09:44.870 Yoon: or whichever wide we’re predicting.
93 00:09:45.440 ⇒ 00:09:50.059 Yoon: And yeah, we can also make a graph. Or
94 00:09:50.280 ⇒ 00:09:55.200 Yoon: we we have the actuals and the the predictive predicted.
95 00:09:55.340 ⇒ 00:09:58.560 Yoon: forecasting a dotted line something like that
96 00:09:59.120 ⇒ 00:10:06.079 Amber Lin: Would we be able to break it down further of forecasting each section? Would that be something plausible
97 00:10:06.530 ⇒ 00:10:07.879 Yoon: Each, section.
98 00:10:08.310 ⇒ 00:10:10.132 Amber Lin: Yeah. Say, maybe
99 00:10:11.510 ⇒ 00:10:23.299 Amber Lin: if we’re forecasting, say, sales, maybe sales for different type of products. Or if we’re forecasting costs that may be different types of costs. And how that’s gonna go, because
100 00:10:23.300 ⇒ 00:10:23.890 Yoon: Yeah.
101 00:10:23.890 ⇒ 00:10:28.619 Amber Lin: If we’re training the model, these are all parameters that we can train
102 00:10:29.810 ⇒ 00:10:35.750 Yoon: So what we what Pius and I were trying to do was
103 00:10:36.170 ⇒ 00:10:41.830 Yoon: forecasting the demand of the top like 2 or 3 products
104 00:10:42.170 ⇒ 00:10:43.770 Amber Lin: Hmm, okay.
105 00:10:43.770 ⇒ 00:10:50.079 Yoon: Yeah, because all of them will have different like demand, history, data
106 00:10:50.080 ⇒ 00:10:50.730 Amber Lin: Okay.
107 00:10:51.850 ⇒ 00:10:59.190 Yoon: And each one of them will eventually. Ha! I mean, unless we aggregate everything together.
108 00:11:00.410 ⇒ 00:11:02.699 Yoon: you’ll have to be at a product level.
109 00:11:03.240 ⇒ 00:11:04.319 Yoon: Yeah, for
110 00:11:04.320 ⇒ 00:11:13.239 Amber Lin: It sounds good. I think that’s good enough for this week to show. I just want us to eventually arrive at something visual.
111 00:11:13.390 ⇒ 00:11:14.560 Amber Lin: So
112 00:11:15.590 ⇒ 00:11:31.999 Amber Lin: this week. I think we can start Monday and Tuesday to look at that and get a feel of what that is, and maybe by Thursday have somewhat of a model and some sort of graph we can show to him. Do you think that’s a reasonable timeline
113 00:11:32.500 ⇒ 00:11:39.359 Yoon: Yeah, I can start looking at the data today. Let me see if I can chat with pious
114 00:11:39.750 ⇒ 00:11:40.370 Amber Lin: Okay.
115 00:11:40.370 ⇒ 00:11:45.760 Yoon: Yeah, it’ll be great if I could just get a like an approval from him.
116 00:11:45.760 ⇒ 00:12:02.560 Uttam: So whatever questions you have, if you don’t mind just putting them in the Channel. I think if you have a chance to call pies. That’s fine, too. But I just want everyone on this call to be able to see the answers like, where’s orders? What are the tables? Things like that. I’m I’m probably the best equipped to answer some of that.
117 00:12:02.700 ⇒ 00:12:10.319 Uttam: So I will respond with with answers. But I definitely want to just try to watch Convo in that channel, so everyone can see
118 00:12:10.910 ⇒ 00:12:12.330 Yoon: Yeah, yeah, sure.
119 00:12:17.650 ⇒ 00:12:18.340 Amber Lin: Right
120 00:12:22.040 ⇒ 00:12:40.660 Amber Lin: right, Luke and Kyle, is there anything on your end? I know that the backlog where we talk about it? It was not that great? Do you think you have capacity to work on this this week? Or is the stack list more important? Because I know the back end stuff don’t doesn’t really show for him.
121 00:12:41.790 ⇒ 00:12:49.482 Luke Daque: Yeah, I should have bandwidth for this week for any kind of data modeling or something like, if you need data models for that
122 00:12:51.170 ⇒ 00:12:56.340 Luke Daque: for like what? What we need for the forecasting? Then maybe we can. We can do that
123 00:12:56.600 ⇒ 00:13:02.370 Amber Lin: Oh, oh, okay, I assume you also have experience working on that together. Right?
124 00:13:04.486 ⇒ 00:13:09.200 Luke Daque: Well, not necessarily in terms of like machine learning. But if we need some
125 00:13:09.840 ⇒ 00:13:11.370 Uttam: Some stuff.
126 00:13:11.370 ⇒ 00:13:17.599 Uttam: Still, Luke and I, Luke and I were the 2 primary engineers on this client.
127 00:13:18.170 ⇒ 00:13:24.290 Uttam: so I think probably any knowledge I have he has as well in terms of the technical modeling. So
128 00:13:24.980 ⇒ 00:13:30.190 Uttam: yeah, and then, I think you know, 1 1 piece of comes to mind, and this is going to be part of like
129 00:13:30.460 ⇒ 00:13:40.429 Uttam: how we sell this, because ultimately the client isn’t. Gonna see, this is, they may not see. This as a priority is is documentation. And I think this is where
130 00:13:40.920 ⇒ 00:13:48.769 Uttam: you know, even across products. I guess I want to understand, like, what is a what is Doc? Great documentation look like for
131 00:13:49.260 ⇒ 00:13:54.850 Uttam: our internal team? And how can we make that a priority for for this client as well
132 00:13:55.010 ⇒ 00:13:55.480 Amber Lin: T
133 00:13:55.480 ⇒ 00:14:00.430 Uttam: I think, to date. We have not really arrived on like what
134 00:14:00.870 ⇒ 00:14:14.670 Uttam: what the end product is. If we were to say cool, we, we want to check a box like we have good documentation. I think we need to work with the data team to understand like what that is. But I feel like for for this client that could definitely be an item on the
135 00:14:14.840 ⇒ 00:14:19.740 Uttam: analytics engineering backlog in addition to the other priorities
136 00:14:20.030 ⇒ 00:14:48.630 Amber Lin: Yeah, thank you for mentioning that, because I was thinking, especially when they’re going through M. And A. I when I wrote on the document for the next phase. I was thinking they need something to standardize their data and everything. And they’re not very organized right now. And that’s going to be a huge problem after they go through M and A, so that could be a selling point of telling them. Hey, you need to organize now and get prepared for the M. And A, so maybe that could be a potential selling point
137 00:14:52.000 ⇒ 00:14:57.780 Uttam: Yeah, I think the big things there are just breaking down like, what are the issues for? If we were to do that, yeah, go ahead. Kai.
138 00:14:58.980 ⇒ 00:15:02.489 Caio Velasco: I know. I just wanna mention that. I mean, I’m not sure exactly
139 00:15:03.460 ⇒ 00:15:09.890 Caio Velasco: what they’re gonna do with whatever we present them now. But if they’re gonna go through an M and a process.
140 00:15:10.230 ⇒ 00:15:15.310 Caio Velasco: and they’re expecting us to deliver like the prediction they used to actually
141 00:15:15.470 ⇒ 00:15:23.729 Caio Velasco: put the price in the company, which is like what happens in a process. Then it is a big thing. So we just have to make sure, like, what are their expectations.
142 00:15:24.250 ⇒ 00:15:25.579 Caio Velasco: If that makes sense
143 00:15:29.710 ⇒ 00:15:30.429 Amber Lin: Oh no!
144 00:15:30.940 ⇒ 00:15:31.980 Aakash Tandel: Yeah, it makes a lot of sense
145 00:15:33.234 ⇒ 00:15:39.800 Amber Lin: Totally. Also forecast machine
146 00:15:40.140 ⇒ 00:15:55.850 Aakash Tandel: I think, Bo, if you and pies come up with like a forecasting model and start to deploy, I think it’d be good to bring to stand up or like, run it by the rest of the team. Just so we have visibility into kind of what’s happening. And also, maybe if Kyle or Luke have.
147 00:15:56.330 ⇒ 00:16:00.309 Aakash Tandel: you know, recommendations or feedback on it, it would be good to have.
148 00:16:00.410 ⇒ 00:16:11.819 Aakash Tandel: Because, yeah, as Kyle mentioned, if if they’re, you know, reliably trying to use this as A as a piece of data for the M and a process. We want this to be as accurate and as polished as possible.
149 00:16:13.170 ⇒ 00:16:26.189 Amber Lin: Totally, and that is a top priority for us. I’ll talk to Pius about how this queue is going and how much time he’s gonna put into that versus the versus the forecasting. I think we know what we need to do
150 00:16:26.360 ⇒ 00:16:27.700 Amber Lin: for this week.
151 00:16:28.317 ⇒ 00:16:48.879 Amber Lin: We do have a short catch of around 10 min or so. For tomorrow. And hopefully, we can have a plan. And we can have an idea of what the forecasting model looks like, and for Bo and Pius, and for Luke and Kyle.
152 00:16:48.880 ⇒ 00:17:05.280 Amber Lin: you guys help with that, and also maybe look at the deep backlog of understanding the issues with documentation and just having an idea of maybe how we’re going to progress with that. So I think today was today, Monday would be a lot about understanding what we need to do
153 00:17:07.140 ⇒ 00:17:11.530 Uttam: Yeah. And then one item is, I can. I can go ahead and try to tee up a meeting for Thursday
154 00:17:12.240 ⇒ 00:17:13.319 Uttam: Let me go ahead and do that
155 00:17:13.329 ⇒ 00:17:14.229 Amber Lin: Okay. Yeah.
156 00:17:14.230 ⇒ 00:17:16.390 Uttam: And like, let’s aim for that as a target.
157 00:17:16.560 ⇒ 00:17:26.319 Uttam: I think another item, and maybe I can take this to work on with the with the analytics engineering team is what does good documentation look like for a client?
158 00:17:26.690 ⇒ 00:17:30.400 Uttam: We had our like spreadsheet. We have like notion.
159 00:17:31.940 ⇒ 00:17:36.729 Uttam: So I think we can maybe agree on that. And that way we can break that up into tickets pretty easily.
160 00:17:37.480 ⇒ 00:17:43.579 Uttam: Whether it’s about or business domain, whether it’s some format. I think we can work on that
161 00:17:45.230 ⇒ 00:17:45.980 Uttam: cool
162 00:17:47.160 ⇒ 00:17:48.340 Amber Lin: Fantastic.
163 00:17:48.620 ⇒ 00:17:51.630 Amber Lin: Let me organize this
164 00:17:54.320 ⇒ 00:18:02.089 Aakash Tandel: Is Pius, gonna be the lone person on the skew work. I’m not sure what that looks like, so I’m not sure if he’s if that’s totally feasible.
165 00:18:02.530 ⇒ 00:18:11.070 Amber Lin: I’m not very sure, and I’m not very sure how much the big concern was that we don’t know how much the client cares about that one, and
166 00:18:11.400 ⇒ 00:18:15.680 Amber Lin: I think he has some progress on it, but it’s kind of stuck on the client’s end.
167 00:18:15.820 ⇒ 00:18:21.510 Amber Lin: So I would need to talk to him to understand this a little bit more
168 00:18:22.140 ⇒ 00:18:35.660 Aakash Tandel: Cool sounds good. Yeah. I just wanted to make sure that. You know, if he is working on forecasting, maybe we can offload the skew work to someone else. But yeah, that’s fine. Just as long as he’s not too burdened by that. It sounds good
169 00:18:36.130 ⇒ 00:18:50.609 Amber Lin: Yeah, okay, I think I’ll ask him to prioritize the forecasting, because that’s what the client wants, and is more important for our next meeting with him and Utam. Thursday sounds good. Thursday or Friday is good, and I think we’ll have something by then.
170 00:18:52.810 ⇒ 00:18:53.500 Uttam: Okay.
171 00:18:53.910 ⇒ 00:18:59.230 Amber Lin: Okay, sounds good. Anything else that you guys can think of for this client
172 00:19:01.520 ⇒ 00:19:14.080 Uttam: I think one item just for folks who haven’t seen it. You can just take a look at the notion, Doc, that Amber put together. It’s in slack, basically has an entire overview of what we’re currently working on and what we’re gonna propose to them.
173 00:19:15.480 ⇒ 00:19:22.349 Uttam: so that would be really helpful to go through and add any comments that that’ll that’ll basically be the base of what we’ll try to present on Thursday
174 00:19:25.090 ⇒ 00:19:27.050 Caio Velasco: The last one. Honestly, right?
175 00:19:27.050 ⇒ 00:19:28.910 Caio Velasco: Yes, that’s okay.
176 00:19:29.840 ⇒ 00:19:40.870 Amber Lin: And and I, later, after I run through all the other kickoffs, I will put in our tasks in notion, so that you guys can. Sorry, not in notion in linear. So we can look at it.
177 00:19:42.460 ⇒ 00:19:42.980 Amber Lin: and then
178 00:19:42.980 ⇒ 00:19:45.188 Aakash Tandel: Another thing on linear if
179 00:19:46.154 ⇒ 00:20:02.960 Aakash Tandel: the engineers like, if you guys feel like there’s a task that’s not currently documented in there, just create the issue with within the actual project team. So we’re pull bars to go and then assign it to Amber, and then Amber can figure out where it goes in terms like projects and stuff like that as well. So feel free to do that
180 00:20:03.240 ⇒ 00:20:09.790 Amber Lin: Yeah, just brain dump. If there’s duplicates doesn’t matter. I I will look over it. I’m meeting with Utam to
181 00:20:10.340 ⇒ 00:20:13.579 Amber Lin: room the backlog. So just dump everything you can think of.
182 00:20:15.240 ⇒ 00:20:16.160 Amber Lin: Okay.
183 00:20:16.470 ⇒ 00:20:21.659 Luke Daque: So maybe the current current tasks in notion we can transfer to linear as well. Right
184 00:20:21.660 ⇒ 00:20:27.119 Amber Lin: Yeah, there’s there’s really not much in notion I look at. There’s like 2
185 00:20:27.650 ⇒ 00:20:28.699 Uttam: Cool. Yeah, Luke.
186 00:20:28.700 ⇒ 00:20:35.840 Uttam: there’s also anything like any sort of backlog like platform stuff we need to fix.
187 00:20:35.940 ⇒ 00:20:36.500 Uttam: It’s probably
188 00:20:37.180 ⇒ 00:20:39.740 Uttam: Me or you are, gonna think about it. So whatever
189 00:20:40.330 ⇒ 00:20:45.050 Uttam: whatever things we can think about in this cleanup process, let’s take care of, yeah.
190 00:20:45.050 ⇒ 00:20:46.970 Luke Daque: Okay. Okay. Sounds good.
191 00:20:46.970 ⇒ 00:20:50.600 Amber Lin: Sounds good. Thank you. Everyone for joining, and I will.
192 00:20:51.300 ⇒ 00:20:52.620 Amber Lin: Maybe tomorrow
193 00:20:53.050 ⇒ 00:20:54.560 Luke Daque: Sounds good
194 00:20:54.930 ⇒ 00:20:55.620 Aakash Tandel: Bye.
195 00:20:55.930 ⇒ 00:20:57.150 Caio Velasco: Move my list! No.