Meeting Title: LMNT | Retail Topic Development Date: 2026-03-23 Meeting participants: Advait Nandakumar Menon, Amber Lin
WEBVTT
1 00:02:50.360 ⇒ 00:02:51.949 Amber Lin: Hey, sorry I’m late.
2 00:02:53.030 ⇒ 00:02:55.149 Advait Nandakumar Menon: Hey, that’s Olga. How’s it going?
3 00:02:55.710 ⇒ 00:03:11.040 Amber Lin: Pretty good, busy morning, but I think today is mostly just gonna be element work. For me, in the morning, and then I have some other client work in the afternoon. I wanted to ask, like, how’s your onboarding going? I know you’re on two clients, right?
4 00:03:12.060 ⇒ 00:03:25.119 Advait Nandakumar Menon: Yeah, so I had a brief call about… I think I mentioned to you on Friday, but Greg pulled me into a meeting with Global Betelink, so I was just shadowing him and trying to understand what’s going on with that.
5 00:03:26.270 ⇒ 00:03:35.390 Advait Nandakumar Menon: So I… it’s going good so far, the onboarding, so I’m just getting used to the workflow you guys are following, and all the tools, and…
6 00:03:35.560 ⇒ 00:03:39.570 Advait Nandakumar Menon: the data layers and everything, so… so far, it’s going good.
7 00:03:39.800 ⇒ 00:03:40.360 Advait Nandakumar Menon: But yeah.
8 00:03:40.360 ⇒ 00:03:40.970 Amber Lin: Okay.
9 00:03:41.200 ⇒ 00:03:48.609 Amber Lin: Sounds good. Do you know how much… I know you just had a call, but do you know how much time you’ll spend on the other project?
10 00:03:49.750 ⇒ 00:03:53.450 Advait Nandakumar Menon: So I had a look at the…
11 00:03:53.700 ⇒ 00:03:57.030 Advait Nandakumar Menon: Like, I think it’s a 10, 30, 60-90 plan.
12 00:03:58.500 ⇒ 00:04:04.320 Advait Nandakumar Menon: they have… I think Robert, Jasmine, and Kayla had, created it for me, so…
13 00:04:05.020 ⇒ 00:04:10.059 Advait Nandakumar Menon: Apparently, in that week 3 is for the other client.
14 00:04:10.220 ⇒ 00:04:16.070 Advait Nandakumar Menon: So, week 1 is mostly about the foundation and setup, and week two is about elements, so…
15 00:04:18.050 ⇒ 00:04:28.109 Advait Nandakumar Menon: I don’t think I’m supposed to work on Global VetLink yet, but if I get time on the side, I might look into it. So, to answer your question, my focus is on Element Funnel.
16 00:04:28.320 ⇒ 00:04:32.800 Amber Lin: Okay, sounds good. That’s all I… that’s all I wanted to know. So…
17 00:04:33.120 ⇒ 00:04:40.710 Amber Lin: Yeah, I think on the onboarding side, we… it seems like things are going well, and you’re getting your I-9
18 00:04:41.020 ⇒ 00:04:45.580 Amber Lin: 983 getting signed, so I… if you…
19 00:04:45.710 ⇒ 00:04:52.730 Amber Lin: Do you have any questions there, or just in general on onboarding, or should we hop in to talk about Element?
20 00:04:53.670 ⇒ 00:05:10.000 Advait Nandakumar Menon: So I… I don’t think I have anything else, apart from the I-98 way, apart from the things we just exchanged messages on. So, I was going to change it back to what’s there on E-Verify, and I was about to inform Rico, but
21 00:05:10.000 ⇒ 00:05:22.390 Advait Nandakumar Menon: I don’t know, maybe I thought for some reason, if he responds that we are gonna… we’re sticking with Brainforge Incorporated, and they might change it on the E-Verify portal. I know that your case is still pending, so I didn’t want that to happen.
22 00:05:22.390 ⇒ 00:05:26.800 Amber Lin: I think just changing it on the form is okay.
23 00:05:26.800 ⇒ 00:05:27.310 Advait Nandakumar Menon: Oh my god.
24 00:05:27.310 ⇒ 00:05:30.869 Amber Lin: But if they, like, if they change it on the…
25 00:05:31.380 ⇒ 00:05:38.209 Amber Lin: Okay. Please do not change all the ear for a verify, because I have sent my application.
26 00:05:38.810 ⇒ 00:05:46.120 Advait Nandakumar Menon: Yeah, yeah, yeah, I understand that. That’s why I didn’t inform to Rico yet, because me informing might lead to that.
27 00:05:46.240 ⇒ 00:05:50.200 Advait Nandakumar Menon: I’m just assuming, so I reached out to you.
28 00:05:50.200 ⇒ 00:05:50.630 Amber Lin: Yeah.
29 00:05:50.630 ⇒ 00:05:52.090 Advait Nandakumar Menon: You can also go…
30 00:05:52.090 ⇒ 00:05:59.620 Amber Lin: grab, like, a group chat between all of us on Slack, and I can tell them, hey, this is… my application’s impending still.
31 00:05:59.980 ⇒ 00:06:02.589 Advait Nandakumar Menon: Okay, maybe, yeah, I can load that after this call.
32 00:06:02.590 ⇒ 00:06:03.750 Amber Lin: Yeah, cool.
33 00:06:03.900 ⇒ 00:06:04.830 Amber Lin: Okay.
34 00:06:05.700 ⇒ 00:06:07.100 Advait Nandakumar Menon: Alright.
35 00:06:07.100 ⇒ 00:06:07.760 Amber Lin: I’m Ellen.
36 00:06:08.830 ⇒ 00:06:10.630 Advait Nandakumar Menon: Yeah, sorry, before we jump into.
37 00:06:10.630 ⇒ 00:06:11.900 Amber Lin: Oh, yeah, yeah, go ahead.
38 00:06:12.670 ⇒ 00:06:20.170 Advait Nandakumar Menon: Yeah, that’s I-983, and did you get a chance to fill the W8 form? I… Yeah, my DSO!
39 00:06:20.690 ⇒ 00:06:24.640 Amber Lin: Yeah, so I got myself… So, have you done that already?
40 00:06:25.310 ⇒ 00:06:32.480 Advait Nandakumar Menon: Yeah, so I contacted my DSO this morning, and they said, like, to leave… I don’t know, did you have a chance to look at it yet?
41 00:06:32.730 ⇒ 00:06:36.630 Amber Lin: No, I can’t look at it right now. One sec.
42 00:06:37.190 ⇒ 00:06:37.950 Advait Nandakumar Menon: Okay.
43 00:06:39.090 ⇒ 00:06:40.249 Amber Lin: What about it?
44 00:06:41.020 ⇒ 00:06:49.249 Advait Nandakumar Menon: Yeah, so it’s pretty simple, the form. So, in Part 1, we are supposed to fill our personal details, but.
45 00:06:49.840 ⇒ 00:06:51.830 Advait Nandakumar Menon: Permanent residence is…
46 00:06:51.980 ⇒ 00:07:04.419 Advait Nandakumar Menon: it should be, like, where we are from, like, so we are on non-immigrant visa, right, here in the US. So, wherever you’re from, in my case, it’s India, so I have to give that address
47 00:07:04.750 ⇒ 00:07:11.169 Advait Nandakumar Menon: In the mailing address… You can give, whatever… are here in the…
48 00:07:11.170 ⇒ 00:07:15.539 Amber Lin: Yeah, I think I actually filled this one for…
49 00:07:17.800 ⇒ 00:07:24.339 Amber Lin: for, like, my bank, because I had interest on my savings, so I actually filled this one.
50 00:07:24.340 ⇒ 00:07:24.880 Advait Nandakumar Menon: Okay.
51 00:07:24.880 ⇒ 00:07:27.169 Amber Lin: So, I think I know what to do, but I…
52 00:07:27.730 ⇒ 00:07:39.110 Amber Lin: I don’t think we’re… oh, I mean, we can claim a tax treaty, I’m… I’m not too sure of the India-US tax treaty, but…
53 00:07:39.540 ⇒ 00:07:45.639 Advait Nandakumar Menon: Yeah, so that’s the part maybe you can check with your DSO. So my DSO said to just leave it as blank.
54 00:07:46.030 ⇒ 00:07:46.860 Amber Lin: Oh.
55 00:07:46.860 ⇒ 00:07:51.199 Advait Nandakumar Menon: what, he said, so… I see. I’m not quite sure.
56 00:07:51.200 ⇒ 00:07:55.600 Amber Lin: Does that mean they will withhold our income at a 30% rate?
57 00:07:56.750 ⇒ 00:08:14.729 Advait Nandakumar Menon: I think so. So, I’m not sure how this works on the 1099, but… so, when I was my previous employer on a W-2, so there are taxes like, the federal tax, I mean, I think it’s called Social Security and Medicaid.
58 00:08:14.730 ⇒ 00:08:16.520 Amber Lin: We don’t have to pay those.
59 00:08:16.740 ⇒ 00:08:30.679 Advait Nandakumar Menon: Yeah, so those weren’t… so we are exempt from that since we are still on F1 visa, so those things didn’t get cut from my paycheck, but the state tax and the federal income tax did get withholded, and I had to…
60 00:08:30.680 ⇒ 00:08:36.749 Advait Nandakumar Menon: Like, we have to file the return for that, so in this case, it’s April 15th this year.
61 00:08:37.980 ⇒ 00:08:39.150 Amber Lin: Yeah, okay.
62 00:08:39.159 ⇒ 00:08:46.689 Advait Nandakumar Menon: But on a 1099, I believe, like, we will get the whole amount and no taxes will be withheld, so it’s our.
63 00:08:46.690 ⇒ 00:08:48.090 Amber Lin: responsibility, then we fight.
64 00:08:48.090 ⇒ 00:08:49.160 Advait Nandakumar Menon: Okay.
65 00:08:49.160 ⇒ 00:08:55.059 Amber Lin: Okay, I mean, I had… I’m going to pay taxes this year, so… that’s fine.
66 00:08:55.620 ⇒ 00:08:56.690 Amber Lin: Alright, self-built that.
67 00:08:56.690 ⇒ 00:09:08.000 Advait Nandakumar Menon: I’m not sure, like, how it will differ for the tenant and him, but make sure you don’t, like, you get that, treaty or exemption for, I think it’s Social Security and Medicare.
68 00:09:08.310 ⇒ 00:09:11.589 Advait Nandakumar Menon: Maybe… You can make sure of that, so that you don’t.
69 00:09:11.590 ⇒ 00:09:27.569 Amber Lin: Yeah, I think if they give us the whole amount, then when we report taxes, as long as we file correctly, it’ll just say that… like, I filed with Sprint Tax this year, because most universities do, and they…
70 00:09:27.700 ⇒ 00:09:30.530 Amber Lin: I think they, they didn’t ask…
71 00:09:31.200 ⇒ 00:09:34.660 Amber Lin: they knew I was a… I was accept, so they didn’t ask me.
72 00:09:34.660 ⇒ 00:09:35.000 Advait Nandakumar Menon: Okay.
73 00:09:35.000 ⇒ 00:09:41.859 Amber Lin: that, because I had to pay the whole amount, and I only paid income tax. I didn’t have to pay the FICA.
74 00:09:42.000 ⇒ 00:09:44.709 Advait Nandakumar Menon: That’s good, then. Yeah, that’s… then it should be fine.
75 00:09:44.910 ⇒ 00:09:45.470 Amber Lin: Yeah.
76 00:09:45.990 ⇒ 00:09:46.800 Amber Lin: Cool.
77 00:09:46.930 ⇒ 00:09:47.840 Amber Lin: Okay.
78 00:09:49.320 ⇒ 00:09:49.920 Amber Lin: Sounds good.
79 00:09:49.920 ⇒ 00:09:56.469 Advait Nandakumar Menon: I don’t think I have anything else on those, apart from those two things, so we can jump into Element.
80 00:09:56.710 ⇒ 00:10:05.150 Amber Lin: Sounds good. So, Element… let’s see… we have two goals this week. It’s one to
81 00:10:05.640 ⇒ 00:10:20.529 Amber Lin: to be done on the retail topic, and two, have, like, dashboard designs for the retail dashboards, which we are not completely owning. Jasmine and Greg said they would contribute most of it, and we’ll just
82 00:10:20.820 ⇒ 00:10:21.990 Amber Lin: be conversing.
83 00:10:21.990 ⇒ 00:10:22.430 Advait Nandakumar Menon: Yeah, with them.
84 00:10:22.430 ⇒ 00:10:27.209 Amber Lin: on how that is, so I think our focus is just the topics, and…
85 00:10:27.210 ⇒ 00:10:27.840 Advait Nandakumar Menon: Okay.
86 00:10:28.280 ⇒ 00:10:42.600 Amber Lin: I think overall… let me pull up the topic… So… We have… Yeah.
87 00:10:43.030 ⇒ 00:10:50.690 Amber Lin: Cool Well, I think… Where we’re at right now, so…
88 00:10:51.040 ⇒ 00:11:00.249 Amber Lin: First, we have… I think the goal of these… this… these topics. I think one is to answer what are existing metrics.
89 00:11:00.360 ⇒ 00:11:08.560 Amber Lin: on the sheets, and answer that correctly in Omni. And two, there’s some questions from the client that they want answered.
90 00:11:09.030 ⇒ 00:11:12.919 Amber Lin: And I think we looked at this together. I think there’s this POS velocity
91 00:11:13.240 ⇒ 00:11:20.289 Amber Lin: we can do, stock outs we can do. And then there’s other topics on, like, PO orders, which I don’t think we can.
92 00:11:20.440 ⇒ 00:11:22.799 Amber Lin: Because we don’t have deal order data.
93 00:11:23.540 ⇒ 00:11:24.260 Advait Nandakumar Menon: Okay.
94 00:11:26.420 ⇒ 00:11:27.590 Advait Nandakumar Menon: S…
95 00:11:27.980 ⇒ 00:11:35.869 Advait Nandakumar Menon: So, the existing sheet reporting metrics, just the table below, are those the only things we need answered out? Because I know the…
96 00:11:36.040 ⇒ 00:11:43.240 Advait Nandakumar Menon: When I went to the spreadsheet, there was a whole list of core metrics, that was listed on the sheet, so…
97 00:11:44.950 ⇒ 00:11:46.439 Amber Lin: Let me see, there’s…
98 00:11:49.570 ⇒ 00:11:50.960 Amber Lin: retail.
99 00:11:51.460 ⇒ 00:12:02.600 Amber Lin: Okay, so there’s… I mean, this is everything from the summary report, and then there’s this. I guess I didn’t include this. This is for…
100 00:12:02.730 ⇒ 00:12:14.299 Amber Lin: This is for Phil, for one of their execs that wanted it to look like this. So, it’s essentially sales by, retailer.
101 00:12:14.300 ⇒ 00:12:32.399 Amber Lin: by product category, and then we look at, like, units and dollar amount, but essentially this is just the breakdown. We have this modeled out, we have this in Snowflake, but essentially it just… it would just look like this. Date.
102 00:12:32.530 ⇒ 00:12:34.430 Amber Lin: Retail are these fields.
103 00:12:35.440 ⇒ 00:12:37.669 Amber Lin: What it was, but this is, like, a…
104 00:12:37.980 ⇒ 00:12:45.660 Amber Lin: I asked Awish to, can you model out the same day last week, same day last month, because they have…
105 00:12:45.790 ⇒ 00:12:55.210 Amber Lin: specific definitions of what they wanted to be. Like, they wanted to be based on the business day. Like, same day last week means same
106 00:12:55.380 ⇒ 00:13:03.510 Amber Lin: Friday last week, and same day last month means the first week last month, that’s… and it’s Friday, so, like, I…
107 00:13:03.510 ⇒ 00:13:04.160 Advait Nandakumar Menon: Oh, okay.
108 00:13:04.160 ⇒ 00:13:09.289 Amber Lin: model this out, because it’s a bit harder to do, because it’s not date-based.
109 00:13:10.560 ⇒ 00:13:17.330 Amber Lin: So we have all of this in a model. I guess we can debate if we want that as its own topic.
110 00:13:18.460 ⇒ 00:13:19.080 Advait Nandakumar Menon: Okay?
111 00:13:19.080 ⇒ 00:13:25.960 Amber Lin: like, I don’t know, because I think the grain we have here for Phil’s model is…
112 00:13:26.070 ⇒ 00:13:32.809 Amber Lin: by day, by SKU, which I… which I think we can join into our topic, no problem, because that’s what we…
113 00:13:33.230 ⇒ 00:13:39.189 Amber Lin: already have, kind of. Like, our smallest grain is by days, by scoop.
114 00:13:39.930 ⇒ 00:13:47.790 Amber Lin: Okay. Maybe not… maybe, like, by day by scuba store, but, like, this is a pretty small grain as well.
115 00:13:48.910 ⇒ 00:13:49.630 Advait Nandakumar Menon: Okay.
116 00:13:50.270 ⇒ 00:13:50.990 Amber Lin: Yeah.
117 00:13:51.260 ⇒ 00:13:58.069 Amber Lin: Mmm… So, like, I can ask the team on this.
118 00:13:58.310 ⇒ 00:14:05.829 Amber Lin: I mean, it’s for another topic anyway, so I think we should just talk about topic one right now.
119 00:14:05.830 ⇒ 00:14:06.500 Advait Nandakumar Menon: Okay.
120 00:14:06.500 ⇒ 00:14:16.339 Amber Lin: Yeah, I saw some of your messages. Can you walk me through a bit? I know you said, like, some things might overlap, and some things might double count. What do you think?
121 00:14:17.310 ⇒ 00:14:21.829 Advait Nandakumar Menon: Yeah, so… I’ll just pull up my message.
122 00:14:31.200 ⇒ 00:14:37.789 Advait Nandakumar Menon: Yeah, so for this topic one, like, questions that it’s asked were, I think we need it
123 00:14:38.120 ⇒ 00:14:42.300 Advait Nandakumar Menon: By daily, and by store, and by SKU,
124 00:14:42.300 ⇒ 00:14:42.680 Amber Lin: Oh my god.
125 00:14:42.680 ⇒ 00:14:43.869 Advait Nandakumar Menon: Correct on that?
126 00:14:44.440 ⇒ 00:14:45.130 Amber Lin: Yeah.
127 00:14:45.820 ⇒ 00:15:01.739 Advait Nandakumar Menon: Yeah, so for that purpose, and also for the purpose of the inventory questions, I think using the existing summary view within the topic in Omni right now, I don’t think it has by store, it’s by retailer, right?
128 00:15:02.520 ⇒ 00:15:10.120 Amber Lin: I… yeah, I think what we included there is…
129 00:15:10.770 ⇒ 00:15:20.829 Amber Lin: I… I doubt, don’t. I… I mean, it doesn’t matter as much if we’re building a new one, but I think we don’t have it by… by re… by store.
130 00:15:21.360 ⇒ 00:15:38.279 Advait Nandakumar Menon: Yeah, so for that reason, like you have listed over here, I think using the fax sales and the inventory by location table… So, I want to understand, like, in the inventory by location table, there is the location type equal to store.
131 00:15:38.410 ⇒ 00:15:42.290 Advait Nandakumar Menon: So, are we only focusing on those records?
132 00:15:43.420 ⇒ 00:15:47.839 Amber Lin: Mmm. You’re saying there’s some of it that’s, like, online, right?
133 00:15:48.370 ⇒ 00:15:49.240 Advait Nandakumar Menon: Yeah, the…
134 00:15:49.240 ⇒ 00:15:49.839 Amber Lin: the issue.
135 00:15:49.840 ⇒ 00:15:50.370 Advait Nandakumar Menon: I’m specifically.
136 00:15:50.370 ⇒ 00:15:56.360 Amber Lin: I see. It still gets picked up at a store, right? Let’s pull it up and see.
137 00:16:09.760 ⇒ 00:16:10.670 Amber Lin: Okay.
138 00:16:12.220 ⇒ 00:16:15.870 Amber Lin: So, retail…
139 00:16:19.970 ⇒ 00:16:21.750 Amber Lin: Inventory by location.
140 00:16:24.650 ⇒ 00:16:32.070 Amber Lin: I mean, each one of these still have a store ID. I think you’re saying some of these are not stores, right?
141 00:16:32.520 ⇒ 00:16:33.000 Advait Nandakumar Menon: Yeah.
142 00:16:33.510 ⇒ 00:16:35.839 Amber Lin: I see, let’s…
143 00:16:37.720 ⇒ 00:16:40.480 Advait Nandakumar Menon: Yeah, each of them has a store ID, so I’m confused, like…
144 00:16:40.480 ⇒ 00:16:46.770 Amber Lin: What is the other value? Like, apart from store, what is the other location type?
145 00:16:47.790 ⇒ 00:16:52.990 Advait Nandakumar Menon: I think I saw DC, if I recall it correctly,
146 00:16:55.130 ⇒ 00:16:57.400 Advait Nandakumar Menon: Maybe it’s worth querying it once.
147 00:16:57.670 ⇒ 00:17:02.370 Amber Lin: Yeah, let’s… let’s do that… copy name.
148 00:17:18.349 ⇒ 00:17:21.479 Amber Lin: Store location?
149 00:17:22.720 ⇒ 00:17:24.319 Amber Lin: location type.
150 00:17:25.300 ⇒ 00:17:25.900 Advait Nandakumar Menon: Yes.
151 00:17:31.800 ⇒ 00:17:36.650 Advait Nandakumar Menon: Yeah, it’s DC, so… I see. I’m not sure what DC exactly is.
152 00:17:36.650 ⇒ 00:17:40.979 Amber Lin: Yeah, let’s, let’s, let’s try.
153 00:17:57.990 ⇒ 00:17:58.890 Amber Lin: What?
154 00:18:00.370 ⇒ 00:18:05.480 Amber Lin: Why is there… okay, it’s Walmart, is it? Wait, what is it? Yeah, it’s a Walmart.
155 00:18:05.480 ⇒ 00:18:06.379 Advait Nandakumar Menon: And the store ID?
156 00:18:06.380 ⇒ 00:18:08.150 Amber Lin: center. Oh.
157 00:18:08.150 ⇒ 00:18:08.820 Advait Nandakumar Menon: Okay.
158 00:18:09.010 ⇒ 00:18:11.120 Advait Nandakumar Menon: And the store ID seems to be null as well.
159 00:18:11.120 ⇒ 00:18:14.579 Amber Lin: I see, so we should be fine if we only filter by store ID.
160 00:18:14.910 ⇒ 00:18:22.860 Advait Nandakumar Menon: Yeah, yeah, so that’s what I was thinking. That’s why I think it’s worth, like, filtering location type is just equal to store.
161 00:18:24.040 ⇒ 00:18:34.090 Amber Lin: This stinks… Sore… Let’s see if there’s anything.
162 00:18:34.630 ⇒ 00:18:35.910 Advait Nandakumar Menon: Apart from Nalia.
163 00:18:36.180 ⇒ 00:18:37.149 Amber Lin: No, there’s nothing.
164 00:18:37.150 ⇒ 00:18:38.209 Advait Nandakumar Menon: It’s just not.
165 00:18:38.620 ⇒ 00:18:44.649 Amber Lin: Okay, I’m gonna… Let’s just screenshot this and put in an appendix.
166 00:18:45.830 ⇒ 00:18:46.420 Advait Nandakumar Menon: Jeff?
167 00:18:58.530 ⇒ 00:19:05.740 Amber Lin: It includes… Hoggie’s and Walmart.
168 00:19:05.940 ⇒ 00:19:09.980 Amber Lin: Source from…
169 00:19:22.690 ⇒ 00:19:25.710 Amber Lin: Okay, cool. So we know definitely.
170 00:19:25.920 ⇒ 00:19:28.969 Amber Lin: Alright, so with this.
171 00:19:28.970 ⇒ 00:19:29.650 Advait Nandakumar Menon: Yeah.
172 00:19:30.160 ⇒ 00:19:39.729 Advait Nandakumar Menon: Yeah, so as I was saying, so we can take fax sales and that inventory by location, and join it with the DIM product, DIMM stores.
173 00:19:39.730 ⇒ 00:19:40.080 Amber Lin: I’m cute.
174 00:19:40.080 ⇒ 00:19:43.409 Advait Nandakumar Menon: even dim calendar, if required.
175 00:19:43.410 ⇒ 00:19:46.860 Amber Lin: I think it might be helpful if they want to ask based on, like.
176 00:19:47.100 ⇒ 00:19:52.799 Amber Lin: They think them calendar is just the Walmart, like, the retailer’s calendars, probably.
177 00:19:54.070 ⇒ 00:19:57.219 Amber Lin: We can, we can, we can track.
178 00:19:59.820 ⇒ 00:20:04.620 Advait Nandakumar Menon: I didn’t check the data on it yet, but I assumed that
179 00:20:05.590 ⇒ 00:20:08.060 Advait Nandakumar Menon: It’s to help with the level of
180 00:20:08.520 ⇒ 00:20:10.470 Advait Nandakumar Menon: detail you’re looking at, like, daily, monthly.
181 00:20:10.470 ⇒ 00:20:11.180 Amber Lin: Yeah.
182 00:20:11.650 ⇒ 00:20:17.380 Amber Lin: I think they’re just, like, day number, week number… Yeah.
183 00:20:17.540 ⇒ 00:20:23.549 Amber Lin: I mean, oh, oh, if we join this in, I think we can do Phil’s view?
184 00:20:23.960 ⇒ 00:20:32.330 Amber Lin: If we take, like, oh, do this number… And find the…
185 00:20:33.390 ⇒ 00:20:39.539 Amber Lin: you know what, it’s too com- I think the modeling’s too complex to do in Omni, so we can just use a wishes model.
186 00:20:40.150 ⇒ 00:20:42.490 Amber Lin: But I do think the calendar will be helpful.
187 00:20:43.310 ⇒ 00:20:44.050 Advait Nandakumar Menon: Okay.
188 00:20:45.510 ⇒ 00:20:51.020 Advait Nandakumar Menon: Mate, can you repeat that? Are you asking me if it’s helpful.
189 00:20:51.020 ⇒ 00:20:53.720 Amber Lin: Oh, no, no, no, I think this will be helpful, so…
190 00:20:53.720 ⇒ 00:20:54.470 Advait Nandakumar Menon: Okay.
191 00:20:54.470 ⇒ 00:20:59.949 Amber Lin: we can… I think we can just join it in, like, join on calendar date, probably.
192 00:21:00.780 ⇒ 00:21:05.200 Advait Nandakumar Menon: Okay, but… but don’t we already have date fields within sales?
193 00:21:06.810 ⇒ 00:21:25.060 Amber Lin: Yeah, yeah, we have, like, not, not this, but for example, we do have the business day. I think the calendar just, just, is extra dimensions of, oh, this business day is week number 5 and weekday number 3, so…
194 00:21:25.130 ⇒ 00:21:32.609 Amber Lin: like, those are just extra dimensions, and sometimes Walmart or Target may use a different…
195 00:21:32.770 ⇒ 00:21:42.560 Amber Lin: like, they might be on a different calendar for their reporting, so sometimes it might be helpful to have that information, too. And it doesn’t hurt to have that topic in there.
196 00:21:42.560 ⇒ 00:21:43.820 Advait Nandakumar Menon: Okay, fair enough.
197 00:21:44.160 ⇒ 00:21:46.100 Amber Lin: Okay, cool.
198 00:21:46.100 ⇒ 00:21:52.270 Advait Nandakumar Menon: Yeah, so I think these should cover the questions that’s listed over here, like the goal of this topic.
199 00:21:52.420 ⇒ 00:21:57.680 Advait Nandakumar Menon: But my other concern is that, within…
200 00:21:57.880 ⇒ 00:22:09.690 Advait Nandakumar Menon: like, the fact sales table. There also seems to be, like, fields, like, that splits it by channel, like, service channel, fulfillment type.
201 00:22:11.040 ⇒ 00:22:18.999 Advait Nandakumar Menon: and order channel as well, so will that make it even more granular than, daily, by store, by SKU?
202 00:22:20.980 ⇒ 00:22:24.789 Amber Lin: Let’s see, so you’re saying that there’s more…
203 00:22:24.900 ⇒ 00:22:28.859 Amber Lin: Like, fulfillment type and order channel.
204 00:22:29.800 ⇒ 00:22:31.019 Advait Nandakumar Menon: Yeah, and the service channel.
205 00:22:31.020 ⇒ 00:22:32.660 Amber Lin: Oh, hmm.
206 00:22:33.380 ⇒ 00:22:34.090 Amber Lin: Hmm.
207 00:22:34.850 ⇒ 00:22:36.770 Amber Lin: Well, that makes sense.
208 00:22:37.010 ⇒ 00:22:46.430 Amber Lin: So, you’re saying that this might be even more granular than just a daily summary? Because it’s by day, by order channel?
209 00:22:46.830 ⇒ 00:22:56.760 Advait Nandakumar Menon: Yeah, on its own, that’s not an issue, even if it’s more granular, but when you’re joining with inventory, maybe will that make things duplicated? That’s my concern, really.
210 00:22:57.420 ⇒ 00:22:59.739 Amber Lin: Oh,
211 00:22:59.900 ⇒ 00:23:05.920 Amber Lin: Because you’re saying we’ll have multiple entries on the sales side for each, like, product and store combination.
212 00:23:05.920 ⇒ 00:23:07.670 Advait Nandakumar Menon: Yeah, yeah.
213 00:23:07.670 ⇒ 00:23:10.230 Amber Lin: Mmm… Hmm.
214 00:23:11.960 ⇒ 00:23:13.010 Amber Lin: Okay.
215 00:23:13.470 ⇒ 00:23:21.060 Amber Lin: Let’s… I mean, let’s go… let’s go check if there’s actually, like, a… multiple entries.
216 00:23:21.250 ⇒ 00:23:25.010 Amber Lin: Which I think you were right, I didn’t notice. I think there will be.
217 00:23:26.460 ⇒ 00:23:34.629 Advait Nandakumar Menon: I, I think… There is, as per the definitions, but yeah, okay, that’s probably worth checking.
218 00:23:35.480 ⇒ 00:23:45.945 Amber Lin: Yeah, let’s say… Prod… 2 effects… Same…
219 00:24:02.920 ⇒ 00:24:05.870 Amber Lin: Do we do, like, a… I think we do a…
220 00:24:06.760 ⇒ 00:24:10.180 Amber Lin: Group by, or we can select a sample.
221 00:24:10.760 ⇒ 00:24:17.319 Amber Lin: I mean, we can do a… Try this.
222 00:24:20.240 ⇒ 00:24:21.730 Amber Lin: Where…
223 00:24:33.770 ⇒ 00:24:36.219 Amber Lin: And I guess we did this.
224 00:24:38.020 ⇒ 00:24:39.240 Amber Lin: But…
225 00:24:50.230 ⇒ 00:24:55.710 Amber Lin: Or… Dirt by business… Business.
226 00:25:12.300 ⇒ 00:25:14.730 Amber Lin: Same product, by day.
227 00:25:17.490 ⇒ 00:25:18.520 Amber Lin: Oh.
228 00:25:18.880 ⇒ 00:25:19.800 Amber Lin: What?
229 00:25:22.570 ⇒ 00:25:24.170 Amber Lin: Maybe…
230 00:25:27.400 ⇒ 00:25:30.960 Amber Lin: count business days? Wait, how do we…
231 00:25:30.960 ⇒ 00:25:33.139 Advait Nandakumar Menon: What are you trying to do?
232 00:25:33.540 ⇒ 00:25:47.430 Amber Lin: I guess I’m trying to figure out if there’s… if there’s multiple entries for each day, for each product, because if they have multiple entries, like, buy fulfillment and order channel, I guess is what I’m trying to figure out.
233 00:25:48.320 ⇒ 00:25:53.140 Advait Nandakumar Menon: For each product and store, For the same day, is what you’re saying?
234 00:25:53.660 ⇒ 00:25:54.850 Amber Lin: Yeah.
235 00:25:55.060 ⇒ 00:25:58.589 Amber Lin: I don’t know if my curry’s the right way to go, though.
236 00:25:59.450 ⇒ 00:26:04.000 Advait Nandakumar Menon: Yeah, I think… You can…
237 00:26:07.250 ⇒ 00:26:09.479 Advait Nandakumar Menon: You need to use group by… group by for this?
238 00:26:09.480 ⇒ 00:26:10.400 Amber Lin: Yeah.
239 00:26:10.520 ⇒ 00:26:14.230 Advait Nandakumar Menon: So, you need to group by the store ID and the product.
240 00:26:15.050 ⇒ 00:26:22.410 Amber Lin: store, and… Right? The product… And then I’ll say…
241 00:26:23.010 ⇒ 00:26:29.059 Advait Nandakumar Menon: Yeah, and you can select the same as well, store ID and product, and have a count star next to it.
242 00:26:30.710 ⇒ 00:26:33.050 Amber Lin: Say that again.
243 00:26:33.720 ⇒ 00:26:40.560 Advait Nandakumar Menon: Yeah, so you have the store ID and the product fields, right? Just select the same, add it to the select statement as well.
244 00:26:43.210 ⇒ 00:26:43.730 Amber Lin: And bye.
245 00:26:43.730 ⇒ 00:26:44.339 Advait Nandakumar Menon: Don’t start.
246 00:26:44.620 ⇒ 00:26:45.810 Amber Lin: Challenge…
247 00:26:47.020 ⇒ 00:26:54.529 Advait Nandakumar Menon: Yeah, you want to look at where the counts are greater than 1, right? So, having count…
248 00:26:55.980 ⇒ 00:26:59.649 Advait Nandakumar Menon: you can… No, no, after the group, bye, I mean…
249 00:27:00.690 ⇒ 00:27:01.730 Amber Lin: Awesome.
250 00:27:02.260 ⇒ 00:27:03.400 Advait Nandakumar Menon: having count.
251 00:27:06.710 ⇒ 00:27:07.989 Amber Lin: That’s cool.
252 00:27:08.640 ⇒ 00:27:10.449 Amber Lin: Not done that before.
253 00:27:12.320 ⇒ 00:27:18.849 Amber Lin: Okay. Wait… But we’re not looking at the same business day.
254 00:27:19.240 ⇒ 00:27:20.050 Advait Nandakumar Menon: Yeah.
255 00:27:20.190 ⇒ 00:27:21.290 Advait Nandakumar Menon: So, shed.
256 00:27:21.290 ⇒ 00:27:22.440 Amber Lin: business day?
257 00:27:22.950 ⇒ 00:27:24.060 Advait Nandakumar Menon: Sure, yeah.
258 00:27:26.390 ⇒ 00:27:29.039 Amber Lin: Okay, I think I need to put this up there.
259 00:27:29.290 ⇒ 00:27:32.120 Advait Nandakumar Menon: Yeah, up there as well, the business date.
260 00:27:32.560 ⇒ 00:27:33.350 Amber Lin: Yeah.
261 00:27:36.170 ⇒ 00:27:39.649 Amber Lin: I think the answer is yes, there are multiple channels.
262 00:27:40.420 ⇒ 00:27:41.340 Amber Lin: Yes.
263 00:27:43.870 ⇒ 00:27:47.980 Amber Lin: Okay, great call. Let’s put this in appendix as well.
264 00:27:49.140 ⇒ 00:27:49.870 Amber Lin: Bing.
265 00:27:50.900 ⇒ 00:28:00.489 Advait Nandakumar Menon: So, the product SK field, this might be there in the platform doc, but… is it the product ID?
266 00:28:01.470 ⇒ 00:28:08.190 Amber Lin: I think we’re using the product… Key…
267 00:28:08.500 ⇒ 00:28:11.490 Amber Lin: My answer is, I don’t know. I think so, but I don’t know.
268 00:28:11.840 ⇒ 00:28:13.090 Advait Nandakumar Menon: Okay, okay.
269 00:28:14.910 ⇒ 00:28:18.309 Amber Lin: Effect sales…
270 00:28:29.700 ⇒ 00:28:30.300 Amber Lin: Oh…
271 00:28:58.900 ⇒ 00:29:05.839 Amber Lin: Okay, so when we do that, we need to do, like, a sum… by… store.
272 00:29:09.340 ⇒ 00:29:10.160 Advait Nandakumar Menon: Yes.
273 00:29:10.160 ⇒ 00:29:14.300 Amber Lin: Yeah, do we want this daily sales summary table?
274 00:29:17.120 ⇒ 00:29:21.380 Advait Nandakumar Menon: I… I don’t think so, because…
275 00:29:22.490 ⇒ 00:29:26.659 Advait Nandakumar Menon: That’s the table that’s being used in the existing topic. Like I said, I…
276 00:29:29.610 ⇒ 00:29:33.540 Advait Nandakumar Menon: Oh… I don’t think it has the stored level.
277 00:29:33.980 ⇒ 00:29:38.959 Advait Nandakumar Menon: correct me if I’m wrong, but does it have the store-level grain that you’re looking for?
278 00:29:39.090 ⇒ 00:29:41.840 Advait Nandakumar Menon: If we do, then we can just reuse this.
279 00:29:42.660 ⇒ 00:29:45.500 Amber Lin: No, it doesn’t have, like, a store level.
280 00:29:45.630 ⇒ 00:29:57.720 Amber Lin: Like, we can add it. It’s not a… it’s not… it wouldn’t… we can make changes to the modeling. If we say, like, this is what we should do, we should model it, and then include it.
281 00:29:57.850 ⇒ 00:30:03.829 Amber Lin: Do you think that that would be helpful if we don’t just use the basic fact sales?
282 00:30:06.060 ⇒ 00:30:12.249 Advait Nandakumar Menon: I mean, this would be built on top of the basic fact sales, right? This table?
283 00:30:13.240 ⇒ 00:30:17.889 Amber Lin: Yeah, oh, I see. This one is for…
284 00:30:18.000 ⇒ 00:30:20.330 Amber Lin: This… oh, this is the wrong table.
285 00:30:21.210 ⇒ 00:30:27.700 Amber Lin: Daily sales covering.
286 00:30:36.620 ⇒ 00:30:41.689 Amber Lin: Yeah, alright, this is just… This is, like, a very high-level view.
287 00:30:44.330 ⇒ 00:30:45.010 Advait Nandakumar Menon: Near.
288 00:30:45.270 ⇒ 00:30:49.680 Amber Lin: Like, I think the difference here is this is by SKU.
289 00:30:51.530 ⇒ 00:30:57.410 Amber Lin: I think by retailer, by SKU… and this is just by retailer.
290 00:30:58.510 ⇒ 00:30:59.020 Advait Nandakumar Menon: So…
291 00:30:59.020 ⇒ 00:31:05.790 Amber Lin: Both of these are on higher grain than what… what we… might need…
292 00:31:07.060 ⇒ 00:31:11.900 Advait Nandakumar Menon: Yeah, and that’s why I don’t think this… we can use these, but,
293 00:31:12.290 ⇒ 00:31:20.470 Advait Nandakumar Menon: we can go two ways about it, really. We can modify this to model it the way we want, or we can have something new as well.
294 00:31:20.470 ⇒ 00:31:22.770 Amber Lin: Yeah, makes sense.
295 00:31:32.640 ⇒ 00:31:37.560 Amber Lin: Here, there’s also… I think, yeah, there’s also this.
296 00:31:37.770 ⇒ 00:31:43.639 Amber Lin: Like, this one, the monthly sales summary, or the weekly sales summary, has these metrics.
297 00:31:43.810 ⇒ 00:31:46.229 Amber Lin: Which actually will look like…
298 00:31:53.820 ⇒ 00:31:55.840 Amber Lin: For example, look like this.
299 00:31:55.960 ⇒ 00:31:56.740 Amber Lin: These are the.
300 00:31:56.740 ⇒ 00:31:57.560 Advait Nandakumar Menon: metric. Oh, okay.
301 00:31:57.560 ⇒ 00:32:08.030 Amber Lin: calculated. I guess, I heard on the, like, fact sales and how we want to model it, I don’t know how we’re going to do this. Do you think we start…
302 00:32:08.030 ⇒ 00:32:08.730 Advait Nandakumar Menon: It’s not short.
303 00:32:08.730 ⇒ 00:32:18.170 Amber Lin: models. Yeah. Not for the POS revenue, I think we can calculate that pretty easily, but for, like, active stores, newly turned stores.
304 00:32:19.030 ⇒ 00:32:21.310 Amber Lin: Like, new stores, just on the…
305 00:32:21.760 ⇒ 00:32:24.500 Amber Lin: Store side, like, what do you think?
306 00:32:25.940 ⇒ 00:32:32.630 Advait Nandakumar Menon: Yeah, I did add a comment on this on Slack on Friday, so…
307 00:32:33.190 ⇒ 00:32:50.929 Advait Nandakumar Menon: So whenever they ask, like, active stores, or whatever, will they ask as of a current date, or will they ask, like, as of a… for example, last week, or the month before, how did it look like? Is there any possibility.
308 00:32:50.930 ⇒ 00:32:51.730 Amber Lin: Yeah.
309 00:32:51.730 ⇒ 00:32:53.579 Advait Nandakumar Menon: Ask historical questions.
310 00:32:53.580 ⇒ 00:33:13.389 Amber Lin: I think they sometimes want to see the trend. Like, for example, here, they want to see how are we… how are we growing over time, and of course, sometimes they will want, like, a… what is it right now? And, like, if they want to ask, hey, what was the trend for the past 3 months? What was the trend for the past, like, weeks? I think that’s also something they may want to see.
311 00:33:14.550 ⇒ 00:33:23.379 Advait Nandakumar Menon: Okay, so, that’s even more the reason that we need to do something in dbt instead of Omni, because,
312 00:33:23.740 ⇒ 00:33:27.050 Advait Nandakumar Menon: Let’s say we have,
313 00:33:27.560 ⇒ 00:33:34.049 Advait Nandakumar Menon: An as-of date, or, like, last order date, or days since last order date field.
314 00:33:34.310 ⇒ 00:33:37.589 Advait Nandakumar Menon: That can change as the new sales come in, right?
315 00:33:38.040 ⇒ 00:33:38.750 Amber Lin: Yeah.
316 00:33:39.490 ⇒ 00:33:46.909 Advait Nandakumar Menon: So, if that happens, how will we answer the historical question? Like, the previous date would get overturned, right?
317 00:33:47.810 ⇒ 00:33:48.740 Amber Lin: Makes sense.
318 00:33:48.740 ⇒ 00:33:49.130 Advait Nandakumar Menon: So.
319 00:33:49.130 ⇒ 00:33:51.290 Amber Lin: So, what is Snapshot.
320 00:33:51.520 ⇒ 00:33:53.750 Amber Lin: We have, like, a daily snapshot.
321 00:33:53.990 ⇒ 00:33:55.290 Amber Lin: Does that work?
322 00:33:56.290 ⇒ 00:33:57.600 Advait Nandakumar Menon: a snapshot table.
323 00:33:58.120 ⇒ 00:34:07.170 Amber Lin: Can’t we have this, oh, maybe that’s where… maybe that was for wholesale.
324 00:34:07.390 ⇒ 00:34:10.739 Amber Lin: Like, for instance, in wholesale, we had the…
325 00:34:11.670 ⇒ 00:34:15.079 Amber Lin: We have a partner status daily snapshot.
326 00:34:15.800 ⇒ 00:34:16.400 Amber Lin: Okay.
327 00:34:16.409 ⇒ 00:34:22.569 Advait Nandakumar Menon: So this will, like, capture the status or information for each date, is that what it does?
328 00:34:22.770 ⇒ 00:34:23.650 Amber Lin: Yeah.
329 00:34:23.940 ⇒ 00:34:24.630 Amber Lin: Yeah.
330 00:34:24.639 ⇒ 00:34:36.249 Advait Nandakumar Menon: Yeah, I would say we need something similar for retail, because if someone… if the customer’s gonna ask, okay, I have the status as of today, but what about 3 weeks back? What about a couple of months back?
331 00:34:36.679 ⇒ 00:34:37.379 Advait Nandakumar Menon: if…
332 00:34:37.380 ⇒ 00:34:37.830 Amber Lin: Hmm…
333 00:34:37.830 ⇒ 00:34:45.150 Advait Nandakumar Menon: If someone is going to, like, ask that to Blobby, then there is no data point we can refer to now to…
334 00:34:46.340 ⇒ 00:34:47.680 Advait Nandakumar Menon: I’m satisfied that…
335 00:34:48.050 ⇒ 00:34:49.639 Amber Lin: I mean, right now, we…
336 00:34:49.760 ⇒ 00:35:02.759 Amber Lin: do, but that’s, like, a pre-modeled version. Like, right now, we know at weekly snapshots and monthly snapshots, and… sorry, one sec. For… so we need…
337 00:35:04.320 ⇒ 00:35:09.600 Amber Lin: Active stores… Newly churned stores.
338 00:35:09.810 ⇒ 00:35:11.270 Amber Lin: Turn stores.
339 00:35:14.000 ⇒ 00:35:18.990 Amber Lin: So, let’s look at the definitions. Essentially, we just need these things.
340 00:35:19.150 ⇒ 00:35:23.170 Amber Lin: Like, how do we… Boom.
341 00:35:25.480 ⇒ 00:35:35.250 Amber Lin: like, technically, I think we can take this, for example, the sales for the past 365 days and say, okay, give me count distinct stores.
342 00:35:36.010 ⇒ 00:35:36.559 Advait Nandakumar Menon: Huh?
343 00:35:37.520 ⇒ 00:35:43.100 Amber Lin: And then give me, like… or… let’s say we’re looking at
344 00:35:43.450 ⇒ 00:35:56.239 Amber Lin: January 1st this year, right? We want to look at that date, and we can say total stores is… we take the sales up to that date and say, okay, give me the count stores. Active stores, we say, give me the past…
345 00:35:56.600 ⇒ 00:36:02.580 Amber Lin: whole 3… 365 days and do, like, a… Count distinct stores and…
346 00:36:03.060 ⇒ 00:36:07.710 Amber Lin: Like, new stores, we can also maybe do that?
347 00:36:08.880 ⇒ 00:36:12.019 Amber Lin: Do you think that’s… The way we should do it.
348 00:36:13.100 ⇒ 00:36:21.189 Advait Nandakumar Menon: That’s another way, like, basically implementing logic of sorts to compute the number.
349 00:36:21.770 ⇒ 00:36:32.909 Advait Nandakumar Menon: Or the other way, like, having a snapshot for each date, or week, or month, or whatever. There are two ways about it, but which one would give us the accurate.
350 00:36:33.940 ⇒ 00:36:35.860 Amber Lin: What modeling would be more accurate.
351 00:36:36.230 ⇒ 00:36:39.979 Amber Lin: My calculating it in Omni would be bad, would be a bad idea.
352 00:36:39.980 ⇒ 00:36:42.729 Advait Nandakumar Menon: Yeah, that’s what… I’m also thinking.
353 00:36:43.800 ⇒ 00:36:47.940 Amber Lin: Okay, I mean, we have the weekly and monthly calculations already.
354 00:36:48.300 ⇒ 00:36:54.019 Amber Lin: So, I think the only thing we need to do is do a… Let’s do a daily calculation.
355 00:36:55.240 ⇒ 00:36:55.940 Advait Nandakumar Menon: Okay.
356 00:36:56.190 ⇒ 00:36:59.859 Advait Nandakumar Menon: Yeah. You mean the… and monthly snapshot is there, and…
357 00:36:59.860 ⇒ 00:37:10.190 Amber Lin: I mean, this is our… this is our monthly, right? It already tells us, okay, active stores as of this month’s start date is… is this… is this value.
358 00:37:10.500 ⇒ 00:37:17.669 Amber Lin: And then we have, same for weekly, of giving this week’s start date, there was this number of active stores.
359 00:37:18.660 ⇒ 00:37:24.189 Advait Nandakumar Menon: Okay, and we can do a subtraction or whatever when they ask a specific question, is what you’re saying, right?
360 00:37:24.940 ⇒ 00:37:27.030 Amber Lin: Repeat that again?
361 00:37:27.410 ⇒ 00:37:35.029 Advait Nandakumar Menon: Like, a minus between both the numbers will give us the as-of date metric, is that what you’re saying?
362 00:37:36.930 ⇒ 00:37:44.900 Amber Lin: Yeah, I think so. So we’ll say as of this week’s start date, there was this many active stores.
363 00:37:45.350 ⇒ 00:37:46.070 Advait Nandakumar Menon: Huh.
364 00:37:46.470 ⇒ 00:37:47.040 Amber Lin: Yeah.
365 00:37:47.300 ⇒ 00:37:57.580 Amber Lin: We don’t yet have it for daily, I think, but we can easily model that. My question is, do you think we need all three of weekly
366 00:37:57.740 ⇒ 00:37:59.910 Amber Lin: Monthly and daily.
367 00:38:02.980 ⇒ 00:38:12.939 Advait Nandakumar Menon: Is there a possibility for them to ask, like, on a specific date, like, as of…
368 00:38:13.310 ⇒ 00:38:18.239 Advait Nandakumar Menon: Example, 15 January, what the active stores were. Is there…
369 00:38:18.910 ⇒ 00:38:24.779 Advait Nandakumar Menon: if there’s a probability of them asking questions like that, I would include daily in that case, because
370 00:38:25.900 ⇒ 00:38:29.030 Advait Nandakumar Menon: Do you think there’s any other way to answer a specific question like that?
371 00:38:29.030 ⇒ 00:38:37.169 Amber Lin: I think the most likely one would be asking, what is it like today, which means we need a daily snapshot.
372 00:38:38.670 ⇒ 00:38:39.380 Advait Nandakumar Menon: Okay.
373 00:38:40.420 ⇒ 00:38:42.700 Amber Lin: Right, or some version of that.
374 00:38:43.100 ⇒ 00:38:44.440 Advait Nandakumar Menon: Yeah, yeah.
375 00:38:44.440 ⇒ 00:38:45.560 Amber Lin: Yeah, okay.
376 00:38:46.160 ⇒ 00:38:46.840 Amber Lin: Mmm.
377 00:38:46.840 ⇒ 00:38:53.720 Advait Nandakumar Menon: I think, to not make it too complicated, I guess we can just have weekly and monthly for now.
378 00:38:53.720 ⇒ 00:38:54.420 Amber Lin: Okay.
379 00:38:54.610 ⇒ 00:38:55.700 Advait Nandakumar Menon: For the snapshot.
380 00:38:57.100 ⇒ 00:39:00.630 Amber Lin: Yeah, and then for the snapshot, I guess, like…
381 00:39:01.020 ⇒ 00:39:06.040 Amber Lin: If they’re asking just of today, I think Omni can… Calculate…
382 00:39:06.480 ⇒ 00:39:14.250 Amber Lin: That… I don’t know, I don’t think Omni can, because Omni needs to know the definition of churned, for example.
383 00:39:14.380 ⇒ 00:39:15.090 Amber Lin: And that’s it.
384 00:39:15.090 ⇒ 00:39:15.680 Advait Nandakumar Menon: Huh.
385 00:39:15.680 ⇒ 00:39:22.609 Amber Lin: specific definition. Anyways, let’s… let’s do the monthly… Summary, and the weekly summary.
386 00:39:22.610 ⇒ 00:39:23.480 Advait Nandakumar Menon: weekly funnel.
387 00:39:23.480 ⇒ 00:39:25.120 Amber Lin: Will we join that in?
388 00:39:26.730 ⇒ 00:39:28.980 Amber Lin: They’ll be joined on date, right?
389 00:39:29.640 ⇒ 00:39:30.690 Advait Nandakumar Menon: Mmm…
390 00:39:33.280 ⇒ 00:39:37.240 Amber Lin: Because that’s just one start date. It will have to be joined on the date.
391 00:39:39.050 ⇒ 00:39:42.169 Advait Nandakumar Menon: Yeah, join between what and what, sorry?
392 00:39:42.320 ⇒ 00:39:48.860 Amber Lin: Like, creating a topic needs a base table, and then you can join things in.
393 00:39:49.700 ⇒ 00:39:50.420 Advait Nandakumar Menon: Okay.
394 00:39:50.740 ⇒ 00:39:53.250 Amber Lin: So…
395 00:39:53.820 ⇒ 00:40:00.540 Amber Lin: So I guess now we’re on to the specifics of how do we join things, what’s the base table, how do we design this?
396 00:40:01.170 ⇒ 00:40:03.389 Amber Lin: topic. We have 20 minutes left.
397 00:40:03.520 ⇒ 00:40:07.859 Amber Lin: Do you think it should be fact sales? Or should it be, like, a…
398 00:40:11.270 ⇒ 00:40:13.619 Advait Nandakumar Menon: The base table is what you’re asking me?
399 00:40:13.620 ⇒ 00:40:14.849 Amber Lin: Yeah, yeah.
400 00:40:16.010 ⇒ 00:40:21.370 Advait Nandakumar Menon: Yeah, I, I think it should be fax sa… fax sales,
401 00:40:22.870 ⇒ 00:40:27.670 Advait Nandakumar Menon: Joined by, inventory by location to know whether it’s a store or not.
402 00:40:28.180 ⇒ 00:40:33.050 Advait Nandakumar Menon: Because that information is not there in fact sales, right? It’s just a retailer.
403 00:40:34.910 ⇒ 00:40:38.989 Amber Lin: Fat Sales has store ID, so you can actually…
404 00:40:38.990 ⇒ 00:40:41.089 Advait Nandakumar Menon: But does it… but does it…
405 00:40:42.360 ⇒ 00:40:43.660 Amber Lin: Right?
406 00:40:43.660 ⇒ 00:40:48.709 Advait Nandakumar Menon: So, indicate what is actually a store versus something like a distribution center?
407 00:40:49.230 ⇒ 00:40:56.170 Amber Lin: I mean, it will have the store number, right? So, it’ll have the store.
408 00:40:56.170 ⇒ 00:41:01.960 Advait Nandakumar Menon: Okay, if you’re saying… if you’re saying store ID is null, then it’s not a store, is that what you’re trying to say? In the file?
409 00:41:01.960 ⇒ 00:41:07.400 Amber Lin: Yeah, because, I mean, we saw that any distribution center doesn’t have a store ID.
410 00:41:09.660 ⇒ 00:41:15.489 Advait Nandakumar Menon: Yeah, that’s what we saw in inventory by location, but do you want to check for null store IDs in…
411 00:41:15.710 ⇒ 00:41:18.519 Advait Nandakumar Menon: The vaccine stable as well, and…
412 00:41:20.140 ⇒ 00:41:26.810 Advait Nandakumar Menon: I think that’s another thing worth checking, like, if both of them are matching, basically. I’m not sure if you’re understanding what I’m saying.
413 00:41:26.810 ⇒ 00:41:28.790 Amber Lin: Yeah, I think, I think so. Where…
414 00:41:28.790 ⇒ 00:41:29.380 Advait Nandakumar Menon: Okay.
415 00:41:29.670 ⇒ 00:41:38.710 Amber Lin: Store… Store ID… is…
416 00:41:38.710 ⇒ 00:41:39.279 Advait Nandakumar Menon: there’s no.
417 00:41:40.100 ⇒ 00:41:45.099 Amber Lin: I think, also, if we join DIM stores, that should be helpful.
418 00:41:45.370 ⇒ 00:41:53.730 Amber Lin: There’s nothing. There’s none, whereas… no. Like, DIM stores would have… Oh.
419 00:41:54.410 ⇒ 00:41:56.869 Amber Lin: Walmart store, Target store.
420 00:41:57.200 ⇒ 00:42:09.429 Amber Lin: Yeah, I think it doesn’t… it won’t have any, because as long as it’s… if we want to check, we can also always join into DIMS stores and say, hey, tell me if it’s in here.
421 00:42:09.800 ⇒ 00:42:13.039 Amber Lin: And distribution centers should not be in here.
422 00:42:13.980 ⇒ 00:42:17.249 Advait Nandakumar Menon: Okay, so this will be just the stores, DIM stores, right?
423 00:42:17.460 ⇒ 00:42:18.130 Amber Lin: Yeah.
424 00:42:19.550 ⇒ 00:42:20.170 Advait Nandakumar Menon: Okay.
425 00:42:20.350 ⇒ 00:42:20.950 Amber Lin: Yeah.
426 00:42:21.660 ⇒ 00:42:30.739 Amber Lin: Oh… Sorry, I forgot what we decided on here. Are we creating, like, a daily by store, by SKU?
427 00:42:31.600 ⇒ 00:42:31.950 Advait Nandakumar Menon: Dude.
428 00:42:32.180 ⇒ 00:42:37.059 Amber Lin: Some… a model like that, or are we just using fat sales as it is?
429 00:42:37.960 ⇒ 00:42:42.780 Advait Nandakumar Menon: I would say, daily, by store, by SKU.
430 00:42:43.220 ⇒ 00:42:53.300 Advait Nandakumar Menon: Okay. That’s what some of the questions here need, right? Like… If I’m not wrong.
431 00:42:54.250 ⇒ 00:42:59.250 Amber Lin: Yeah, I don’t think they’ll be asking by, like, fulfillment.
432 00:42:59.690 ⇒ 00:43:03.050 Amber Lin: I ordered service channel yet.
433 00:43:05.070 ⇒ 00:43:05.750 Advait Nandakumar Menon: Okay.
434 00:43:06.170 ⇒ 00:43:13.710 Amber Lin: Yeah, I guess my main question is, like, do we need to model it, or is it enough in Omni to say, like, do a sum based on that?
435 00:43:18.030 ⇒ 00:43:20.169 Advait Nandakumar Menon: the daily store by SKU thing?
436 00:43:20.370 ⇒ 00:43:24.289 Amber Lin: Yeah. Like, is it enough if we just have Omni
437 00:43:24.510 ⇒ 00:43:28.189 Amber Lin: do the sum over there, it’s a pretty simple calculation.
438 00:43:34.260 ⇒ 00:43:42.289 Advait Nandakumar Menon: So, we have, for the daily, we have the business date and the… calendar… DIM calendar, right?
439 00:43:44.340 ⇒ 00:43:45.799 Advait Nandakumar Menon: Is that what we’re using?
440 00:43:46.780 ⇒ 00:43:50.520 Amber Lin: Mmm… Repeat that again?
441 00:43:51.080 ⇒ 00:43:55.949 Advait Nandakumar Menon: For bi-daily, by store, by SKU, for…
442 00:43:56.480 ⇒ 00:43:57.910 Advait Nandakumar Menon: bi-daily aspect.
443 00:43:58.870 ⇒ 00:44:07.200 Advait Nandakumar Menon: Do we… are we relying on the business date field in fact, sales, or the DIM calendar table?
444 00:44:08.230 ⇒ 00:44:19.770 Amber Lin: I think we would use this business date. I mean, DIM cal… all the DIM tables are just to, like, additional information, so I don’t think these matter as much.
445 00:44:20.770 ⇒ 00:44:25.290 Advait Nandakumar Menon: Okay, and there is also the store level of detail.
446 00:44:25.660 ⇒ 00:44:30.750 Advait Nandakumar Menon: impact sales, and there is SKU as well, right? I don’t… I don’t think we talked about that.
447 00:44:30.750 ⇒ 00:44:40.740 Amber Lin: Yeah, there should be, let’s, let’s read this DIM products table. Yeah, okay, so you’re right, this product SK is there, just, like.
448 00:44:41.410 ⇒ 00:44:46.559 Amber Lin: product ID, because that’s what they have in… DIM products.
449 00:44:47.710 ⇒ 00:44:48.380 Advait Nandakumar Menon: Okay.
450 00:44:48.940 ⇒ 00:44:49.530 Amber Lin: Yeah.
451 00:44:50.600 ⇒ 00:44:53.890 Amber Lin: So we’ll say this… Sorry, go ahead.
452 00:44:54.470 ⇒ 00:44:59.090 Advait Nandakumar Menon: Yeah, so we have the information in fact, sale, so… If it’s…
453 00:44:59.460 ⇒ 00:45:04.209 Advait Nandakumar Menon: That’s a pretty simple sound, and I think Omni should be able to do it.
454 00:45:04.210 ⇒ 00:45:08.170 Amber Lin: Okay. Yeah, let’s… let’s try it in Omni, and then let’s see how it…
455 00:45:08.330 ⇒ 00:45:17.489 Amber Lin: Let’s see how it goes. If it fails on the questions, we’ll make a model. Stores, we can join on store, SK…
456 00:45:18.390 ⇒ 00:45:25.250 Amber Lin: Base… Face painful.
457 00:45:25.880 ⇒ 00:45:37.609 Amber Lin: And then… inventory… by location… Okay, we can join on product… SK and…
458 00:45:38.350 ⇒ 00:45:40.620 Amber Lin: Do they have a Storis K here?
459 00:45:41.350 ⇒ 00:45:42.310 Amber Lin: Hmm.
460 00:45:44.410 ⇒ 00:45:48.540 Amber Lin: I think we’ll have to… Wait, what?
461 00:45:49.880 ⇒ 00:45:56.449 Amber Lin: I think we’ll have to… let’s investigate this table, because you see they have a store ID and a DC number.
462 00:45:57.010 ⇒ 00:45:59.330 Amber Lin: I think this one will include…
463 00:45:59.720 ⇒ 00:46:06.399 Amber Lin: Oh, you’re… this does include, like, a… Distribu… distribution center, and…
464 00:46:06.720 ⇒ 00:46:07.920 Advait Nandakumar Menon: Yeah, the store type.
465 00:46:07.920 ⇒ 00:46:08.420 Amber Lin: Let’s go.
466 00:46:08.420 ⇒ 00:46:12.330 Advait Nandakumar Menon: Redfield indicates whether it’s a store or a distribution center.
467 00:46:12.330 ⇒ 00:46:12.700 Amber Lin: I swear.
468 00:46:12.700 ⇒ 00:46:14.340 Advait Nandakumar Menon: I mean, the location type, the location.
469 00:46:14.340 ⇒ 00:46:19.290 Amber Lin: How will we join that in? Inventory…
470 00:46:21.020 ⇒ 00:46:27.029 Amber Lin: Like, how do we plan to join that? Because I think we have to join on multiple?
471 00:46:32.160 ⇒ 00:46:33.950 Advait Nandakumar Menon: Yeah, let me check that.
472 00:46:34.230 ⇒ 00:46:34.940 Amber Lin: Yeah.
473 00:47:27.840 ⇒ 00:47:36.170 Amber Lin: I guess we can use store… there should be store ID in… Facts.
474 00:47:36.170 ⇒ 00:47:36.640 Advait Nandakumar Menon: stores?
475 00:47:36.640 ⇒ 00:47:43.069 Amber Lin: Yeah, there definitely is in dim stores, but I’m just thinking if we join it to the base table…
476 00:47:43.520 ⇒ 00:47:48.369 Amber Lin: Is there… yeah, there’s a… there’s a store ID, so I guess we can join on.
477 00:47:51.250 ⇒ 00:47:53.579 Amber Lin: Join on store ID, and…
478 00:47:57.660 ⇒ 00:48:03.010 Amber Lin: Join our product screens for ID.
479 00:48:09.080 ⇒ 00:48:11.409 Amber Lin: And business day, I think?
480 00:48:11.890 ⇒ 00:48:14.750 Amber Lin: And business date, yeah.
481 00:48:16.550 ⇒ 00:48:19.259 Amber Lin: is not a state.
482 00:48:19.670 ⇒ 00:48:20.330 Advait Nandakumar Menon: Okay.
483 00:48:24.240 ⇒ 00:48:25.190 Amber Lin: Alright.
484 00:48:25.670 ⇒ 00:48:29.290 Amber Lin: If we want to join these in.
485 00:48:30.030 ⇒ 00:48:32.059 Advait Nandakumar Menon: Now, this is for snapshots specifically.
486 00:48:32.060 ⇒ 00:48:36.739 Amber Lin: Yeah, do you think we should have the snapshots as, like, a different topic?
487 00:48:36.740 ⇒ 00:48:39.359 Advait Nandakumar Menon: I… I was about to…
488 00:48:39.770 ⇒ 00:48:43.520 Amber Lin: Right? Because this one is so big already. Okay, let’s…
489 00:48:43.520 ⇒ 00:48:44.370 Advait Nandakumar Menon: Yeah.
490 00:48:46.680 ⇒ 00:48:50.710 Amber Lin: Because this number of stores and all that, like.
491 00:48:51.210 ⇒ 00:48:53.420 Amber Lin: I don’t think they’re gonna ask about…
492 00:48:53.690 ⇒ 00:48:57.309 Amber Lin: Revenue and number of stores in the same question?
493 00:49:00.000 ⇒ 00:49:04.390 Advait Nandakumar Menon: So multiple topics can serve the same dashboard, right?
494 00:49:04.910 ⇒ 00:49:05.819 Amber Lin: Yeah, yeah.
495 00:49:07.470 ⇒ 00:49:08.190 Advait Nandakumar Menon: Okay.
496 00:49:09.240 ⇒ 00:49:15.380 Advait Nandakumar Menon: So, how does… this is maybe dumb to ask, because I’m so new to Omni.
497 00:49:15.540 ⇒ 00:49:19.439 Advait Nandakumar Menon: How will… when the user asks some question, how will…
498 00:49:19.580 ⇒ 00:49:23.310 Advait Nandakumar Menon: Blobby or whatever will know which topic to refer to.
499 00:49:23.310 ⇒ 00:49:24.399 Amber Lin: Yeah, there’s some…
500 00:49:25.050 ⇒ 00:49:34.970 Amber Lin: Yeah, keep going, keep going. I just… I just recalled Greg educated me on this, like, a day ago, so I’m… I just got the answer.
501 00:49:35.850 ⇒ 00:49:47.409 Advait Nandakumar Menon: Yeah, no, that’s fine. So, my question is, like, so we are interacting with the dashboard, and it has, like, 2 or 3 topics under it. One will be related to the revenue and the sales, which…
502 00:49:47.550 ⇒ 00:49:51.670 Advait Nandakumar Menon: This is the first topic we were working on. But what if…
503 00:49:52.190 ⇒ 00:49:59.699 Advait Nandakumar Menon: for the snapshot, we are building another topic. How will Omni or Blobby know to go refer to that topic? So, how will we know that?
504 00:50:00.010 ⇒ 00:50:11.230 Amber Lin: Yeah, when we build the dashboards, we can select a topic here, so that’s not an issue. I think the main thing is we’ll blobby select the right one, and I think,
505 00:50:12.450 ⇒ 00:50:17.150 Amber Lin: If we’re just in the dashboard, I think it will ask different…
506 00:50:17.290 ⇒ 00:50:20.170 Amber Lin: Like, right now, I don’t know how I can…
507 00:50:20.370 ⇒ 00:50:22.880 Amber Lin: Do this, but if we just go to…
508 00:50:23.370 ⇒ 00:50:26.739 Amber Lin: The regular blobby, you can select a topic.
509 00:50:27.860 ⇒ 00:50:28.460 Amber Lin: Or…
510 00:50:28.460 ⇒ 00:50:30.459 Advait Nandakumar Menon: It will be up to the user to select.
511 00:50:31.020 ⇒ 00:50:35.320 Advait Nandakumar Menon: Will it be up to the user to select the correct topic and ask the correct question? Is that what is.
512 00:50:36.810 ⇒ 00:50:47.229 Amber Lin: it can auto-select, and if you know what topic you’re talking about, it can… you can select, like, there’s an option to manually select, that’s all.
513 00:50:47.760 ⇒ 00:50:52.030 Advait Nandakumar Menon: Okay, so how good it is… is it at auto-selecting is probably…
514 00:50:52.030 ⇒ 00:51:03.869 Amber Lin: Auto-selecting will be… let me show you, when we develop topics, this is… this is actually really helpful. So, when we develop topics, let’s say element…
515 00:51:04.000 ⇒ 00:51:08.750 Amber Lin: And then we have a topic here, let’s say this is for…
516 00:51:08.930 ⇒ 00:51:14.379 Amber Lin: Retail product performance. You’ll give us some context.
517 00:51:14.540 ⇒ 00:51:24.050 Amber Lin: So, when you create it, you’ll have contacts here, say, use this topic when you want to do these things, do these things, do these things. So, if we…
518 00:51:24.220 ⇒ 00:51:31.560 Amber Lin: prompted correctly, when AI selects, it will know. So this is the key to having it select correctly, and we can.
519 00:51:31.560 ⇒ 00:51:32.500 Advait Nandakumar Menon: The AI conflict.
520 00:51:32.500 ⇒ 00:51:37.359 Amber Lin: around with, like, how we describe this here, to see if it… Okay.
521 00:51:38.740 ⇒ 00:51:39.440 Advait Nandakumar Menon: Okay.
522 00:51:39.660 ⇒ 00:51:46.699 Amber Lin: Yeah, let’s see, you can define what the grain is here, like, what joins we used, like.
523 00:51:46.800 ⇒ 00:51:52.380 Amber Lin: Description, AI context… So, yeah, that’s… that was a great question.
524 00:51:53.160 ⇒ 00:52:00.959 Advait Nandakumar Menon: Okay, so these topics we create… is that why you asked me to install the CLI? We’ll be doing it all here on Courser?
525 00:52:00.960 ⇒ 00:52:01.570 Amber Lin: Yeah.
526 00:52:01.710 ⇒ 00:52:03.759 Advait Nandakumar Menon: And push it forward. Because that makes it…
527 00:52:03.760 ⇒ 00:52:12.550 Amber Lin: Yeah, that makes… if you want to update, say, 5 things at a time, you just tell Cursor, can you do it, and you don’t have to go in and find each one of them.
528 00:52:14.010 ⇒ 00:52:24.240 Advait Nandakumar Menon: Okay, but… so, that was one of my questions, is why we’re doing it through CLI. I know it’s easier here, but is there the option to do it on the browser UI as well, like…
529 00:52:24.240 ⇒ 00:52:37.349 Amber Lin: Yeah, yeah, there’s… you can totally do it on the browser. I’m just thinking more of, it’s… for me, it’s easier to read what I have here, and I… and, like…
530 00:52:38.500 ⇒ 00:52:45.229 Amber Lin: when we build dashboards, I think we will… I think we have the option to do it on…
531 00:52:45.410 ⇒ 00:52:51.470 Amber Lin: the browser or do it in here. I just think it’ll be easier if we have all the context
532 00:52:51.570 ⇒ 00:52:56.500 Amber Lin: Down here, just in case you want to use AI to develop dashboards in the future.
533 00:52:57.650 ⇒ 00:53:01.790 Advait Nandakumar Menon: Okay. And obviously, the dashboard will be fully code here, right?
534 00:53:02.860 ⇒ 00:53:07.590 Amber Lin: I think so, I think they just came out with, like, an Omni…
535 00:53:07.990 ⇒ 00:53:15.230 Amber Lin: I think Utam sent the link that you… we can build dashboards with AI now, here. I think they just developed something.
536 00:53:15.880 ⇒ 00:53:17.950 Advait Nandakumar Menon: Oh, okay. Yeah. Okay.
537 00:53:19.970 ⇒ 00:53:23.919 Amber Lin: Cool. We have 5 minutes left, I think.
538 00:53:23.920 ⇒ 00:53:25.669 Advait Nandakumar Menon: Yeah, sorry for going off the…
539 00:53:25.670 ⇒ 00:53:32.239 Amber Lin: No, no, no, all good. I think we arrived at a pretty good thing, like, we want a different topic here, right? We want…
540 00:53:32.580 ⇒ 00:53:40.170 Amber Lin: Retail sales performance… Stores Overview.
541 00:53:40.850 ⇒ 00:53:43.340 Advait Nandakumar Menon: Yeah, that is more for a snapshot, right?
542 00:53:43.800 ⇒ 00:53:54.400 Amber Lin: Yeah, for… Snapshots… Number… For stores, active stores, trend stores…
543 00:53:57.290 ⇒ 00:54:02.749 Amber Lin: Wait, do you think that’s the only thing they will be asking in the SOAR overview?
544 00:54:04.140 ⇒ 00:54:14.309 Amber Lin: Like, do you think if a user asks, for example, I don’t know.
545 00:54:17.690 ⇒ 00:54:22.910 Amber Lin: Do you think… I guess my question is, do you think we can answer these questions now with this… with this topic?
546 00:54:24.090 ⇒ 00:54:30.720 Advait Nandakumar Menon: Maybe, do you mean, like, something like, what is the sales of…
547 00:54:32.190 ⇒ 00:54:35.389 Advait Nandakumar Menon: the churn stores, or something like that. Are you…
548 00:54:36.120 ⇒ 00:54:43.149 Advait Nandakumar Menon: thinking of questions wherein the context for the… I mean, the two different topics can be combined and a.
549 00:54:43.150 ⇒ 00:54:44.279 Amber Lin: Yeah, yeah, yeah.
550 00:54:44.280 ⇒ 00:54:44.940 Advait Nandakumar Menon: let us…
551 00:54:44.940 ⇒ 00:54:49.900 Amber Lin: Yeah, that’s… that’s a perfect summary. I was like, would they be asking these two things at the same time?
552 00:54:49.900 ⇒ 00:54:58.940 Advait Nandakumar Menon: So, then again, I would ask you, like, how will Lobier, like, Omni will know to combine both of them?
553 00:54:58.940 ⇒ 00:55:09.950 Amber Lin: I don’t think it comb… let me… let’s, let’s, let’s try. Can you answer questions?
554 00:55:10.200 ⇒ 00:55:16.890 Amber Lin: from… By using more than one topic?
555 00:55:22.870 ⇒ 00:55:26.180 Amber Lin: It can’t, but it can rumse up a crease.
556 00:55:27.150 ⇒ 00:55:29.460 Amber Lin: And the input results into another.
557 00:55:32.200 ⇒ 00:55:32.849 Amber Lin: Let’s see…
558 00:55:38.470 ⇒ 00:55:39.330 Amber Lin: Yeah.
559 00:55:40.460 ⇒ 00:55:46.010 Advait Nandakumar Menon: I mean… They can ask, there’s a possibility, like.
560 00:55:47.070 ⇒ 00:55:55.940 Advait Nandakumar Menon: Something that combines the… the overall view, like the daily sales, and, like, combining with the snapshot.
561 00:55:56.810 ⇒ 00:56:02.420 Advait Nandakumar Menon: topic, I think it’s a possibility, but then, with respect to modeling, then it…
562 00:56:03.150 ⇒ 00:56:04.049 Amber Lin: And it makes us so.
563 00:56:04.050 ⇒ 00:56:04.670 Advait Nandakumar Menon: etc.
564 00:56:04.670 ⇒ 00:56:15.369 Amber Lin: It makes it so big. I… you know what, let’s… let’s just keep it like this, and then we’ll keep these things. I… I’m just worried they’ll start asking, hey, give me the…
565 00:56:15.520 ⇒ 00:56:21.469 Amber Lin: Like, if they were to ask, like, number of stores out of stock, I think it will go to topic 2, right?
566 00:56:23.130 ⇒ 00:56:30.749 Amber Lin: If they ask, are retailers stocking out, it will go to, like, this table and look at the out-of-stock tag.
567 00:56:30.970 ⇒ 00:56:31.940 Amber Lin: I think.
568 00:56:31.940 ⇒ 00:56:32.720 Advait Nandakumar Menon: Yeah.
569 00:56:32.720 ⇒ 00:56:46.559 Amber Lin: Okay, okay, sounds good. So for this, we’ll just create a different topic. Wait, how do… how would this weekly and monthly sales summer be in the same topic?
570 00:56:47.130 ⇒ 00:56:49.069 Amber Lin: Would it just be different topics?
571 00:56:50.030 ⇒ 00:56:53.850 Advait Nandakumar Menon: Yeah, I don’t think you can… Oh.
572 00:56:54.290 ⇒ 00:56:58.070 Amber Lin: Like, technically, we can… no, I don’t… I don’t think we can.
573 00:56:58.070 ⇒ 00:57:01.709 Advait Nandakumar Menon: Do you think this is,
574 00:57:01.820 ⇒ 00:57:09.590 Advait Nandakumar Menon: Do you think this is a situation wherein if we have a daily summary, we can just work our way up to weekly and then monthly?
575 00:57:10.250 ⇒ 00:57:15.250 Amber Lin: That’s not… Okay, my question here is, say it’s…
576 00:57:15.430 ⇒ 00:57:24.819 Amber Lin: It’s, let’s pull up this table. I also… I don’t think I have anything after this, so if you can stay for, like, 5 more minutes, that would be great.
577 00:57:24.820 ⇒ 00:57:29.590 Advait Nandakumar Menon: Yeah, I had… I had another meeting, but it’s rescheduled to tomorrow, so…
578 00:57:29.590 ⇒ 00:57:34.619 Amber Lin: Oh, okay. Cool. So, let’s look at this definition here. It says.
579 00:57:35.440 ⇒ 00:57:44.760 Amber Lin: just, just, like, these top ones. Total source, these. So if we had a daily one, can we roll it up to weekly and monthly?
580 00:57:44.990 ⇒ 00:57:46.720 Amber Lin: I think some of these?
581 00:57:47.690 ⇒ 00:57:52.579 Amber Lin: Like, new stores, we can do a cumulative, but total stores…
582 00:57:52.890 ⇒ 00:57:56.440 Amber Lin: It’s like an end-of-month snapshot, right?
583 00:57:56.740 ⇒ 00:57:59.889 Amber Lin: Like, some of these don’t roll up in the same way.
584 00:58:01.930 ⇒ 00:58:02.460 Amber Lin: It’s what…
585 00:58:02.460 ⇒ 00:58:03.420 Advait Nandakumar Menon: Focus.
586 00:58:07.440 ⇒ 00:58:16.670 Advait Nandakumar Menon: I mean… won’t, like, if you sum up, for example, February, you’re summing up all the days.
587 00:58:16.920 ⇒ 00:58:23.219 Advait Nandakumar Menon: one that… reflect the end of month for February, will it not? Or am I wrong?
588 00:58:23.220 ⇒ 00:58:33.009 Amber Lin: Yeah, I’m just… I think for some of these, it will work, but say we have a daily snapshot, and every day there’s, like.
589 00:58:33.210 ⇒ 00:58:40.429 Amber Lin: for this whole month, it was at 5,000. We can’t sum it up to say 30 times 5,000 is your total stores.
590 00:58:40.960 ⇒ 00:58:48.439 Amber Lin: Because that’s, like, that’s just a daily snapshot. So at the end of month, we can only take the end of month snapshot.
591 00:58:48.440 ⇒ 00:58:52.460 Advait Nandakumar Menon: Oh, yung… Oh, you mean, like.
592 00:58:52.460 ⇒ 00:58:58.850 Amber Lin: I mean, like, each of these different metrics will roll out differently from a daily to a monthly view.
593 00:59:00.770 ⇒ 00:59:01.500 Advait Nandakumar Menon: Okay.
594 00:59:02.000 ⇒ 00:59:12.569 Advait Nandakumar Menon: Okay. You mean, for example, for the example February example I just said, the daily snapshot will start with 5,000 and…
595 00:59:12.780 ⇒ 00:59:21.609 Advait Nandakumar Menon: The end of month snapshot will end up with 6,000. You can’t really add those and say that’s the total… Yeah. That’s what you mean, right?
596 00:59:21.610 ⇒ 00:59:31.529 Amber Lin: Yeah, because, like, look at this, this is the weekly, but I can’t say at end of month, I can’t say you have 20,000 stores. They have, like, maybe a little bit more than this.
597 00:59:31.720 ⇒ 00:59:33.099 Amber Lin: Right? This is what they.
598 00:59:33.100 ⇒ 00:59:33.850 Advait Nandakumar Menon: Okay.
599 00:59:34.100 ⇒ 00:59:46.819 Advait Nandakumar Menon: In that case, in that case, like, obviously, since it’s daily, so end of February, we are saying it’s 6,000, but we… we can take max, right, max day.
600 00:59:47.160 ⇒ 00:59:50.859 Advait Nandakumar Menon: The month, and then use that as the final value for that month.
601 00:59:51.070 ⇒ 00:59:52.659 Advait Nandakumar Menon: Instead of summing it up.
602 00:59:53.740 ⇒ 00:59:54.430 Amber Lin: Yeah.
603 00:59:55.330 ⇒ 00:59:56.230 Amber Lin: like…
604 00:59:56.490 ⇒ 01:00:14.120 Amber Lin: I think, yes, yes, I agree. Do you think Omni will be smart enough and not just do a sum? Like, where would… where would we tell it to say, hey, you have to take a max for this? We can do it on a dashboard, but if it’s asked via AI, I don’t know.
605 01:00:18.700 ⇒ 01:00:27.340 Advait Nandakumar Menon: maybe we need to specify it in the AI context, like, if…
606 01:00:28.340 ⇒ 01:00:31.819 Advait Nandakumar Menon: Accumulative values asked for that month.
607 01:00:32.000 ⇒ 01:00:38.900 Advait Nandakumar Menon: Then use the max, because… The value we see on a day-to-day, that is the daily basis, is
608 01:00:39.630 ⇒ 01:00:47.820 Advait Nandakumar Menon: Not a val- not the value that’s representing each day, but it’s something that’s getting cumulative over the days, so…
609 01:00:48.990 ⇒ 01:00:57.410 Advait Nandakumar Menon: with that in mind, maybe take the max value. Can we give some context like that in the AI context section you just showed?
610 01:00:58.600 ⇒ 01:01:03.989 Amber Lin: We can… we can try. I haven’t tried that yet, so that could be possible. Like, let…
611 01:01:03.990 ⇒ 01:01:07.149 Advait Nandakumar Menon: Is this the first implementation for Omni where
612 01:01:07.340 ⇒ 01:01:11.579 Advait Nandakumar Menon: going with? I mean, to the client, is this going to be the first one?
613 01:01:12.870 ⇒ 01:01:19.420 Amber Lin: Yeah, I mean, we showed them a demo, but they haven’t reused it at all, because we’re.
614 01:01:19.420 ⇒ 01:01:25.380 Advait Nandakumar Menon: No, no, I mean, with respect to Brainforge and Omni, like, is this the first client we are going to deliver something like this, or have we.
615 01:01:25.380 ⇒ 01:01:31.939 Amber Lin: We did one of this for Eden, so I… we do have someone we can ask on, like, contacts.
616 01:01:31.940 ⇒ 01:01:32.580 Advait Nandakumar Menon: Okay.
617 01:01:32.850 ⇒ 01:01:33.420 Amber Lin: Yeah.
618 01:01:33.710 ⇒ 01:01:39.199 Amber Lin: So, I guess what we’re deciding is, do we just do a daily view, or do we.
619 01:01:39.200 ⇒ 01:01:40.380 Advait Nandakumar Menon: Yeah, I would…
620 01:01:40.380 ⇒ 01:01:42.490 Amber Lin: Monthly, daily. I would say…
621 01:01:43.200 ⇒ 01:01:48.380 Advait Nandakumar Menon: I would say a daily would be helpful, because having a monthly, weekly, and daily is…
622 01:01:48.760 ⇒ 01:01:54.700 Advait Nandakumar Menon: When we’re talking about it a couple of minutes back, it’s adding to the complexity of the modeling, right?
623 01:01:54.800 ⇒ 01:02:02.459 Advait Nandakumar Menon: So… if we have a daily, I think we can work our way upwards. Like, having it at the…
624 01:02:03.140 ⇒ 01:02:07.870 Advait Nandakumar Menon: Lowest grain and then working its way upwards would make sense…
625 01:02:09.230 ⇒ 01:02:17.060 Advait Nandakumar Menon: Provided as long as Omni or the context we are going to set it up for Omni and Blobby would…
626 01:02:17.190 ⇒ 01:02:21.219 Advait Nandakumar Menon: Help to answer weekly and monthly questions as well.
627 01:02:22.390 ⇒ 01:02:23.240 Amber Lin: Gotcha.
628 01:02:23.560 ⇒ 01:02:26.680 Amber Lin: Daily total storage.
629 01:02:37.010 ⇒ 01:02:39.550 Advait Nandakumar Menon: If not, I think we need to have
630 01:02:39.770 ⇒ 01:02:48.530 Advait Nandakumar Menon: 3 different topics for 3 different, like, monthly, weekly, and daily, because I don’t think there’s any way to join it otherwise.
631 01:02:48.760 ⇒ 01:02:53.769 Amber Lin: Yeah, I’m… yeah, I agree, because they’re on different grains, like.
632 01:02:54.630 ⇒ 01:02:55.510 Advait Nandakumar Menon: Yeah.
633 01:02:55.510 ⇒ 01:03:02.140 Amber Lin: It’s… this is by… by… Retailer?
634 01:03:02.680 ⇒ 01:03:03.350 Amber Lin: Bye.
635 01:03:03.350 ⇒ 01:03:04.860 Advait Nandakumar Menon: But retail, just by.
636 01:03:04.860 ⇒ 01:03:07.089 Amber Lin: I think just by retail.
637 01:03:07.360 ⇒ 01:03:08.050 Amber Lin: I think.
638 01:03:09.170 ⇒ 01:03:11.929 Amber Lin: Let’s… let’s see… yeah, just buy retail one.
639 01:03:13.530 ⇒ 01:03:14.280 Advait Nandakumar Menon: Okay.
640 01:03:14.280 ⇒ 01:03:16.379 Amber Lin: Do you think we can join it by that?
641 01:03:17.980 ⇒ 01:03:20.130 Amber Lin: I don’t know, it sounds weird.
642 01:03:20.870 ⇒ 01:03:22.210 Advait Nandakumar Menon: Just by the retail.
643 01:03:22.620 ⇒ 01:03:27.469 Amber Lin: Yeah, just joined by, like, retailer, and…
644 01:03:27.660 ⇒ 01:03:30.439 Amber Lin: We can also join by,
645 01:03:31.290 ⇒ 01:03:34.900 Amber Lin: What is it? The quarter start date?
646 01:03:36.830 ⇒ 01:03:38.400 Amber Lin: And then say…
647 01:03:38.540 ⇒ 01:03:44.589 Amber Lin: Hey, these rows have the same quarter start date. I don’t know if that’s the right way to go.
648 01:03:45.260 ⇒ 01:03:49.669 Advait Nandakumar Menon: Quarter start date is a field that’s gonna repeat across the weekly, monthly, and daily.
649 01:03:50.760 ⇒ 01:03:51.990 Advait Nandakumar Menon: Will that be the case?
650 01:03:52.470 ⇒ 01:03:59.490 Amber Lin: Technically, yeah, but, like, it just… like, we artificially determined, hey, we added a quarter start date.
651 01:03:59.610 ⇒ 01:04:03.679 Amber Lin: So… Maybe that’s just not a possible thing to do.
652 01:04:04.970 ⇒ 01:04:09.650 Advait Nandakumar Menon: Okay, I guess if retailer and quarter date
653 01:04:10.030 ⇒ 01:04:13.870 Advait Nandakumar Menon: It’s something that’s going to be consistent across the daily, weekly, and monthly
654 01:04:14.230 ⇒ 01:04:18.840 Advait Nandakumar Menon: tables, then I guess we can join on that, and we can try it out, maybe?
655 01:04:18.840 ⇒ 01:04:22.469 Amber Lin: Can we? Okay, we’ll try it out. I, like, my mind’s not…
656 01:04:23.070 ⇒ 01:04:35.369 Amber Lin: comprehending how the, like, multiple days in a week are going to join to, like, one week. So, like, I don’t completely comprehend that yet. We’ll see.
657 01:04:35.370 ⇒ 01:04:39.540 Advait Nandakumar Menon: You mean, working from the lower level to the upper level, is that what you’re saying?
658 01:04:39.750 ⇒ 01:04:55.630 Amber Lin: Yeah, like, if… what if this week’s start day has 7 days? Like, what will the join do? Where is all the… because all of the other days are rows, so how is… how is the values… where are the values gonna go if we join by, like, week start date?
659 01:04:56.230 ⇒ 01:04:59.180 Amber Lin: Go join it on… I just don’t…
660 01:04:59.180 ⇒ 01:05:09.280 Advait Nandakumar Menon: We can’t join on weeks that way, that’s for sure, like, since the monthly, weekly, and daily, the brain is going to be different, we can’t…
661 01:05:09.520 ⇒ 01:05:12.799 Advait Nandakumar Menon: Really join on something that’s gonna differ between the…
662 01:05:12.960 ⇒ 01:05:23.560 Advait Nandakumar Menon: three tables, so if what you’re saying is true, that the quarter field and the retailer field is going to be consistent across the three tables, then that’s what I would use to join.
663 01:05:24.390 ⇒ 01:05:30.850 Amber Lin: I see. What would happen if we join it? I guess it’s… okay, like, a month…
664 01:05:30.850 ⇒ 01:05:31.579 Advait Nandakumar Menon: Okay, so this is.
665 01:05:31.580 ⇒ 01:05:35.170 Amber Lin: Where would the… if we joined them, what would it look like?
666 01:05:36.380 ⇒ 01:05:40.309 Advait Nandakumar Menon: So what will you join? So that, that was the weekly view, right?
667 01:05:40.730 ⇒ 01:05:46.479 Amber Lin: Yeah, so if we were to join weekly, monthly, and daily together, what is that table going to look like?
668 01:05:49.510 ⇒ 01:05:55.929 Advait Nandakumar Menon: So, it will have the metric, it’ll have the retailer, and it’ll have the quarter as the consistent fields, right?
669 01:05:56.370 ⇒ 01:05:59.110 Advait Nandakumar Menon: Then it should look like…
670 01:05:59.490 ⇒ 01:06:03.879 Advait Nandakumar Menon: Week start date and its value, month start date and its…
671 01:06:04.080 ⇒ 01:06:08.640 Advait Nandakumar Menon: value, and then the daily… I mean, this is all tier…
672 01:06:08.750 ⇒ 01:06:10.659 Advait Nandakumar Menon: In theory, it should look like that.
673 01:06:12.180 ⇒ 01:06:17.640 Amber Lin: I see, so sometimes the week’s start date would just be… Beep.
674 01:06:18.090 ⇒ 01:06:25.370 Amber Lin: blank? Like, the… or some row, so you’re saying there’s one row for each day, and then
675 01:06:25.570 ⇒ 01:06:30.589 Amber Lin: Like, for example, 4 rows will have the same value because it’s the same week.
676 01:06:30.990 ⇒ 01:06:31.970 Amber Lin: Yeah.
677 01:06:32.000 ⇒ 01:06:33.910 Advait Nandakumar Menon: The month will… yeah.
678 01:06:33.910 ⇒ 01:06:42.010 Amber Lin: The month will look the same for those 4 rows, and then maybe… the week will look the same for those 4 rows, and then, say, like, 30 rows would have the same.
679 01:06:42.010 ⇒ 01:06:42.550 Advait Nandakumar Menon: Yeah.
680 01:06:42.550 ⇒ 01:06:44.250 Amber Lin: Month start date.
681 01:06:44.560 ⇒ 01:06:47.949 Advait Nandakumar Menon: Yeah, I think now that I think of it, that’s…
682 01:06:48.930 ⇒ 01:06:51.799 Advait Nandakumar Menon: I think it’s gonna complicate things a little.
683 01:06:51.800 ⇒ 01:06:54.029 Amber Lin: It sounds a little silly.
684 01:06:54.030 ⇒ 01:06:54.910 Advait Nandakumar Menon: Yeah.
685 01:06:55.600 ⇒ 01:07:02.230 Advait Nandakumar Menon: Yeah, that’s one way to join, but if you want to join, or, like, we can keep it separate topics as well, but…
686 01:07:02.950 ⇒ 01:07:09.809 Amber Lin: Okay. I mean, it… it… It kind of makes sense to me, and then we can try it.
687 01:07:10.060 ⇒ 01:07:11.449 Amber Lin: We can try.
688 01:07:11.660 ⇒ 01:07:14.279 Amber Lin: But we’ll, we’ll see.
689 01:07:14.740 ⇒ 01:07:17.529 Amber Lin: We’ll need to… let’s make a daily view first.
690 01:07:18.130 ⇒ 01:07:21.550 Amber Lin: Okay. And then we’ll, we’ll see if the join works.
691 01:07:22.000 ⇒ 01:07:22.580 Amber Lin: Okay.
692 01:07:22.580 ⇒ 01:07:30.760 Advait Nandakumar Menon: So, the daily view is not something, even on the spreadsheet, or is it just not on Snowflake… not in Snowflake, we don’t have it.
693 01:07:30.760 ⇒ 01:07:42.389 Amber Lin: Yeah, because I don’t think they had a need for the daily one when we created the spreadsheet, but I can generate a model pretty easily based on
694 01:07:42.520 ⇒ 01:07:48.139 Amber Lin: like, the… the weekly sales summary, I think I can generate one.
695 01:07:48.760 ⇒ 01:07:52.959 Amber Lin: I think I made one earlier, I just didn’t approve it in GitHub.
696 01:07:53.270 ⇒ 01:07:55.780 Amber Lin: I think I have. I think I have this.
697 01:07:56.310 ⇒ 01:07:56.860 Amber Lin: Yeah.
698 01:07:56.860 ⇒ 01:07:58.609 Advait Nandakumar Menon: Did you use cursor for this?
699 01:07:58.610 ⇒ 01:08:06.890 Amber Lin: Yeah, I just said, hey, this is the model. I sent it the weekly and the monthly, and I said, hey, generate a model for me.
700 01:08:07.230 ⇒ 01:08:14.869 Amber Lin: And I think it’s what it did, but this might… I might be hallucinating, I might have did that for wholesale instead of retail.
701 01:08:16.200 ⇒ 01:08:16.899 Advait Nandakumar Menon: Okay.
702 01:08:17.390 ⇒ 01:08:20.639 Advait Nandakumar Menon: And you pushed it to DBT, is it? This one?
703 01:08:20.640 ⇒ 01:08:21.899 Amber Lin: Now push it to GitHub.
704 01:08:22.439 ⇒ 01:08:23.599 Advait Nandakumar Menon: You’re talking, okay.
705 01:08:23.600 ⇒ 01:08:24.300 Amber Lin: Yeah.
706 01:08:24.300 ⇒ 01:08:26.010 Advait Nandakumar Menon: And dbt will take it from there.
707 01:08:28.540 ⇒ 01:08:43.800 Amber Lin: I think dbt is just orchestrating how we execute the SQL, so I haven’t touched dbt at all. I think I just say dbt just as an easy way to refer all the modeling that happens, but, like, the modeling.
708 01:08:43.800 ⇒ 01:08:48.029 Advait Nandakumar Menon: Yeah, I mean, dbt is used for transformation, so I’m just trying to understand what the…
709 01:08:48.450 ⇒ 01:08:52.499 Advait Nandakumar Menon: process flow here is, so that’s the reason I asked. Yeah, okay.
710 01:08:52.930 ⇒ 01:09:03.249 Amber Lin: Cool. How do you want to split the work here? I think we’ve determined, one, we need to set up a daily model, two, we need to update to,
711 01:09:04.939 ⇒ 01:09:08.560 Amber Lin: And that… that’s… that’s it, mainly.
712 01:09:09.540 ⇒ 01:09:11.249 Advait Nandakumar Menon: Yeah, both the topics.
713 01:09:11.729 ⇒ 01:09:18.489 Amber Lin: Yeah. Do you want to take a try at setting up this retail topic? I mean, we already have a lot of stuff
714 01:09:18.689 ⇒ 01:09:24.539 Amber Lin: like… We already… this is the wrong page. We already have…
715 01:09:25.239 ⇒ 01:09:29.029 Amber Lin: Like, some level of topic set up.
716 01:09:29.509 ⇒ 01:09:31.619 Amber Lin: Do you want to try and create that?
717 01:09:32.470 ⇒ 01:09:36.840 Advait Nandakumar Menon: Sure, so do I just ask… so I haven’t worked with topics before, so do I just.
718 01:09:37.439 ⇒ 01:09:39.359 Advait Nandakumar Menon: Like, use cursor to do this.
719 01:09:39.600 ⇒ 01:09:52.969 Amber Lin: By giving it the context. Yes, so this will be helpful when we start working on later topics as well, so that’s why I wanted you to try and do this. So you will first give it some context.
720 01:09:53.109 ⇒ 01:10:06.610 Amber Lin: Like, there’s some… Like, these are the topics I gave… I think we have this…
721 01:10:07.030 ⇒ 01:10:10.460 Amber Lin: So… this doc…
722 01:10:10.970 ⇒ 01:10:25.350 Amber Lin: this stock, I think you should feed… feed it, and maybe you should download this… our Notion, download this and give it to Omni, because we already have, like, very detailed of what the joins are, and, like, how we want to do that.
723 01:10:25.560 ⇒ 01:10:38.010 Amber Lin: So feed that to Omni, and then give us some… sorry, feed that to Kirscher, and give us some, like, Omni-topic best practices, where… which you should be able to find…
724 01:10:38.610 ⇒ 01:10:42.570 Amber Lin: Somewhere here. Yeah, like, these are…
725 01:10:43.400 ⇒ 01:10:48.909 Amber Lin: the best practices, so just dump these into Kirscher as well.
726 01:10:48.980 ⇒ 01:11:02.310 Amber Lin: And you should… I think you said you already set up the CLI, so you only need to ask, hey, create these topics, and then you can tweak it as we go, and then once you create it here.
727 01:11:02.310 ⇒ 01:11:14.779 Amber Lin: If you just Ctrl-Command-S, save it, and you should be able to see it on Omni. And Omni might tell you, hey, I can’t find this join, I see this error here, and then you can go back and…
728 01:11:14.780 ⇒ 01:11:29.189 Amber Lin: like, adjust how the joins are working, adjust the AI context, but if you don’t know anything, just type it back into cursors to say, hey, I don’t know this, can you research this, or can you tell me what to do? .
729 01:11:29.190 ⇒ 01:11:29.760 Advait Nandakumar Menon: Okay.
730 01:11:30.290 ⇒ 01:11:46.260 Amber Lin: Yeah, so when you start, I would say give it context, start with the planning mode, plan… adjust the plan as you see fit, and then ask it to execute, and if you have any questions along the way, like, just ping me or give me a screenshot, and I can go help you.
731 01:11:46.260 ⇒ 01:11:46.960 Advait Nandakumar Menon: Sure.
732 01:11:47.090 ⇒ 01:11:51.069 Advait Nandakumar Menon: So, I have to work with this in planning mode, not agent, right?
733 01:11:51.070 ⇒ 01:11:59.240 Amber Lin: You can… planning just so that AI knows the steps to do, but it needs to be in agent mode to do anything.
734 01:11:59.240 ⇒ 01:12:02.550 Advait Nandakumar Menon: Actually, yeah, create this… Okay.
735 01:12:02.550 ⇒ 01:12:05.279 Amber Lin: I just find the plan helpful, of…
736 01:12:05.400 ⇒ 01:12:14.220 Amber Lin: sometimes I also just have it write a plan, and then ask it to follow this, because it’s more permanent, but, like, it’s… it’s up to you.
737 01:12:15.520 ⇒ 01:12:16.260 Advait Nandakumar Menon: Okay.
738 01:12:16.520 ⇒ 01:12:31.570 Advait Nandakumar Menon: So in order, like, so I have the CLI, like, the local folder, but should I be, should it be in sync mode? Should I execute the command to terminal in sync mode so that it gets returned to Omni?
739 01:12:32.460 ⇒ 01:12:42.010 Amber Lin: Yes, be in… establish a branch, and then enable sync mode, so that your topics will get synced, and then you can check it in Omni real-time.
740 01:12:42.900 ⇒ 01:12:53.809 Advait Nandakumar Menon: Okay, and the one last thing is, I did ask it in the engineering channel that day, but, so you see the small circle near the chat, like, the content.
741 01:12:53.810 ⇒ 01:12:56.420 Amber Lin: Oh, that’s… context. I see.
742 01:12:56.650 ⇒ 01:12:58.030 Amber Lin: I remember her.
743 01:12:58.030 ⇒ 01:13:00.080 Advait Nandakumar Menon: Phew! Yeah.
744 01:13:00.080 ⇒ 01:13:02.939 Amber Lin: Yeah, I… For some reason…
745 01:13:03.320 ⇒ 01:13:03.920 Advait Nandakumar Menon: Yeah.
746 01:13:03.920 ⇒ 01:13:07.780 Amber Lin: See, are you logged into our, like…
747 01:13:08.230 ⇒ 01:13:10.500 Amber Lin: Do they give you a Kirscher account?
748 01:13:11.540 ⇒ 01:13:15.539 Advait Nandakumar Menon: Yeah, I did accept the invitation. I did sign up through Brainforge.
749 01:13:15.960 ⇒ 01:13:24.700 Amber Lin: Okay, I usually start different chats based on different topics, like, if I’m just asking you to
750 01:13:25.190 ⇒ 01:13:31.170 Amber Lin: do one thing. Like, I haven’t ran out of it since, so I don’t… I don’t actually know.
751 01:13:31.470 ⇒ 01:13:35.599 Amber Lin: You can always, also, if you do run out of context, you can click here.
752 01:13:35.600 ⇒ 01:13:36.309 Advait Nandakumar Menon: Huh.
753 01:13:36.310 ⇒ 01:13:38.990 Amber Lin: And say, export transcript.
754 01:13:39.960 ⇒ 01:13:50.090 Amber Lin: Add it to a folder here, and when you can just start a new chat and pull that transcript in to say, hey, this is the past context. Like, just keep referring to that for your historicals.
755 01:13:50.890 ⇒ 01:13:58.979 Advait Nandakumar Menon: Okay, yeah, that makes sense. I was also about to ask, like, if you use any specific, like, Claude, or GPT 5.4, or you just said.
756 01:13:58.980 ⇒ 01:14:09.469 Amber Lin: I just do auto for now. Like, I’m not running any models, I’m just making a plan, so I think it’s alright on auto.
757 01:14:10.440 ⇒ 01:14:11.130 Advait Nandakumar Menon: Okay.
758 01:14:11.500 ⇒ 01:14:12.090 Amber Lin: Cool.
759 01:14:12.090 ⇒ 01:14:18.319 Advait Nandakumar Menon: Cool, so I will… Try to, tackle topic 2.
760 01:14:18.320 ⇒ 01:14:19.320 Amber Lin: Indeed. Yeah.
761 01:14:19.320 ⇒ 01:14:25.479 Advait Nandakumar Menon: Notion, and with whatever you said, I’ll try to do it with Cursor, and I’ll let you know how that goes.
762 01:14:25.480 ⇒ 01:14:30.709 Amber Lin: Yeah, sounds good, and I will try to create, like, a model here.
763 01:14:31.370 ⇒ 01:14:32.569 Advait Nandakumar Menon: Okay, sounds good.
764 01:14:32.570 ⇒ 01:14:35.150 Amber Lin: Alright, thanks for the call, this is productive.
765 01:14:35.150 ⇒ 01:14:37.959 Advait Nandakumar Menon: Yeah. Thank you as well. Talk soon. Bye-bye.
766 01:14:37.960 ⇒ 01:14:38.570 Amber Lin: Bye.