Meeting Title: ABC Standup Date: 2025-08-07 Meeting participants: Casie Aviles, Mustafa Raja, Awaish Kumar, Vashdev Heerani, Amber Lin
WEBVTT
1 00:03:00.230 ⇒ 00:03:01.380 Amber Lin: Hi, there!
2 00:03:04.740 ⇒ 00:03:12.100 Amber Lin: Let’s see on the ABC side. Are you guys meeting again on the inspector sheet?
3 00:03:14.170 ⇒ 00:03:20.250 Casie Aviles: We we don’t have like a a meeting set for this. But it did
4 00:03:20.600 ⇒ 00:03:28.400 Casie Aviles: test already, and made some updates to the inspector sheet, and I think maybe a review would help, just to be sure that
5 00:03:29.570 ⇒ 00:03:33.029 Casie Aviles: the triage is working as expected.
6 00:03:38.320 ⇒ 00:03:44.920 Amber Lin: okay, is there a way, I can test it.
7 00:03:46.370 ⇒ 00:03:55.429 Casie Aviles: Yeah, it’s still pretty much the same where you talk with Andy, and then you give the you give the zip codes, and the inspector.
8 00:03:57.017 ⇒ 00:04:01.489 Amber Lin: I see, are the feedback from the Csr. Is resolved.
9 00:04:01.850 ⇒ 00:04:08.099 Amber Lin: I know there was a lot of a lot of feedback on me about the inspector Sheet. Have we looked at those yet?
10 00:04:08.100 ⇒ 00:04:18.539 Casie Aviles: Yes, yes, I did try to recreate. So when so given in the document that I created I those were questions directly pulled from the Csr sheet, like the feedback sheet.
11 00:04:20.120 ⇒ 00:04:23.769 Casie Aviles: And yeah, I just recreated, reproduced
12 00:04:24.050 ⇒ 00:04:26.320 Casie Aviles: the exact question that they gave.
13 00:04:27.510 ⇒ 00:04:30.480 Amber Lin: Hmm. Okay, second
14 00:04:36.370 ⇒ 00:04:38.010 Amber Lin: sounds good.
15 00:04:39.660 ⇒ 00:04:40.890 Amber Lin: Oh.
16 00:04:45.680 ⇒ 00:04:50.290 Amber Lin: I’ll see if I can check in on that later.
17 00:04:51.160 ⇒ 00:04:54.020 Amber Lin: Oh, let’s see.
18 00:04:57.810 ⇒ 00:05:04.039 Amber Lin: Oh, for the ABC. Side rushed up. How’s connecting the transcript? Api going.
19 00:05:12.220 ⇒ 00:05:20.360 Vashdev Heerani: I started that project. Then I stop and work on the other Admin project.
20 00:05:20.620 ⇒ 00:05:22.559 Vashdev Heerani: So currently working on that project.
21 00:05:24.180 ⇒ 00:05:29.229 Vashdev Heerani: I see how many hours do you have in total.
22 00:05:29.400 ⇒ 00:05:31.840 Amber Lin: Do you work 40 h.
23 00:05:34.330 ⇒ 00:05:36.060 Vashdev Heerani: 4 h per day.
24 00:05:38.140 ⇒ 00:05:39.699 Amber Lin: I see Gotcha
25 00:05:41.400 ⇒ 00:05:50.620 Amber Lin: a wish based on what Russia has on Eden. It it does. He have enough time to work on ABC or data platform.
26 00:05:53.200 ⇒ 00:05:58.729 Awaish Kumar: And we have decided to have 10 h per week for Irian
27 00:05:59.730 ⇒ 00:06:02.639 Awaish Kumar: and the other of these 2 points.
28 00:06:03.400 ⇒ 00:06:07.960 Amber Lin: Yeah, I don’t think that’s the case right now. Right?
29 00:06:08.110 ⇒ 00:06:12.030 Amber Lin: I mean, is there? How many hours are we spending on? He did.
30 00:06:18.575 ⇒ 00:06:21.019 Awaish Kumar: I’m not. I’m not sure, like how many
31 00:06:21.190 ⇒ 00:06:24.610 Awaish Kumar: to get reassigned for this spring to actually.
32 00:06:30.433 ⇒ 00:06:31.840 Amber Lin: Let’s see.
33 00:06:32.200 ⇒ 00:06:46.520 Vashdev Heerani: Like the current ticket that I am working on on the Admin side is I I need more. I I needed more context information. That’s why I’m taking the time to to understand the transformation.
34 00:06:47.660 ⇒ 00:06:53.190 Amber Lin: Yeah, that that makes sense. I I guess. Tristan
35 00:06:53.320 ⇒ 00:07:00.540 Amber Lin: general we have. This can be next site. I don’t know could be could be this week.
36 00:07:03.286 ⇒ 00:07:07.870 Amber Lin: We do have quite a lot on which I don’t think.
37 00:07:07.870 ⇒ 00:07:11.450 Awaish Kumar: Well, not a lot of it is going to win this week, right? We.
38 00:07:11.660 ⇒ 00:07:12.380 Amber Lin: True.
39 00:07:13.030 ⇒ 00:07:16.020 Amber Lin: 6, 1, 8 is not needed in this week.
40 00:07:16.020 ⇒ 00:07:21.420 Amber Lin: This next cycle 6, 1, 6, 1.
41 00:07:22.340 ⇒ 00:07:22.870 Amber Lin: But.
42 00:07:24.270 ⇒ 00:07:35.870 Awaish Kumar: And also like, yeah, 6, 5, 0 is kind of a part of 6, 1, 5, because he needs to join the data between, on a channel level between 2.
43 00:07:36.110 ⇒ 00:07:36.450 Amber Lin: Hmm.
44 00:07:36.450 ⇒ 00:07:44.170 Awaish Kumar: 2 different sources, and he for that, he need to standardize the naming. So it’s kind of a subtask of from 6, 1, 5.
45 00:07:46.700 ⇒ 00:07:47.470 Amber Lin: Okay.
46 00:07:51.820 ⇒ 00:07:55.110 Amber Lin: Oh, sounds good.
47 00:07:55.110 ⇒ 00:08:01.740 Awaish Kumar: And I don’t know when 6, 1, 9, I don’t know.
48 00:08:04.930 ⇒ 00:08:09.649 Awaish Kumar: Okay, that’s also can be in the next week. Right? It’s not urgent.
49 00:08:10.410 ⇒ 00:08:11.040 Amber Lin: Oh!
50 00:08:15.830 ⇒ 00:08:16.560 Awaish Kumar: Okay.
51 00:08:18.980 ⇒ 00:08:22.304 Awaish Kumar: So only now we have marketing work like
52 00:08:23.050 ⇒ 00:08:23.660 Awaish Kumar: So.
53 00:08:23.660 ⇒ 00:08:24.160 Amber Lin: Yeah.
54 00:08:24.160 ⇒ 00:08:26.960 Awaish Kumar: 6, 4, 6, 4, 6.
55 00:08:31.430 ⇒ 00:08:36.770 Amber Lin: Yeah, these are all from marketing. And for the ad spend stuff.
56 00:08:37.860 ⇒ 00:08:44.660 Awaish Kumar: Okay, yeah, we can add it to the backlog right? Because we today is Thursday, like, we only have Friday left, right
57 00:08:44.970 ⇒ 00:08:45.500 Amber Lin: Yeah.
58 00:08:45.500 ⇒ 00:08:46.320 Awaish Kumar: So for the market.
59 00:08:46.320 ⇒ 00:08:50.490 Awaish Kumar: I’m like, data is already. I have already worked on updating the data.
60 00:08:50.620 ⇒ 00:08:53.690 Awaish Kumar: So data is already updated.
61 00:08:54.050 ⇒ 00:09:03.294 Awaish Kumar: But the only yeah, Vashev needs to work on 2 tasks, which I added like spiking on, adding new sources along with adding the
62 00:09:03.850 ⇒ 00:09:06.900 Awaish Kumar: like product information for.
63 00:09:07.490 ⇒ 00:09:10.529 Amber Lin: Oh, I thought that was for Demo Lade, my bad!
64 00:09:11.030 ⇒ 00:09:11.780 Amber Lin: Let me.
65 00:09:12.710 ⇒ 00:09:18.540 Awaish Kumar: So, and that these both, I I don’t think, can be done in this week. Right? They they need to be.
66 00:09:18.540 ⇒ 00:09:18.815 Amber Lin: Oh.
67 00:09:19.640 ⇒ 00:09:27.549 Amber Lin: yeah. So there, we can’t add the product names all the product names if we don’t have these sources. So
68 00:09:27.760 ⇒ 00:09:33.210 Amber Lin: probably this is something for life. Cycle.
69 00:09:35.720 ⇒ 00:09:38.650 Amber Lin: This one was the vibrant.
70 00:09:38.650 ⇒ 00:09:41.549 Awaish Kumar: Yeah, that’s that’s like, done like, that’s done.
71 00:09:41.720 ⇒ 00:09:47.770 Amber Lin: Okay, that is done. Did the choir review it? Can I see it done?
72 00:09:50.287 ⇒ 00:09:54.090 Awaish Kumar: I don’t know like it’s done from my side. So we can add it in like.
73 00:09:54.760 ⇒ 00:09:57.310 Awaish Kumar: okay, and we send it to them. Right?
74 00:10:01.040 ⇒ 00:10:03.070 Awaish Kumar: Yeah, I will send an update.
75 00:10:03.290 ⇒ 00:10:04.720 Awaish Kumar: But it was about
76 00:10:05.170 ⇒ 00:10:10.520 Awaish Kumar: I. I did tell them that data is updated. But I will let let them know again.
77 00:10:10.520 ⇒ 00:10:13.577 Amber Lin: Okay, yeah, that’s awesome.
78 00:10:15.850 ⇒ 00:10:30.770 Amber Lin: so similar to standardize a Channel name. Annie wants to see if we can standardize, standardize the pharmacy name. But this is lower priority than the Channel name, she said. So I’m just keeping it here. If we don’t finish it, we’ll move it
79 00:10:31.110 ⇒ 00:10:31.960 Awaish Kumar: That’s cool.
80 00:10:31.960 ⇒ 00:10:34.399 Amber Lin: So mainly. It’s just the spike
81 00:10:35.077 ⇒ 00:10:40.449 Amber Lin: the Channel name. And oh, my bad! This was a
82 00:10:41.510 ⇒ 00:10:45.140 Amber Lin: ad hoc request from the data board.
83 00:10:46.510 ⇒ 00:10:47.240 Amber Lin: It’s like a.
84 00:10:47.240 ⇒ 00:10:48.859 Awaish Kumar: In next week, right.
85 00:10:50.460 ⇒ 00:10:51.560 Amber Lin: Okay.
86 00:10:51.950 ⇒ 00:10:54.509 Awaish Kumar: But I don’t know even if it
87 00:10:55.260 ⇒ 00:10:58.649 Awaish Kumar: is it needed, because at the bottom you commented, that
88 00:10:59.150 ⇒ 00:11:01.750 Awaish Kumar: I see your comment that, like we need a
89 00:11:02.010 ⇒ 00:11:09.749 Awaish Kumar: refactor probe old code. But that’s what I already did. Right. I’m not. I’m not sure if there is anything new.
90 00:11:11.950 ⇒ 00:11:17.759 Amber Lin: Oh, oh, I see, it’s for the same thing. I didn’t know that.
91 00:11:17.930 ⇒ 00:11:33.829 Amber Lin: My bad and yes, okay. So we are. Client review.
92 00:11:34.950 ⇒ 00:11:39.990 Amber Lin: Okay. It sounds like on Eden. There’s 2 tasks that.
93 00:11:39.990 ⇒ 00:11:40.780 Awaish Kumar: 3 m.
94 00:11:41.040 ⇒ 00:11:52.400 Amber Lin: 3 3 tasks. That’s in peer review, and so back on ABC. We should have.
95 00:11:54.270 ⇒ 00:12:00.019 Amber Lin: Do you think we still have 5 h to spare for ABC.
96 00:12:01.610 ⇒ 00:12:02.440 Awaish Kumar: Oh, yeah.
97 00:12:03.060 ⇒ 00:12:03.474 Amber Lin: Okay.
98 00:12:04.120 ⇒ 00:12:09.120 Amber Lin: So when should I reasonably expect this by not today? But
99 00:12:09.360 ⇒ 00:12:11.889 Amber Lin: when should I reasonably expect this? By.
100 00:12:16.920 ⇒ 00:12:17.450 Awaish Kumar: Oh!
101 00:12:17.450 ⇒ 00:12:18.769 Amber Lin: End of day, Friday.
102 00:12:20.440 ⇒ 00:12:22.450 Vashdev Heerani: That watch the camera also.
103 00:12:22.987 ⇒ 00:12:28.359 Vashdev Heerani: Okay, okay, you can expect this for Friday tomorrow.
104 00:12:28.360 ⇒ 00:12:28.930 Amber Lin: Okay.
105 00:12:29.580 ⇒ 00:12:35.220 Amber Lin: yeah, let me know if you get blocked on anything, or if you need more time, and then we’ll see what’s.
106 00:12:35.690 ⇒ 00:12:36.170 Vashdev Heerani: Sure.
107 00:12:36.170 ⇒ 00:12:41.190 Amber Lin: Okay, yeah, I think that’s all.
108 00:12:48.200 ⇒ 00:12:51.769 Amber Lin: Oh, I think one thing.
109 00:12:52.660 ⇒ 00:13:03.640 Amber Lin: Casey, I don’t know if this is they confirmed on the spreadsheet. Sorry they confirmed on the document. Can Casey, can you add this to the Central Doc
110 00:13:03.910 ⇒ 00:13:05.830 Amber Lin: to the mechanical Central Dock?
111 00:13:06.875 ⇒ 00:13:09.670 Casie Aviles: So just basting it. Yeah, gotcha do that.
112 00:13:09.670 ⇒ 00:13:12.329 Amber Lin: In the bottom section. Yeah, that’ll be great.
113 00:13:20.440 ⇒ 00:13:21.390 Amber Lin: okay.
114 00:13:25.130 ⇒ 00:13:26.080 Amber Lin: phone.
115 00:13:34.170 ⇒ 00:13:35.400 Amber Lin: Progress.
116 00:13:36.930 ⇒ 00:13:38.479 Amber Lin: Da- da- da-da.
117 00:13:39.610 ⇒ 00:13:40.630 Amber Lin: Okay?
118 00:13:41.799 ⇒ 00:13:49.949 Amber Lin: I think. One last thing I had a question on on ABC is when new feedback is
119 00:13:50.960 ⇒ 00:13:54.089 Amber Lin: added. Where is the spreadsheet?
120 00:13:56.070 ⇒ 00:13:57.380 Amber Lin: Oh, wow!
121 00:13:59.520 ⇒ 00:14:03.479 Amber Lin: So when new feedback gets added.
122 00:14:04.132 ⇒ 00:14:16.679 Amber Lin: it’s not in the table. So it gets added at the bottom, and it’s not part of the table. And so when we filter for things, it doesn’t include the new entries.
123 00:14:16.950 ⇒ 00:14:21.750 Amber Lin: What should we do for this? Should we just remove that
124 00:14:21.940 ⇒ 00:14:29.580 Amber Lin: table format and just have columns so that it includes these new entries? How should we do this?
125 00:14:31.180 ⇒ 00:14:32.000 Casie Aviles: Hmm.
126 00:14:33.042 ⇒ 00:14:41.099 Casie Aviles: I’m not sure like how how table lot logic works, because but but if we want to have everything.
127 00:14:41.890 ⇒ 00:14:43.650 Casie Aviles: then I guess. The 1st thing I have
128 00:14:43.910 ⇒ 00:14:46.640 Casie Aviles: my mind is to just, you know. Remove the table format.
129 00:14:48.340 ⇒ 00:14:49.400 Amber Lin: Yeah, but.
130 00:14:50.400 ⇒ 00:14:54.123 Casie Aviles: Yeah, I’m not sure if you guys want to do that to do that,
131 00:14:54.680 ⇒ 00:15:02.719 Amber Lin: I see, like another thing is just, we’ll we’ll have to manually right now. I just manually add that to the table
132 00:15:03.150 ⇒ 00:15:03.750 Amber Lin: whenever.
133 00:15:04.112 ⇒ 00:15:08.817 Casie Aviles: Because any 10 adds it as a row, and not to the table.
134 00:15:11.900 ⇒ 00:15:18.750 Amber Lin: I see, that’s okay. I just wanna point it out. I’ll document it here, just in case
135 00:15:20.460 ⇒ 00:15:24.419 Amber Lin: oh, it back, sheet.
136 00:15:32.740 ⇒ 00:15:33.400 Amber Lin: you.
137 00:15:36.350 ⇒ 00:15:44.523 Amber Lin: Okay? For the data platform. Buddha. Review the docs, he said. We’re good to go.
138 00:15:45.110 ⇒ 00:15:48.320 Amber Lin: We. I talked to him as well to see
139 00:15:48.920 ⇒ 00:15:56.930 Amber Lin: who’s gonna take the dashboarding. I think we’re gonna assign someone else to do the dashboarding because Annie’s doing the client work.
140 00:15:59.480 ⇒ 00:16:02.799 Amber Lin: How can we best assign this? A wish.
141 00:16:04.450 ⇒ 00:16:08.010 Awaish Kumar: We can assign, like vascular for only modeling work. And
142 00:16:08.860 ⇒ 00:16:12.759 Awaish Kumar: and we’ll like the dashboarding work for Casey.
143 00:16:13.830 ⇒ 00:16:23.180 Amber Lin: Okay, okay, sounds good. Do we still need to? Eda, the data. Can someone do that?
144 00:16:24.370 ⇒ 00:16:28.529 Amber Lin: Is, can you do the EPA or.
145 00:16:29.130 ⇒ 00:16:35.310 Awaish Kumar: Yeah, we just like, I don’t think we need some like, what is that like creating documents?
146 00:16:37.760 ⇒ 00:16:39.360 Amber Lin: Huh? Sorry. What was that?
147 00:16:40.790 ⇒ 00:16:46.059 Awaish Kumar: So, Eda, like, what is the the output from these tickets like a document or.
148 00:16:47.827 ⇒ 00:16:54.009 Amber Lin: Do we know all the fields we have? That’s the main thing. Do we know all the fields we have? If we do, we don’t need.
149 00:16:54.010 ⇒ 00:16:56.920 Awaish Kumar: Yeah, like, we we do know, like.
150 00:16:56.920 ⇒ 00:16:57.390 Amber Lin: Oh, okay.
151 00:16:57.390 ⇒ 00:17:00.040 Awaish Kumar: We have everything we could get from these sources.
152 00:17:00.400 ⇒ 00:17:01.830 Amber Lin: Awesome. Okay. Canceled.
153 00:17:01.830 ⇒ 00:17:11.919 Awaish Kumar: The task is now to like, have some sample dashboards ready, like we need to like the like. For example, we have a source clock if I
154 00:17:12.540 ⇒ 00:17:13.284 Awaish Kumar: and
155 00:17:16.760 ⇒ 00:17:32.070 Awaish Kumar: like Vast, is working on modeling stuff so he can work on modeling like how to join these different sources, classify and the operating and the linear and create a unified model which can be further used
156 00:17:32.920 ⇒ 00:17:38.690 Awaish Kumar: to create a dashboard but until that is being done, like Casey can in the
157 00:17:38.920 ⇒ 00:17:41.889 Awaish Kumar: in parallel can work on just an individual
158 00:17:42.629 ⇒ 00:17:48.309 Awaish Kumar: sources like that. We have linear data so he can build a dashboard to see like, how we are
159 00:17:48.460 ⇒ 00:17:52.619 Awaish Kumar: closing the tickets, how we how for each project, how we are
160 00:17:53.590 ⇒ 00:18:00.560 Awaish Kumar: catering tickets each week! How then we are closing them, and then for each person how the tickets are signed.
161 00:18:00.780 ⇒ 00:18:05.999 Awaish Kumar: how many story points, and like he can use AI to get more help? On
162 00:18:06.350 ⇒ 00:18:23.090 Awaish Kumar: what other different metrics or the questions we can answer, using the linear data and build a kind of a 1st version of a dashboard which then, I or you and Rico can basically review and give feedback.
163 00:18:23.820 ⇒ 00:18:31.140 Amber Lin: Okay, yeah, that sounds that sounds great, Casey. Do you feel comfortable? Building dashboard with cursor is in real.
164 00:18:32.115 ⇒ 00:18:37.150 Casie Aviles: Yeah, I I could give it a shot as well. I mean, I’ve done it before, just you know.
165 00:18:39.000 ⇒ 00:18:40.659 Amber Lin: Okay, I can get.
166 00:18:40.660 ⇒ 00:18:41.750 Casie Aviles: That’s not the choice.
167 00:18:42.760 ⇒ 00:18:44.810 Amber Lin: Okay, sounds good.
168 00:18:45.020 ⇒ 00:18:55.230 Amber Lin: So I wish, let’s, it sounds like we should break down the dashboarding Mvp by like by source.
169 00:18:55.380 ⇒ 00:19:02.460 Amber Lin: Right? So should I do one for clockify and one for linear. Is that how we should do these circuits?
170 00:19:11.660 ⇒ 00:19:14.079 Amber Lin: Oh, I wish you’re on mute if you’re talking.
171 00:19:14.620 ⇒ 00:19:18.230 Awaish Kumar: Yeah, like, clockify linear and operating.
172 00:19:18.460 ⇒ 00:19:21.730 Awaish Kumar: can we? The this like the
173 00:19:23.290 ⇒ 00:19:29.309 Awaish Kumar: can, we can have, like a some simple dashboard for them, showcasing individual
174 00:19:30.084 ⇒ 00:19:42.089 Awaish Kumar: data from these sources, and then we can review if we need them, or how we can improve that. In the meantime, until the once they build the model
175 00:19:42.300 ⇒ 00:19:46.209 Awaish Kumar: where we have all this data together, then we have one more dashboard
176 00:19:46.410 ⇒ 00:19:51.149 Awaish Kumar: where we can basically come, show a combined picture of all these
177 00:19:51.740 ⇒ 00:19:57.940 Awaish Kumar: sources for each client. How the hours are logged on, graphify how it looks in operating.
178 00:19:58.430 ⇒ 00:20:01.930 Awaish Kumar: then how it looks in linear for each person.
179 00:20:02.820 ⇒ 00:20:05.410 Amber Lin: So sounds like we’re just making a
180 00:20:06.900 ⇒ 00:20:09.490 Amber Lin: just a dashboard to show what data we have.
181 00:20:10.010 ⇒ 00:20:21.669 Amber Lin: So I’m gonna assign these not to wish to, Casey, and say that to do in cycle
182 00:20:22.477 ⇒ 00:20:26.519 Amber Lin: Casey here is, let me find it.
183 00:20:26.720 ⇒ 00:20:30.599 Amber Lin: Docs so should be here.
184 00:20:30.790 ⇒ 00:20:35.770 Amber Lin: So I think Vashtop’s document has all the different sources.
185 00:20:35.970 ⇒ 00:20:46.970 Amber Lin: And this document has the dashboard plan and some suggestions. To clarify
186 00:20:47.120 ⇒ 00:20:50.579 Amber Lin: and to confirm. We just want all of the
187 00:20:50.750 ⇒ 00:20:55.820 Amber Lin: data shown. We don’t necessarily need any complex charts. Yet. Right
188 00:21:09.620 ⇒ 00:21:16.660 Amber Lin: sorry. Can someone confirm this for me? I wish this is, I just wanna make sure Casey knows what he needs to do.
189 00:21:19.650 ⇒ 00:21:24.080 Awaish Kumar: So we already have operating data in for Snowflake.
190 00:21:24.560 ⇒ 00:21:29.440 Awaish Kumar: We’ll just need to connect Real with Snowflake and start working with.
191 00:21:32.890 ⇒ 00:21:35.430 Amber Lin: I thought real, and Snowflake was connected.
192 00:21:35.940 ⇒ 00:21:36.760 Amber Lin: You mean to.
193 00:21:36.760 ⇒ 00:21:42.590 Awaish Kumar: Yeah, it is connected to some of like he, he needs to connect to specific table or whatever he needs.
194 00:21:43.640 ⇒ 00:21:48.630 Amber Lin: Gotcha. Okay, easy. Do you feel? Pro? Do you feel that you have enough requirements to do this?
195 00:21:52.210 ⇒ 00:21:58.579 Awaish Kumar: And also for local development, for local development Casey for internal service user.
196 00:21:58.920 ⇒ 00:22:02.810 Awaish Kumar: The the connection string is available in one pass.
197 00:22:03.530 ⇒ 00:22:06.350 Awaish Kumar: So you guys search for internal server
198 00:22:06.630 ⇒ 00:22:09.350 Awaish Kumar: service user and you can find it in the one pass.
199 00:22:11.220 ⇒ 00:22:12.652 Casie Aviles: Okay, sounds good.
200 00:22:14.190 ⇒ 00:22:22.189 Casie Aviles: yeah. I think this is, I’ll so it’s up to me to like, think define what needs to be shown.
201 00:22:23.140 ⇒ 00:22:28.530 Awaish Kumar: Yeah, you can use this. Yeah, you can use like help of Chat Gpt, or
202 00:22:28.870 ⇒ 00:22:30.709 Awaish Kumar: anything to figure out like
203 00:22:30.910 ⇒ 00:22:36.999 Awaish Kumar: what needs to be. If we want to measure the team productivity given the linear tickets.
204 00:22:37.687 ⇒ 00:22:44.039 Awaish Kumar: What kind of metrics you want to look for something like that. And you can get the suggestion from AI.
205 00:22:44.410 ⇒ 00:22:45.460 Casie Aviles: Yes. Okay.
206 00:22:45.850 ⇒ 00:22:48.139 Amber Lin: Which one do we prioritize? First.st
207 00:22:50.750 ⇒ 00:22:51.909 Awaish Kumar: Maybe linear.
208 00:22:52.730 ⇒ 00:22:53.460 Casie Aviles: Okay.
209 00:22:54.690 ⇒ 00:22:56.470 Amber Lin: How much.
210 00:22:56.790 ⇒ 00:23:03.779 Amber Lin: how long should each of these take? Are each of them like 2 points, each of them 3 points or 1 point. What? What is it like.
211 00:23:05.560 ⇒ 00:23:07.351 Casie Aviles: I think maybe we could
212 00:23:08.170 ⇒ 00:23:11.670 Casie Aviles: put them at 2 points for now pick each. Okay.
213 00:23:15.380 ⇒ 00:23:20.449 Amber Lin: So after linear, I think it should be clockify and then operating.
214 00:23:22.060 ⇒ 00:23:27.949 Amber Lin: Let’s do it in that order. Alright, I will check in.
215 00:23:28.400 ⇒ 00:23:31.940 Amber Lin: Oh, I’ll check in tomorrow when we meet.
216 00:23:37.600 ⇒ 00:23:43.120 Amber Lin: I don’t think we need this cancel.
217 00:23:43.510 ⇒ 00:23:45.410 Amber Lin: Okay? Sounds good.
218 00:23:46.020 ⇒ 00:23:47.299 Amber Lin: Thanks. Everyone.
219 00:23:48.220 ⇒ 00:23:48.980 Casie Aviles: Thank you.
220 00:23:49.540 ⇒ 00:23:50.100 Amber Lin: Alright!
221 00:23:50.570 ⇒ 00:23:51.740 Amber Lin: Talk to you tomorrow.