Meeting Title: MatterMore | internal Standup Date: 2025-06-17 Meeting participants: Luke Daque, Annie Yu, Amber Lin
WEBVTT
1 00:01:43.810 ⇒ 00:01:45.009 Luke Daque: Hi! Annie!
2 00:01:47.440 ⇒ 00:01:48.510 Annie Yu: Oh, Hi Luke!
3 00:01:49.940 ⇒ 00:01:50.939 Luke Daque: How’s it going.
4 00:01:52.570 ⇒ 00:01:55.379 Annie Yu: Not too bad. It’s getting
5 00:01:55.890 ⇒ 00:02:07.960 Annie Yu: busy. I feel like lots of my stuff got unblocked this week. So that means there are like multiple different things that I have to get done.
6 00:02:07.960 ⇒ 00:02:10.780 Luke Daque: Yeah, I feel the same way as well. So
7 00:02:16.950 ⇒ 00:02:23.580 Luke Daque: it’s like the faucet just turned on like a lot of things.
8 00:02:23.580 ⇒ 00:02:24.480 Amber Lin: Hi.
9 00:02:24.710 ⇒ 00:02:26.680 Annie Yu: Yeah. Hello. Amber?
10 00:02:26.680 ⇒ 00:02:27.295 Amber Lin: Hello!
11 00:02:28.040 ⇒ 00:02:30.240 Amber Lin: How is everyone?
12 00:02:32.060 ⇒ 00:02:33.530 Luke Daque: Doing well, how are you.
13 00:02:34.655 ⇒ 00:02:43.570 Amber Lin: I totally forgot that this meeting was in place. I was working on other projects, and I I’m a little bit late.
14 00:02:43.970 ⇒ 00:02:44.890 Luke Daque: Oh, yeah.
15 00:02:45.984 ⇒ 00:02:48.209 Amber Lin: I’m looking. I think we’re
16 00:02:48.870 ⇒ 00:03:01.690 Amber Lin: pretty good on progress. I mean, we’re unblocked, which I’m so happy about. So this is just like a quick check in on everybody’s progress to see if you need anything and how tickets are going.
17 00:03:04.340 ⇒ 00:03:06.190 Amber Lin: I’m gonna share my screen.
18 00:03:07.230 ⇒ 00:03:08.730 Amber Lin: Let’s go here.
19 00:03:13.950 ⇒ 00:03:15.280 Amber Lin: Were these.
20 00:03:17.480 ⇒ 00:03:26.509 Amber Lin: Annie, would you be able to review them? Is that possible like, how does who do you? Who do you need to review these.
21 00:03:26.510 ⇒ 00:03:39.330 Luke Daque: Okay, yeah, anybody basically can review. Maybe, Annie, if you like, to review the models like, if they
22 00:03:41.136 ⇒ 00:03:42.620 Luke Daque: what do you call this
23 00:03:44.450 ⇒ 00:03:48.619 Luke Daque: like, if they yeah, basically, if if we need to update anything
24 00:03:48.840 ⇒ 00:03:52.259 Luke Daque: from the walls, I already did add, like just.
25 00:03:52.830 ⇒ 00:03:55.480 Luke Daque: The Pre Pre post office mandate, for example.
26 00:03:55.480 ⇒ 00:03:55.800 Amber Lin: I’m sorry.
27 00:03:56.380 ⇒ 00:03:58.130 Luke Daque: Some random dates there.
28 00:03:58.130 ⇒ 00:03:58.520 Amber Lin: Okay.
29 00:03:58.670 ⇒ 00:04:02.119 Luke Daque: And yeah, the worker type in location should be in
30 00:04:02.480 ⇒ 00:04:05.660 Luke Daque: in there. I also like
31 00:04:07.100 ⇒ 00:04:12.589 Luke Daque: like what? What Annie raised before, where it was just like aggregating for the last 30 days. I removed that.
32 00:04:13.240 ⇒ 00:04:14.230 Luke Daque: Already, like.
33 00:04:14.430 ⇒ 00:04:18.749 Amber Lin: I see all time. And stuff like that. So okay.
34 00:04:19.567 ⇒ 00:04:27.619 Amber Lin: well, I think Annie can help you verify if this is what she needs. But I think if you want modeling logic review, we can tag a wish.
35 00:04:29.520 ⇒ 00:04:33.270 Luke Daque: Yeah, I don’t know if we need that. But yeah, we can. We can do that.
36 00:04:34.190 ⇒ 00:04:42.159 Amber Lin: Okay, how would Ally be able to review? If this is like, this is what she needs? Is this can she see this in power? Bi.
37 00:04:45.530 ⇒ 00:04:51.870 Luke Daque: Based on what we tested yesterday. The power bi is still limited, because, like.
38 00:04:52.376 ⇒ 00:04:56.059 Amber Lin: The Brainforge user doesn’t have access to bigquery, so it cannot see.
39 00:04:56.790 ⇒ 00:04:59.690 Luke Daque: The data, even in power Bi, which is.
40 00:05:01.600 ⇒ 00:05:11.539 Annie Yu: Oh, I think that was blocked if I’m not wrong. After our call, look, amber provided
41 00:05:11.790 ⇒ 00:05:16.489 Annie Yu: like a authentication process which I went through, so I think I.
42 00:05:16.490 ⇒ 00:05:16.960 Luke Daque: Oh!
43 00:05:17.480 ⇒ 00:05:21.559 Annie Yu: The the models that you published on power. Bi.
44 00:05:21.560 ⇒ 00:05:22.300 Amber Lin: Yay!
45 00:05:22.300 ⇒ 00:05:27.200 Annie Yu: Rbi service. So I think those 4 that are published.
46 00:05:27.400 ⇒ 00:05:28.400 Luke Daque: Nice.
47 00:05:28.680 ⇒ 00:05:55.809 Amber Lin: Awesome. I guess. My question, then, is when we publish these models, did it include these worker types and Pre post office mandate? Because when I think, based on my chat Gpt research. Whenever we update the models, we probably would want to republish again. But if we only update the data and not the models in bigquery. We don’t need to publish another time.
48 00:05:56.570 ⇒ 00:05:58.670 Luke Daque: Yeah, that’s a good question. We’ll have to check.
49 00:05:58.900 ⇒ 00:06:00.030 Amber Lin: Okay.
50 00:06:00.190 ⇒ 00:06:02.290 Luke Daque: Yeah, we’ll have to check it again.
51 00:06:02.580 ⇒ 00:06:02.960 Amber Lin: Okay.
52 00:06:03.040 ⇒ 00:06:08.980 Luke Daque: Because, like we, we we use direct query, I will.
53 00:06:08.980 ⇒ 00:06:17.959 Luke Daque: I would think it should show if there are any updates, because it’s direct query. It’s not imported to power. Bi.
54 00:06:17.960 ⇒ 00:06:18.450 Amber Lin: Okay.
55 00:06:18.450 ⇒ 00:06:20.689 Luke Daque: But yeah, we can, we can double check.
56 00:06:24.760 ⇒ 00:06:28.769 Amber Lin: Okay. Sounds good. I’m gonna comment this.
57 00:06:33.480 ⇒ 00:06:40.890 Amber Lin: And then this should be done because I think Annie did see all of them.
58 00:06:42.580 ⇒ 00:06:50.230 Amber Lin: Alright sorry this one current bigquery views. Do you want a wish to help you review this.
59 00:06:51.920 ⇒ 00:06:53.549 Luke Daque: Yeah, we can tag a wish.
60 00:06:53.790 ⇒ 00:06:55.320 Amber Lin: Okay, sounds good.
61 00:06:58.670 ⇒ 00:07:00.900 Amber Lin: I think I might need to.
62 00:07:03.460 ⇒ 00:07:05.910 Amber Lin: Yeah, let me copy these.
63 00:07:06.230 ⇒ 00:07:08.330 Amber Lin: I’ll just go as a wish.
64 00:07:11.820 ⇒ 00:07:13.829 Amber Lin: Is it a Pr or.
65 00:07:14.980 ⇒ 00:07:18.344 Luke Daque: Yeah, I can. I can add the Pr to the linear.
66 00:07:18.650 ⇒ 00:07:19.210 Amber Lin: Okay.
67 00:07:21.440 ⇒ 00:07:22.910 Luke Daque: Let me do that.
68 00:07:27.300 ⇒ 00:07:28.780 Amber Lin: Okay, sounds good.
69 00:07:30.620 ⇒ 00:07:39.639 Amber Lin: And any well, Lucas, adding that, how is the power? Bi? I fleshed out this ticket so it might be more clear now.
70 00:07:40.340 ⇒ 00:07:44.359 Annie Yu: Yeah, I haven’t started I’ll start today.
71 00:07:44.730 ⇒ 00:07:45.320 Amber Lin: Hmm.
72 00:07:47.850 ⇒ 00:08:01.970 Amber Lin: when is this? Yeah, how much do you think you’ll? Well, I’ll ask tomorrow. Let me know how much you think you can get done by Friday, just as just so I can like show the matter more people. Some things.
73 00:08:02.540 ⇒ 00:08:08.520 Annie Yu: Okay, yeah, I in an ideal world, I think we can get done by Friday. But I.
74 00:08:08.520 ⇒ 00:08:11.990 Amber Lin: Oh, really, I’m not gonna push you to do that. I’m I said, good here.
75 00:08:12.423 ⇒ 00:08:13.290 Annie Yu: Next Monday.
76 00:08:13.290 ⇒ 00:08:17.240 Annie Yu: I’ll let you know tomorrow, because I I think I can get started today and then have.
77 00:08:17.870 ⇒ 00:08:20.199 Annie Yu: Clear idea. But I I’ll let you know tomorrow.
78 00:08:20.200 ⇒ 00:08:21.909 Amber Lin: Okay, okay, awesome.
79 00:08:26.070 ⇒ 00:08:31.229 Amber Lin: And then this is still waiting. Response.
80 00:08:32.080 ⇒ 00:08:37.619 Amber Lin: Yeah, think, look, since all the other remaining modeling is done, I think
81 00:08:37.880 ⇒ 00:08:51.649 Amber Lin: you have time now to focus on making the synthetic data set for tool usage. And we you already got the Apis. So I think our next step to creating the synthetic data set is pretty clear.
82 00:08:57.410 ⇒ 00:08:58.130 Amber Lin: Hello.
83 00:08:58.130 ⇒ 00:09:00.990 Luke Daque: Oh, sorry I was on mute. But yeah, I can work on that.
84 00:09:01.630 ⇒ 00:09:02.340 Amber Lin: Okay.
85 00:09:02.520 ⇒ 00:09:31.679 Amber Lin: Sounds good. Let me know if this is a good estimate, I think copilot, there’s only one source. But I think for office 3, 65. There was a few sources, so that might take a little bit more time. So let me know if the estimate is correct, once you start on it. I’ll check in tomorrow on if there’s any blockers, and then we’ll see when we can start the modeling. But I think as we’re on good track. Things are more clear much better.
86 00:09:31.680 ⇒ 00:09:32.480 Luke Daque: Just a much better.
87 00:09:32.480 ⇒ 00:09:33.856 Amber Lin: Other than last month.
88 00:09:34.530 ⇒ 00:09:37.120 Luke Daque: Question on the co-pilot, though, because there’s
89 00:09:37.680 ⇒ 00:09:45.139 Luke Daque: 2 Api endpoints, for, like general endpoints for co-pilot, there’s the co-pilot for.
90 00:09:46.900 ⇒ 00:09:49.490 Luke Daque: I think the Vs code extension? Or is it like Github.
91 00:09:50.070 ⇒ 00:09:55.999 Luke Daque: And there’s also a co-pilot that’s like the Microsoft actual co-pilot. One
92 00:09:57.150 ⇒ 00:09:58.100 Amber Lin: But then.
93 00:09:58.100 ⇒ 00:10:02.817 Luke Daque: And I think, based on my research. If we want to see those
94 00:10:04.950 ⇒ 00:10:10.750 Luke Daque: the data that we wanted, which was like number of users, or to how how many times the tools were access and stuff.
95 00:10:11.450 ⇒ 00:10:18.499 Luke Daque: The Github. One would be like we can get it from there. But though we won’t have
96 00:10:18.660 ⇒ 00:10:21.079 Luke Daque: that data in coming from Microsoft.
97 00:10:22.060 ⇒ 00:10:24.460 Amber Lin: So we should use the Microsoft one.
98 00:10:25.300 ⇒ 00:10:27.990 Luke Daque: Then, in that case we won’t be able to get the
99 00:10:28.170 ⇒ 00:10:35.910 Luke Daque: data that we want, which is like how many times the tools was used, or something, because it’s not available in Microsoft.
100 00:10:36.800 ⇒ 00:10:42.670 Amber Lin: Yeah. So let’s let’s use this one. Then let’s use the Microsoft Api.
101 00:10:43.340 ⇒ 00:10:46.929 Luke Daque: Yeah, like I mentioned, if we use that it, we won’t be able to get
102 00:10:47.160 ⇒ 00:10:48.659 Luke Daque: the data that we want.
103 00:10:49.050 ⇒ 00:10:52.760 Amber Lin: Oh, sorry I I interpreted the opposite way.
104 00:10:54.042 ⇒ 00:10:56.650 Amber Lin: Okay, let me go.
105 00:10:57.970 ⇒ 00:11:00.150 Amber Lin: Yeah, let me go. Confirm with them.
106 00:11:02.850 ⇒ 00:11:17.339 Amber Lin: I will. Then I think we can start with the other 1 first, st because I believe all all of them are from Microsoft, so there! There won’t be any confusion there, so we’ll start on that. I’ll go. I’ll go. Confirm with this.
107 00:11:17.340 ⇒ 00:11:18.500 Luke Daque: Okay. Sounds good.
108 00:11:18.500 ⇒ 00:11:19.860 Amber Lin: Yeah. Awesome.
109 00:11:22.110 ⇒ 00:11:23.420 Amber Lin: All right.
110 00:11:24.180 ⇒ 00:11:29.330 Luke Daque: Okay, I just added the Pr and linear.
111 00:11:32.140 ⇒ 00:11:35.160 Amber Lin: Okay. Yay, awesome, that’s all from me.
112 00:11:35.470 ⇒ 00:11:37.379 Amber Lin: I’ll go check in with the clients.
113 00:11:37.640 ⇒ 00:11:39.230 Annie Yu: Sounds good.
114 00:11:39.230 ⇒ 00:11:42.820 Annie Yu: We have Thursday off, right? I just really.
115 00:11:42.820 ⇒ 00:11:51.848 Amber Lin: Yeah. I checked. There’s I didn’t put a meeting. That’s not a meeting. Yeah, that’s
116 00:11:52.940 ⇒ 00:11:58.569 Amber Lin: that should be canceled because they are off as well. Okay, yeah. No meeting on Thursdays.
117 00:11:59.130 ⇒ 00:12:00.260 Annie Yu: Okay. Cool.
118 00:12:00.260 ⇒ 00:12:01.060 Amber Lin: Hey?
119 00:12:01.920 ⇒ 00:12:03.810 Amber Lin: Okay, Hi! I’ll.
120 00:12:04.560 ⇒ 00:12:05.640 Annie Yu: Alright, thanks. Team.
121 00:12:05.640 ⇒ 00:12:06.580 Luke Daque: It’s in the way.
122 00:12:06.900 ⇒ 00:12:07.240 Amber Lin: Bye.