Meeting Title: MatterMore x Brainforge | Standup Date: 2025-05-02 Meeting participants: Annie Yu, Trevor Cohen, Luke Daque, Uttam Kumaran, Amber Lin, Mathew
WEBVTT
1 00:01:04.140 ⇒ 00:01:05.360 Uttam Kumaran: Hello!
2 00:01:05.750 ⇒ 00:01:06.390 Mathew: Morning.
3 00:01:07.200 ⇒ 00:01:08.799 Uttam Kumaran: Us, and the note takers.
4 00:01:09.210 ⇒ 00:01:10.870 Mathew: This us and the noise takers.
5 00:01:11.350 ⇒ 00:01:12.760 Mathew: How lucky are we.
6 00:01:14.530 ⇒ 00:01:15.930 Amber Lin: Hi.
7 00:01:19.000 ⇒ 00:01:23.179 Amber Lin: Matthew, is your zoom image AI generated.
8 00:01:23.330 ⇒ 00:01:24.340 Mathew: Let me look.
9 00:01:25.920 ⇒ 00:01:27.370 Mathew: No, that’s real.
10 00:01:28.310 ⇒ 00:01:30.190 Amber Lin: Really, wow.
11 00:01:30.190 ⇒ 00:01:31.429 Mathew: He’s knocking on the door once.
12 00:01:37.340 ⇒ 00:01:38.130 Trevor Cohen: Hello!
13 00:01:38.130 ⇒ 00:01:39.130 Uttam Kumaran: Blue.
14 00:01:44.540 ⇒ 00:01:46.259 Trevor Cohen: Blaz, are you trying to speak?
15 00:01:50.410 ⇒ 00:01:51.610 Trevor Cohen: You’re on mute.
16 00:01:54.050 ⇒ 00:01:56.219 Mathew: Someone is here to look at the air conditioner.
17 00:01:57.180 ⇒ 00:02:01.139 Trevor Cohen: Okay, cool. We have. We have other people on this call. Did you know that.
18 00:02:05.970 ⇒ 00:02:07.520 Uttam Kumaran: This roomy drama.
19 00:02:07.900 ⇒ 00:02:11.049 Trevor Cohen: Yeah, if only you knew.
20 00:02:15.610 ⇒ 00:02:17.200 Trevor Cohen: Hello! Hey? Siem.
21 00:02:17.390 ⇒ 00:02:18.070 Uttam Kumaran: Leave.
22 00:02:18.070 ⇒ 00:02:20.340 Luke Daque: Nice everyone.
23 00:02:25.380 ⇒ 00:02:25.899 Trevor Cohen: Thank you.
24 00:02:25.900 ⇒ 00:02:26.450 Trevor Cohen: So.
25 00:02:29.350 ⇒ 00:02:31.630 Uttam Kumaran: Amber feel free to take this wherever.
26 00:02:31.630 ⇒ 00:02:34.060 Amber Lin: Oh, okay, let’s
27 00:02:34.300 ⇒ 00:02:49.389 Amber Lin: look. I’m gonna pull up my linear. Just so then nothing slips my mind and let me share screen. So we have. We have a few updates I want to confirm on a few things, and also I know we will also want to talk about
28 00:02:49.850 ⇒ 00:02:59.119 Amber Lin: our time, how to track our time allocation and how to be mindful of Trevor’s time as well.
29 00:02:59.240 ⇒ 00:03:03.809 Amber Lin: So let’s just start here. Let’s look at our linear
30 00:03:05.370 ⇒ 00:03:18.139 Amber Lin: a few updates. Here we have the success factors. We have the synthetic data sheet for both the success better success factors and the Microsoft Graphs and.
31 00:03:18.550 ⇒ 00:03:24.830 Amber Lin: Right now we’re going going through internal review, and then we’ll look at how to join them.
32 00:03:24.950 ⇒ 00:03:28.550 Amber Lin: So. And, Luke, I’ll let you give an update on that.
33 00:03:29.840 ⇒ 00:03:30.970 Trevor Cohen: That’s awesome.
34 00:03:33.760 ⇒ 00:03:43.412 Luke Daque: Sure I can do a quick update. So yeah, and and I have been like working together to create synthetic data, basically for both success factors and
35 00:03:44.120 ⇒ 00:03:52.860 Luke Daque: the the Microsoft stuff we do, have we? We already created like Prs for that for the Python scripts, and also, like the
36 00:03:53.040 ⇒ 00:03:56.140 Luke Daque: the Csv output files. And
37 00:03:57.377 ⇒ 00:04:04.679 Luke Daque: yeah, so that’s basically next step we’ll be doing is to like, figure out like how to join those great like
38 00:04:04.810 ⇒ 00:04:12.530 Luke Daque: final tables that we can probably use for like final output. Basically.
39 00:04:12.995 ⇒ 00:04:17.709 Luke Daque: we haven’t loaded it in bigquery yet. But yeah, that’s also like the the next step that we’d be doing.
40 00:04:18.470 ⇒ 00:04:22.539 Trevor Cohen: Cool. And have you guys all been able to access bigquery with that shared email address.
41 00:04:23.040 ⇒ 00:04:25.379 Luke Daque: Yeah. But unfortunately, I think we
42 00:04:25.560 ⇒ 00:04:33.966 Luke Daque: can no longer access it. And we’re trying to like, figure out the new password, can you? Is it possible for you to reset the password.
43 00:04:34.290 ⇒ 00:04:36.560 Trevor Cohen: Like it worked before. But it doesn’t anymore.
44 00:04:36.560 ⇒ 00:04:37.899 Amber Lin: Yeah, it looks like that.
45 00:04:37.900 ⇒ 00:04:42.520 Uttam Kumaran: And if can I can, I share my screen just to share the synthetic data? Really quick.
46 00:04:42.520 ⇒ 00:04:45.360 Amber Lin: Yeah, I think I think we can all share now.
47 00:04:46.240 ⇒ 00:04:46.870 Uttam Kumaran: Okay,
48 00:04:48.710 ⇒ 00:04:59.299 Uttam Kumaran: yeah. So like as context, Matthew, like, this is like, actually the synthetic data we generated. So all of this that you’re seeing sorry if it’s like a little bit too small.
49 00:05:00.090 ⇒ 00:05:03.800 Uttam Kumaran: It’s just fake and so
50 00:05:05.040 ⇒ 00:05:21.840 Uttam Kumaran: to bring the whole thing home. This is what we were saying, where we’ve basically written scripts where each of the formats of, like the name, the address, the email, are all sort of formatted in the way we should expect the data to come from the source
51 00:05:21.970 ⇒ 00:05:33.189 Uttam Kumaran: systems themselves. Like a good example. If we look at like the call records. You’re gonna see that they have ids. They have names, they have, you know. So it’s actually like
52 00:05:33.300 ⇒ 00:05:42.636 Uttam Kumaran: pretty good. And for us to model and they’re all being generated by these python scripts here that I’m I’m just doing like one more pass on.
53 00:05:44.144 ⇒ 00:05:48.209 Uttam Kumaran: you’re right. The next thing to do is just basically manage the joins between them. So.
54 00:05:49.461 ⇒ 00:05:53.160 Trevor Cohen: Sweet, awesome, nice work, guys, this is really exciting.
55 00:05:53.160 ⇒ 00:06:00.719 Annie Yu: I do have one question. I think when we generated the data, we did them separately. So I
56 00:06:01.050 ⇒ 00:06:08.760 Annie Yu: doubt that we can join them now, just because they might be different group of people. So we probably had to fine tune the script to make sure.
57 00:06:08.760 ⇒ 00:06:11.719 Uttam Kumaran: Yeah, we can. That’s what that’s what I I’m gonna look at. Yeah.
58 00:06:11.720 ⇒ 00:06:22.240 Annie Yu: Yeah, great. And but for the success factors, those 2 are joinable. So those 2 have share users. So I’m not sure if we want to use those people and feed them
59 00:06:22.450 ⇒ 00:06:23.310 Annie Yu: into this.
60 00:06:23.310 ⇒ 00:06:31.440 Uttam Kumaran: So basically, what I’m gonna do is like we will. We will actually generate the Uids in a separate script. And that list will get used
61 00:06:32.000 ⇒ 00:06:38.000 Uttam Kumaran: in subsequent scripts for the uid column. So you you basically always have them.
62 00:06:38.390 ⇒ 00:06:39.500 Annie Yu: Nice nice.
63 00:06:39.500 ⇒ 00:06:42.339 Uttam Kumaran: I just need to put that comment in here. Yeah.
64 00:06:42.550 ⇒ 00:06:43.170 Trevor Cohen: Cool.
65 00:06:43.560 ⇒ 00:06:45.130 Uttam Kumaran: That’s a small think, yeah.
66 00:06:45.420 ⇒ 00:06:46.809 Trevor Cohen: Cool. This is great.
67 00:06:48.530 ⇒ 00:06:49.440 Uttam Kumaran: Yeah, it is.
68 00:06:49.440 ⇒ 00:06:59.950 Trevor Cohen: I’m excited to like, dig into this and start working with it. And yeah, this really sets us up well, to be able to like, get a ton of the work we want to do in place.
69 00:07:00.120 ⇒ 00:07:04.940 Trevor Cohen: Now, which is, yeah, extremely helpful. So thank you guys for this.
70 00:07:05.470 ⇒ 00:07:11.370 Uttam Kumaran: Yeah. And I think even for future Demos, this is like pretty good where you could probably just like get the
71 00:07:11.990 ⇒ 00:07:17.420 Uttam Kumaran: Api endpoints docs, and probably like, just modify these quickly to
72 00:07:17.890 ⇒ 00:07:19.910 Uttam Kumaran: support any other synthetic data set.
73 00:07:20.140 ⇒ 00:07:28.609 Uttam Kumaran: probably, and turn something around that’s like viewable within, between, like the 1st meeting and like the sales meeting to close right? So that’s like.
74 00:07:28.750 ⇒ 00:07:32.059 Uttam Kumaran: that’s where we’ve found this this sort of stuff to be really helpful.
75 00:07:32.300 ⇒ 00:07:36.379 Trevor Cohen: Cool. And and can I ask to what extent have you
76 00:07:36.900 ⇒ 00:07:39.690 Trevor Cohen: like? Try to make the metrics
77 00:07:40.030 ⇒ 00:07:44.509 Trevor Cohen: like believable, correlated when they should be things like that?
78 00:07:45.360 ⇒ 00:07:51.946 Uttam Kumaran: Yeah, so like, let me give you like an example of sort of like what this looks like. At.
79 00:07:52.620 ⇒ 00:08:13.319 Uttam Kumaran: I like the peak. This is like a demo we’re putting in front of, like some customers on the like on the pot, like we’re we’re pitching to some people who do podcast analytics. This is all like fake data. And so you’re right in that, we want to introduce seasonality and noise. And those are all things we could totally do. This is all fake
80 00:08:14.132 ⇒ 00:08:38.020 Uttam Kumaran: but like again, when it’s when you put this in front of someone’s very powerful, because then they just layer on what they know about their business, and you get them to start being like, oh, I’ve never seen platforms without this way. Blah blah blah as you can see here, like there’s some stuff that’s like variable to serve stuff that’s like, not we could just introduce this noise quick adjustments in the script and reintroduce this this sort of noise. I don’t know what the sort of like
81 00:08:38.630 ⇒ 00:08:41.590 Uttam Kumaran: I haven’t thought through what that is in sort of people analytics?
82 00:08:41.967 ⇒ 00:08:47.240 Uttam Kumaran: But, like again, we would just modify the scripts to introduce any sort of patterns that we need to.
83 00:08:47.440 ⇒ 00:08:48.240 Luke Daque: Cool. We did.
84 00:08:48.240 ⇒ 00:08:55.144 Luke Daque: We did include like nuances like for the emails, for example, when, like, there’s a reply like messages
85 00:08:56.354 ⇒ 00:09:02.759 Luke Daque: maybe help. You can help me out here, Annie, in terms of like nuances where? Where someone messages somebody. And then
86 00:09:02.860 ⇒ 00:09:05.419 Luke Daque: the next message is a reply to the other guy.
87 00:09:05.580 ⇒ 00:09:06.390 Uttam Kumaran: Oh, thanks!
88 00:09:06.390 ⇒ 00:09:15.309 Luke Daque: So, yeah, so we did that kind of stuff like, yeah. And then the 3rd one would forward it to another people, person, or something like that. So, yeah.
89 00:09:15.310 ⇒ 00:09:15.640 Trevor Cohen: Nice.
90 00:09:15.640 ⇒ 00:09:17.600 Luke Daque: Like nuances that you added, Yeah.
91 00:09:17.600 ⇒ 00:09:25.395 Trevor Cohen: That’s great, cool, alright sweet! That’s that’s awesome.
92 00:09:29.160 ⇒ 00:09:29.710 Uttam Kumaran: Cool
93 00:09:30.252 ⇒ 00:09:37.079 Uttam Kumaran: amber. What else do we want to cover today? So I think that that Pr, I’ll make sure it’s approved. And we can get that going.
94 00:09:37.752 ⇒ 00:09:39.690 Uttam Kumaran: What else is on the list?
95 00:09:40.100 ⇒ 00:09:46.659 Amber Lin: So that’s updates on the project itself. I think we wanted to also talk about
96 00:09:47.440 ⇒ 00:10:04.669 Amber Lin: one, how we’re gonna review the different hours into how we can be mindful of Trevor’s time, because right now you guys are in a big sprint and we wanna make sure that we use your time wisely. So, Matthew, if you wanna like start off.
97 00:10:04.670 ⇒ 00:10:15.750 Mathew: Yeah, no, I’m I’m fine. I was. I saw the latest invoice. I think it’s I was just asking from that 1st week, just like, how are, how are things like ramping up between across the team, like, how many hours are we consuming? And then.
98 00:10:16.158 ⇒ 00:10:22.590 Mathew: yeah, the main thing with Trevor. We spoke. I think I think we messaged you, or there was some back and forth
99 00:10:23.117 ⇒ 00:10:35.272 Mathew: around that. We just want to make sure that as much that hits Trevor’s has been like reviewed and checked so that he can do more like checking and confirming, and then leading versus
100 00:10:36.730 ⇒ 00:10:42.479 Mathew: you know, just about like there’s gonna be moments where he has to roll up his sleeves and get in there. But we need. I need him focused on core tech.
101 00:10:44.670 ⇒ 00:10:52.090 Mathew: above and beyond everything. So that you know when it’s time, we can actually take all the things that you’re building and pipe it.
102 00:10:54.450 ⇒ 00:10:57.480 Uttam Kumaran: Yeah, I feel overall this week. Since that I think we’ve done.
103 00:10:57.700 ⇒ 00:11:06.809 Uttam Kumaran: That’s fine. We’ve we’ve relied way more on Async. I think the team is now like a lot more familiar. So I feel fine. I don’t know, Trevor. If you have any feedback there.
104 00:11:06.810 ⇒ 00:11:11.890 Trevor Cohen: Yeah, I know I feel good. I mean, I always want to get in the weeds with you guys. But I need to try to.
105 00:11:11.890 ⇒ 00:11:12.450 Uttam Kumaran: Totally.
106 00:11:12.450 ⇒ 00:11:13.020 Trevor Cohen: That.
107 00:11:13.220 ⇒ 00:11:13.830 Uttam Kumaran: Yeah.
108 00:11:14.110 ⇒ 00:11:18.749 Uttam Kumaran: I think I think we’re gonna I think we’re doing well, like we’re fully async remote team, too. So
109 00:11:19.130 ⇒ 00:11:25.819 Uttam Kumaran: even stuff internally, we we rely heavily on like looms and ways to sort of do things just whenever you have time to get to it. So
110 00:11:25.980 ⇒ 00:11:29.880 Uttam Kumaran: I think we’re on the same page. There, cool. I think. Yeah.
111 00:11:29.880 ⇒ 00:11:31.099 Trevor Cohen: It’s been good.
112 00:11:31.220 ⇒ 00:11:41.279 Uttam Kumaran: Okay? And then, yeah, probably amber. I think we can have a cadence where we share out sort of how hours are going with, you know, Matthew and Trevor. And we can do that as part of just our typical Friday updates
113 00:11:41.410 ⇒ 00:11:42.430 Uttam Kumaran: over email.
114 00:11:43.390 ⇒ 00:12:05.360 Mathew: And if there are things that you need from Trevor that, like, you know, say, there’s this, and we’re doing this with our, we’re doing a lot of work with the the security people, if you have like a bunch of requests from him. And you’re like, this is a showstopper. This is a nice to have or like. It’s not urgent. Feel free to like DM, me, or just like tag me in that. And I can work with him, because I’m also trying to see like what other work streams we have.
115 00:12:07.050 ⇒ 00:12:12.780 Mathew: so if you’re and if you’re not hearing back from him, he’s probably like deep in again, deep in cortex. So just like
116 00:12:12.970 ⇒ 00:12:25.519 Mathew: ping me and I’ll and and be like this is a huge block, or this is slowing us down. Can you get him to take a look at this like? Don’t don’t hesitate like. Consider me like a an internal Pm. On our side, for whatever you know, whatever I can do to clear clear that stuff.
117 00:12:27.770 ⇒ 00:12:31.510 Uttam Kumaran: So then, amber, what’s priorities for this next week?
118 00:12:32.420 ⇒ 00:12:58.849 Amber Lin: So in my perspective, we’re pretty decently close or 1 2 steps away from the finish line, because ultimately we want to have a mock dashboard right? And to get there, we have all this synthetic data, we it might not be in the perfect form, but we’re there at least. And now we got it in between of one gotta figure out the join logic. Figure that one out simultaneously. Figure out. Dvt. Of how do we
119 00:12:59.220 ⇒ 00:13:11.460 Amber Lin: stack those SQL. Codes together, and then, just from there, of what do we want to be in a dashboard? What questions can we answer? What are the most important questions that that
120 00:13:11.460 ⇒ 00:13:27.319 Amber Lin: the client wants, and I think that’s something that we’ll go after next week. And Luke and Annie just want to hear from you how much time do you think it’s needed for figuring out Dbt. Figuring on the joints and getting a mock up dashboard.
121 00:13:28.810 ⇒ 00:13:29.330 Uttam Kumaran: Cool.
122 00:13:32.380 ⇒ 00:13:41.799 Luke Daque: That shouldn’t take too long. As we get the data into bigquery. We’ll the data modeling shouldn’t take long for us to do. We’ll yeah, we’ll
123 00:13:41.900 ⇒ 00:13:48.149 Luke Daque: definitely discuss that with Annie in terms of like, what needs to be shown in the dashboard and stuff like that. So yeah.
124 00:13:50.340 ⇒ 00:13:55.499 Trevor Cohen: Let me. So okay, so just to clarify, you guys need me to reset the password for that.
125 00:13:56.630 ⇒ 00:14:04.999 Trevor Cohen: I still I still, I’m I’m still, not understanding exactly why each of you doesn’t have individual access to that data set.
126 00:14:05.000 ⇒ 00:14:10.199 Uttam Kumaran: Yeah, I agree. Cause I, I look at all the settings on my side, and we actually have another client
127 00:14:10.440 ⇒ 00:14:14.500 Uttam Kumaran: where we are sharing individual access.
128 00:14:14.840 ⇒ 00:14:16.580 Luke Daque: Yeah, can you?
129 00:14:17.180 ⇒ 00:14:20.530 Luke Daque: You did send out an invite for us, or something.
130 00:14:20.780 ⇒ 00:14:26.680 Trevor Cohen: But you did. But you were able to get access through that shared that shared email address.
131 00:14:26.680 ⇒ 00:14:37.099 Amber Lin: Yeah, I was in bigquery. I was able to get in bigquery. But again there was just no projects in there. I was under the matter more space. But then there was just still no.
132 00:14:37.100 ⇒ 00:14:41.040 Trevor Cohen: Oh, so it was the same as wasn’t. That was the same as what when I.
133 00:14:41.040 ⇒ 00:14:47.489 Uttam Kumaran: Yeah, Trev Trevor, are you doing on the roles? Are you doing like Gcp, big? Are you doing big bigquery viewer?
134 00:14:47.980 ⇒ 00:14:49.139 Trevor Cohen: Data editor.
135 00:14:50.840 ⇒ 00:14:51.320 Luke Daque: Changes.
136 00:14:51.940 ⇒ 00:14:52.560 Luke Daque: Bigquery.
137 00:14:52.660 ⇒ 00:14:53.740 Luke Daque: Yeah.
138 00:14:54.910 ⇒ 00:14:59.849 Uttam Kumaran: What usually, usually I just slam anything that says bigquery. I just turn it on.
139 00:15:00.200 ⇒ 00:15:02.460 Trevor Cohen: I just. I’m giving you everything except admin.
140 00:15:02.780 ⇒ 00:15:03.160 Uttam Kumaran: Okay.
141 00:15:04.758 ⇒ 00:15:07.509 Trevor Cohen: Just slam, just slam, slam. Yes.
142 00:15:08.017 ⇒ 00:15:12.390 Trevor Cohen: I I but Utam, didn’t you get? Weren’t you able to.
143 00:15:12.390 ⇒ 00:15:14.990 Uttam Kumaran: It was fine for me. Yeah, it was fine for me.
144 00:15:14.990 ⇒ 00:15:20.039 Amber Lin: Awesome. Is it fine for you still, cause I’m gonna share? Oh, this is the wrong screen.
145 00:15:20.576 ⇒ 00:15:29.989 Amber Lin: Is this the over here? Yeah, this is what I see. I’m already. I’m in the right email. I’m in here. There’s just no projects.
146 00:15:30.835 ⇒ 00:15:34.499 Trevor Cohen: And which one. And this is for your email or for the.
147 00:15:34.500 ⇒ 00:15:37.160 Amber Lin: No, this is a shared email.
148 00:15:37.160 ⇒ 00:15:38.410 Trevor Cohen: You? What do you get.
149 00:15:38.410 ⇒ 00:15:39.290 Amber Lin: This is this one.
150 00:15:40.975 ⇒ 00:15:41.740 Uttam Kumaran: Let me try.
151 00:15:41.740 ⇒ 00:15:44.600 Trevor Cohen: For you when you go to your own personal one or your.
152 00:15:44.935 ⇒ 00:15:50.300 Amber Lin: I don’t have big current projects because I don’t have. I’m not doing data work here.
153 00:15:50.300 ⇒ 00:15:53.020 Trevor Cohen: Yeah, I have you all as a data editor for.
154 00:15:57.550 ⇒ 00:16:03.900 Annie Yu: Sorry, guys, I have to drop for another meeting, so I’ll feel soon. But add me if you need me.
155 00:16:03.900 ⇒ 00:16:10.209 Uttam Kumaran: Yeah, okay, I’m gonna I’ll just let me sign into mine and see what I see.
156 00:16:13.370 ⇒ 00:16:15.659 Uttam Kumaran: Yeah, I’m in. I see Brainforge prod.
157 00:16:17.070 ⇒ 00:16:20.070 Uttam Kumaran: Do you matter more analytics, brain forge, prod like.
158 00:16:20.270 ⇒ 00:16:22.070 Trevor Cohen: But you don’t see synthetic.
159 00:16:23.420 ⇒ 00:16:26.156 Uttam Kumaran: I owe, like the
160 00:16:26.710 ⇒ 00:16:27.840 Trevor Cohen: Static data set.
161 00:16:30.240 ⇒ 00:16:32.110 Uttam Kumaran: Oh, I do see. Cynthia. Yeah.
162 00:16:32.390 ⇒ 00:16:33.320 Trevor Cohen: Yeah.
163 00:16:33.320 ⇒ 00:16:36.119 Amber Lin: Utah. Why do you get to see all these things.
164 00:16:37.120 ⇒ 00:16:44.539 Uttam Kumaran: No, I don’t know. I would have given you my, except, this is like my Google account. I can’t. I don’t really like I can’t really share that, because it’s like how it gets signed into everything.
165 00:16:44.740 ⇒ 00:16:47.340 Amber Lin: Can you see the? Can you see any.
166 00:16:47.340 ⇒ 00:16:47.950 Uttam Kumaran: I can. Maybe.
167 00:16:47.950 ⇒ 00:16:49.549 Amber Lin: Like I can try to.
168 00:16:49.550 ⇒ 00:16:52.694 Uttam Kumaran: I can’t go into. I I can’t do any Iam stuff.
169 00:16:54.420 ⇒ 00:17:01.530 Trevor Cohen: It’s so weird. Okay, I may also delete you and then re add you all except you. You know.
170 00:17:05.160 ⇒ 00:17:07.329 Uttam Kumaran: I mean, I can. Yeah, I just.
171 00:17:10.930 ⇒ 00:17:22.829 Trevor Cohen: Yeah, because the only the only sense in which I added you is that you’re a member of the brainforge@mattermore.ai Google group which I gave. And I gave the Google group access like bigquery data editor
172 00:17:23.069 ⇒ 00:17:32.002 Trevor Cohen: access to that data set. And so whatever you have, everyone else should have also like it doesn’t make any sense that they’re different.
173 00:17:34.860 ⇒ 00:17:35.680 Uttam Kumaran: Yeah,
174 00:17:38.795 ⇒ 00:17:39.470 Trevor Cohen: Not.
175 00:17:39.470 ⇒ 00:17:43.549 Uttam Kumaran: No, I just like it’s like, Yeah, I I guess I can.
176 00:17:44.770 ⇒ 00:17:50.359 Uttam Kumaran: I went and looked, and there was no settings like preventing external access. But I can go.
177 00:17:51.650 ⇒ 00:18:00.930 Trevor Cohen: And then and then separately, amber, I added you as a like your own personal email, as a data editor. But it didn’t seem to do anything.
178 00:18:02.170 ⇒ 00:18:06.359 Amber Lin: Let me check if I can switch to my
179 00:18:07.450 ⇒ 00:18:11.080 Amber Lin: bring for Gmail, and then see if I can get access there.
180 00:18:11.080 ⇒ 00:18:11.830 Trevor Cohen: Okay.
181 00:18:12.250 ⇒ 00:18:12.890 Amber Lin: Okay.
182 00:18:13.560 ⇒ 00:18:14.010 Amber Lin: Look.
183 00:18:14.010 ⇒ 00:18:17.949 Trevor Cohen: Alright, I’ll I’ll I’ll chat with chat, gpt, and.
184 00:18:18.170 ⇒ 00:18:24.700 Uttam Kumaran: Yeah, I’m gonna do the same thing. I basically, I’m just gonna find out if there’s anything I need to enable, or people in a workspace to access external.
185 00:18:25.990 ⇒ 00:18:26.510 Amber Lin: Well.
186 00:18:26.850 ⇒ 00:18:27.510 Uttam Kumaran: Yeah.
187 00:18:27.510 ⇒ 00:18:30.320 Amber Lin: That’s good, that’s all. From that’s all from me.
188 00:18:30.320 ⇒ 00:18:31.380 Trevor Cohen: Okay, cool, thanks, guys.
189 00:18:31.750 ⇒ 00:18:33.420 Uttam Kumaran: Thank you. Talk soon.
190 00:18:33.960 ⇒ 00:18:34.440 Trevor Cohen: Bye.
191 00:18:34.440 ⇒ 00:18:35.050 Trevor Cohen: M.
192 00:18:35.330 ⇒ 00:18:35.909 Uttam Kumaran: You too.