Meeting Title: AI Tools Case Study Sync Date: 2025-11-14 Meeting participants: Hannah Wang, Gabriel Lam
WEBVTT
1 00:00:24.980 ⇒ 00:00:26.290 Gabriel Lam: Hello.
2 00:00:27.720 ⇒ 00:00:28.870 Hannah Wang: Hello?
3 00:00:29.020 ⇒ 00:00:30.229 Gabriel Lam: How are you?
4 00:00:31.030 ⇒ 00:00:32.520 Hannah Wang: Friday.
5 00:00:33.280 ⇒ 00:00:34.700 Hannah Wang: I feel that.
6 00:00:36.090 ⇒ 00:00:40.219 Hannah Wang: How’s your week? How’s… how’s working here?
7 00:00:40.220 ⇒ 00:00:45.790 Gabriel Lam: It’s pretty good. I am… Still wrapping my head around.
8 00:00:47.230 ⇒ 00:00:50.050 Gabriel Lam: code, but we’re getting there.
9 00:00:50.360 ⇒ 00:00:58.660 Gabriel Lam: No, but I’m just, like, looking up PRs, and I’m like, how do I make sense of all this?
10 00:00:59.180 ⇒ 00:01:01.950 Hannah Wang: Do you need to do that, though, or are you just doing it because…
11 00:01:01.950 ⇒ 00:01:05.700 Gabriel Lam: Less so… I mean, I think that’s more Sam’s area.
12 00:01:05.700 ⇒ 00:01:06.200 Hannah Wang: Yeah.
13 00:01:06.200 ⇒ 00:01:16.050 Gabriel Lam: But I feel like eventually I would like to know what I’m looking at, aside from, like, hey, you know, cursor, what does this repo meme do, you know?
14 00:01:16.050 ⇒ 00:01:16.750 Hannah Wang: Hmm.
15 00:01:17.430 ⇒ 00:01:22.979 Gabriel Lam: Aside from that, things are pretty good. Jerry’s a little sick, though, so… working through that.
16 00:01:23.160 ⇒ 00:01:23.910 Hannah Wang: Yeah.
17 00:01:24.090 ⇒ 00:01:28.450 Hannah Wang: It’s… it’s sick season. Everyone… everyone’s getting sick.
18 00:01:28.450 ⇒ 00:01:37.519 Gabriel Lam: Yeah, she’s been getting, like, eczema, like, she’s itching, like, for a week straight, and it’s like, we don’t know what the reasoning is, so…
19 00:01:37.910 ⇒ 00:01:40.109 Hannah Wang: Does she usually have eczema?
20 00:01:40.110 ⇒ 00:01:43.400 Gabriel Lam: No, very rare, so, yeah.
21 00:01:44.060 ⇒ 00:01:44.900 Hannah Wang: Oh.
22 00:01:44.900 ⇒ 00:01:46.330 Gabriel Lam: That’s…
23 00:01:46.710 ⇒ 00:01:47.830 Hannah Wang: That’s not fun.
24 00:01:48.460 ⇒ 00:01:51.440 Gabriel Lam: I’ll put that in the prayer, so…
25 00:01:51.440 ⇒ 00:01:52.819 Hannah Wang: Yeah, yeah, yeah.
26 00:01:54.020 ⇒ 00:01:58.689 Hannah Wang: Oh, man. It’s… it’s so gloomy. It’s, like, raining here. Apparently.
27 00:01:58.690 ⇒ 00:01:59.660 Gabriel Lam: It’s go.
28 00:01:59.880 ⇒ 00:02:01.420 Hannah Wang: storm.
29 00:02:01.420 ⇒ 00:02:02.510 Gabriel Lam: Whoa, wow.
30 00:02:02.510 ⇒ 00:02:02.980 Hannah Wang: Oh my god.
31 00:02:02.980 ⇒ 00:02:04.359 Gabriel Lam: That’s a rarity.
32 00:02:04.360 ⇒ 00:02:12.369 Hannah Wang: I know, but, I don’t re- know if you remember… Alice and Eugene.
33 00:02:12.970 ⇒ 00:02:13.570 Gabriel Lam: Yeah.
34 00:02:13.570 ⇒ 00:02:15.709 Hannah Wang: Yeah, so I’m dog-sitting…
35 00:02:15.710 ⇒ 00:02:17.620 Gabriel Lam: Mmm! Dog.
36 00:02:17.620 ⇒ 00:02:28.630 Hannah Wang: And, yeah, taking her out is gonna be interesting in the rain. They, like, bought her a raincoat and everything, so we tested it out this morning.
37 00:02:28.630 ⇒ 00:02:29.480 Gabriel Lam: Adorable.
38 00:02:29.480 ⇒ 00:02:30.590 Hannah Wang: Yeah, but…
39 00:02:30.590 ⇒ 00:02:31.379 Gabriel Lam: Where do they know?
40 00:02:31.830 ⇒ 00:02:34.649 Hannah Wang: They’re in Napa, they’re…
41 00:02:34.650 ⇒ 00:02:35.160 Gabriel Lam: Okay.
42 00:02:35.160 ⇒ 00:02:38.460 Hannah Wang: Doing… so they just… they gave birth 2 weeks ago.
43 00:02:38.460 ⇒ 00:02:40.829 Gabriel Lam: I did hear about that.
44 00:02:40.830 ⇒ 00:02:49.899 Hannah Wang: And so, I think Eugene is getting kind of stir-crazy, so they’re experimenting with how early they can take their son out to.
45 00:02:49.900 ⇒ 00:02:50.490 Gabriel Lam: Yeah.
46 00:02:50.490 ⇒ 00:02:54.510 Hannah Wang: So they… they’re… they made a road trip to Napa yesterday.
47 00:02:54.510 ⇒ 00:02:55.020 Gabriel Lam: Whoa.
48 00:02:55.360 ⇒ 00:02:59.999 Hannah Wang: So let’s see how that goes. Yeah, taking a 2-week-old…
49 00:03:00.840 ⇒ 00:03:01.910 Gabriel Lam: It’s crazy.
50 00:03:01.910 ⇒ 00:03:03.330 Hannah Wang: Maybe? Yeah.
51 00:03:03.680 ⇒ 00:03:04.780 Gabriel Lam: Wow.
52 00:03:04.780 ⇒ 00:03:06.300 Hannah Wang: I know.
53 00:03:06.590 ⇒ 00:03:11.739 Hannah Wang: That’s their personality, though, like, they do, like, fun stuff like that. Yeah.
54 00:03:11.920 ⇒ 00:03:19.200 Hannah Wang: But… Yeah, it’s been… it’s been good having her, but it’s for everywhere, because she…
55 00:03:19.750 ⇒ 00:03:25.419 Hannah Wang: She sheds a lot, so my outfit is just fur, but that’s okay.
56 00:03:25.650 ⇒ 00:03:28.110 Gabriel Lam: It’s a little enrollee kind of day.
57 00:03:28.110 ⇒ 00:03:30.659 Hannah Wang: Yeah. Oh yeah, I have lint rollers everywhere.
58 00:03:31.100 ⇒ 00:03:32.609 Hannah Wang: Even with the dogs, so…
59 00:03:32.610 ⇒ 00:03:35.090 Gabriel Lam: We’re just like, okay.
60 00:03:35.570 ⇒ 00:03:35.960 Gabriel Lam: that.
61 00:03:35.960 ⇒ 00:03:43.160 Hannah Wang: But yeah, walk me through… or I guess I can… you can send me the Heroku link, and I can play around with it.
62 00:03:45.790 ⇒ 00:03:52.870 Gabriel Lam: Yes, let me get that to you, and maybe I can walk you through…
63 00:03:53.610 ⇒ 00:03:55.969 Gabriel Lam: Oh, you can share a screen and I can walk you through.
64 00:03:57.290 ⇒ 00:03:58.680 Gabriel Lam: Interfaces.
65 00:04:03.170 ⇒ 00:04:05.280 Gabriel Lam: There we go, I put it on Slack.
66 00:04:05.280 ⇒ 00:04:06.000 Hannah Wang: Okay.
67 00:04:06.540 ⇒ 00:04:08.480 Gabriel Lam: Yeah, yeah, yeah, yeah.
68 00:04:09.590 ⇒ 00:04:18.469 Hannah Wang: Alright, let me… Sign in… And then share my screen.
69 00:04:21.940 ⇒ 00:04:22.335 Hannah Wang: cover.
70 00:04:24.730 ⇒ 00:04:27.209 Gabriel Lam: So, it’s in the AI tools.
71 00:04:27.400 ⇒ 00:04:29.150 Gabriel Lam: Good. Yes.
72 00:04:29.630 ⇒ 00:04:30.669 Hannah Wang: Correct.
73 00:04:30.830 ⇒ 00:04:33.489 Gabriel Lam: I should put that in the pre-read email.
74 00:04:33.830 ⇒ 00:04:34.910 Hannah Wang: Oh, please.
75 00:04:36.530 ⇒ 00:04:37.550 Hannah Wang: Alright.
76 00:04:37.550 ⇒ 00:04:41.100 Gabriel Lam: So, you would just go to Create Case Study…
77 00:04:43.950 ⇒ 00:04:48.330 Gabriel Lam: I mean, you can feel free to, like, chime in and be like, I don’t like this, or…
78 00:04:49.610 ⇒ 00:04:53.030 Hannah Wang: Alright, let me actually pull up, like, an upcoming…
79 00:04:53.030 ⇒ 00:04:55.490 Gabriel Lam: Case study, if we have one.
80 00:04:59.070 ⇒ 00:05:00.460 Gabriel Lam: Okay. Yeah.
81 00:05:03.880 ⇒ 00:05:13.489 Gabriel Lam: In the very, very small chance case that anything doesn’t work, Katie, would you be open to doing your own loot section, if that comes down to it?
82 00:05:13.720 ⇒ 00:05:14.960 Gabriel Lam: Or, you know, okay.
83 00:05:14.960 ⇒ 00:05:18.189 Hannah Wang: Like, I don’t know who’s the POC for these.
84 00:05:19.640 ⇒ 00:05:20.350 Gabriel Lam: Okay.
85 00:05:23.820 ⇒ 00:05:27.270 Gabriel Lam: Okay, yeah, it hasn’t come out yet, but just, just in case, yeah.
86 00:05:28.610 ⇒ 00:05:30.300 Hannah Wang: I feel like you should wear it.
87 00:05:30.860 ⇒ 00:05:32.130 Gabriel Lam: Alright.
88 00:05:33.560 ⇒ 00:05:35.050 Gabriel Lam: Okay, take care.
89 00:05:35.920 ⇒ 00:05:37.010 Gabriel Lam: Bye!
90 00:05:43.260 ⇒ 00:05:48.980 Hannah Wang: Here we go. Whoa, okay.
91 00:05:52.160 ⇒ 00:05:52.940 Hannah Wang: Hip.
92 00:05:54.470 ⇒ 00:05:58.250 Hannah Wang: Alright, so let’s try this. So, this is a…
93 00:05:58.760 ⇒ 00:06:06.149 Hannah Wang: I don’t know the priority for any of these, to be honest, but I’m just gonna use this as an example. So, project name is…
94 00:06:07.360 ⇒ 00:06:09.280 Hannah Wang: hip access…
95 00:06:10.990 ⇒ 00:06:15.029 Gabriel Lam: You could just also do, like, Data Strategy Audit, that’s the title.
96 00:06:15.030 ⇒ 00:06:15.910 Hannah Wang: Yeah.
97 00:06:16.370 ⇒ 00:06:17.470 Hannah Wang: Data strategy.
98 00:06:17.920 ⇒ 00:06:20.249 Hannah Wang: I don’t know what any of these mean.
99 00:06:20.250 ⇒ 00:06:21.000 Gabriel Lam: Yeah.
100 00:06:21.000 ⇒ 00:06:23.459 Hannah Wang: Oh, cause client is under it.
101 00:06:24.390 ⇒ 00:06:31.369 Hannah Wang: Okay, this is hip… Can I just, like, paste that whole thing?
102 00:06:31.370 ⇒ 00:06:32.180 Gabriel Lam: Yep.
103 00:06:33.410 ⇒ 00:06:41.069 Hannah Wang: Awash. So he’s gonna get a Slack. Theoretically, he’ll get a Slack message.
104 00:06:41.070 ⇒ 00:06:44.709 Gabriel Lam: Theoretically, I… when I tested it, I…
105 00:06:44.860 ⇒ 00:06:52.280 Gabriel Lam: did not. So, either Sam has not pushed it up yet, or… A wish might get some.
106 00:06:52.280 ⇒ 00:07:01.970 Hannah Wang: Okay, that’s okay, he’ll… if he gets it, then… so, if he gets it, is it in, like, a group chat with me and him, or is it just his DM? His, like…
107 00:07:01.970 ⇒ 00:07:06.930 Gabriel Lam: It’s just… so… For now, it’s just to him.
108 00:07:07.350 ⇒ 00:07:08.970 Gabriel Lam: Eventually.
109 00:07:09.390 ⇒ 00:07:19.019 Gabriel Lam: we want to also… once he’s done, you would get a Slack notification from this to be like, hey, he’s done it. There’s a copy.
110 00:07:19.020 ⇒ 00:07:19.410 Hannah Wang: Okay.
111 00:07:19.410 ⇒ 00:07:24.700 Gabriel Lam: I’m not sure if Sam was able to get to that yet, but I think we were setting it up so that
112 00:07:25.500 ⇒ 00:07:26.620 Gabriel Lam: It could be.
113 00:07:27.730 ⇒ 00:07:29.460 Hannah Wang: Got it. Would it be…
114 00:07:29.790 ⇒ 00:07:40.099 Hannah Wang: not to add any technical level, like, this might be technically too difficult, or, like, it might… yeah, but would it be better if…
115 00:07:41.760 ⇒ 00:07:45.699 Hannah Wang: It sends a message in a group
116 00:07:46.910 ⇒ 00:07:52.609 Hannah Wang: chat with, like… like, I don’t know if you signed up for the donut thing yet, but…
117 00:07:52.610 ⇒ 00:07:53.419 Gabriel Lam: Not yet.
118 00:07:53.610 ⇒ 00:07:57.940 Hannah Wang: No worries, let me just show you that. Like, there’s a donut
119 00:07:58.090 ⇒ 00:08:03.149 Hannah Wang: Agent, and then it sets up a group chat with me.
120 00:08:03.290 ⇒ 00:08:11.180 Hannah Wang: Sam and Donut, so I’m just wondering if that would be easier? Because…
121 00:08:11.730 ⇒ 00:08:17.939 Hannah Wang: You’re saying that it would send to my… my… himself, like, this version for him.
122 00:08:18.830 ⇒ 00:08:20.040 Gabriel Lam: I think so.
123 00:08:20.040 ⇒ 00:08:20.810 Hannah Wang: Okay.
124 00:08:23.200 ⇒ 00:08:29.540 Hannah Wang: Okay, this is super knit, it doesn’t… I think it’s okay. So, let’s just… if he gets it, and he’s like, what the heck is this? And I’ll just say.
125 00:08:29.540 ⇒ 00:08:30.170 Gabriel Lam: Yeah.
126 00:08:30.170 ⇒ 00:08:31.090 Hannah Wang: Yeah.
127 00:08:32.309 ⇒ 00:08:32.699 Hannah Wang: Okay.
128 00:08:32.820 ⇒ 00:08:42.029 Gabriel Lam: So, yeah, so they would send it over to him. The link he would get is the same link as if you pressed that open interview link.
129 00:08:42.890 ⇒ 00:08:46.760 Gabriel Lam: And so we are doing it my auth, so, like.
130 00:08:47.390 ⇒ 00:08:56.410 Gabriel Lam: Not anyone can take it, but for you, if you were to go back and, click into that row, just anywhere on that row.
131 00:08:57.710 ⇒ 00:09:03.470 Gabriel Lam: You should be able to get to this page. And so you would see when he…
132 00:09:05.010 ⇒ 00:09:09.020 Gabriel Lam: When he does the interview, You would get the transcript.
133 00:09:10.490 ⇒ 00:09:19.029 Gabriel Lam: Or it would say who’s interviewed, what the transcript is, and then it would automatically generate a case study based on whatever.
134 00:09:20.390 ⇒ 00:09:25.750 Gabriel Lam: either agent. And I know there are two, and so the hope was if we needed to
135 00:09:26.030 ⇒ 00:09:31.000 Gabriel Lam: Polish it or refine it for certain industries or certain people.
136 00:09:31.760 ⇒ 00:09:34.259 Gabriel Lam: Or, you know, a certain market.
137 00:09:34.740 ⇒ 00:09:36.789 Gabriel Lam: area, it would…
138 00:09:37.880 ⇒ 00:09:43.789 Gabriel Lam: we had the ability to do that, but for now, it’s the same agent. It’s the one on the Notion bot, basically.
139 00:09:44.400 ⇒ 00:09:45.300 Hannah Wang: Okay.
140 00:09:45.800 ⇒ 00:09:46.315 Gabriel Lam: Sweet.
141 00:09:46.890 ⇒ 00:09:55.520 Hannah Wang: So, like, this… This one is, like, the… Like, let’s say that… the case study…
142 00:09:55.830 ⇒ 00:10:04.629 Hannah Wang: we wanted to send it out to a head of growth and, like, a head of product. I feel like they care about different things, so you’re saying that
143 00:10:04.740 ⇒ 00:10:06.930 Hannah Wang: This would be able to generate
144 00:10:07.220 ⇒ 00:10:11.169 Hannah Wang: Copy that’s specific for both personas.
145 00:10:11.170 ⇒ 00:10:13.939 Gabriel Lam: So, that’s the next.
146 00:10:14.100 ⇒ 00:10:14.440 Hannah Wang: Okay.
147 00:10:14.440 ⇒ 00:10:19.560 Gabriel Lam: layer. For now, we just literally took the one from Notion.
148 00:10:19.560 ⇒ 00:10:20.130 Hannah Wang: Yeah.
149 00:10:20.130 ⇒ 00:10:21.800 Gabriel Lam: And…
150 00:10:22.010 ⇒ 00:10:29.229 Gabriel Lam: The only reason why we separated it was so if we wanted to slide in and plug in and plug out a different…
151 00:10:29.490 ⇒ 00:10:31.220 Gabriel Lam: Promptly could do that.
152 00:10:31.940 ⇒ 00:10:38.299 Hannah Wang: Got it. Would it be… could you move… this is super knit, but could you move this to be first?
153 00:10:38.700 ⇒ 00:10:39.240 Gabriel Lam: Okay.
154 00:10:39.630 ⇒ 00:10:41.459 Hannah Wang: Not that it matters, but…
155 00:10:42.300 ⇒ 00:10:50.869 Hannah Wang: when I just look at it visually, I would probably look at this by accident if I just… I see. Huge heading, which I wouldn’t, but…
156 00:10:52.970 ⇒ 00:11:02.960 Hannah Wang: Yeah, and then let me message Oilishrou real quick, in case… I mean, he’s offline, but, I’m testing out…
157 00:11:04.790 ⇒ 00:11:09.160 Hannah Wang: Okay, setting assistant on the facilities.
158 00:11:18.460 ⇒ 00:11:19.250 Hannah Wang: Okay.
159 00:11:19.360 ⇒ 00:11:22.070 Hannah Wang: Cool.
160 00:11:22.330 ⇒ 00:11:32.440 Hannah Wang: And then, let me just, like, click around and stuff. Alright, awaiting interview. And then, once he submits it, it would… I’m assuming the statuses would automatically change?
161 00:11:32.440 ⇒ 00:11:33.060 Gabriel Lam: Yeah.
162 00:11:33.060 ⇒ 00:11:33.450 Hannah Wang: Okay.
163 00:11:33.450 ⇒ 00:11:34.130 Gabriel Lam: Yes.
164 00:11:36.210 ⇒ 00:11:36.710 Hannah Wang: Awesome.
165 00:11:36.710 ⇒ 00:11:41.220 Gabriel Lam: Yeah, and so I think… I think the goal is to… cat.
166 00:11:41.340 ⇒ 00:11:47.150 Gabriel Lam: the interview part out of the picture. I think something we’re trying to figure out now, either. Well.
167 00:11:47.490 ⇒ 00:11:53.449 Gabriel Lam: I think Uta brought it up earlier, which is, like, which ones to do, which I think is a separate problem.
168 00:11:53.450 ⇒ 00:11:54.770 Hannah Wang: Totally, yep.
169 00:11:54.770 ⇒ 00:11:58.260 Gabriel Lam: And then after that… Yeah. Yeah, go for it.
170 00:11:58.260 ⇒ 00:11:59.599 Hannah Wang: Oh, I was just saying, I f-
171 00:12:00.050 ⇒ 00:12:04.119 Hannah Wang: I mean, he mentioned, oh, every Monday in the delivery meetings or whatever.
172 00:12:04.290 ⇒ 00:12:09.530 Hannah Wang: I think he was just saying, oh, now they’re gonna let me know, so I think that’s…
173 00:12:10.260 ⇒ 00:12:18.000 Hannah Wang: Less of a… Blocker than this was, like, getting the interview.
174 00:12:18.750 ⇒ 00:12:36.260 Hannah Wang: content itself, because for the part… the one… the problem of which case study, like, that would only take… at least for me, that would only be a simple message to send. Maybe it’s harder for the people gathering the interviews and… or gathering the projects, and, like, telling.
175 00:12:36.710 ⇒ 00:12:37.609 Hannah Wang: what it is.
176 00:12:37.710 ⇒ 00:12:40.280 Hannah Wang: But for me, that’s, like, not…
177 00:12:42.870 ⇒ 00:12:46.690 Hannah Wang: terrible, but I will say, like, it’d be helpful if…
178 00:12:47.390 ⇒ 00:12:50.690 Hannah Wang: Once we start working on that end of it, like…
179 00:12:53.760 ⇒ 00:12:56.360 Hannah Wang: If they can just, like, help prioritize.
180 00:12:56.730 ⇒ 00:13:04.219 Hannah Wang: Within, like, a big backlog of case studies, like, which one to do next, and,
181 00:13:04.630 ⇒ 00:13:12.430 Hannah Wang: Because right now, it’s basically me kind of guessing and, like, asking every week, hey, which one do you want done? Or, like…
182 00:13:12.690 ⇒ 00:13:21.400 Hannah Wang: Stuff like that. Yeah. But anyway, we can grab time for that aspect.
183 00:13:21.830 ⇒ 00:13:29.979 Hannah Wang: the project. But yeah, this is awesome. Like, I think this will be super helpful, just so that I don’t have to…
184 00:13:30.520 ⇒ 00:13:41.920 Hannah Wang: go in and interview people. I guess, yeah, my other questions… I don’t know if you want to answer the questions I sent to you in, Slack, but yeah, like, how does the…
185 00:13:42.730 ⇒ 00:13:49.149 Hannah Wang: interview… Agent thing, like, know what to ask and stuff.
186 00:13:49.150 ⇒ 00:13:56.640 Gabriel Lam: So, we… Had the list of questions that you typically would have in an interview.
187 00:13:57.470 ⇒ 00:14:06.059 Gabriel Lam: So that serves as a guideline to… Basically, if it doesn’t… Have the answer.
188 00:14:06.630 ⇒ 00:14:11.640 Gabriel Lam: It’ll… or if it can’t infer the answer, it’ll try to ask more explicitly.
189 00:14:11.850 ⇒ 00:14:12.350 Hannah Wang: Right.
190 00:14:12.350 ⇒ 00:14:28.779 Gabriel Lam: we did overfit it at the beginning, and so, like, if you try to test the edge cases, it kind of falls apart, and so that’s why I noticed when… I think I, I, briefly checked out the call you did on Wednesday, Thursday, with Henry, about Eden.
191 00:14:28.780 ⇒ 00:14:29.210 Hannah Wang: Aya.
192 00:14:29.210 ⇒ 00:14:31.820 Gabriel Lam: that…
193 00:14:32.670 ⇒ 00:14:40.669 Gabriel Lam: We took that as the norm, where it’s, like, a little more prescriptive, and you’re able to go through each and every question. But the way, for example.
194 00:14:40.820 ⇒ 00:14:45.360 Gabriel Lam: the way Utam tested it was, like, he just, like, said everything in 5 minutes.
195 00:14:45.870 ⇒ 00:14:52.249 Gabriel Lam: Got it. And then he was like, I’m done. I don’t need to add anything. And then the AI was like, oh, what about context? What about…
196 00:14:52.780 ⇒ 00:15:00.740 Gabriel Lam: I don’t need that. So that’s why it’s a little more… free flow, I think…
197 00:15:01.000 ⇒ 00:15:05.170 Gabriel Lam: I mean, we can always edit it, and we can always add more to the prompt.
198 00:15:05.970 ⇒ 00:15:09.000 Gabriel Lam: So that’s… that’s really the first question. I think.
199 00:15:09.150 ⇒ 00:15:11.150 Gabriel Lam: We have the list of questions, and
200 00:15:11.660 ⇒ 00:15:16.240 Gabriel Lam: If your answer covers any of them, it will…
201 00:15:16.790 ⇒ 00:15:19.670 Gabriel Lam: like, cross it off the list, it’ll answer…
202 00:15:19.810 ⇒ 00:15:26.930 Gabriel Lam: The other… it’ll ask the other questions that it thinks it hasn’t covered. And then the last thing is, if it…
203 00:15:27.480 ⇒ 00:15:36.320 Gabriel Lam: If you want to end the interview and you haven’t provided anything for that answer, so one example would be, like, what are the results and metrics, and
204 00:15:36.320 ⇒ 00:15:49.040 Gabriel Lam: if you’re like, I don’t know what it is, or I can’t give it to you, or, like, let’s just end the interview, I need to go, then it’ll leave it, like, as I don’t have the information there, and maybe that’s something to follow up, or maybe it’s, you know, a culture of…
205 00:15:49.870 ⇒ 00:15:53.729 Gabriel Lam: training people to talk to an AI.
206 00:15:54.710 ⇒ 00:16:04.670 Gabriel Lam: Yeah, the second question during the demo, this isn’t interrupted you when there was a small pause. I think the way we were thinking about it was…
207 00:16:05.320 ⇒ 00:16:08.940 Gabriel Lam: How long we have that pause be.
208 00:16:09.290 ⇒ 00:16:19.110 Gabriel Lam: Cause… It… we’re using a real-time model, and so essentially, the second you stop talking, it can start processing.
209 00:16:19.110 ⇒ 00:16:19.710 Hannah Wang: Yeah.
210 00:16:19.710 ⇒ 00:16:24.209 Gabriel Lam: And so we tried to put in a little gap, and…
211 00:16:24.590 ⇒ 00:16:30.339 Gabriel Lam: I think the way that I presented it is a little… I try to stuff as much information in as possible.
212 00:16:30.340 ⇒ 00:16:33.239 Hannah Wang: It sounded like from when you were talking with Henry, there’s…
213 00:16:33.600 ⇒ 00:16:34.720 Gabriel Lam: Most like it.
214 00:16:35.000 ⇒ 00:16:36.789 Gabriel Lam: You know, a 2-3 sentence.
215 00:16:37.550 ⇒ 00:16:38.650 Gabriel Lam: Answer.
216 00:16:38.960 ⇒ 00:16:42.410 Gabriel Lam: And then, the next question. But…
217 00:16:42.670 ⇒ 00:16:43.540 Hannah Wang: if…
218 00:16:43.540 ⇒ 00:16:49.580 Gabriel Lam: If people are noticing that it’s interrupting us, then… Yes.
219 00:16:49.790 ⇒ 00:16:52.280 Gabriel Lam: We can increase the pause.
220 00:16:52.280 ⇒ 00:16:53.519 Hannah Wang: Thank you.
221 00:16:53.520 ⇒ 00:16:59.650 Gabriel Lam: The other thing is you can interrupt the AI assistant, so you don’t need to feel like you have to let it finish.
222 00:17:00.080 ⇒ 00:17:00.620 Hannah Wang: I see.
223 00:17:00.620 ⇒ 00:17:02.219 Gabriel Lam: And then there…
224 00:17:02.220 ⇒ 00:17:04.000 Hannah Wang: walking over it, and I was like, okay, yeah.
225 00:17:04.000 ⇒ 00:17:08.539 Gabriel Lam: Yeah, and then there is a pause and unpause. There is a pause and then resume.
226 00:17:08.930 ⇒ 00:17:11.030 Gabriel Lam: And then there’s also a restart button.
227 00:17:11.030 ⇒ 00:17:11.700 Hannah Wang: Okay.
228 00:17:11.700 ⇒ 00:17:17.889 Gabriel Lam: So this is… that’s more for the interviewee, which is maybe less for you, and I think the more people get used to it.
229 00:17:18.510 ⇒ 00:17:23.660 Gabriel Lam: Maybe there’s more feedback, which is what we’re really hoping for, but… Yeah, I…
230 00:17:23.660 ⇒ 00:17:32.919 Hannah Wang: I’m totally fair… it’s, like, fair game for just, like, people can just blah at the…
231 00:17:33.140 ⇒ 00:17:47.510 Hannah Wang: AI agent, because I know… I feel like everyone’s so different, right? So it’s hard to, like, build a one-size-fits-all thing for the way people talk, and, like, how much information they give. So, for example, I feel like UTAM tends to be more…
232 00:17:47.510 ⇒ 00:17:55.699 Hannah Wang: long-winded, which is great, you know, like, he provides all the context, like, everything. But some people might be more…
233 00:17:55.700 ⇒ 00:18:00.960 Hannah Wang: Succinct, and terse, and… Like, not give.
234 00:18:01.090 ⇒ 00:18:07.220 Hannah Wang: just, like, literally just answer that question and not expand on it, so… I guess that’s…
235 00:18:08.660 ⇒ 00:18:18.029 Hannah Wang: that’s, like, my one concern, I guess, is that, like, the case study is lacking… it’s just lacking it, because… lacking robustness because…
236 00:18:18.170 ⇒ 00:18:19.739 Gabriel Lam: the interviewee.
237 00:18:20.690 ⇒ 00:18:29.099 Hannah Wang: just doesn’t talk as much by nature. Yeah. So… but I think we’ll, like, start to figure that out once people start to use it.
238 00:18:29.100 ⇒ 00:18:34.580 Gabriel Lam: Yeah, I mean, I mean, I could do a short live demo where I’m a little more terse.
239 00:18:34.580 ⇒ 00:18:35.060 Hannah Wang: Okay.
240 00:18:35.060 ⇒ 00:18:41.550 Gabriel Lam: And maybe that… I mean, we’re also just figuring that out as well, so we shall see.
241 00:18:42.410 ⇒ 00:18:46.120 Hannah Wang: Like, I think Utam… sorry, before you begin… Yeah, good.
242 00:18:46.250 ⇒ 00:18:58.620 Hannah Wang: I think people are used to interview… interviewing with me, and so they know, like, okay, she’s gonna ask this question, this question, this question. I think that’s why they tend to stop talking.
243 00:18:59.030 ⇒ 00:19:02.999 Hannah Wang: and just answer my question. So, like, I don’t know…
244 00:19:05.530 ⇒ 00:19:15.079 Hannah Wang: Yeah, so it’s good, I guess, the way you built it, where they can just blah at it, and if they miss anything, then it’ll come back and ask it.
245 00:19:15.080 ⇒ 00:19:25.979 Hannah Wang: I just feel like those guiding questions are helpful for new people, like, who’ve never interviewed with me, because basically everyone here has done an interview with me, and they know, like, how I do it.
246 00:19:26.040 ⇒ 00:19:28.360 Hannah Wang: So, I guess a good, like.
247 00:19:30.360 ⇒ 00:19:43.189 Hannah Wang: test case would be for a new person, like, using the agent for the first time to see how the case study comes out, but yeah, that’s, like, a continual iteration type of thing that we’ll get to. So yeah.
248 00:19:43.890 ⇒ 00:19:48.009 Gabriel Lam: So, let’s just try to… I’ll redo this interview.
249 00:19:48.010 ⇒ 00:19:48.640 Hannah Wang: Okay.
250 00:19:49.830 ⇒ 00:19:51.379 Gabriel Lam: Let’s see if I can…
251 00:19:54.900 ⇒ 00:20:02.560 Gabriel Lam: So now that it’s generated… I will… I’ll just redo this.
252 00:20:02.560 ⇒ 00:20:03.220 Hannah Wang: Okay.
253 00:20:04.370 ⇒ 00:20:05.800 Gabriel Lam: I don’t know if you can hear it.
254 00:20:07.680 ⇒ 00:20:09.269 Audio shared by Gabriel Lam: I can hear you just fine.
255 00:20:09.390 ⇒ 00:20:12.199 Audio shared by Gabriel Lam: Could you tell me a bit about the project you’re involved in?
256 00:20:12.200 ⇒ 00:20:18.310 Gabriel Lam: Yeah, this is the case study assistant that the internal AI team, just released.
257 00:20:20.380 ⇒ 00:20:21.000 Audio shared by Gabriel Lam: Got it.
258 00:20:21.260 ⇒ 00:20:23.439 Audio shared by Gabriel Lam: How long has the project been running so far?
259 00:20:23.910 ⇒ 00:20:24.640 Gabriel Lam: One week.
260 00:20:26.010 ⇒ 00:20:29.000 Audio shared by Gabriel Lam: Thanks. And what’s your specific role in the project?
261 00:20:29.420 ⇒ 00:20:36.430 Gabriel Lam: I mostly did the product roadmap and managed the… Daily, stand-up.
262 00:20:38.150 ⇒ 00:20:38.810 Audio shared by Gabriel Lam: Got it.
263 00:20:38.910 ⇒ 00:20:41.869 Audio shared by Gabriel Lam: And who else was on the team with you, and what were their roles?
264 00:20:41.870 ⇒ 00:20:49.150 Gabriel Lam: So, Sam, Mustafa, and Casey, Sam mostly handled… All the front end…
265 00:20:49.260 ⇒ 00:20:53.060 Gabriel Lam: And UI Mustafa did the AI voice.
266 00:20:54.000 ⇒ 00:21:04.910 Gabriel Lam: And, yeah, refining and developing the algorithm there, and then Casey did the prompt refinement for the agents in the backend.
267 00:21:06.670 ⇒ 00:21:14.639 Audio shared by Gabriel Lam: Thanks for that. So, before you started this project, what was the situation like? Were there any particular challenges or constraints you were facing?
268 00:21:14.900 ⇒ 00:21:17.789 Gabriel Lam: Yeah, so when we started the…
269 00:21:18.020 ⇒ 00:21:22.530 Gabriel Lam: Case studies took a long time to do. There was the issue of
270 00:21:23.620 ⇒ 00:21:27.329 Gabriel Lam: Scheduling time with people, we had to…
271 00:21:27.700 ⇒ 00:21:36.850 Gabriel Lam: carve out, 30 minutes for each call, and then in each call, Hannah would have to go in and actually talk to each and every one person, and it couldn’t be done asynchronously.
272 00:21:38.690 ⇒ 00:21:45.849 Audio shared by Gabriel Lam: Right, so a lot of manual effort and synchronous communication. Were there any previous attempts to improve that process before this?
273 00:21:45.850 ⇒ 00:21:52.619 Gabriel Lam: I think there’s other problems, but this is just one way to solve one of the pain points in that workflow.
274 00:21:54.380 ⇒ 00:22:00.140 Audio shared by Gabriel Lam: Got it. So, focusing on that specific challenge, what kind of solution did you implement with the case study assistant?
275 00:22:00.480 ⇒ 00:22:05.310 Gabriel Lam: So that’s sort of how we think it goes, and if we wanted to resume it, we can resume it.
276 00:22:06.130 ⇒ 00:22:11.420 Hannah Wang: Great. I think this is great. Like, this is basically what I would do.
277 00:22:11.750 ⇒ 00:22:16.729 Hannah Wang: Yeah, like, I… I mean, I just tend to ask extraneous questions
278 00:22:16.860 ⇒ 00:22:25.550 Hannah Wang: Because I’m, like, curious, I guess, and, like, want to know, like, learn a little bit more, so I don’t know which of the Henry ones you watched. I did two with him, but…
279 00:22:25.550 ⇒ 00:22:27.099 Gabriel Lam: I think I only watched the first one.
280 00:22:27.330 ⇒ 00:22:30.819 Hannah Wang: Okay, is that the one where I was like, oh, what is attribution versus.
281 00:22:31.470 ⇒ 00:22:35.899 Hannah Wang: Yeah, so, like, stuff like that, I don’t know how helpful it is for…
282 00:22:36.320 ⇒ 00:22:38.539 Hannah Wang: You know, the case study, but…
283 00:22:38.700 ⇒ 00:22:41.540 Hannah Wang: I do try to ask questions.
284 00:22:42.570 ⇒ 00:22:45.570 Hannah Wang: Like that, to just make the…
285 00:22:46.120 ⇒ 00:22:56.269 Hannah Wang: copy more rich, but I think this is, like, a very great start, and then we can also just ask Utam and Robert what their feedback is,
286 00:22:56.470 ⇒ 00:23:05.180 Hannah Wang: As well, like, the generated copy, because they have more of, like, the technical lens and, like, the storytelling lens to it, so…
287 00:23:05.580 ⇒ 00:23:11.699 Hannah Wang: Yeah, we’ll just ask for feedback and get people to start using this and see how it goes.
288 00:23:11.940 ⇒ 00:23:12.570 Gabriel Lam: Okay.
289 00:23:13.180 ⇒ 00:23:25.830 Gabriel Lam: Yeah, I think the reason why I asked for, like, previous copies that you have done was I wanted to get Mustafa and Sam to redo some of those interviews and see whether they match with each other.
290 00:23:25.830 ⇒ 00:23:26.460 Hannah Wang: Hmm.
291 00:23:26.460 ⇒ 00:23:31.399 Gabriel Lam: But I haven’t gone around to them yet, so hoping to get them out before.
292 00:23:31.480 ⇒ 00:23:32.789 Hannah Wang: Yeah. We sign off.
293 00:23:33.130 ⇒ 00:23:36.350 Hannah Wang: Yeah, great. That’s a smart idea.
294 00:23:37.730 ⇒ 00:23:52.020 Hannah Wang: Yeah, and we’ll see, like, how much of my, like, human input actually changed the copy versus, like, them just answering the questions, based on the AI interviewer.
295 00:23:52.430 ⇒ 00:23:58.160 Hannah Wang: Yeah, great. I don’t have any other questions. Great.
296 00:23:58.160 ⇒ 00:23:59.260 Gabriel Lam: Awesome.
297 00:23:59.260 ⇒ 00:24:03.869 Hannah Wang: Yeah, I think just… let’s just get people to start using this, and then we’ll see how it goes.
298 00:24:04.260 ⇒ 00:24:09.090 Hannah Wang: And I think this definitely will save… time…
299 00:24:09.090 ⇒ 00:24:09.630 Gabriel Lam: time.
300 00:24:09.630 ⇒ 00:24:15.739 Hannah Wang: Yeah, just cause, you know, scheduling, like, I have to find everyone’s time zones, and yeah, it’s just not…
301 00:24:15.740 ⇒ 00:24:16.400 Gabriel Lam: Ow.
302 00:24:16.420 ⇒ 00:24:20.689 Hannah Wang: Out of curiosity, sorry to interrupt, how… typically, how long…
303 00:24:20.780 ⇒ 00:24:24.600 Gabriel Lam: From, like, when, you know, either Utam or Robert, like, hey, we need this out.
304 00:24:24.700 ⇒ 00:24:31.359 Gabriel Lam: And then to schedule an interview, and then to actually do it. How long does that usually take?
305 00:24:35.830 ⇒ 00:24:38.370 Hannah Wang: It depends on how much context
306 00:24:39.480 ⇒ 00:24:47.350 Hannah Wang: Tom and Robert give me, so if they’re just like, oh, this case study, I’m like, okay, who did it? Like…
307 00:24:47.520 ⇒ 00:24:55.020 Hannah Wang: Tell me the POC, and then… or I don’t know if POC is the right word. Tell me the person who worked on the project that I can interview, and then…
308 00:24:55.650 ⇒ 00:25:00.689 Hannah Wang: Yeah, and then I would schedule it manually on Google.
309 00:25:02.860 ⇒ 00:25:13.429 Hannah Wang: I think for, like, the Philippines team, it’s okay, because they work U.S. hour… like, work central time hours, I think, mostly. But, like, for people, like.
310 00:25:13.840 ⇒ 00:25:18.699 Hannah Wang: Awash, I think he, like, signs… he doesn’t stay up all night.
311 00:25:19.150 ⇒ 00:25:20.169 Hannah Wang: For sure, it…
312 00:25:20.480 ⇒ 00:25:38.150 Hannah Wang: That… that varies. Like, the… the case study that I did with Ovation Henry, like, that one was originally supposed to be for Tuesday, but it kept getting pushed, because, like, their schedules are jam-packed in their working hours, and I couldn’t find a time to squeeze in the case study earlier in the week. Yeah.
313 00:25:40.050 ⇒ 00:25:41.789 Hannah Wang: So I guess, like, it takes…
314 00:25:43.630 ⇒ 00:25:50.290 Hannah Wang: Like, the actual action of scheduling stuff takes, like.
315 00:25:50.410 ⇒ 00:25:54.379 Hannah Wang: 15 minutes, but it’s a lot of, like, mental, like…
316 00:25:54.530 ⇒ 00:26:08.420 Hannah Wang: okay, I gotta keep track of, like, who I already asked, and, like, make sure that I get the interview done in time for me to hand it over to Anne, who designs it,
317 00:26:08.710 ⇒ 00:26:13.809 Hannah Wang: So actually, I think this would save her a lot of time, because I tend to just pass off
318 00:26:13.810 ⇒ 00:26:31.649 Hannah Wang: the trans… the interview meeting link to her, and then I tell her, like, okay, yeah, use these GPTs to generate the copy, and I don’t know how long that takes her. So this not only helps me, but also helps her when she designs it, because the copy’s right there, so she can just, like, paste it into the Figma.
319 00:26:31.650 ⇒ 00:26:33.429 Hannah Wang: file.
320 00:26:35.220 ⇒ 00:26:38.949 Gabriel Lam: I don’t know if that answered any of her questions, but… No, I think that’s…
321 00:26:38.950 ⇒ 00:26:39.590 Hannah Wang: Yeah.
322 00:26:40.710 ⇒ 00:26:43.830 Gabriel Lam: That’s totally fine, that’s perfect.
323 00:26:44.500 ⇒ 00:26:46.890 Gabriel Lam: Yeah, no, that… I think that makes a lot of sense. It’s…
324 00:26:48.080 ⇒ 00:26:55.490 Gabriel Lam: Well, I… I think Casey and Mustafa also work very, very late, so I’m also seeing a very different view onto it, and…
325 00:26:56.180 ⇒ 00:27:02.050 Gabriel Lam: Like, instead of getting it out in days, we can get it out, you know, maybe before people sign off that night.
326 00:27:02.530 ⇒ 00:27:04.219 Gabriel Lam: I think that’d be really good.
327 00:27:04.510 ⇒ 00:27:08.949 Hannah Wang: Yeah, I… my… my main… one of my main concerns is just, like.
328 00:27:09.280 ⇒ 00:27:13.099 Hannah Wang: Oh, are they gonna get this done as urgently as if…
329 00:27:13.100 ⇒ 00:27:13.810 Gabriel Lam: I see.
330 00:27:13.810 ⇒ 00:27:31.110 Hannah Wang: As if I… because I think scheduling a meeting is good, because it’s accountability, and, like, they have to show up, you know? But I’m like, oh, if people, like, are like, it’s not a person thing, I’ll just, like, push it off, like… I mean, that’s not on me, I guess that’s on, like, the PM.
331 00:27:31.110 ⇒ 00:27:31.900 Gabriel Lam: Yeah.
332 00:27:31.900 ⇒ 00:27:44.760 Hannah Wang: of the team, to be like, oh, you should prioritize this, so… yeah, that’s just… that’s my main, I guess, concern with this, but that’s, like, a people problem and not, like, your product problem, so… I think it’s…
333 00:27:45.810 ⇒ 00:27:52.380 Hannah Wang: it’s fine. Like, it’s neither of our faults if they don’t, you know, do the interview, so…
334 00:27:52.910 ⇒ 00:27:53.460 Gabriel Lam: Yeah.
335 00:27:54.980 ⇒ 00:28:02.780 Hannah Wang: Yeah, this is awesome, though. Yeah, I’m like, I… the interviews take, like, 20 to 25 minutes, and it’s like…
336 00:28:03.220 ⇒ 00:28:03.740 Gabriel Lam: Okay.
337 00:28:03.740 ⇒ 00:28:05.820 Hannah Wang: A lot of me, like, trying to…
338 00:28:06.180 ⇒ 00:28:12.549 Hannah Wang: yeah, it’s just a lot, because, like, I don’t know any of the projects, so I’m also trying to, like, understand and process
339 00:28:12.770 ⇒ 00:28:17.769 Hannah Wang: What they’re talking about, but they throw a lot of, like, technical jargon, which they should, and…
340 00:28:17.990 ⇒ 00:28:26.410 Hannah Wang: It’s just, like, context switching, so this will be helpful. Yeah, and then…
341 00:28:26.720 ⇒ 00:28:34.209 Hannah Wang: I have, like, other ideas for how you can help me in terms of, like, the AI stuff. I don’t know, like, when…
342 00:28:34.750 ⇒ 00:28:40.139 Hannah Wang: You want me to, like, share that with you, so you can just, like, put it on the roadmap for the future?
343 00:28:40.330 ⇒ 00:28:44.920 Hannah Wang: Yeah, let me know.
344 00:28:46.100 ⇒ 00:28:48.520 Gabriel Lam: Sorry, I’m just replying to a quick…
345 00:28:48.930 ⇒ 00:28:49.540 Hannah Wang: You’re good.
346 00:29:13.480 ⇒ 00:29:18.489 Gabriel Lam: Sorry. Yeah, you were saying that you were looking for some AI help.
347 00:29:19.530 ⇒ 00:29:26.539 Hannah Wang: Or just, like, things revolving around case studies and stuff like that.
348 00:29:26.780 ⇒ 00:29:27.400 Gabriel Lam: Okay.
349 00:29:27.930 ⇒ 00:29:29.050 Hannah Wang: I don’t know, like…
350 00:29:29.180 ⇒ 00:29:37.190 Hannah Wang: we can talk next week or something about other project ideas I might have to help assist the marketing and sales team.
351 00:29:37.190 ⇒ 00:29:37.840 Gabriel Lam: Yeah.
352 00:29:39.080 ⇒ 00:29:43.130 Hannah Wang: So yeah, we can, like, schedule a 30-minute… Call next week or something.
353 00:29:43.130 ⇒ 00:29:49.140 Gabriel Lam: Sure. Yeah, I think… I think that’d be good. I think the ultimate goal is… 2…
354 00:29:49.790 ⇒ 00:29:52.719 Gabriel Lam: Unload a lot of this thinking from.
355 00:29:53.360 ⇒ 00:29:55.960 Gabriel Lam: the two of them, Utom and Robert.
356 00:29:55.960 ⇒ 00:29:56.610 Hannah Wang: Yeah.
357 00:29:56.760 ⇒ 00:29:58.619 Gabriel Lam: If we can drive.
358 00:29:59.020 ⇒ 00:30:05.340 Gabriel Lam: roadmaps and drive features, I think. As long as they’re like, that sounds good, that’s the thing we need, and I think that’ll…
359 00:30:05.760 ⇒ 00:30:06.869 Gabriel Lam: That’ll be great.
360 00:30:07.160 ⇒ 00:30:17.439 Hannah Wang: Yeah, like, Robert sent a bunch of, like, messages before, like, tagging me and Sam, like, oh, this would be nice, this would be nice, so I’ll try to, like, gather all of that, and then also…
361 00:30:17.790 ⇒ 00:30:21.069 Hannah Wang: Gather my ideas,
362 00:30:22.760 ⇒ 00:30:30.690 Hannah Wang: And maybe I’ll DM you about it. DM you… just send you a message so I don’t forget, and then we can talk about it in person next week, or on a call.
363 00:30:31.560 ⇒ 00:30:32.140 Gabriel Lam: Okay.
364 00:30:32.510 ⇒ 00:30:33.660 Gabriel Lam: That sounds great.
365 00:30:34.590 ⇒ 00:30:35.520 Hannah Wang: Alright.
366 00:30:35.990 ⇒ 00:30:37.580 Gabriel Lam: Alright…
367 00:30:37.580 ⇒ 00:30:39.479 Hannah Wang: Anything else you need from me?
368 00:30:39.480 ⇒ 00:30:48.050 Gabriel Lam: That’s all, that’s… We’re all set. Any, any, any other shoots this weekend, or you are all…
369 00:30:48.260 ⇒ 00:30:51.460 Hannah Wang: This weekend? No, I…
370 00:30:53.200 ⇒ 00:31:02.180 Hannah Wang: No, none for this week, and next weekend, which is, like, a bummer, because I want to shoot more, just to build my portfolio, but…
371 00:31:02.460 ⇒ 00:31:07.850 Hannah Wang: Yeah, I actually got in contact with Robert and Rachel’s photographer.
372 00:31:07.850 ⇒ 00:31:09.810 Gabriel Lam: Has he, like, posted…
373 00:31:09.810 ⇒ 00:31:27.290 Hannah Wang: something on Facebook, and I sent a screenshot to Robert, and he’s like, wait, that’s my photographer. And I was like, oh, lol, that’s funny. So, got in contact with him. So yeah, I’m realizing entrepreneurship and just, like, life is all networking, which…
374 00:31:28.580 ⇒ 00:31:35.450 Hannah Wang: pros and cons to that. It’s just people who you know and the connections you have can get you far, so…
375 00:31:36.240 ⇒ 00:31:44.300 Hannah Wang: Anyway, yeah, no shoots this week, so… but that’s okay. I’ll take a break.
376 00:31:45.540 ⇒ 00:31:46.360 Hannah Wang: Yeah.
377 00:31:48.870 ⇒ 00:31:51.959 Gabriel Lam: Alright, well, I… sounds like you can rest, at least.
378 00:31:52.170 ⇒ 00:31:55.609 Hannah Wang: Yeah, I hope you have a restful weekend, too. Any plans?
379 00:31:55.610 ⇒ 00:32:02.219 Gabriel Lam: Thank you. Our… one of our friends is… getting proposed, is proposing.
380 00:32:02.750 ⇒ 00:32:08.449 Gabriel Lam: So we are… kind of pull the same thing as he did with Robert and Rachel.
381 00:32:09.480 ⇒ 00:32:11.839 Gabriel Lam: Yeah. Like a surprise.
382 00:32:11.840 ⇒ 00:32:14.570 Hannah Wang: Like, they don’t know you’re gonna be there type of thing.
383 00:32:16.490 ⇒ 00:32:19.109 Gabriel Lam: Well, the guy knows, because he’s the one who asked.
384 00:32:19.110 ⇒ 00:32:19.979 Hannah Wang: Right, but, like, the…
385 00:32:19.980 ⇒ 00:32:22.569 Gabriel Lam: But the… the girl doesn’t…
386 00:32:23.120 ⇒ 00:32:24.460 Hannah Wang: Is this in Boston, or somewhere?
387 00:32:24.460 ⇒ 00:32:25.439 Gabriel Lam: This is in Boston.
388 00:32:25.440 ⇒ 00:32:26.180 Hannah Wang: Okay.
389 00:32:26.340 ⇒ 00:32:30.450 Hannah Wang: I mean, you even showed up for, yeah, we were both there at Roberts.
390 00:32:30.450 ⇒ 00:32:32.810 Gabriel Lam: Yeah, yeah, yeah, yeah, in Utah.
391 00:32:32.810 ⇒ 00:32:36.530 Hannah Wang: Yeah, that was insane. I can’t believe we did that.
392 00:32:36.530 ⇒ 00:32:37.800 Gabriel Lam: Damn.
393 00:32:38.670 ⇒ 00:32:42.230 Hannah Wang: Cool. How exciting. We are in that era of everyone.
394 00:32:42.230 ⇒ 00:32:42.970 Gabriel Lam: Nice.
395 00:32:43.290 ⇒ 00:32:50.590 Gabriel Lam: Carrie’s flying out in 6 days to Asia for a bunch of weddings, so… Right.
396 00:32:50.590 ⇒ 00:32:54.469 Hannah Wang: Is that the one that, Rachel’s also going to, I think.
397 00:32:54.590 ⇒ 00:32:56.310 Gabriel Lam: Yeah, yeah, yeah, yeah.
398 00:32:56.310 ⇒ 00:32:57.110 Hannah Wang: Okay.
399 00:32:57.110 ⇒ 00:32:57.690 Gabriel Lam: So…
400 00:32:58.040 ⇒ 00:33:01.050 Hannah Wang: Oh, you gotta… the home all to yourself.
401 00:33:01.390 ⇒ 00:33:03.690 Hannah Wang: It’s too empty.
402 00:33:04.100 ⇒ 00:33:05.860 Hannah Wang: I feel that, yeah.
403 00:33:07.140 ⇒ 00:33:10.300 Gabriel Lam: Alright, I’ll let you be. Have a great weekend.
404 00:33:10.300 ⇒ 00:33:10.960 Hannah Wang: vaccine.
405 00:33:10.960 ⇒ 00:33:12.160 Gabriel Lam: Bye.