Meeting Title: Brainforge AI Team Project Sync Date: 2025-11-18 Meeting participants: Gabriel Lam, Hannah Wang
WEBVTT
1 00:00:15.630 ⇒ 00:00:18.369 Gabriel Lam: Hello, how are you?
2 00:00:18.650 ⇒ 00:00:20.059 Hannah Wang: Good, how are you?
3 00:00:20.230 ⇒ 00:00:23.419 Gabriel Lam: I’m doing good. Just had lunch.
4 00:00:24.410 ⇒ 00:00:25.930 Gabriel Lam: Wow, we live…
5 00:00:25.930 ⇒ 00:00:31.949 Hannah Wang: Wow, the time zone makes such a difference, oh my goodness.
6 00:00:31.950 ⇒ 00:00:32.720 Gabriel Lam: Hmm.
7 00:00:33.060 ⇒ 00:00:40.719 Hannah Wang: Okay, well, yeah, I just wanted to kind of walk through other ideas I had.
8 00:00:41.150 ⇒ 00:00:49.399 Hannah Wang: and ways that I could use the AI team’s help, so… Sure, that’s not a problem. Okay, great.
9 00:00:49.840 ⇒ 00:00:57.640 Hannah Wang: Let me go back to our Slack, because I forgot what I said.
10 00:00:59.350 ⇒ 00:01:01.840 Hannah Wang: Duchess life. Let’s see.
11 00:01:01.840 ⇒ 00:01:02.629 Gabriel Lam: so true.
12 00:01:02.800 ⇒ 00:01:09.710 Hannah Wang: Yeah, especially in a busy place like this, I forget a lot of things.
13 00:01:10.170 ⇒ 00:01:16.919 Hannah Wang: Okay, so… I’m just gonna share… my screen.
14 00:01:22.070 ⇒ 00:01:23.140 Hannah Wang: Alright.
15 00:01:24.050 ⇒ 00:01:26.040 Hannah Wang: This is Chimongo.
16 00:01:27.950 ⇒ 00:01:34.610 Hannah Wang: So… this one… Yeah, so basically this is more for…
17 00:01:34.790 ⇒ 00:01:40.609 Hannah Wang: Like, the go-to-market motions that we have, because we send a lot of our collateral to…
18 00:01:40.850 ⇒ 00:01:45.339 Hannah Wang: leads and prospects, and I think it’s just hard
19 00:01:45.470 ⇒ 00:01:53.410 Hannah Wang: for the team, i.e. mainly me, and I guess Ryan, and whoever’s on the go-to-market team, to
20 00:01:53.690 ⇒ 00:02:04.000 Hannah Wang: Just send case studies that match the needs and wants of Whoever we’re sending it to.
21 00:02:04.000 ⇒ 00:02:04.450 Gabriel Lam: I just don’.
22 00:02:04.450 ⇒ 00:02:10.099 Hannah Wang: hit, because I feel like we’re not sending relevant case studies to them.
23 00:02:10.229 ⇒ 00:02:15.000 Hannah Wang: Yeah, so this is Robert just giving me an example of something. I sent a…
24 00:02:15.150 ⇒ 00:02:26.060 Hannah Wang: about a GoCo case study, and he was like, oh, this doesn’t make sense. Great Orange is a SaaS company, and why are we sending them, like, a case study that’s related to inventory stock outs?
25 00:02:26.890 ⇒ 00:02:33.189 Hannah Wang: I… did use AI previously to help match
26 00:02:33.860 ⇒ 00:02:46.640 Hannah Wang: them, like, match the case study, so I didn’t just pull this out of nowhere, like, I tried to be strategic, and I thought about the conference theme as a whole, because Gray Orange was from a conference that we attended.
27 00:02:46.720 ⇒ 00:02:54.140 Hannah Wang: Or targeted, I guess, and I think that conference was, like, a CPG conference or something.
28 00:02:54.740 ⇒ 00:02:59.890 Hannah Wang: that’s why I thought maybe it would be relevant, but, no. So…
29 00:03:00.430 ⇒ 00:03:11.289 Hannah Wang: Robert is like, oh, maybe you can talk with the AI team to build a system that helps me, I guess, match, like, any collateral to the leads. So,
30 00:03:13.130 ⇒ 00:03:16.169 Hannah Wang: Yeah, so let me actually share…
31 00:03:16.170 ⇒ 00:03:16.770 Gabriel Lam: Oh.
32 00:03:17.410 ⇒ 00:03:27.750 Hannah Wang: So this… So what… what I’ve used before was,
33 00:03:27.980 ⇒ 00:03:36.659 Hannah Wang: GPT that Ryan made. It’s more for job applications, so…
34 00:03:37.090 ⇒ 00:03:43.110 Hannah Wang: I don’t know if it’s the best, but… okay, let me share again.
35 00:03:44.220 ⇒ 00:03:48.330 Hannah Wang: Yeah, so it’s this Java application GPT, this is used
36 00:03:48.850 ⇒ 00:04:06.219 Hannah Wang: when we apply to jobs, and, it kind of helps pick out case studies for us, that’s, like, in the prompt, but… and so what I prompt the GPT is, like, oh, this is not a job application, it’s, like, a company, and we want to, like, send them relevant case studies, like, help me
37 00:04:06.370 ⇒ 00:04:10.259 Hannah Wang: find the relevant case study, but I think GPT is not very good at
38 00:04:10.810 ⇒ 00:04:14.500 Hannah Wang: picking out good ones. I think it sends, like, very general…
39 00:04:14.720 ⇒ 00:04:16.130 Gabriel Lam: Doesn’t have the context.
40 00:04:16.500 ⇒ 00:04:22.390 Hannah Wang: Yeah, it doesn’t have the context, like… yeah, it just doesn’t have… like.
41 00:04:23.140 ⇒ 00:04:42.360 Hannah Wang: all… like, I’m sure, like, having all the calls and stuff, like, is helpful. Yeah, just having, like, any and all the context of who Brainforge is, who the client, who the lead is, like, what type of company they are, like, what they’re looking for, their pain points, like, yeah, I don’t… I guess I have to, like, feed that into
42 00:04:43.200 ⇒ 00:04:59.390 Hannah Wang: the GPT somehow. I thought I was doing that by sending them the link of the website of the company and stuff, but yeah, it… I don’t think it’s been working that well. So, what Robert and I briefly, like, brainstormed together was…
43 00:05:01.050 ⇒ 00:05:10.069 Hannah Wang: yeah, these tags are great, and they’re helpful, but I think we might need more metadata, so that we can
44 00:05:10.180 ⇒ 00:05:16.320 Hannah Wang: match it to the company. And what I mean by that is… Like, maybe there’s a…
45 00:05:16.530 ⇒ 00:05:35.470 Hannah Wang: world in which the AI, like, looks through this case study, it combs through, like, the context, the challenge, everything. It also pulls in the tools used, because sometimes, like, some CPG brands, for example, may need help with, like, MixedPanel, so we want to send them, like, all the Mixpanel-related case studies, and
46 00:05:35.470 ⇒ 00:05:41.119 Hannah Wang: Yeah, even, like, the content itself, like, some of them might be dashboard building, others are, like.
47 00:05:41.250 ⇒ 00:05:47.550 Hannah Wang: cleanups, others are AI-related, so yeah, if somehow, like, the…
48 00:05:47.720 ⇒ 00:05:52.969 Hannah Wang: AI can pull all of that from just looking at
49 00:05:53.280 ⇒ 00:05:55.199 Hannah Wang: Not only just looking at this…
50 00:05:55.490 ⇒ 00:05:58.949 Hannah Wang: Page, but also maybe pulling in
51 00:05:59.050 ⇒ 00:06:12.980 Hannah Wang: information from the calls that we’ve had with the client related to this, and then basically, like, adding different metadata tags, similar to this.
52 00:06:13.110 ⇒ 00:06:20.310 Hannah Wang: like, case study AI, like, econ, but even more specific. So, for example, like,
53 00:06:20.560 ⇒ 00:06:25.519 Hannah Wang: this, like, AI assistant, like,
54 00:06:28.540 ⇒ 00:06:31.120 Hannah Wang: I can’t really think of a good…
55 00:06:32.700 ⇒ 00:06:49.819 Hannah Wang: other good examples, but hopefully you get the gist of what I’m saying. Like, another pill… all the tools can be a metadata tag, too, like Snowflake, real, N-A-N, and so, like, later, I don’t really know what the UI would look like, but…
56 00:06:50.000 ⇒ 00:06:51.810 Hannah Wang: I’d be able to, like.
57 00:06:53.500 ⇒ 00:07:10.750 Hannah Wang: or maybe it’s not just the UI, but, like, once we have all the tags for the case study, it could be, like, a set number of tags. Like, I think we need to probably brainstorm, like, what are… what is the most general… generalized tag that we can create that’s, like, applicable to
58 00:07:10.850 ⇒ 00:07:15.859 Hannah Wang: The case studies, and then… With those tags, like, matching…
59 00:07:16.020 ⇒ 00:07:27.769 Hannah Wang: it to the tags that, for example, like, we can scrape from sending over, like, the Gray Orange’s website. So, like, maybe the AI, we, like, feed it
60 00:07:28.550 ⇒ 00:07:37.360 Hannah Wang: Yeah, the client, website, and so it scrapes through the entire website and, maps
61 00:07:37.720 ⇒ 00:07:53.130 Hannah Wang: basically, like, maps it to the existing tags that we have, whatever that may be, and whatever, like, the highest number of matches there is, like, it just picks out the case study that matches the most with the pills. Does that make sense? I don’t know if I’m explaining anything.
62 00:07:53.130 ⇒ 00:07:55.710 Gabriel Lam: No, no, I think you’re good. Yeah.
63 00:07:57.350 ⇒ 00:08:02.480 Gabriel Lam: Yeah, that makes sense. I think it sort of reminds… if you’ve ever used Airtable, it kind of reminds me of that.
64 00:08:02.630 ⇒ 00:08:02.950 Hannah Wang: Hmm.
65 00:08:02.950 ⇒ 00:08:07.320 Gabriel Lam: Where it’s like a l… layers of…
66 00:08:08.140 ⇒ 00:08:11.679 Gabriel Lam: Filtering, and then ranking them off of that.
67 00:08:11.680 ⇒ 00:08:14.070 Hannah Wang: Yeah, exactly.
68 00:08:14.290 ⇒ 00:08:17.700 Hannah Wang: Yeah, it’s like… There’s, like, a…
69 00:08:18.020 ⇒ 00:08:27.399 Hannah Wang: X number of, yeah, labels or tags that we have, and it just, like, filters and ranks it by, oh, this is the 90% match, 85% match, like.
70 00:08:27.910 ⇒ 00:08:35.310 Hannah Wang: Yeah, because… I think that’ll probably help me, because right now, I, I, like.
71 00:08:35.530 ⇒ 00:08:40.509 Hannah Wang: I feel… I’m like, okay, forget AI, it’s not very helpful, so, like, I…
72 00:08:40.730 ⇒ 00:08:50.849 Hannah Wang: try to work that muscle in my brain. I think for healthcare, it makes a little bit more sense, but some of these are, like, too ambiguous for me, or too technical, so…
73 00:08:50.850 ⇒ 00:08:51.300 Gabriel Lam: Yeah.
74 00:08:51.300 ⇒ 00:09:05.849 Hannah Wang: like, a lot of the SaaS-related ones, I’m like, I don’t really understand. Maybe, like, CPG and healthcare econ makes a little bit more sense, because I… I’ve used, like, e-comm websites and stuff like that, so… But anyway, like, even, like…
75 00:09:07.830 ⇒ 00:09:16.280 Hannah Wang: Yeah, a lot of these are just words that go over my head, so maybe that’s, like, another… maybe I should, like, do go-to-market stuff when I’m most awake.
76 00:09:17.190 ⇒ 00:09:25.879 Hannah Wang: Yeah, and sometimes it just doesn’t… I get it. It doesn’t work. My brain doesn’t work that way, I left… I’m not a dev for a reason, so…
77 00:09:25.880 ⇒ 00:09:26.809 Gabriel Lam: I hate that.
78 00:09:27.800 ⇒ 00:09:29.480 Hannah Wang: Yeah, so I think that…
79 00:09:29.650 ⇒ 00:09:35.950 Hannah Wang: is just one idea. Obviously we don’t have to do, like, the ranking, like, matching with the.
80 00:09:36.270 ⇒ 00:09:39.610 Hannah Wang: Tags and whatever, like, maybe there’s some other…
81 00:09:39.750 ⇒ 00:09:44.439 Hannah Wang: Solution that we can implement, but…
82 00:09:44.550 ⇒ 00:09:48.660 Hannah Wang: yeah, I think that’s… that’s just one idea.
83 00:09:48.780 ⇒ 00:09:56.920 Hannah Wang: And feel free to, like, message me if you have other ideas, and you can kind of go back and forth, with developing that.
84 00:09:57.120 ⇒ 00:10:04.410 Hannah Wang: Yeah, I think that’s probably… The biggest thing that would… Help a lot.
85 00:10:05.450 ⇒ 00:10:09.510 Hannah Wang: Yeah, it’s just with the case studies and matching it to…
86 00:10:09.720 ⇒ 00:10:13.040 Hannah Wang: what I can send. And then the second…
87 00:10:13.360 ⇒ 00:10:17.050 Hannah Wang: one. And feel free to interrupt me if you have any questions.
88 00:10:17.050 ⇒ 00:10:22.379 Gabriel Lam: I will… I will let you finish, and then I will ask a bunch of questions.
89 00:10:22.620 ⇒ 00:10:27.399 Hannah Wang: Sure. Another second one maybe is less…
90 00:10:27.850 ⇒ 00:10:34.109 Hannah Wang: urgent, or it’s already addressed. Like, I remember in the Friday meeting.
91 00:10:35.120 ⇒ 00:10:41.549 Hannah Wang: Or in some meeting, Utam was saying, oh, in our Monday… in his Monday, like, delivery team meetings.
92 00:10:41.790 ⇒ 00:10:55.019 Hannah Wang: they’ll try to, like, see what project is wrapped up, and then Bhutan will, like, message me what to prioritize. But anyway, I feel like it’d be better in general if he doesn’t have to do that, so this.
93 00:10:55.530 ⇒ 00:10:58.909 Hannah Wang: might be helpful in the long run. But basically, this…
94 00:10:59.190 ⇒ 00:11:14.150 Hannah Wang: this is, like, oh, the pre-case study part of, like, actually finding out what asset, or what work to turn into assets. I think I already mentioned this to you, but, yeah, Robert’s like, we’re too dependent on the SME retro…
95 00:11:14.330 ⇒ 00:11:21.950 Hannah Wang: weeks after a project, like, basically, where I’m too dependent on… the design team is too dependent on people to tell us what to make.
96 00:11:22.270 ⇒ 00:11:22.720 Gabriel Lam: Which…
97 00:11:23.200 ⇒ 00:11:30.489 Hannah Wang: I don’t blame myself for doing that, because I don’t know what projects we work on, so that’s not my fault, I feel like. But…
98 00:11:31.030 ⇒ 00:11:36.100 Hannah Wang: Yeah, if there’s, like, some… way to…
99 00:11:37.280 ⇒ 00:11:46.979 Hannah Wang: maybe through, like, linear… a linear automation, like, once a project is closed, it, like, notifies the design channel, like, oh, this is…
100 00:11:47.640 ⇒ 00:11:51.070 Hannah Wang: This project is done, and…
101 00:11:51.200 ⇒ 00:12:00.139 Hannah Wang: I feel like, well, still, I would need Utam and or Robert’s input on if it’s a priority or not, like, in terms of transforming it into a case study, but…
102 00:12:00.280 ⇒ 00:12:13.949 Hannah Wang: yeah, that’s… I feel like that’s not as much of, like, a lift compared to typing out, this is the project, like, this blah blah blah, this is the context, etc.
103 00:12:14.820 ⇒ 00:12:18.980 Hannah Wang: Yeah, so if there’s some way where… Once a project
104 00:12:19.400 ⇒ 00:12:32.409 Hannah Wang: it lets me know… it lets me know the project, it lets me know who I should send the interview assistant to, so they can do an interview, and then if I can know the priority,
105 00:12:32.690 ⇒ 00:12:40.340 Hannah Wang: I think that would be… helpful, and currently, there is no…
106 00:12:40.500 ⇒ 00:12:47.260 Hannah Wang: We have no working database for the backlog of case studies.
107 00:12:47.490 ⇒ 00:12:52.600 Hannah Wang: Ex… except on linear, so… Okay. It’s just,
108 00:12:53.520 ⇒ 00:13:01.979 Hannah Wang: yeah, like, all of the ones that are prefixed with Case City are the ones that we have in backlog, and, like, earlier…
109 00:13:02.200 ⇒ 00:13:06.679 Gabriel Lam: So the Notion thing that you had sent… Over… like, that is…
110 00:13:07.650 ⇒ 00:13:08.660 Hannah Wang: That’s old.
111 00:13:08.660 ⇒ 00:13:09.180 Gabriel Lam: old.
112 00:13:09.180 ⇒ 00:13:10.959 Hannah Wang: It’s not up to date.
113 00:13:11.750 ⇒ 00:13:17.260 Gabriel Lam: I remember you said you were still looking through it to make sure there weren’t any overlaps. Has that been done, or…
114 00:13:17.370 ⇒ 00:13:17.940 Hannah Wang: Yes.
115 00:13:17.940 ⇒ 00:13:20.520 Gabriel Lam: Okay, so linear is the most up-to-date, and…
116 00:13:20.520 ⇒ 00:13:29.890 Hannah Wang: Correct. Yeah, so I messaged Rico last week, so I combed through it, and then…
117 00:13:30.120 ⇒ 00:13:36.639 Hannah Wang: I… and then he helped me prioritize, so… Okay. These are all the ones…
118 00:13:36.820 ⇒ 00:13:48.389 Hannah Wang: or… there’s a lot, but basically, you can see here Utom, like, helped prioritize them. Yeah. So I think this is all I really need, a high and low, and then we’ll, like, get through all of them eventually, but…
119 00:13:48.390 ⇒ 00:13:48.820 Gabriel Lam: Okay.
120 00:13:48.820 ⇒ 00:13:54.049 Hannah Wang: Yeah, if there’s… Yeah, that’s the second, I guess, request I have.
121 00:13:55.350 ⇒ 00:13:59.590 Hannah Wang: Yeah, so I think that’s my spiel. Do you have any questions?
122 00:13:59.760 ⇒ 00:14:01.910 Gabriel Lam: I do, sure.
123 00:14:02.370 ⇒ 00:14:07.389 Gabriel Lam: I guess my first question, if we go back to the marketing assets stuff.
124 00:14:07.390 ⇒ 00:14:07.970 Hannah Wang: Yep.
125 00:14:11.120 ⇒ 00:14:16.459 Gabriel Lam: I, like, I’m curious what your thought process and workflow is when you’re like, okay.
126 00:14:16.670 ⇒ 00:14:22.890 Gabriel Lam: We need to send this case study out to this person, and you were looking… for…
127 00:14:24.210 ⇒ 00:14:30.970 Gabriel Lam: The industry, and then you’re looking for… the… Like, the ti- the- the…
128 00:14:32.010 ⇒ 00:14:36.500 Gabriel Lam: product, industry, and then function of the case study, I would assume.
129 00:14:36.710 ⇒ 00:14:39.889 Hannah Wang: Yes. And so, the goal…
130 00:14:40.020 ⇒ 00:14:44.250 Gabriel Lam: Maybe is to… Match it better?
131 00:14:44.670 ⇒ 00:14:47.400 Gabriel Lam: Am I saying that correctly?
132 00:14:48.290 ⇒ 00:14:54.899 Hannah Wang: It’s… Basically, just send them the most relevant case studies that’ll be like, oh.
133 00:14:55.020 ⇒ 00:14:59.670 Hannah Wang: this is relevant to my company, and I want to work with Brainforge.
134 00:15:00.780 ⇒ 00:15:12.409 Hannah Wang: So I feel like, yeah, I’m just not maybe matching… it… Correctly, let me go back…
135 00:15:15.580 ⇒ 00:15:22.909 Hannah Wang: Yeah, like, this is a very… this is a good example. It’s like, oh, I sent the VitalCoco one to a SaaS company, but I feel like there’s other…
136 00:15:23.170 ⇒ 00:15:31.030 Hannah Wang: maybe there’s other SAS-related case studies that I could have sent instead of, like, a CPG-related case study.
137 00:15:31.130 ⇒ 00:15:35.879 Hannah Wang: So… Yeah, it’s just…
138 00:15:37.650 ⇒ 00:15:48.369 Hannah Wang: You would think that it’s, like, not that hard, but it’s been hard for me, and I can’t really know why, or I don’t really know why.
139 00:15:49.370 ⇒ 00:15:54.329 Gabriel Lam: So, yeah, I’m also curious about when you’re like, oh, I am running it through this prompt.
140 00:15:54.540 ⇒ 00:16:00.119 Gabriel Lam: Like, what does the prompt spit out, and is it saying, like, oh, you know, this is the…
141 00:16:02.390 ⇒ 00:16:04.360 Gabriel Lam: I’m also… yeah, I guess I’m curious…
142 00:16:04.360 ⇒ 00:16:05.050 Hannah Wang: Yes.
143 00:16:05.050 ⇒ 00:16:09.349 Gabriel Lam: Once you are like, hey, I need SaaS-related case studies for this client.
144 00:16:09.350 ⇒ 00:16:12.760 Hannah Wang: Sure, let’s just do an actual run-through.
145 00:16:12.760 ⇒ 00:16:13.200 Gabriel Lam: Yeah.
146 00:16:13.200 ⇒ 00:16:24.669 Hannah Wang: Yeah. So, granted, like, this GPT is not for… it’s for a job application, so maybe that’s where I went wrong, but I did use, like.
147 00:16:24.910 ⇒ 00:16:27.830 Hannah Wang: other GPTs that other people have made throughout.
148 00:16:28.300 ⇒ 00:16:38.750 Hannah Wang: weeks, like, oh, ICP fit, like, is this company… this is, like, for seeing if the company’s an ICP fit, and I’m like, okay, like, is this company…
149 00:16:39.220 ⇒ 00:16:48.420 Hannah Wang: given this company that is an ICP fit, give me the case studies, but I feel like this is probably the best GPT, because we actually uploaded
150 00:16:48.550 ⇒ 00:16:51.170 Hannah Wang: A bunch of our case studies, so it has, like, the content.
151 00:16:51.190 ⇒ 00:16:51.680 Gabriel Lam: Hmm.
152 00:16:51.680 ⇒ 00:17:02.040 Hannah Wang: So I can be like, I want to send relevant… a relevant case study to gray, orange, white, gray…
153 00:17:02.580 ⇒ 00:17:08.020 Hannah Wang: Orange, here’s… oh, I actually… I also don’t know if the GPT can go through
154 00:17:08.450 ⇒ 00:17:12.630 Hannah Wang: And scrape the website, so maybe that’s… the problem.
155 00:17:12.630 ⇒ 00:17:14.669 Gabriel Lam: Maybe we can just ask, and…
156 00:17:16.720 ⇒ 00:17:20.490 Hannah Wang: Are you able to open the website?
157 00:17:21.960 ⇒ 00:17:27.099 Hannah Wang: Scrape through it, and… Find.
158 00:17:27.770 ⇒ 00:17:29.870 Hannah Wang: Yeah, find the most relevant.
159 00:17:30.020 ⇒ 00:17:37.420 Hannah Wang: Does anybody give me the reasoning for why you think… It’s… Don’t match.
160 00:17:39.850 ⇒ 00:17:45.609 Hannah Wang: I also haven’t done this in a while, because I’ve been just relying on my own brain. Right.
161 00:17:45.950 ⇒ 00:17:51.820 Hannah Wang: And the more recent case studies have been health… health-related, and I feel like health is a little bit…
162 00:17:52.550 ⇒ 00:17:59.039 Hannah Wang: slightly easier, but even then, I think the problem is I just don’t know all the technical terms.
163 00:17:59.400 ⇒ 00:17:59.950 Gabriel Lam: Hmm.
164 00:17:59.950 ⇒ 00:18:04.140 Hannah Wang: a full funnel attribution. I’m just like, what the heck does that mean?
165 00:18:04.140 ⇒ 00:18:04.780 Gabriel Lam: Yeah.
166 00:18:04.780 ⇒ 00:18:15.719 Hannah Wang: Like, even attribution, I was like, I had no idea what that meant until, like, Henry explained it in the demo, and also in, like, the case study interviews,
167 00:18:16.540 ⇒ 00:18:22.789 Hannah Wang: Yeah, so I think it’s just also, like, a knowledge gap for me, like, not knowing what all these
168 00:18:23.160 ⇒ 00:18:27.080 Hannah Wang: more corporate lingo, like, marketing lingoists, because I…
169 00:18:27.310 ⇒ 00:18:32.299 Hannah Wang: yeah, like, as a tech person, I didn’t… like, I wasn’t in, like, a…
170 00:18:32.630 ⇒ 00:18:43.660 Hannah Wang: you know, CPG company, I wasn’t in… like, my company… I don’t even know what Ring is. Is it, like, a… it’s a product… I don’t know, like a D2C… CP… no, I don’t even know, see? So, like…
171 00:18:43.840 ⇒ 00:18:46.310 Hannah Wang: Maybe there’s just, like, a knowledge gap.
172 00:18:46.420 ⇒ 00:18:51.439 Hannah Wang: For me. So yeah, I’m just like, bro, why are you sending me the Vita Coco case study?
173 00:18:51.440 ⇒ 00:18:52.670 Gabriel Lam: Mmm.
174 00:18:52.670 ⇒ 00:19:04.389 Hannah Wang: So yeah, it’s like, oh, gray-orange sells AI-driven or orchestration for warehouses and stores, and it probably saw, like, oh, stock out in stores, so it probably.
175 00:19:04.390 ⇒ 00:19:05.359 Gabriel Lam: When we mapped it.
176 00:19:05.360 ⇒ 00:19:11.680 Hannah Wang: there, omni Child Performance,
177 00:19:17.920 ⇒ 00:19:22.340 Hannah Wang: Yeah, so… to me, like, reading this, I’m like, oh yeah, it makes sense, like…
178 00:19:22.340 ⇒ 00:19:23.190 Gabriel Lam: Mmm.
179 00:19:23.190 ⇒ 00:19:25.799 Hannah Wang: Send it over, but… Yeah, maybe…
180 00:19:27.290 ⇒ 00:19:32.589 Hannah Wang: Maybe it’s also just, like, me not understanding what SaaS companies… what their pain points are.
181 00:19:32.590 ⇒ 00:19:33.360 Gabriel Lam: Hmm…
182 00:19:33.360 ⇒ 00:19:35.319 Hannah Wang: Like, for each industry, like, what…
183 00:19:35.690 ⇒ 00:19:44.619 Hannah Wang: Because I’m sure, like, every industry, it’s the same set of problems that they have, and I feel like Robert and Tom have more experience, because they’re client-facing, so they’ve worked
184 00:19:44.720 ⇒ 00:19:49.169 Hannah Wang: with the clients, but yeah, I just don’t know,
185 00:19:50.430 ⇒ 00:19:56.419 Hannah Wang: Yeah, so that’s where I guess, the AI needs to help me, because there’s only, like, I can try to learn and stuff, but…
186 00:19:56.740 ⇒ 00:20:02.530 Hannah Wang: I feel like I tend to not… be as accurate.
187 00:20:04.130 ⇒ 00:20:09.310 Gabriel Lam: And then when you have uploaded all the case studies into this agent.
188 00:20:09.530 ⇒ 00:20:10.090 Hannah Wang: Yeah.
189 00:20:10.090 ⇒ 00:20:13.960 Gabriel Lam: Like, are… there’s 26, are you uploading them?
190 00:20:14.240 ⇒ 00:20:17.080 Gabriel Lam: Regularly, or is it just… Generally. Okay.
191 00:20:17.330 ⇒ 00:20:17.870 Hannah Wang: Excellent.
192 00:20:17.870 ⇒ 00:20:19.219 Gabriel Lam: Are they up-to-date, or is…
193 00:20:19.220 ⇒ 00:20:24.699 Hannah Wang: Nope, right now it’s not up to date. Yeah, so that’s, like, the… there’s another… that’s another barrier. It’s like.
194 00:20:24.760 ⇒ 00:20:44.109 Hannah Wang: okay, I have to not only export it from Figma, I have to make a slide deck version of it, so I have to remember to do that. And then at the way end, like, and then I have to upload it via GitHub, and then I have to, like, upload it to this GPT. Like, there’s just too many steps for me to remember, so I tend to forget,
195 00:20:44.860 ⇒ 00:20:48.639 Hannah Wang: So yeah, like, I’m sure there’s a lot more case studies, maybe, like, 30.
196 00:20:48.750 ⇒ 00:20:52.329 Hannah Wang: I think there’s, like, 4 missing.
197 00:20:53.430 ⇒ 00:20:57.239 Hannah Wang: Yeah, I’m exposing my desktop, but let me show you, like…
198 00:20:58.540 ⇒ 00:21:18.520 Hannah Wang: Don’t mind. Like, yeah, the case studies, like, I was like, okay, I just have to label the ones in green that are on the GPT, and, like, I have to remember to upload the ones that are not in green, but sometimes I forget to even add it to my personal folder here. So yeah, that’s…
199 00:21:18.570 ⇒ 00:21:22.559 Hannah Wang: That’s, where I’m at.
200 00:21:22.560 ⇒ 00:21:29.539 Gabriel Lam: I see. So, when you are done with the case study, say you’re done on Figma, you’re ready to export it to a PDF, then it goes.
201 00:21:29.540 ⇒ 00:21:30.020 Hannah Wang: it’s uploaded.
202 00:21:30.020 ⇒ 00:21:32.350 Gabriel Lam: loaded to marketing assets.
203 00:21:32.830 ⇒ 00:21:33.410 Hannah Wang: Yep.
204 00:21:33.690 ⇒ 00:21:37.059 Gabriel Lam: And then from marketing assets, then you have to, like, save it.
205 00:21:37.620 ⇒ 00:21:39.519 Gabriel Lam: To your personal, or…
206 00:21:39.760 ⇒ 00:21:46.389 Hannah Wang: Yeah, so… when I… So this is the case study kit where we design everything.
207 00:21:46.830 ⇒ 00:21:53.270 Hannah Wang: And then, so, what happens is you just export it, and then it goes to your desktop.
208 00:21:53.270 ⇒ 00:21:53.660 Gabriel Lam: Right.
209 00:21:53.660 ⇒ 00:21:54.510 Hannah Wang: But then…
210 00:21:54.660 ⇒ 00:22:04.170 Hannah Wang: when I go to GitHub Desktop, which is what I use to upload files to the platform,
211 00:22:05.350 ⇒ 00:22:17.519 Hannah Wang: there’s, like, a separate, like, tree, like a… I forgot what this is called, but, you know, you know what I’m saying. And so, what usually happens is that I just, like, drag the file into here.
212 00:22:17.520 ⇒ 00:22:18.440 Gabriel Lam: But then…
213 00:22:18.440 ⇒ 00:22:27.090 Hannah Wang: it doesn’t make a copy, it just, like, moves it, and so I forget, like, oh shoot, I should have made a copy and not, like, moved it to…
214 00:22:27.360 ⇒ 00:22:28.130 Hannah Wang: this…
215 00:22:28.720 ⇒ 00:22:29.740 Gabriel Lam: I see.
216 00:22:29.740 ⇒ 00:22:32.710 Hannah Wang: And so I forget to, like, move it to my…
217 00:22:33.100 ⇒ 00:22:43.699 Hannah Wang: personal folder of case studies, which is… and then, like, label everything, and like, I’m sure there’s a better way to do it, but I… I see. This is, like…
218 00:22:44.050 ⇒ 00:22:48.430 Hannah Wang: in the thick… in the… in the moment, I’m like, oh my gosh, I need to do something, so…
219 00:22:48.430 ⇒ 00:22:57.149 Gabriel Lam: But the… yeah. But the typical… it seems like the exporting or uploading to marketing assets is, like, the least of your problem. It’s like…
220 00:22:57.980 ⇒ 00:23:02.780 Gabriel Lam: In this… in… Hasa, I’m… I’m… let me… let me… Okay. …think about it.
221 00:23:03.010 ⇒ 00:23:06.770 Gabriel Lam: Like, when you are uploading it to Marketing Assets.
222 00:23:07.840 ⇒ 00:23:16.960 Gabriel Lam: it’s the follow-up stuff after that, where you’re like, oh, I forgot to copy it, I forgot to do that, but if there was a way for you to just take it from marketing assets.
223 00:23:17.320 ⇒ 00:23:23.350 Gabriel Lam: And have it… have that context. Like, that seems to be more helpful than having you
224 00:23:23.500 ⇒ 00:23:27.139 Gabriel Lam: Re-explore it, and then upload to…
225 00:23:27.140 ⇒ 00:23:27.740 Hannah Wang: Yeah.
226 00:23:27.740 ⇒ 00:23:28.610 Gabriel Lam: Like.
227 00:23:28.610 ⇒ 00:23:39.330 Hannah Wang: Well, it’s a GPT… yeah. Okay. Yeah, so, like, if the… if the solution… solution of, like, matching case studies to the client or whatever, like, if that was in the platform.
228 00:23:39.510 ⇒ 00:23:57.780 Hannah Wang: already, like, that’d be super helpful. Like, if there’s just, like, a button here, it’s like, oh, want to find the most relevant case study? And I click on it, and it’s like, okay, this is the company website, find me the case study, and then it just sends me the link, and why it’s a match.
229 00:23:57.840 ⇒ 00:24:03.729 Hannah Wang: And even, like, what message I can send to the lead,
230 00:24:03.830 ⇒ 00:24:09.760 Hannah Wang: That would be helpful, because I literally pull things out of my butt, so let me…
231 00:24:09.760 ⇒ 00:24:10.390 Gabriel Lam: Mmm.
232 00:24:10.390 ⇒ 00:24:13.800 Hannah Wang: Maybe just show an example, like…
233 00:24:14.860 ⇒ 00:24:21.040 Hannah Wang: Okay, Jenna Glover, I think she’s from Headspace,
234 00:24:25.390 ⇒ 00:24:29.770 Hannah Wang: Yeah, so usually for go-to-market, there’s, like, a three-part sequence.
235 00:24:30.540 ⇒ 00:24:36.699 Hannah Wang: Typically, for when we connect with someone who’s going to an event that we are targeting, so…
236 00:24:36.810 ⇒ 00:24:44.329 Hannah Wang: she was going to a health-related conference, and obviously health is an industry that we work in, with, like, Eden, LE Mental Health, like, HIP.
237 00:24:44.450 ⇒ 00:25:03.849 Hannah Wang: So she’s the CCO, so this is kind of the connection request message that we send, and then the first message is, hey, thanks for the connect, want to hear more, here’s our website. And after a couple days, this is where the case study comes in. It’s like, hey, we recently blah blah blah with a relevant client, and
238 00:25:04.000 ⇒ 00:25:12.819 Hannah Wang: thought you might find it interesting, and then I always try to, like, ask a question at the end that’s, like, related to the company.
239 00:25:13.190 ⇒ 00:25:20.719 Hannah Wang: And the case study that I just sent. So, like, this, like, coming up with this is also hard.
240 00:25:20.780 ⇒ 00:25:38.469 Hannah Wang: not just picking out the case study, like, I wish I could just send the file and not have to do messaging, but, like, I guess that’s all middle-of-funnel stuff, that I’m helping with, so even this is, like, hard. The second step in the sequence is always the hardest part, because the third one is just, like, a generic
241 00:25:38.790 ⇒ 00:25:41.210 Hannah Wang: you know, whatever. Yeah.
242 00:25:41.490 ⇒ 00:25:45.099 Hannah Wang: Copy-paste, but yeah, this has to be more like…
243 00:25:45.630 ⇒ 00:25:49.540 Hannah Wang: niche to… or specific to the lead. So…
244 00:25:50.080 ⇒ 00:25:54.989 Hannah Wang: Yeah, lots of stuff I have a hard time with.
245 00:25:55.520 ⇒ 00:26:00.810 Hannah Wang: I feel like at least getting just… the case study.
246 00:26:01.240 ⇒ 00:26:03.509 Hannah Wang: What might… might be helpful.
247 00:26:03.630 ⇒ 00:26:07.810 Hannah Wang: Nice. But again, like, you know, GPT… like, AI is not perfect, so…
248 00:26:07.810 ⇒ 00:26:08.310 Gabriel Lam: No.
249 00:26:08.310 ⇒ 00:26:10.249 Hannah Wang: I would still have to, like.
250 00:26:10.530 ⇒ 00:26:16.250 Hannah Wang: fact check it, I guess? But… I think…
251 00:26:16.440 ⇒ 00:26:20.979 Hannah Wang: using AI’s assistance would be more helpful than me just trying to do it on my own.
252 00:26:21.250 ⇒ 00:26:25.929 Hannah Wang: Which is so sad to say, because we didn’t have AI before, so the fact that I’m, like.
253 00:26:25.930 ⇒ 00:26:31.109 Gabriel Lam: I have to rely on AI, it’s terrifying. Like, I can’t do this myself, what the heck?
254 00:26:31.380 ⇒ 00:26:32.909 Hannah Wang: Yeah.
255 00:26:34.530 ⇒ 00:26:35.470 Gabriel Lam: I see.
256 00:26:36.470 ⇒ 00:26:37.370 Gabriel Lam: Okay.
257 00:26:39.760 ⇒ 00:26:43.599 Gabriel Lam: I guess my next question is… Do you think
258 00:26:46.570 ⇒ 00:26:49.590 Gabriel Lam: it makes more sense. Well, my first…
259 00:26:50.010 ⇒ 00:26:53.010 Gabriel Lam: ass of an impression would be like, oh, I think it would make more sense to have
260 00:26:53.500 ⇒ 00:27:07.990 Gabriel Lam: something at marketing assets, because that’s where you already use it, it’s where you already have all the white papers, all your one-pagers, all your case studies, maybe it makes more sense to be… to bring it here, as opposed to, like, having a new page.
261 00:27:08.960 ⇒ 00:27:12.099 Hannah Wang: Totally. Totally, yeah. Just wherever…
262 00:27:12.710 ⇒ 00:27:14.059 Gabriel Lam: So, like, maybe adding an agent…
263 00:27:15.490 ⇒ 00:27:19.369 Gabriel Lam: here to be like, hey, I have a new client, I want to send something up to them.
264 00:27:21.220 ⇒ 00:27:27.709 Hannah Wang: Yeah, maybe just, like, even adding a little button here, it’s like, oh, need to find Relevant case studies?
265 00:27:27.710 ⇒ 00:27:28.359 Gabriel Lam: And then I couldn’.
266 00:27:28.360 ⇒ 00:27:29.000 Hannah Wang: on it, it’s a.
267 00:27:29.000 ⇒ 00:27:31.530 Gabriel Lam: Maybe, yeah, yeah, maybe, like… Something. Yeah.
268 00:27:32.430 ⇒ 00:27:38.170 Hannah Wang: Oh, I know, I was gonna say maybe a modal, but Utom hates modals, so maybe not a modal.
269 00:27:39.070 ⇒ 00:27:46.390 Hannah Wang: I don’t know what else it would be, but… Yeah, something, something like that.
270 00:27:46.810 ⇒ 00:27:47.339 Hannah Wang: Like, this.
271 00:27:47.340 ⇒ 00:27:48.349 Gabriel Lam: There’s a model.
272 00:27:48.350 ⇒ 00:27:49.190 Hannah Wang: Okay.
273 00:27:49.570 ⇒ 00:27:52.730 Hannah Wang: This.
274 00:27:53.600 ⇒ 00:28:01.669 Hannah Wang: Refresh… Yeah, definitely something here, or adjacent to… to here.
275 00:28:03.820 ⇒ 00:28:15.180 Hannah Wang: like, yeah, we can have, like, an agent or a tool for it, but it’s just also, like, two clicks for me. You know, I have to go here first, and then I have to go to here to get the asset, so…
276 00:28:15.470 ⇒ 00:28:15.850 Gabriel Lam: Okay.
277 00:28:15.850 ⇒ 00:28:17.360 Hannah Wang: is good, I think.
278 00:28:19.470 ⇒ 00:28:20.330 Gabriel Lam: I see.
279 00:28:21.760 ⇒ 00:28:27.380 Gabriel Lam: Has… maybe on a different question, has the…
280 00:28:28.320 ⇒ 00:28:33.499 Gabriel Lam: Has the interview thing been helpful, to do it async? Have you gotten any…
281 00:28:35.070 ⇒ 00:28:38.919 Gabriel Lam: Anyone that do it? Test from it?
282 00:28:38.920 ⇒ 00:28:39.860 Hannah Wang: Yay!
283 00:28:40.030 ⇒ 00:28:43.529 Hannah Wang: I asked Ryan… To do…
284 00:28:43.690 ⇒ 00:28:50.669 Hannah Wang: it, but, I mean, obviously everyone’s, like, super busy with their other stuff, so I haven’t gotten a chance, but…
285 00:28:50.820 ⇒ 00:28:54.129 Gabriel Lam: I will let you… I will for sure let you know, like, once…
286 00:28:54.130 ⇒ 00:29:01.800 Hannah Wang: Once it’s, been tested by people. Yeah, like, I… I need to find stakeholders for all of these, like, I don’t…
287 00:29:02.020 ⇒ 00:29:07.569 Hannah Wang: know who I should be interviewing, so I just… like, I asked Ryan to do it because
288 00:29:07.750 ⇒ 00:29:16.060 Hannah Wang: his name was there already, like, the stakeholder, but some of these, I’m like, I don’t know who the stakeholder is, like, who the interviewee should be, so…
289 00:29:16.060 ⇒ 00:29:16.810 Gabriel Lam: Hmm…
290 00:29:16.810 ⇒ 00:29:25.109 Hannah Wang: that’s why I haven’t asked other people. But I was just thinking before this meeting to maybe ask the data team, like, who can help me?
291 00:29:25.990 ⇒ 00:29:30.609 Hannah Wang: Yeah, like, this is so… vague. I’m like…
292 00:29:30.730 ⇒ 00:29:33.209 Hannah Wang: Bruh, I don’t… I don’t know. Like, who…
293 00:29:33.210 ⇒ 00:29:33.730 Gabriel Lam: Yeah.
294 00:29:33.730 ⇒ 00:29:36.479 Hannah Wang: Which client used Snowflake? I don’t know.
295 00:29:36.840 ⇒ 00:29:37.700 Gabriel Lam: Yeah.
296 00:29:38.560 ⇒ 00:29:41.610 Hannah Wang: So, no. Short an… long answer is short, no.
297 00:29:41.610 ⇒ 00:29:42.590 Gabriel Lam: Not yet.
298 00:29:43.450 ⇒ 00:29:46.149 Hannah Wang: But I’m sure it will be helpful. Like, I…
299 00:29:46.330 ⇒ 00:29:53.059 Hannah Wang: the fact that I don’t have to schedule interviews is already helpful for me going into this week.
300 00:30:00.200 ⇒ 00:30:01.360 Gabriel Lam: I see.
301 00:30:02.200 ⇒ 00:30:03.080 Gabriel Lam: Okay.
302 00:30:04.110 ⇒ 00:30:06.670 Hannah Wang: Yeah, I know it’s a lot.
303 00:30:07.910 ⇒ 00:30:13.760 Gabriel Lam: No, I think it’s helpful, because I think these are all things that… R…
304 00:30:14.020 ⇒ 00:30:18.589 Gabriel Lam: Maybe we’re a little more divorced from, or maybe, like, more distant from.
305 00:30:18.700 ⇒ 00:30:21.439 Gabriel Lam: Like, an example would be, like, this week. Well, this week.
306 00:30:21.650 ⇒ 00:30:26.450 Gabriel Lam: Apparently, it’s one of the busiest weeks for a lot of the client teams.
307 00:30:26.840 ⇒ 00:30:31.270 Gabriel Lam: And so… like, Sam and Mustafa are, like.
308 00:30:31.320 ⇒ 00:30:34.659 Hannah Wang: More focused on other things, as opposed to internal…
309 00:30:34.840 ⇒ 00:30:35.820 Gabriel Lam: initiatives.
310 00:30:36.100 ⇒ 00:30:36.540 Hannah Wang: Yes.
311 00:30:36.680 ⇒ 00:30:43.229 Gabriel Lam: But… Like, something we’re working on… we were thinking of working on was, like.
312 00:30:43.460 ⇒ 00:30:48.020 Gabriel Lam: how to make linear tickets a little easier to handle, like, let’s…
313 00:30:48.400 ⇒ 00:30:54.439 Gabriel Lam: have a meeting, and you’re like, well, now we gotta turn it into a summary and next steps, and what we have right now kind of sucks.
314 00:30:54.610 ⇒ 00:30:57.570 Gabriel Lam: But I think this…
315 00:31:00.160 ⇒ 00:31:07.660 Gabriel Lam: this, I think, is, like, the follow-up to last week, of, like, okay, now we’ve gotten the interview step down, how do we… Yeah.
316 00:31:08.040 ⇒ 00:31:13.350 Gabriel Lam: take the next step forward. I guess I’m also curious, like.
317 00:31:15.520 ⇒ 00:31:23.470 Gabriel Lam: from the time it takes… you know, let’s say, you know, Robert or Utham’s like, hey, we need this out this week, and you schedule an interview, you get it.
318 00:31:23.470 ⇒ 00:31:26.829 Hannah Wang: and then you handle the Figma part, how long does that take?
319 00:31:27.630 ⇒ 00:31:36.040 Hannah Wang: designing it, if we have the copy, probably, like, an hour. Like, not that long. Not long at all, just because…
320 00:31:36.590 ⇒ 00:31:39.490 Hannah Wang: We already have, like, a tr…
321 00:31:39.600 ⇒ 00:31:51.400 Hannah Wang: I wouldn’t say tried and true, but we have a template already, so it’s just a matter of, oh, duplicate this… this frame, and just start changing everything, which doesn’t take…
322 00:31:51.450 ⇒ 00:32:03.930 Hannah Wang: take long. That’s, like, the easiest step for me. Like, anything related to… like, in an ideal world, I would just be designing, and I’d have all the requirements laid out for me.
323 00:32:03.930 ⇒ 00:32:04.840 Gabriel Lam: Like, oh.
324 00:32:04.840 ⇒ 00:32:08.919 Hannah Wang: This is the copy for this area, just paste it in, make it look pretty.
325 00:32:09.000 ⇒ 00:32:11.959 Gabriel Lam: But that’s not how we work here, so…
326 00:32:11.960 ⇒ 00:32:15.820 Hannah Wang: No, it’s definitely not structured enough for that. Yeah.
327 00:32:15.820 ⇒ 00:32:17.479 Gabriel Lam: I get it. Okay.
328 00:32:20.080 ⇒ 00:32:22.249 Gabriel Lam: Okay, I have some ideas…
329 00:32:25.480 ⇒ 00:32:26.420 Gabriel Lam: Hmm.
330 00:32:28.140 ⇒ 00:32:30.390 Hannah Wang: And also, like, this is not, I guess.
331 00:32:30.600 ⇒ 00:32:40.530 Hannah Wang: Like, if you need to prioritize the linear one, like, that’s totally fine by me, because linear affects every team, internal and delivery.
332 00:32:40.530 ⇒ 00:32:51.249 Hannah Wang: teams, because we all use linear, so if that’s more urgent, like, that’s totally fine. I will survive, like, it’s okay. Robert knows that I struggle with it, and…
333 00:32:51.250 ⇒ 00:32:59.709 Hannah Wang: yeah, he knows it’s hard for me, and I will… I will survive, so it’s okay if you get to it in a couple weeks. Doesn’t need to be, like.
334 00:32:59.750 ⇒ 00:33:00.750 Hannah Wang: Right away.
335 00:33:00.750 ⇒ 00:33:07.059 Gabriel Lam: No, but I think the heads-up is good. I think we can also… I can also just…
336 00:33:08.190 ⇒ 00:33:13.689 Gabriel Lam: either you or I can put it in one of the channels and be like, hey, this is something to think about,
337 00:33:14.800 ⇒ 00:33:18.490 Gabriel Lam: I… just want to preface that, I think, this week.
338 00:33:18.730 ⇒ 00:33:28.609 Gabriel Lam: the AI internal team is gonna be a little on the backseat. Like, I’m a little… a little bit… I’m a little struggling on what I’m gonna do this week. I’m like.
339 00:33:28.610 ⇒ 00:33:28.930 Hannah Wang: I don’.
340 00:33:28.930 ⇒ 00:33:30.660 Gabriel Lam: have that much going on.
341 00:33:31.120 ⇒ 00:33:37.390 Gabriel Lam: But I’m also writing a PRD for the first time, and I also have no idea what I’m doing, so there’s that.
342 00:33:38.190 ⇒ 00:33:38.850 Gabriel Lam: Welcome to…
343 00:33:38.850 ⇒ 00:33:41.670 Hannah Wang: No, I don’t know what I’m doing, but I… do it.
344 00:33:41.670 ⇒ 00:33:44.370 Gabriel Lam: Yeah. So we’ll get that out.
345 00:33:44.800 ⇒ 00:33:48.140 Gabriel Lam: But I appreciate this, I think this is really good, and I think… what’s that?
346 00:33:48.590 ⇒ 00:33:56.719 Hannah Wang: sorry, what… what does a PRD stand for again? I’ve heard it thrown everywhere, like, I’ve heard it used, product, I forgot about.
347 00:33:56.720 ⇒ 00:33:58.510 Gabriel Lam: Like, a product requirement.
348 00:33:58.510 ⇒ 00:33:58.990 Hannah Wang: requirement.
349 00:33:58.990 ⇒ 00:34:00.100 Gabriel Lam: document.
350 00:34:00.110 ⇒ 00:34:01.530 Hannah Wang: Okay.
351 00:34:01.730 ⇒ 00:34:06.979 Gabriel Lam: I think it’s… that’s… The… it basically highlights
352 00:34:07.590 ⇒ 00:34:16.450 Gabriel Lam: in, you know, 5 pa- like, 2 to 5 pages, what feature you’re building, and why, and how,
353 00:34:17.500 ⇒ 00:34:21.749 Gabriel Lam: So for this, it’d be like, oh, you know, this is the pain point we’re facing, this is how…
354 00:34:22.170 ⇒ 00:34:27.999 Gabriel Lam: we think we can do it, this is why we think it’s good, and these are the metrics we can use to measure it.
355 00:34:28.000 ⇒ 00:34:28.819 Hannah Wang: I see.
356 00:34:28.820 ⇒ 00:34:33.439 Gabriel Lam: I also… haven’t used GitHub in, like, a year.
357 00:34:33.440 ⇒ 00:34:34.260 Hannah Wang: Oh, yes.
358 00:34:34.260 ⇒ 00:34:39.130 Gabriel Lam: So I was, like, I was, like, pushing to Maine, and I was like, nope, not… Correct.
359 00:34:39.880 ⇒ 00:34:44.579 Gabriel Lam: Delete! So, that was me last night.
360 00:34:44.580 ⇒ 00:34:46.190 Hannah Wang: I feel you.
361 00:34:46.190 ⇒ 00:34:53.129 Gabriel Lam: Like, oh shoot, I pushed something to Maine, I didn’t make a branch. How do you even make a branch? I don’t know how to…
362 00:34:53.130 ⇒ 00:34:54.830 Hannah Wang: merge everything, I don’t know.
363 00:34:54.830 ⇒ 00:34:57.390 Gabriel Lam: I was like, yeah, I was like, oh man.
364 00:34:57.550 ⇒ 00:34:58.980 Gabriel Lam: So… Okay.
365 00:34:58.980 ⇒ 00:35:03.419 Hannah Wang: Why did you have to push something? Are you coding as well?
366 00:35:03.740 ⇒ 00:35:06.989 Gabriel Lam: Well, not really, but I think they want to have…
367 00:35:07.180 ⇒ 00:35:10.039 Gabriel Lam: these documents as markdowns on GitHub.
368 00:35:11.740 ⇒ 00:35:19.179 Gabriel Lam: And I think it’s also just maybe a good habit for me to get used to looking at any pull requests if I ever have extra time.
369 00:35:20.330 ⇒ 00:35:26.580 Hannah Wang: Oh, boy. Yeah, even when I was working at Ring, like.
370 00:35:27.020 ⇒ 00:35:31.139 Hannah Wang: I was still, like, googling, how do I, like, merge
371 00:35:31.360 ⇒ 00:35:38.119 Hannah Wang: How to Center Reduce. Yes, literally me.
372 00:35:38.160 ⇒ 00:35:39.489 Gabriel Lam: All the time.
373 00:35:39.700 ⇒ 00:35:40.880 Gabriel Lam: Huh.
374 00:35:41.490 ⇒ 00:35:44.920 Hannah Wang: Yeah, like, GitHub is so confu- like, it…
375 00:35:45.490 ⇒ 00:35:51.770 Hannah Wang: I think it… you just… it just… it’s a long learning curve, like, a steep learning curve, because there’s so many, like.
376 00:35:52.370 ⇒ 00:36:01.310 Hannah Wang: scenarios, like, oh, if this person is two branches ahead of you, but then you need a, like, merchant on top of theirs, it’s like, oh my gosh, what is happening? So…
377 00:36:02.100 ⇒ 00:36:08.100 Hannah Wang: But yeah, this… maybe if you have time and need something to work on, you can ponder on this.
378 00:36:08.100 ⇒ 00:36:09.149 Gabriel Lam: I will definitely…
379 00:36:09.150 ⇒ 00:36:11.120 Hannah Wang: Two ideas, yeah.
380 00:36:11.450 ⇒ 00:36:27.150 Hannah Wang: And I’ll… I’ll try to see, like, if there’s any other optimization help that I need for, like, not only just design, but also, like, go-to-market. That’s… that… being in that team has definitely been a challenge.
381 00:36:27.360 ⇒ 00:36:27.740 Gabriel Lam: Yeah.
382 00:36:27.740 ⇒ 00:36:31.220 Hannah Wang: Ugh, and… Yeah.
383 00:36:31.410 ⇒ 00:36:32.820 Hannah Wang: Surviving.
384 00:36:32.820 ⇒ 00:36:40.440 Gabriel Lam: You got this. I am curious about, like, I saw your AI, what is it, services offerings?
385 00:36:40.640 ⇒ 00:36:41.420 Gabriel Lam: deck…
386 00:36:42.900 ⇒ 00:36:45.539 Gabriel Lam: I’m curious what you guys are doing there.
387 00:36:45.830 ⇒ 00:36:46.570 Hannah Wang: AI.
388 00:36:46.570 ⇒ 00:36:47.790 Gabriel Lam: If you had it open.
389 00:36:47.790 ⇒ 00:36:49.370 Hannah Wang: Like, literally.
390 00:36:49.770 ⇒ 00:37:03.370 Gabriel Lam: In Figma. Oh, is it the data capabilities? I’m not sure. I literally, like, as you were showing me stuff earlier, I just saw, like, a giant slide deck, and I was like, oh, I didn’t know, like, what we were marketing.
391 00:37:03.750 ⇒ 00:37:05.010 Hannah Wang: Oh, yeah.
392 00:37:05.510 ⇒ 00:37:07.250 Hannah Wang: Sure, yeah, so…
393 00:37:07.860 ⇒ 00:37:09.430 Gabriel Lam: Was it this one?
394 00:37:10.130 ⇒ 00:37:15.269 Gabriel Lam: Yeah, was it Navy? I don’t know if it was an ARC or something, either.
395 00:37:15.270 ⇒ 00:37:17.540 Hannah Wang: Yeah, so… haha.
396 00:37:17.750 ⇒ 00:37:21.020 Hannah Wang: We… I like to design in Figma, obviously.
397 00:37:21.020 ⇒ 00:37:21.680 Gabriel Lam: understand.
398 00:37:21.680 ⇒ 00:37:23.709 Hannah Wang: slides is kind of bad.
399 00:37:23.710 ⇒ 00:37:24.080 Gabriel Lam: Nope.
400 00:37:24.080 ⇒ 00:37:34.190 Hannah Wang: But, Utam was like, oh, it’d be nice to have a Google Slide version of it. And I was like, huh, you want me to, like, trans… like, somehow, like, recreate.
401 00:37:34.530 ⇒ 00:37:35.390 Gabriel Lam: It’s…
402 00:37:35.390 ⇒ 00:37:46.990 Hannah Wang: But, so what I did was just export it as a PNG and attach it, so that’s probably why you saw it in Arc. Well, also my Figma was open in Arc, but anyway.
403 00:37:47.540 ⇒ 00:37:56.360 Hannah Wang: Yeah, this is… this is kind of maybe outdated. It was made in August. I’m sure things have been adjusted by now, but…
404 00:37:56.820 ⇒ 00:38:00.839 Hannah Wang: Yeah, it’s just talking about… Our process, like.
405 00:38:01.840 ⇒ 00:38:06.340 Hannah Wang: I guess, age… evaluate evals,
406 00:38:07.920 ⇒ 00:38:14.140 Hannah Wang: like, yeah, I don’t really know what these tools are, like, golden data sets, like, yeah, measuring stuff, like,
407 00:38:14.390 ⇒ 00:38:21.269 Hannah Wang: Evals, like, oh, we embed it right into your workspace, and like…
408 00:38:21.630 ⇒ 00:38:34.459 Hannah Wang: this is our stack, like, governance, data diag… like, architecture diagrams, shouting out certain tools that we use, like NAN clay.
409 00:38:34.690 ⇒ 00:38:45.349 Hannah Wang: AI-related case studies, yeah, so we have, like, a bunch of decks, AI deck, data, data deck.
410 00:38:45.710 ⇒ 00:38:50.930 Hannah Wang: like, an over… a general capabilities deck that includes both data and AI, so…
411 00:38:50.930 ⇒ 00:38:52.099 Gabriel Lam: Hmm, I see.
412 00:38:52.100 ⇒ 00:38:54.250 Hannah Wang: Yeah, there’s just a lot of decks everywhere.
413 00:38:54.250 ⇒ 00:38:55.539 Gabriel Lam: I get it.
414 00:38:56.030 ⇒ 00:39:02.120 Hannah Wang: We’re constantly… Trying to… Yeah.
415 00:39:02.120 ⇒ 00:39:02.599 Gabriel Lam: I get it.
416 00:39:02.600 ⇒ 00:39:14.389 Hannah Wang: And I have to, like, make more decks now, too, with, like, other stuff, and decks. That’s not the worst thing, though. I don’t mind doing decks, because it’s design, and…
417 00:39:14.520 ⇒ 00:39:17.040 Hannah Wang: It works a different part of my brain.
418 00:39:17.040 ⇒ 00:39:17.770 Gabriel Lam: Oh.
419 00:39:19.130 ⇒ 00:39:20.310 Hannah Wang: Yeah!
420 00:39:21.300 ⇒ 00:39:29.959 Gabriel Lam: Alright, I appreciate this. Very helpful. I… yeah. We’ll try to get to it, I think is what I can say.
421 00:39:29.960 ⇒ 00:39:34.719 Hannah Wang: Yeah, no, no rush. I’ve lived without it this far, and…
422 00:39:34.720 ⇒ 00:39:35.330 Gabriel Lam: Okay.
423 00:39:35.530 ⇒ 00:39:42.300 Hannah Wang: Yeah, it’s… the holidays are coming too, so I’m like, work, whatever.
424 00:39:42.300 ⇒ 00:39:42.630 Gabriel Lam: Yeah.
425 00:39:42.810 ⇒ 00:39:45.870 Hannah Wang: I need a break, so… and it’s okay.
426 00:39:46.150 ⇒ 00:39:46.850 Gabriel Lam: Okay.
427 00:39:48.030 ⇒ 00:39:54.239 Hannah Wang: Alright, well, yeah, let me know if you have any other questions, and, yeah, we can…
428 00:39:54.350 ⇒ 00:39:56.630 Hannah Wang: back and forth on… on Slack, if you have…
429 00:39:56.630 ⇒ 00:39:58.370 Gabriel Lam: Sounds good, sounds good.
430 00:39:58.370 ⇒ 00:40:01.810 Hannah Wang: Yeah. I might just try to spend the next, like.
431 00:40:01.850 ⇒ 00:40:08.630 Gabriel Lam: 20 minutes to see if there’s a better prompt that we can come up with, and then I’ll send it over and see what happens there. But otherwise…
432 00:40:08.630 ⇒ 00:40:09.849 Hannah Wang: Oh, sure.
433 00:40:10.330 ⇒ 00:40:16.159 Hannah Wang: Yeah, I can also send you a link to the Notion that has all of our prompts. I don’t know if you.
434 00:40:16.160 ⇒ 00:40:18.299 Gabriel Lam: I have it out. I… Okay. Yeah.
435 00:40:18.300 ⇒ 00:40:25.399 Hannah Wang: Great, you got it. Yeah, the job application GPT. Again, it’s for job applications, so you need to tailor it a lot, but yeah.
436 00:40:25.900 ⇒ 00:40:26.330 Gabriel Lam: Okay.
437 00:40:26.330 ⇒ 00:40:27.210 Hannah Wang: Alrighty.
438 00:40:27.210 ⇒ 00:40:27.920 Gabriel Lam: Alrighty.
439 00:40:27.920 ⇒ 00:40:28.560 Hannah Wang: in.
440 00:40:28.560 ⇒ 00:40:29.690 Gabriel Lam: Talk soon!
441 00:40:29.690 ⇒ 00:40:30.270 Hannah Wang: Bye.
442 00:40:30.270 ⇒ 00:40:31.200 Gabriel Lam: i.e.