Meeting Title: Event Scraping Process Overview Date: 2025-06-24 Meeting participants: Casie Aviles, Hannah Wang
WEBVTT
1 00:00:33.810 ⇒ 00:00:34.970 Hannah Wang: Hello!
2 00:00:35.720 ⇒ 00:00:36.460 Casie Aviles: Hey! Anna!
3 00:00:38.260 ⇒ 00:00:39.280 Hannah Wang: How are you?
4 00:00:40.760 ⇒ 00:00:42.220 Casie Aviles: Doing fine. How about you?
5 00:00:42.220 ⇒ 00:00:44.029 Hannah Wang: Nice doing? Well?
6 00:00:45.782 ⇒ 00:00:48.670 Hannah Wang: Yeah. So I think
7 00:00:49.420 ⇒ 00:01:06.629 Hannah Wang: I’m not sure how much work you’ve done on this yet. But I can kind of. Just walk you through what I do manually, right now and then. You can just ask me questions. If you have any, so let me share my screen.
8 00:01:07.490 ⇒ 00:01:11.614 Hannah Wang: Hold on! I have so many things open.
9 00:01:16.940 ⇒ 00:01:21.370 Casie Aviles: I’m just checking out the ticket before this this meeting. So I don’t know if much.
10 00:01:21.370 ⇒ 00:01:22.400 Casie Aviles: It’s okay checks yet.
11 00:01:23.280 ⇒ 00:01:32.020 Hannah Wang: Okay, no worries. So basically, I think one of our big pushes for marketing and
12 00:01:32.160 ⇒ 00:01:41.229 Hannah Wang: promotions. Is to be able to attend events. That are in New York, Austin, la, or wherever
13 00:01:41.724 ⇒ 00:01:54.795 Hannah Wang: people are based in so probably primarily New York and Austin. Since you, Tom, and Robert are there. And then. The reason why we want to do this is to basically
14 00:01:55.690 ⇒ 00:02:04.139 Hannah Wang: yeah, get potential clients or leads and just network and talk to people. So I think.
15 00:02:05.170 ⇒ 00:02:09.750 Hannah Wang: let me pull up the linear ticket as well.
16 00:02:12.430 ⇒ 00:02:15.510 Hannah Wang: Could you send me the link again in zoom.
17 00:02:15.800 ⇒ 00:02:16.540 Casie Aviles: Oh, sure.
18 00:02:22.620 ⇒ 00:02:23.510 Casie Aviles: Okay.
19 00:02:24.170 ⇒ 00:02:25.590 Casie Aviles: Yeah. I see it.
20 00:02:26.380 ⇒ 00:02:26.980 Hannah Wang: Great.
21 00:02:27.900 ⇒ 00:02:29.010 Hannah Wang: So
22 00:02:31.460 ⇒ 00:02:44.480 Hannah Wang: yeah, I feel like, maybe wrote this with AI the required fields. Yeah, they are all helpful. So I think you can just keep it as it is. But
23 00:02:45.230 ⇒ 00:03:08.957 Hannah Wang: I kinda added, more context at the bottom. Here. As to why we want to do this. So the 1st is kind of to attend events in person, to network and to potentially also speak at because that also helps with marketing and just getting ourselves getting our name out there. And then the second category is
24 00:03:10.209 ⇒ 00:03:33.130 Hannah Wang: persons we are events we can’t attend, but we can maybe leverage like the attendee list, to build an outreach list. And we can just like target those people. And like just cold email, those types of people. So a lot of the conferences and events are really expensive. So we probably can’t go to all of them.
25 00:03:33.702 ⇒ 00:03:44.509 Hannah Wang: But we can. Still, if we can get like the Attendee list, we can use that to just market ourselves for sales. Another
26 00:03:45.000 ⇒ 00:03:50.619 Hannah Wang: goal is events that we can sponsor or co-host, maybe with like another
27 00:03:51.040 ⇒ 00:03:58.819 Hannah Wang: yeah company. Again. This is just like to put ourselves out there and then the last one is just maybe
28 00:03:59.520 ⇒ 00:04:21.120 Hannah Wang: lower priority. But just cause. There’s just so many virtual events. But if there’s any particular niche, virtual events that Robert or Tom, or whoever else can attend that’s good also for networking, obviously. And also, if we can get the attendee list, that’d be helpful, too. So
29 00:04:21.510 ⇒ 00:04:22.770 Hannah Wang: that’s kind of
30 00:04:22.870 ⇒ 00:04:44.860 Hannah Wang: yeah. That’s kinda yeah, hopefully, gives you more context as to why we’re doing this. So it’s not just finding events that we can attend. But even for the ones that we cannot go to. We can still leverage the attendee list, or even email, the host and get connected that way. So.
31 00:04:44.860 ⇒ 00:04:48.910 Casie Aviles: Yes, that makes sense. Okay, cool. Yeah.
32 00:04:49.920 ⇒ 00:04:51.439 Hannah Wang: Did you have any questions?
33 00:04:51.440 ⇒ 00:05:00.749 Casie Aviles: Yes, yes, I guess my first, st my my question right now is, how? How do we like? Do we have like a criteria to identify which
34 00:05:02.123 ⇒ 00:05:03.840 Casie Aviles: based on these goals like.
35 00:05:04.050 ⇒ 00:05:06.132 Casie Aviles: how do we know the event is?
36 00:05:06.970 ⇒ 00:05:12.159 Casie Aviles: some. Yeah, something we can attend, or you know, something we could co-host instead.
37 00:05:12.400 ⇒ 00:05:15.099 Casie Aviles: Do you have like a way to find that.
38 00:05:15.100 ⇒ 00:05:16.900 Hannah Wang: Criteria for that. Yeah.
39 00:05:17.960 ⇒ 00:05:42.840 Hannah Wang: I mean, I like, talk to Robert briefly about this. I initially thought we could maybe just target like AI events or tech events, AI tech and data. Obviously, because that’s what we do. Right? So, for example, like an Aws conference or like a dbt meetup stuff like that. But I think even because our clients are also
40 00:05:43.010 ⇒ 00:05:59.490 Hannah Wang: some of our clients are not even in that sector, right? So like ABC home, for example, like they’re a pest control company. So it’s not. They’re not necessarily like a service or provider for data and AI, but they still need our services. So I think
41 00:06:02.200 ⇒ 00:06:05.609 Hannah Wang: you can be more broad in the beginning.
42 00:06:05.990 ⇒ 00:06:33.340 Hannah Wang: Like just kind of target, all events. And then I think Robert and Newton can look through it and just filter it themselves. And then, later on, if we need to update or optimize anything, or niche down a little bit more, we can do that. So I I would say, maybe err on the side of being more broad and targeting like any like type of obviously not like a
43 00:06:34.090 ⇒ 00:06:38.179 Hannah Wang: I’m trying to think of an example that wouldn’t apply, maybe like a
44 00:06:41.240 ⇒ 00:06:59.609 Hannah Wang: like a food tasting event like, obviously, maybe not those types of things. But anything that’s kind of remotely related to data or AI, or maybe has, like the phrase, data and AI in it. So I know there is like a conference that I looked at.
45 00:07:01.430 ⇒ 00:07:08.380 Hannah Wang: It was. I know this is really small. Oh, you’re also on this, too, which is great. It’s
46 00:07:10.993 ⇒ 00:07:29.160 Hannah Wang: I guess these are all data. And AI related. But I found someone that some conference that was more for like real estate but using AI and real estate. So that’s like an example where we can obviously like, go and attend. So obviously, real estate, like it’s not.
47 00:07:29.840 ⇒ 00:07:40.904 Hannah Wang: you know, are like data or AI, but they’re trying to utilize AI and data in it. So yeah, that’s just like, kind of an example.
48 00:07:42.300 ⇒ 00:07:43.840 Hannah Wang: okay, cool.
49 00:07:45.110 ⇒ 00:07:46.889 Hannah Wang: Any other questions.
50 00:07:48.820 ⇒ 00:07:53.300 Casie Aviles: Hmm! I guess I’ll just check out these websites. I haven’t really.
51 00:07:53.840 ⇒ 00:07:56.281 Casie Aviles: Yeah. Have you clicked through them yet?
52 00:07:56.630 ⇒ 00:08:02.376 Hannah Wang: Okay, yeah. So I guess I’ll just kind of share with you what I do manually.
53 00:08:02.916 ⇒ 00:08:03.350 Casie Aviles: Yes. Yeah.
54 00:08:03.350 ⇒ 00:08:10.289 Hannah Wang: So. Oh, I I just type in like AI events in New York, for example.
55 00:08:10.650 ⇒ 00:08:23.639 Hannah Wang: And clearly, clearly, I’ve like click through a lot of these. So I try to go to websites like eventbrite or Luma and then so for example, if I click on here.
56 00:08:23.820 ⇒ 00:08:32.549 Hannah Wang: Yeah, so and then what I do is I just kinda like scroll through these. Obviously the ones for today. We can’t
57 00:08:32.750 ⇒ 00:08:52.100 Hannah Wang: do cause it’s too late notice. So I kind of maybe go like 3 days to a week out, and then I start looking at these. So for now, because it’s it’s a manual process. I’ve been ignoring the ones that are sold out, but
58 00:08:52.470 ⇒ 00:08:55.459 Hannah Wang: I feel like, even if we can’t attend these
59 00:08:55.898 ⇒ 00:09:02.679 Hannah Wang: maybe we can like reach out to the host and get the attendee list. So this I think this can also be
60 00:09:03.160 ⇒ 00:09:06.326 Hannah Wang: maybe fair game to include
61 00:09:07.320 ⇒ 00:09:07.690 Casie Aviles: Yes.
62 00:09:07.690 ⇒ 00:09:25.780 Hannah Wang: But for ones that, like, I’m looking specifically for ones that we can attend. So I kind of go through. And I’m like, okay, like, okay, it’s on June 26.th a little bit too short notice. But let’s say that this is interesting. And I look at this. And then I’m like, I just kinda like, read through
63 00:09:26.010 ⇒ 00:09:27.735 Hannah Wang: what it is.
64 00:09:29.820 ⇒ 00:09:41.299 Hannah Wang: And I look at the price and stuff like that. And I feel, and if I feel like it’s okay. I just literally copy the link. I add the title, copy the link and add the details here.
65 00:09:41.894 ⇒ 00:09:52.755 Hannah Wang: Obviously, all of these details. They’re not up to date. They’re not the same as the one in the linear ticket. So we would probably want to update that
66 00:09:53.230 ⇒ 00:10:00.810 Hannah Wang: And then, after I copy that in, I kind of just go back and look at other events.
67 00:10:02.420 ⇒ 00:10:13.190 Hannah Wang: yeah. So I mean, when it’s manual, like, it takes a while. So I just kinda like, I’m a lot more picky with the ones that I add into the
68 00:10:13.350 ⇒ 00:10:19.443 Hannah Wang: the sheet. But if we can automate it, we can just include like a bunch.
69 00:10:20.830 ⇒ 00:10:22.539 Hannah Wang: So I’m thinking.
70 00:10:22.710 ⇒ 00:10:35.079 Hannah Wang: I know the tickets said that you we can add it to this, Doc. I’m thinking maybe we can create more tabs. So maybe this one can be in person.
71 00:10:35.370 ⇒ 00:10:45.880 Hannah Wang: events that we can attend. Maybe the next tab is in person events, we can’t attend another tab can be virtual events.
72 00:10:47.220 ⇒ 00:10:49.320 Hannah Wang: And then maybe.
73 00:10:49.590 ⇒ 00:10:59.800 Hannah Wang: like another tab, could be meetups, cause I think meetups are different than like conferences, for example. So maybe we can like talk through
74 00:11:00.300 ⇒ 00:11:09.563 Hannah Wang: yeah, breaking down the categories and creating different tabs. Cause I think if we put everything into this sheet. It’s just a lot to look at.
75 00:11:09.920 ⇒ 00:11:10.500 Casie Aviles: Yes.
76 00:11:11.300 ⇒ 00:11:13.770 Hannah Wang: Yeah. So let me go back to the ticket.
77 00:11:15.093 ⇒ 00:11:19.429 Hannah Wang: So maybe the 1st tab yeah in person.
78 00:11:19.920 ⇒ 00:11:26.739 Hannah Wang: Eventually, maybe I’ll just copy this over here. We can’t attend
79 00:11:32.093 ⇒ 00:11:42.700 Hannah Wang: events. I think maybe that’s we don’t need to include that virtual events and maybe meetups.
80 00:11:43.370 ⇒ 00:11:52.250 Hannah Wang: And by events, I mean like conferences. Yeah, mostly bigger things like conferences.
81 00:11:53.900 ⇒ 00:12:00.480 Hannah Wang: because that’s different than meetups. Meetups is more of like, you know, you go. And you network but
82 00:12:00.620 ⇒ 00:12:05.265 Hannah Wang: events like conferences, there’s like an agenda and a schedule and everything.
83 00:12:06.340 ⇒ 00:12:11.449 Hannah Wang: yeah. So maybe we can start with that, for now and then within each of those tabs we can have.
84 00:12:13.006 ⇒ 00:12:15.723 Hannah Wang: These required fields.
85 00:12:18.750 ⇒ 00:12:24.768 Hannah Wang: I feel like I don’t really know what this means to be honest. So I think you can leave this out for now?
86 00:12:26.770 ⇒ 00:12:29.482 Hannah Wang: and so maybe I’ll cross that out
87 00:12:30.480 ⇒ 00:12:32.939 Hannah Wang: Where is the shortcut? Okay.
88 00:12:33.350 ⇒ 00:12:38.259 Casie Aviles: And we can. Just we can start with just these 2 websites, right, Luma, and meet up.
89 00:12:39.697 ⇒ 00:12:40.799 Hannah Wang: Yeah, you can.
90 00:12:40.800 ⇒ 00:12:43.171 Casie Aviles: Do we want? Do we want more?
91 00:12:45.380 ⇒ 00:12:53.508 Hannah Wang: I think maybe more, if possible. Because, for example, or this is like eventbrite is also another
92 00:12:56.230 ⇒ 00:13:02.180 Hannah Wang: conference kind of platform or like event platform. So I can write that down as well.
93 00:13:05.620 ⇒ 00:13:13.920 Hannah Wang: But do you think it’s possible to also just scrape like a Google search cause, for example, like.
94 00:13:14.190 ⇒ 00:13:25.919 Hannah Wang: if it’s a very big conference like they’re not. I feel like the link of that conference just shows up on the Google search like we don’t need to go within Luma to dig it out.
95 00:13:26.160 ⇒ 00:13:31.210 Hannah Wang: So like this is a huge conference. I feel like
96 00:13:32.440 ⇒ 00:13:41.699 Hannah Wang: and I don’t know if this would be in Luma or eventbrite. It’s just like the 1st event that pops up on a Google search. So
97 00:13:41.870 ⇒ 00:13:46.030 Hannah Wang: I don’t know if that’s like, too, it makes it too broad, like scraping everything.
98 00:13:48.510 ⇒ 00:13:53.512 Casie Aviles: Yeah, I mean, I could, I could do like a Google search first.st And then, you know, just
99 00:13:54.540 ⇒ 00:13:58.050 Casie Aviles: get the top results, I guess for to yeah.
100 00:13:58.590 ⇒ 00:14:09.803 Hannah Wang: Okay, yeah, that that works. And obviously, if there’s overlap, like, maybe, let’s say, Ml, con is listed in a Luma event like, we wanna not have duplicates. Obviously.
101 00:14:11.570 ⇒ 00:14:14.019 Hannah Wang: yes, let me write that down.
102 00:14:22.650 ⇒ 00:14:29.204 Hannah Wang: and I don’t know if this is possible. But I know a lot of people like. I don’t follow the right people, but
103 00:14:29.840 ⇒ 00:14:37.357 Hannah Wang: I know some people like post oh, or like hosting a conference, or like something like that.
104 00:14:38.170 ⇒ 00:14:52.350 Hannah Wang: like? Is it possible to scrape? I don’t know who whose account we can scrape, but like, let’s say, utam, he obviously follows a lot of people, and maybe his followers like, maybe some of them are like Ceos of the
105 00:14:53.220 ⇒ 00:15:02.269 Hannah Wang: like a data company. And maybe they have like an event, or something like, is it possible to scrape like, let’s say.
106 00:15:03.320 ⇒ 00:15:07.989 Casie Aviles: Yeah, for for Linkedin. I’m not sure how to do scraping there yet.
107 00:15:07.990 ⇒ 00:15:08.330 Hannah Wang: Okay.
108 00:15:08.330 ⇒ 00:15:09.280 Casie Aviles: So, yeah.
109 00:15:09.280 ⇒ 00:15:09.990 Hannah Wang: No worries.
110 00:15:10.620 ⇒ 00:15:11.810 Casie Aviles: I have to check.
111 00:15:12.780 ⇒ 00:15:15.623 Hannah Wang: Okay, I’ll write that down for now.
112 00:15:20.026 ⇒ 00:15:26.619 Hannah Wang: in case you like find a way. But I think I think, yeah, Google search. And then
113 00:15:27.366 ⇒ 00:15:31.259 Hannah Wang: platforms like this, would be good.
114 00:15:35.500 ⇒ 00:15:37.370 Hannah Wang: Yeah. And then
115 00:15:41.800 ⇒ 00:15:43.289 Hannah Wang: I don’t know if.
116 00:15:44.060 ⇒ 00:15:50.040 Hannah Wang: Okay, yeah. Cause I know we want like different cities and stuff, but I think it’s fine if we just include
117 00:15:50.190 ⇒ 00:15:52.286 Hannah Wang: all of them here.
118 00:15:56.310 ⇒ 00:16:04.149 Casie Aviles: I guess. Another question. How how often do you do this this scraping like? So I I know how often we want the
119 00:16:04.510 ⇒ 00:16:06.759 Casie Aviles: the like, the the pipeline to run.
120 00:16:06.890 ⇒ 00:16:07.430 Casie Aviles: Yeah.
121 00:16:07.430 ⇒ 00:16:12.090 Hannah Wang: I see like.
122 00:16:12.420 ⇒ 00:16:14.019 Hannah Wang: Oh, I feel like this is not
123 00:16:14.320 ⇒ 00:16:30.680 Hannah Wang: accurate of what I would normally do. So I I’ve just been looking every day, just because Robert asked if we can have like a list by Wednesday. But obviously a Google search is not updated that frequently, I think,
124 00:16:31.650 ⇒ 00:16:38.279 Hannah Wang: like I. If I was doing this consistently, maybe I would do this like
125 00:16:39.550 ⇒ 00:16:42.430 Hannah Wang: once or twice a week, maybe
126 00:16:42.910 ⇒ 00:16:43.600 Casie Aviles: Okay.
127 00:16:43.820 ⇒ 00:16:46.800 Hannah Wang: Yeah, we could schedule the the yeah.
128 00:16:47.270 ⇒ 00:16:55.639 Hannah Wang: yeah, maybe we can start, maybe with once a week for now, just to be more generous. But then, later, if we find that we’re not getting new results, maybe we can
129 00:16:55.870 ⇒ 00:17:03.620 Hannah Wang: change it to like twice a week like a different cadence. Yeah. And then, I think
130 00:17:04.200 ⇒ 00:17:11.674 Hannah Wang: for the results you can limit it to end of Q. 3. So I think that’s September.
131 00:17:13.349 ⇒ 00:17:14.830 Hannah Wang: let me look it up.
132 00:17:15.869 ⇒ 00:17:16.480 Casie Aviles: Yeah.
133 00:17:16.680 ⇒ 00:17:33.400 Hannah Wang: Yeah, end of September. So just so that we don’t have like a Bajillion conferences. So you can just filter it to end of September. So any event from now to then, I think, is good. Cause we can’t plan that far out, obviously.
134 00:17:36.190 ⇒ 00:17:37.670 Hannah Wang: And then.
135 00:17:40.900 ⇒ 00:17:53.230 Hannah Wang: yeah, I think that’s about it. I don’t think you need sponsor vendor list, or event.
136 00:17:54.980 ⇒ 00:17:58.240 Hannah Wang: Actually, yeah, maybe event type and industry focus
137 00:17:59.890 ⇒ 00:18:05.180 Hannah Wang: are also good. If if it’s there, if not, no worries.
138 00:18:08.910 ⇒ 00:18:11.489 Hannah Wang: just so that we kind of know.
139 00:18:15.680 ⇒ 00:18:24.139 Hannah Wang: yeah. So, for example, like a hackathon, obviously, we don’t have the bandwidth to attend that or participate in that. So
140 00:18:25.607 ⇒ 00:18:29.903 Hannah Wang: that wouldn’t go in like in person events.
141 00:18:30.810 ⇒ 00:18:35.680 Hannah Wang: But maybe it can be like, maybe another category can be like other
142 00:18:36.980 ⇒ 00:18:46.019 Hannah Wang: and that’s where maybe hackathons or those types of event of events. Can live in the in the Google sheet? Yeah.
143 00:18:51.780 ⇒ 00:18:55.489 Hannah Wang: okay, yeah. Do you have any more questions?
144 00:18:57.310 ⇒ 00:19:01.530 Casie Aviles: No, I think, yeah, this is good to start with, for now. If ever I have.
145 00:19:01.530 ⇒ 00:19:01.860 Hannah Wang: Okay.
146 00:19:01.860 ⇒ 00:19:04.849 Casie Aviles: More than I’ll just, you know. I’ll just send a message.
147 00:19:05.540 ⇒ 00:19:18.810 Hannah Wang: Okay, cool. And yeah, if anything’s not clear, yeah, feel free to ping me. But I think this will be a good start anything, any automation, even if it’s like the simplest one, I’m sure, is better than me just
148 00:19:19.050 ⇒ 00:19:35.009 Hannah Wang: manually doing this. So I also do like oh, in Austin. In la I sometimes check. Sf, no one lives in Sf. From our company. San Francisco, but
149 00:19:35.240 ⇒ 00:19:43.201 Hannah Wang: I’m sure if it’s like a big enough thing. They’re willing to fly to California, to San Francisco here.
150 00:19:44.310 ⇒ 00:19:46.830 Hannah Wang: So maybe I can also write that down.
151 00:19:51.230 ⇒ 00:19:56.029 Hannah Wang: So, Austin, maybe not even just Austin, maybe.
152 00:19:57.520 ⇒ 00:20:03.629 Hannah Wang: Texas, I don’t know if that’s too big. Hold on. Let me look at the map of Texas because it’s huge
153 00:20:07.995 ⇒ 00:20:10.100 Hannah Wang: yeah, awesome.
154 00:20:11.430 ⇒ 00:20:18.683 Hannah Wang: I forget how big Austin is or Texas is. It’s yeah. Okay. It’s a 2 h drive.
155 00:20:21.360 ⇒ 00:20:31.640 Hannah Wang: I still think that’s okay. So how about we do, Austin Houston, San Antonio.
156 00:20:32.310 ⇒ 00:20:35.919 Hannah Wang: These are all in Texas, and then New York.
157 00:20:36.170 ⇒ 00:20:42.430 Hannah Wang: which is obviously in New York. Actually, let me look up.
158 00:20:51.270 ⇒ 00:21:02.109 Hannah Wang: Yeah. So I don’t know if you know much about New York. But New York is also the State. Okay? Sure. So New York is a State. But New York is also a city within New York. So
159 00:21:02.860 ⇒ 00:21:06.240 Hannah Wang: that’s kind of confusing. So maybe I’ll specify.
160 00:21:06.930 ⇒ 00:21:15.719 Hannah Wang: like New York, the State not just the city, because within all of New York. Here
161 00:21:16.020 ⇒ 00:21:18.279 Hannah Wang: the city of New York is
162 00:21:19.100 ⇒ 00:21:22.241 Hannah Wang: different than like the city of Manhattan.
163 00:21:22.690 ⇒ 00:21:23.460 Casie Aviles: I see.
164 00:21:23.640 ⇒ 00:21:29.299 Hannah Wang: So yeah, there’s like, a yeah. New York city is different than
165 00:21:29.550 ⇒ 00:21:33.709 Hannah Wang: New York. So maybe the search can be like
166 00:21:34.020 ⇒ 00:21:39.329 Hannah Wang: in New York, not just New York City. So
167 00:21:39.860 ⇒ 00:21:41.799 Casie Aviles: New York is like broader, right?
168 00:21:42.030 ⇒ 00:22:05.779 Hannah Wang: Yes, New York is like, Yeah, broader. And New York is like Texas, for example. And then New York City is like Austin. The Austin is like a city within Texas. New York City is a city within New York. That’s like super confusing. But yeah, and then Los Angeles, San Francisco. So these are both cities within California.
169 00:22:06.000 ⇒ 00:22:09.199 Hannah Wang: So I think that’ll be a good place to start.
170 00:22:10.430 ⇒ 00:22:10.930 Casie Aviles: Okay.
171 00:22:11.310 ⇒ 00:22:11.930 Hannah Wang: Yeah.
172 00:22:12.290 ⇒ 00:22:19.480 Hannah Wang: Okay, okay, cool. I think that was everything I wanted to show you. I mean, yeah.
173 00:22:19.850 ⇒ 00:22:24.030 Hannah Wang: it’s just me clicking through a bunch of things and like adding stuff.
174 00:22:25.420 ⇒ 00:22:37.790 Hannah Wang: so, for example, this, I think, is also maybe something that we can add data. Bricks obviously, is like a data thing. And this is in San Francisco. So
175 00:22:38.030 ⇒ 00:22:41.609 Hannah Wang: oh, it’s next year. Never mind. So
176 00:22:41.880 ⇒ 00:22:47.459 Hannah Wang: yeah. So you kind of see like how I go through, and the process that I use and.
177 00:22:47.630 ⇒ 00:22:48.220 Casie Aviles: Yes.
178 00:22:48.220 ⇒ 00:22:50.209 Hannah Wang: Like that. Awesome. Okay.
179 00:22:50.420 ⇒ 00:23:02.070 Hannah Wang: cool. This was helpful. For me to just explain it to you and give you more context. And looking forward to the automation. Even if it’s super simple, it’ll be helpful.
180 00:23:02.400 ⇒ 00:23:04.249 Casie Aviles: Also very helpful. Thanks. Hannah.
181 00:23:04.470 ⇒ 00:23:06.629 Hannah Wang: Okay, yeah. Thank you. Have a good day.
182 00:23:07.020 ⇒ 00:23:07.690 Casie Aviles: Bye.
183 00:23:08.090 ⇒ 00:23:08.670 Hannah Wang: Bye.