Meeting Title: Friday Brainforge Demos & Retro Date: 2025-05-23 Meeting participants: Annie Yu, Mustafa Raja, Amber Lin, Miguel De Veyra, Casie Aviles, Raymund Verzosa, Awaish Kumar, Ryan Brosas
WEBVTT
1 00:02:28.340 ⇒ 00:02:32.969 Amber Lin: Hello, let me check. Who else needs to join?
2 00:02:37.520 ⇒ 00:02:47.379 Amber Lin: My assumption is that Utam and Robert both won’t be here because Utam’s on vacation, and Robert’s going to the airport, so it will be us.
3 00:02:47.610 ⇒ 00:02:53.450 Amber Lin: So today will be a more hopefully find shorter
4 00:02:53.980 ⇒ 00:02:56.380 Amber Lin: lighthearted meeting not much going on.
5 00:03:28.230 ⇒ 00:03:29.849 Amber Lin: How is everyone?
6 00:03:32.040 ⇒ 00:03:38.850 Amber Lin: And also, if you don’t mind joining me with the cameras, I feel a bit lonely with just me on the screen.
7 00:04:28.500 ⇒ 00:04:29.270 Awaish Kumar: Hello!
8 00:04:35.470 ⇒ 00:04:37.200 Awaish Kumar: How is it still? What you doing.
9 00:04:38.310 ⇒ 00:04:38.960 Amber Lin: No.
10 00:04:40.800 ⇒ 00:04:45.870 Awaish Kumar: Like, maybe we can discuss. What’s what are we going to do on the weekend.
11 00:04:46.873 ⇒ 00:04:51.380 Amber Lin: Okay, I mean, hannah had a
12 00:04:51.670 ⇒ 00:04:55.210 Amber Lin: and I had a fun game for
13 00:04:56.300 ⇒ 00:04:59.560 Amber Lin: for us. I guess we can do that.
14 00:04:59.890 ⇒ 00:05:02.840 Amber Lin: Maybe like, let me share my screen.
15 00:05:03.710 ⇒ 00:05:09.119 Amber Lin: We don’t have too much people today, so we don’t need probably don’t need a breakout room.
16 00:05:09.280 ⇒ 00:05:20.950 Amber Lin: And so we have a new team member. Let me see if you’re here. I think we have
17 00:05:21.620 ⇒ 00:05:24.360 Amber Lin: Ray. Who’s new?
18 00:05:25.575 ⇒ 00:05:29.700 Amber Lin: Ray, would you mind introducing yourself a little bit to everybody?
19 00:05:31.520 ⇒ 00:05:32.049 Raymund Verzosa: Hello!
20 00:05:32.850 ⇒ 00:05:36.990 Raymund Verzosa: I am Raymond Verzosa, but you can just call me Ray.
21 00:05:37.220 ⇒ 00:05:40.309 Raymund Verzosa: I am the new video editor.
22 00:05:40.680 ⇒ 00:05:43.339 Raymund Verzosa: Yeah, looking forward to working with you all.
23 00:05:43.740 ⇒ 00:05:45.490 Amber Lin: Where are you based in right.
24 00:05:46.410 ⇒ 00:05:48.479 Raymund Verzosa: Based in the Philippines.
25 00:05:48.480 ⇒ 00:05:49.740 Amber Lin: Hmm, okay.
26 00:05:52.670 ⇒ 00:06:02.460 Amber Lin: yeah. Let’s see. Okay, one more question. Do you want to tell us about your hobbies or what you like to do outside of work?
27 00:06:04.740 ⇒ 00:06:05.940 Raymund Verzosa: I just.
28 00:06:06.710 ⇒ 00:06:09.929 Raymund Verzosa: I don’t really have one at the moment, but
29 00:06:10.230 ⇒ 00:06:14.649 Raymund Verzosa: I just love watching, you know, Netflix, and stuff.
30 00:06:14.840 ⇒ 00:06:15.420 Amber Lin: Oh!
31 00:06:15.650 ⇒ 00:06:17.179 Raymund Verzosa: Binge, watching and.
32 00:06:17.180 ⇒ 00:06:18.900 Amber Lin: Recent show that you’re watching.
33 00:06:20.340 ⇒ 00:06:27.470 Raymund Verzosa: Right now. Oh, I’m currently watching wheel of time in Brame.
34 00:06:27.790 ⇒ 00:06:28.280 Amber Lin: Huh!
35 00:06:28.280 ⇒ 00:06:31.949 Raymund Verzosa: Yeah, about magic stuff. I have.
36 00:06:31.950 ⇒ 00:06:36.999 Amber Lin: Oh, interesting! Oh, this one!
37 00:06:37.400 ⇒ 00:06:38.010 Raymund Verzosa: Yeah.
38 00:06:38.340 ⇒ 00:06:40.180 Amber Lin: Oh, fun.
39 00:06:40.550 ⇒ 00:06:41.640 Amber Lin: Okay.
40 00:06:41.770 ⇒ 00:06:44.789 Amber Lin: Anyone else watching any shows recently.
41 00:06:45.290 ⇒ 00:06:47.719 Amber Lin: Oh, gosh! Where is this presentation?
42 00:06:50.000 ⇒ 00:06:51.386 Ryan Brosas: Watching, scrub.
43 00:06:52.640 ⇒ 00:06:55.890 Amber Lin: Hmm, bronze.
44 00:06:56.440 ⇒ 00:06:57.310 Ryan Brosas: Yeah.
45 00:06:58.710 ⇒ 00:07:01.080 Amber Lin: You’re lazy. Show.
46 00:07:01.600 ⇒ 00:07:02.430 Ryan Brosas: Yes.
47 00:07:05.910 ⇒ 00:07:11.830 Amber Lin: Oh, I’ll say it’s like a like a doctor show.
48 00:07:13.510 ⇒ 00:07:19.150 Amber Lin: Oh, I was really obsessed with house a few years ago.
49 00:07:19.300 ⇒ 00:07:23.839 Amber Lin: and I watched a lot of their episodes. Have you watched that before?
50 00:07:23.840 ⇒ 00:07:26.969 Ryan Brosas: Yeah, yeah, they’re pretty good.
51 00:07:28.070 ⇒ 00:07:36.132 Amber Lin: Sometimes I feel like I’ll get wrong information from these shows, and someone would die, because then I’ll not be able to show them. I’ll think I’m so good.
52 00:07:39.820 ⇒ 00:07:46.990 Amber Lin: Thank you, Ray, for introducing yourself, and let’s see.
53 00:07:48.360 ⇒ 00:07:58.539 Amber Lin: Okay, we’ll still do some emojis. Please drop it in the chat. Drop a few emojis. That’s either
54 00:07:58.890 ⇒ 00:08:05.459 Amber Lin: a word, a phrase, or, if you’re extra freaky, you can do a sentence, so
55 00:08:06.300 ⇒ 00:08:15.329 Amber Lin: just drop it in the chat. We’ll spend like 5, 10 min on this, and then all the other stuff is pretty quick. So I just want everybody to have some time together.
56 00:08:53.510 ⇒ 00:09:00.620 Amber Lin: Do I need to call on people. No, okay.
57 00:09:02.590 ⇒ 00:09:05.629 Amber Lin: So isn’t. Wasn’t that in the example?
58 00:09:11.380 ⇒ 00:09:12.620 Amber Lin: Oh, okay.
59 00:09:13.940 ⇒ 00:09:15.459 Awaish Kumar: What was the question like.
60 00:09:16.810 ⇒ 00:09:23.230 Amber Lin: Use emojis to make a phrase, a word, or a sentence.
61 00:09:24.650 ⇒ 00:09:25.680 Awaish Kumar: Oh, okay.
62 00:09:57.030 ⇒ 00:09:57.890 Amber Lin: alrighty.
63 00:09:58.280 ⇒ 00:09:59.140 Miguel de Veyra: Hey! Amber.
64 00:09:59.940 ⇒ 00:10:01.440 Amber Lin: Hi.
65 00:10:03.050 ⇒ 00:10:03.590 Miguel de Veyra: Yes.
66 00:10:03.960 ⇒ 00:10:06.100 Miguel de Veyra: Is that your new apartment already.
67 00:10:06.923 ⇒ 00:10:09.240 Amber Lin: No, this is Utam’s house.
68 00:10:09.730 ⇒ 00:10:15.429 Miguel de Veyra: Oh, okay, okay, wait. This is Arizona. I in Argentina.
69 00:10:15.430 ⇒ 00:10:25.089 Amber Lin: In Texas. I will go show you guys. Okay, do you guys want a house tour? He probably doesn’t want this to be shown. But he’s not here. This is his apartment.
70 00:10:26.980 ⇒ 00:10:31.209 Amber Lin: his house rented. This is his regular spot.
71 00:10:31.490 ⇒ 00:10:32.880 Miguel de Veyra: Oh, yeah. Yeah.
72 00:10:35.390 ⇒ 00:10:44.820 Amber Lin: And I live upstairs, but nobody is here except for me right now.
73 00:10:46.200 ⇒ 00:10:50.020 Miguel de Veyra: Oh, okay, going to move to New York.
74 00:10:50.637 ⇒ 00:10:55.809 Amber Lin: I wanted to. It’s not there my lease doesn’t end until August.
75 00:10:55.910 ⇒ 00:11:02.329 Amber Lin: So I already spent a week in spent a week in house.
76 00:11:03.060 ⇒ 00:11:10.789 Amber Lin: And yeah, and then maybe later, I’ll go to. I’m probably visiting New York later.
77 00:11:11.050 ⇒ 00:11:11.990 Amber Lin: But.
78 00:11:12.930 ⇒ 00:11:15.458 Miguel de Veyra: Don’t know if I’m here yet.
79 00:11:15.880 ⇒ 00:11:18.140 Miguel de Veyra: Oh, yeah, that’s why you met the ABC.
80 00:11:18.430 ⇒ 00:11:21.529 Amber Lin: Yeah, yeah, I was there with the ABC. Folks
81 00:11:21.760 ⇒ 00:11:25.430 Amber Lin: in their office. It was. It was very interesting
82 00:11:25.800 ⇒ 00:11:28.079 Amber Lin: to be in person with them.
83 00:11:30.150 ⇒ 00:11:43.940 Amber Lin: Oh, okay. Annie sent crimson shrimp, shrimp, sun fried shrimp tempura sun. Oh.
84 00:11:43.940 ⇒ 00:11:44.439 Miguel de Veyra: Are we doing?
85 00:11:44.440 ⇒ 00:11:53.840 Amber Lin: We’re guessing. We’re guessing words, phrases, phrases, or sentences from emojis.
86 00:11:54.500 ⇒ 00:12:00.150 Miguel de Veyra: And the 1st 2 was very nice, and then Annie said, I don’t know what that is.
87 00:12:03.670 ⇒ 00:12:04.600 Amber Lin: Ops.
88 00:12:05.100 ⇒ 00:12:07.011 Annie Yu: Let me know if you want the answer.
89 00:12:07.250 ⇒ 00:12:08.115 Amber Lin: No
90 00:12:11.340 ⇒ 00:12:17.920 Amber Lin: Well, Ryan sent rock paper scissors, and then I don’t know.
91 00:12:18.450 ⇒ 00:12:20.510 Miguel de Veyra: You know what those this AI.
92 00:12:21.390 ⇒ 00:12:22.240 Amber Lin: Oh.
93 00:12:25.540 ⇒ 00:12:26.700 Amber Lin: toast!
94 00:12:27.700 ⇒ 00:12:28.680 Amber Lin: Who?
95 00:12:31.880 ⇒ 00:12:34.150 Miguel de Veyra: Not. Yeah, it’s not good. It’s not good.
96 00:12:34.150 ⇒ 00:12:38.479 Amber Lin: Okay, okay. Does anyone want to guess at Annie’s emojis?
97 00:12:44.140 ⇒ 00:12:45.460 Miguel de Veyra: Little Day.
98 00:12:48.640 ⇒ 00:12:53.470 Amber Lin: Like, what does the sun mean? Does it mean actually sun or a day.
99 00:12:59.130 ⇒ 00:13:00.240 Annie Yu: You’re close.
100 00:13:11.530 ⇒ 00:13:13.019 Annie Yu: Okay. I’m gonna review.
101 00:13:13.020 ⇒ 00:13:14.299 Amber Lin: Okay. Yes. Please.
102 00:13:14.300 ⇒ 00:13:15.980 Annie Yu: It’s Friday.
103 00:13:16.780 ⇒ 00:13:18.369 Amber Lin: Oh, my! Gosh!
104 00:13:18.370 ⇒ 00:13:19.170 Miguel de Veyra: Whole fry.
105 00:13:21.600 ⇒ 00:13:24.829 Amber Lin: Because the 1st one is fried. Okay.
106 00:13:26.220 ⇒ 00:13:36.183 Amber Lin: okay? Anyone has a guess at Ryan’s emojis raw paper. I think it’s raw paper scissors, something something.
107 00:13:45.810 ⇒ 00:13:46.770 Miguel de Veyra: Naruto.
108 00:13:48.700 ⇒ 00:13:49.470 Amber Lin: Hmm.
109 00:13:49.800 ⇒ 00:13:51.560 Miguel de Veyra: Row, paper, Scissor.
110 00:13:52.870 ⇒ 00:13:57.993 Ryan Brosas: If you watch a big Bang theory, it’s a reference to that.
111 00:13:59.430 ⇒ 00:14:01.371 Miguel de Veyra: I don’t know. I don’t.
112 00:14:03.620 ⇒ 00:14:04.720 Amber Lin: Hmm!
113 00:14:05.000 ⇒ 00:14:06.840 Amber Lin: I have no clue.
114 00:14:07.600 ⇒ 00:14:09.180 Amber Lin: Please reveal.
115 00:14:09.950 ⇒ 00:14:11.290 Amber Lin: Save us.
116 00:14:15.940 ⇒ 00:14:17.780 Amber Lin: Ryan. What is it.
117 00:14:18.510 ⇒ 00:14:22.370 Ryan Brosas: Drop paper, these are these are spot.
118 00:14:24.390 ⇒ 00:14:26.220 Amber Lin: Lizard, spot.
119 00:14:27.070 ⇒ 00:14:28.350 Ryan Brosas: Users call.
120 00:14:28.850 ⇒ 00:14:29.950 Amber Lin: Huh?
121 00:14:31.240 ⇒ 00:14:32.560 Amber Lin: Huh?
122 00:14:33.360 ⇒ 00:14:36.420 Amber Lin: I see that’s a reference that I do not know.
123 00:14:40.260 ⇒ 00:14:41.020 Amber Lin: Okay, anyway.
124 00:14:41.020 ⇒ 00:14:42.000 Casie Aviles: Star, trek.
125 00:14:45.800 ⇒ 00:14:47.129 Amber Lin: I can.
126 00:14:47.330 ⇒ 00:15:00.219 Amber Lin: I’ll do a quick round of updates, just like I’ll take 1 min. This week we had a brain forge event with Paul atomic on Monday in Austin took photos for that, and then for the different
127 00:15:00.330 ⇒ 00:15:04.160 Amber Lin: client hubs, I think urban stems we sent a
128 00:15:04.400 ⇒ 00:15:17.970 Amber Lin: I don’t know if you guys know all these clients. But anyways, urban styles, we sent a renewal. I think they’re gonna sign it hopefully. They’re gonna sign it. It sounded like. They’re just negotiating the prices now, so that should be good.
129 00:15:19.860 ⇒ 00:15:25.769 Amber Lin: Eden, I think they’re negotiating a expansion of the contract from like
130 00:15:26.000 ⇒ 00:15:29.179 Amber Lin: I don’t know if it’s 15 or 20 K to like 30,
131 00:15:29.330 ⇒ 00:15:34.400 Amber Lin: full part is going well, it’s it’s just there. The clients is
132 00:15:34.830 ⇒ 00:15:44.250 Amber Lin: the clients. It’s just been always been like that. ABC. Is good cause I met them in person, madam, or Oh, gosh!
133 00:15:44.906 ⇒ 00:15:56.640 Amber Lin: I think I think we’re going well, I guess I got scolded by the client, and Annie knows this. The client got really frustrated, but I think, overall it. It’s fine.
134 00:15:56.980 ⇒ 00:15:58.409 Amber Lin: And then
135 00:15:58.690 ⇒ 00:16:05.579 Amber Lin: off. The record is pretty good. We’re continuing. Anyways, that’s all the updates. That’s a photo of ABC,
136 00:16:06.621 ⇒ 00:16:10.060 Amber Lin: don’t think we have anything new here.
137 00:16:12.060 ⇒ 00:16:20.410 Amber Lin: Can I get a quick round of shout outs from everybody of who you appreciate.
138 00:16:20.690 ⇒ 00:16:25.250 Amber Lin: This week it was important to let your.
139 00:16:25.250 ⇒ 00:16:29.059 Awaish Kumar: We submitted through the slack workflows. Can we see them.
140 00:16:29.350 ⇒ 00:16:31.340 Amber Lin: Yes, let me go find them.
141 00:16:32.970 ⇒ 00:16:36.620 Amber Lin: Oh, wait! Let me find Hannah.
142 00:16:37.810 ⇒ 00:16:43.829 Amber Lin: Here, there we go. Is that the wrong right one?
143 00:16:46.190 ⇒ 00:16:47.060 Amber Lin: Oh.
144 00:16:49.420 ⇒ 00:16:50.360 Awaish Kumar: That’s interesting.
145 00:16:50.790 ⇒ 00:16:51.790 Amber Lin: Home.
146 00:16:52.020 ⇒ 00:16:54.260 Amber Lin: Wait! Give me a quick second.
147 00:17:02.660 ⇒ 00:17:05.770 Amber Lin: Maybe it’s a notion. Shout out.
148 00:17:14.710 ⇒ 00:17:18.530 Amber Lin: Oh, is it here?
149 00:17:19.390 ⇒ 00:17:20.996 Amber Lin: Oh, no!
150 00:17:31.140 ⇒ 00:17:38.940 Amber Lin: Can everyone just say it if they remember. I’m trying to find this. This notion, Doc.
151 00:17:39.540 ⇒ 00:17:41.310 Amber Lin: but I can’t find it.
152 00:18:08.190 ⇒ 00:18:09.080 Amber Lin: Oh.
153 00:18:18.050 ⇒ 00:18:19.463 Amber Lin: the genuine
154 00:18:20.590 ⇒ 00:18:24.160 Amber Lin: Wanna just save it, perhaps.
155 00:18:26.310 ⇒ 00:18:31.180 Casie Aviles: Yeah, I’ll I’ll go. Yeah, I’ll just shout out Mustafa.
156 00:18:32.110 ⇒ 00:18:34.399 Casie Aviles: He’s taken on some of our.
157 00:18:34.820 ⇒ 00:18:41.080 Casie Aviles: you know, clean up on the AI team side, like our technical debt and stuff. And yeah, he’s been
158 00:18:41.820 ⇒ 00:18:45.420 Casie Aviles: yeah, he’s he’s done pretty well working with us.
159 00:18:47.880 ⇒ 00:18:49.030 Amber Lin: Yay!
160 00:18:49.560 ⇒ 00:18:51.490 Casie Aviles: So yeah, shout out to Mustafa.
161 00:18:52.110 ⇒ 00:18:53.070 Mustafa Raja: Thank you.
162 00:18:57.520 ⇒ 00:19:00.079 Amber Lin: Awesome anyone else.
163 00:19:16.980 ⇒ 00:19:31.729 Amber Lin: Well, I will! Shout out Annie. We got we got we were facing some frustrated clients that single her out in the meeting. I was like Annie. I like the client would not let me talk, but so the pressure kind of
164 00:19:31.730 ⇒ 00:19:47.009 Amber Lin: was faced by Annie, and then afterwards a client called me 2 times, and it was like, You’re not doing your job. It’s like this is not working. They were very frustrated, and then we had to act really fast and give them some output
165 00:19:47.170 ⇒ 00:19:51.899 Amber Lin: this this week. So thank you, Annie, for
166 00:19:53.420 ⇒ 00:19:59.039 Amber Lin: working extra hours and working extra hard to get that done. So that was really awesome.
167 00:19:59.770 ⇒ 00:20:00.450 Amber Lin: Okay?
168 00:20:01.690 ⇒ 00:20:07.410 Amber Lin: Also, I know Robert and Wujang is not there, but I appreciate them for being my emotional outlet
169 00:20:07.750 ⇒ 00:20:12.650 Amber Lin: like. Oh, that was the 1st time of getting an angry client for me. And I was like, Wow.
170 00:20:13.640 ⇒ 00:20:30.540 Amber Lin: like, I know this would happen. But I didn’t think this would trigger my childhood trauma of being an Asian kid and never being enough, I was like, Oh, shit! This is not about a client. I’m not crying about the climb, crying about something else. So that was something this week.
171 00:20:31.580 ⇒ 00:20:35.939 Amber Lin: It’s really nice to have people to talk to after that happened.
172 00:20:36.490 ⇒ 00:20:37.840 Amber Lin: So that’s from me
173 00:20:49.810 ⇒ 00:20:58.150 Annie Yu: Yeah, I also have shout outs definitely shouting out, amber like this girl has like
174 00:20:58.850 ⇒ 00:21:08.840 Annie Yu: patience that I absolutely don’t. So I think a lot of time. My like face to my face shows a lot of what I feel. But amber is like.
175 00:21:09.540 ⇒ 00:21:10.600 Annie Yu: always
176 00:21:11.400 ⇒ 00:21:23.780 Annie Yu: calm and composed, and that’s something I really like, admire her about, and also wanted to shout out, Robert, I just don’t know like this guy does it? But he’s like.
177 00:21:24.460 ⇒ 00:21:27.460 Annie Yu: Always such a good stop partner.
178 00:21:27.760 ⇒ 00:21:36.090 Annie Yu: including things on like higher level or a very like granular detail. So really appreciate the teammates here.
179 00:21:37.740 ⇒ 00:21:43.595 Amber Lin: Yay, I figured this out. Thank you, Hannah, who is not here yet?
180 00:21:44.590 ⇒ 00:21:47.300 Amber Lin: so we have this from last week.
181 00:21:52.310 ⇒ 00:21:55.290 Amber Lin: Row or overshad.
182 00:21:55.680 ⇒ 00:22:01.349 Amber Lin: Okay, yeah. And also, I, I think this is from, I don’t know. Like, a week ago
183 00:22:01.450 ⇒ 00:22:27.570 Amber Lin: Ryan was working very, very hard extra hours to edit the video. So I’m very glad that we have new members to help him with that. I know Ryan’s also doing the go to market automation stuff. So a lot of stuff on his plate. This man’s really leading and making an impact and just doing, taking on all these projects and just learning along the way. So really, really appreciate that we have someone like you in our
184 00:22:27.920 ⇒ 00:22:29.250 Amber Lin: in our org.
185 00:22:32.430 ⇒ 00:22:36.750 Amber Lin: And oh, did that?
186 00:22:37.700 ⇒ 00:22:47.369 Amber Lin: Oh, and wish I’ll let you I’ll let you speak. Your shout outs to our dear colleagues.
187 00:22:48.707 ⇒ 00:22:54.279 Awaish Kumar: Yeah. So my shout out shout out is to the all the members of
188 00:22:56.160 ⇒ 00:22:59.635 Awaish Kumar: data platform team and
189 00:23:00.580 ⇒ 00:23:07.800 Awaish Kumar: like any, for like she part started participating in some technical work on Meta plan
190 00:23:08.631 ⇒ 00:23:14.326 Awaish Kumar: shout out to Luke, like he has been actively working on this stuff and has been
191 00:23:15.882 ⇒ 00:23:20.307 Awaish Kumar: in talks with the Meta plan team. So definitely is
192 00:23:20.820 ⇒ 00:23:23.639 Awaish Kumar: trying hard to to have this tool
193 00:23:23.750 ⇒ 00:23:31.469 Awaish Kumar: for us to monitor all our data and and keep our clients, you know, like, get happy.
194 00:23:33.610 ⇒ 00:23:38.810 Awaish Kumar: Also to Kyle. He has been doing good work on organizing the documentation
195 00:23:39.490 ⇒ 00:23:43.000 Awaish Kumar: and the like, the lineage sheets which we are building
196 00:23:43.370 ⇒ 00:23:48.600 Awaish Kumar: for our AI agents and also for our clients.
197 00:23:49.610 ⇒ 00:23:58.420 Awaish Kumar: And yeah, definitely to damalade. He actually has been. He has helped me with some client
198 00:23:59.184 ⇒ 00:24:00.999 Awaish Kumar: work like where we
199 00:24:01.180 ⇒ 00:24:10.290 Awaish Kumar: had some order enhancement work where we we have some concerns. So he has been trying to resolve those.
200 00:24:11.120 ⇒ 00:24:14.599 Awaish Kumar: Yeah. So yeah, shout out to double a day as well.
201 00:24:15.360 ⇒ 00:24:16.400 Amber Lin: Yeah.
202 00:24:17.160 ⇒ 00:24:26.740 Amber Lin: that was really nice to hear everything going on well. And since we talked about like data platform, where can I ask about how the other projects are doing like, how’s the
203 00:24:26.950 ⇒ 00:24:33.759 Amber Lin: I guess? How’s the AI team doing? I know we’re working on a lot of Demos go in case you want to give us like a
204 00:24:33.940 ⇒ 00:24:37.719 Amber Lin: like 10 second or 30 second overview of what’s going on.
205 00:24:43.431 ⇒ 00:24:49.590 Casie Aviles: Yeah. So I think Miguel and the design team has been working on the
206 00:24:50.560 ⇒ 00:24:56.220 Casie Aviles: I think it. This is the demo page that they’ve they’ve been working on. So I think that’s them the one we could.
207 00:24:57.210 ⇒ 00:24:59.550 Miguel de Veyra: Yeah, sorry guys, we have.
208 00:24:59.550 ⇒ 00:25:00.230 Miguel de Veyra: Yeah, I guess
209 00:25:00.230 ⇒ 00:25:05.269 Miguel de Veyra: stuff we need to change up right now because it’s going live. I’m working on some stuff.
210 00:25:05.270 ⇒ 00:25:15.290 Amber Lin: Oh, I see, that’s okay. It’s good to hear that something’s going live, and then we’ll all get to test test our internal agent, because we haven’t had a chance to play with them yet.
211 00:25:16.180 ⇒ 00:25:20.540 Casie Aviles: Yeah, but just looking at the demo page, I think it looks really good right now.
212 00:25:20.830 ⇒ 00:25:30.450 Amber Lin: Yeah, can you share screen to just let us have a peek? I know we probably can’t use it yet, but if there’s anything we can see that would be very exciting.
213 00:25:32.900 ⇒ 00:25:35.180 Casie Aviles: Yeah, pretty much. It’s this one.
214 00:25:35.180 ⇒ 00:25:37.430 Amber Lin: Wow!
215 00:25:38.020 ⇒ 00:25:39.970 Casie Aviles: Yeah. And over the course of
216 00:25:40.220 ⇒ 00:25:51.729 Casie Aviles: working with Brainforge. A lot of these agents are the ones we’ve worked on for past clients and also internally, like one of the earliest agents we worked on was the lead research agent. And then
217 00:25:52.210 ⇒ 00:25:56.239 Casie Aviles: yep. And then we have this for ABC right, the knowledge creation agent.
218 00:25:56.240 ⇒ 00:25:58.790 Amber Lin: What this is, so cool.
219 00:25:59.350 ⇒ 00:26:00.363 Casie Aviles: Yeah, and
220 00:26:01.200 ⇒ 00:26:11.919 Amber Lin: It looks honestly, it looks so much better than what we had before like. I don’t. I don’t know if we have the before page. But people should see how different this looks.
221 00:26:12.440 ⇒ 00:26:19.679 Casie Aviles: Yeah, this, this is good good work to the to Miguel and the designers work on this, I think Alim and Ann. So
222 00:26:20.592 ⇒ 00:26:24.100 Casie Aviles: yeah, and then and then you could, you know, interact with the.
223 00:26:24.740 ⇒ 00:26:25.130 Amber Lin: Oh!
224 00:26:25.130 ⇒ 00:26:30.999 Casie Aviles: There’s just some changes here that needs to happen. But I think, yeah, we can
225 00:26:31.150 ⇒ 00:26:33.170 Casie Aviles: talk to each of the agents here.
226 00:26:33.390 ⇒ 00:26:38.826 Casie Aviles: But yeah, I get. I I think that’s it for the that, the this website,
227 00:26:39.500 ⇒ 00:26:44.749 Casie Aviles: internally, we just we’re just continuing work on the client of agents. We have been
228 00:26:45.601 ⇒ 00:26:47.639 Casie Aviles: working more on the back end side
229 00:26:48.510 ⇒ 00:26:54.989 Casie Aviles: like with the data. So we’re really making sure that we have the data, we have it clean. And the agents could use it.
230 00:26:56.278 ⇒ 00:26:58.789 Casie Aviles: What else? But yeah. And also.
231 00:26:58.790 ⇒ 00:27:00.009 Miguel de Veyra: What’s just the other thing.
232 00:27:00.800 ⇒ 00:27:03.260 Casie Aviles: Yeah, you could go. Do you want to show that.
233 00:27:05.130 ⇒ 00:27:06.509 Casie Aviles: Or do you want to talk about it?
234 00:27:06.750 ⇒ 00:27:19.480 Miguel de Veyra: Yeah, I think we can just talk about it, for now I cause it was live. It went live, I think, last week at least we deployed it on Heroku. And then this week we’re like, I started working on yesterday the image thing amber.
235 00:27:19.720 ⇒ 00:27:20.970 Amber Lin: Oh, okay.
236 00:27:21.170 ⇒ 00:27:27.049 Miguel de Veyra: So yeah, that one. Yeah, I don’t want to deploy it today, because of course, we we haven’t really qaed it.
237 00:27:27.180 ⇒ 00:27:31.199 Miguel de Veyra: And I checked the pptg.ai. They haven’t really deployed it tighter.
238 00:27:31.200 ⇒ 00:27:38.300 Amber Lin: Yeah, they’re really slow, so I wouldn’t rush you on any and like on your end if if you have other stuff just like.
239 00:27:38.700 ⇒ 00:27:42.140 Miguel de Veyra: Okay. But yeah, I’m already working on it. Tuesday. We should be able to have.
240 00:27:42.140 ⇒ 00:27:43.760 Amber Lin: Okay, okay. Sounds good.
241 00:27:44.510 ⇒ 00:27:50.399 Amber Lin: Yay, oh, Ryan, you had something you want to demo right
242 00:27:50.962 ⇒ 00:27:56.279 Amber Lin: on the was it on the content, or was it on our sales? Go to market.
243 00:27:57.970 ⇒ 00:28:09.559 Ryan Brosas: It’s pretty much the progress of our well, the progress of the traffic on thinking account. So, yeah, I can share my screen.
244 00:28:20.660 ⇒ 00:28:44.405 Ryan Brosas: So yeah. I think. We’re getting like a good progress on our Linkedin campaign and from the previous like initiative from outcome, that is, you know, going to events, to events and heading the the content creation, or the content that we are
245 00:28:45.120 ⇒ 00:29:08.180 Ryan Brosas: doing which is attending to events and like doing interview. And yeah, I think the last event that we did is the atomic. And I think it’s really it. It’s giving us a boost on the algorithm on Linkedin. And we got like a 2,000 plus impression
246 00:29:09.970 ⇒ 00:29:18.355 Ryan Brosas: on on engagement, and hopefully that we can apply this to the front line of of of you know, Rainforge, like
247 00:29:18.830 ⇒ 00:29:38.280 Ryan Brosas: Robert’s account. And you know, Amber’s account also. And yeah, we, I’m still like learning. What’s the what’s the ups and down of the linking algorithm? But yeah, we are with with showing more on on the industry. I think that’s pretty much
248 00:29:38.280 ⇒ 00:29:51.799 Ryan Brosas: how we break the algorithm is showing more expertise and showing the whole like, we are a human. And you, you know, sharing the experience, or like sharing the brand through. This is really helpful for us.
249 00:29:51.880 ⇒ 00:29:54.969 Ryan Brosas: I think that’s all, for for on this progress
250 00:29:55.060 ⇒ 00:29:56.930 Ryan Brosas: and have a great day. You guys.
251 00:29:57.500 ⇒ 00:30:17.650 Amber Lin: Yeah. And I was thinking, because once we know how to do all of this, I I don’t know if you guys also want to post on Linkedin. But say, Ryan knows a lot about how to grow your account, get impressions. So if anyone wants any advice on growing your Linkedin presence, because
252 00:30:17.690 ⇒ 00:30:29.539 Amber Lin: to be completely honest and to be I know Robert is not here like that’s we’re gonna is we’re gonna is what that is. What is going to stay with you
253 00:30:29.900 ⇒ 00:30:38.689 Amber Lin: like your professional presence. So if you we we already have resources of ha developing that. And if you wanna
254 00:30:39.320 ⇒ 00:30:43.620 Amber Lin: grow publicly like. That’s something that
255 00:30:44.050 ⇒ 00:30:56.699 Amber Lin: is really beneficial to you, and also would be really beneficial to the company. So then, Uta Robert is very willing to invest in that because that helps our sales, and that helps you. So if anyone wants
256 00:30:56.720 ⇒ 00:31:15.110 Amber Lin: to even post like once a month or help interact, if have something you want us to help you repost and gain more visibility. I know we’re helping Kyle with that. I’m getting help with that. And so just let let us know, and I think that’s something that’s
257 00:31:15.470 ⇒ 00:31:19.939 Amber Lin: like a extra perk or extra benefit that you can take with you later on as well.
258 00:31:21.760 ⇒ 00:31:23.649 Amber Lin: So that’s all I have to say
259 00:31:26.310 ⇒ 00:31:32.199 Amber Lin: I wouldn’t love to ask everybody about their plans. But if you want, if you think this is like a
260 00:31:32.730 ⇒ 00:31:36.143 Amber Lin: maybe we can take the day off earlier.
261 00:31:39.153 ⇒ 00:31:40.620 Amber Lin: Let’s see.
262 00:31:49.570 ⇒ 00:31:53.359 Amber Lin: Okay, anyone have cool plans this weekend.
263 00:31:59.360 ⇒ 00:32:00.349 Miguel de Veyra: Which are 3.
264 00:32:01.260 ⇒ 00:32:01.910 Amber Lin: No.
265 00:32:03.270 ⇒ 00:32:04.889 Miguel de Veyra: I’m going back to my roots.
266 00:32:06.698 ⇒ 00:32:10.190 Miguel de Veyra: Yeah, yeah, I’m just gonna play. I guess this weekend. And then maybe
267 00:32:11.180 ⇒ 00:32:15.379 Miguel de Veyra: I don’t know if we’ll go golfing again, but hopefully, not because it’s still hot.
268 00:32:16.170 ⇒ 00:32:16.780 Miguel de Veyra: That’s.
269 00:32:16.780 ⇒ 00:32:18.810 Amber Lin: One, you know
270 00:32:18.810 ⇒ 00:32:35.249 Amber Lin: I’ve never really golfed before. Sometimes I don’t understand why you want to be on this empty, empty field and swing swing a ball at and not see what’s going on. But that’s probably because I’ve not played played before, and I just really don’t know like where the ball even is.
271 00:32:35.610 ⇒ 00:32:36.759 Miguel de Veyra: Also hurts a lot.
272 00:32:38.930 ⇒ 00:32:40.789 Miguel de Veyra: I don’t know my dad likes it.
273 00:32:41.700 ⇒ 00:32:43.140 Amber Lin: Sounds very fun.
274 00:32:43.840 ⇒ 00:32:50.030 Amber Lin: Well, I’m gonna explore Austin. Because why not?
275 00:32:50.480 ⇒ 00:32:59.510 Amber Lin: Because the plane tickets were paid for so. And I’m not paying for this house. So I’m gonna go spend money on something else.
276 00:33:01.690 ⇒ 00:33:06.569 Amber Lin: And anyone else have interesting plans. This weekend.
277 00:33:14.770 ⇒ 00:33:19.380 Amber Lin: Okay? Well, everybody. Thank you for coming to this meeting.
278 00:33:19.710 ⇒ 00:33:27.159 Amber Lin: We ended early, so I appreciate you all, and have a really really good weekend.
279 00:33:27.610 ⇒ 00:33:28.530 Miguel de Veyra: Thanks. Everyone.
280 00:33:28.530 ⇒ 00:33:30.300 Annie Yu: Thank you. Have a good one.
281 00:33:30.920 ⇒ 00:33:32.340 Amber Lin: Bye.