Meeting Title: Personal Meeting Room Date: 2025-01-24 Meeting participants: Mariane Cequina, Anne, Nicolas Sucari, Uttam Kumaran, Robert Tseng, Miguel De Veyra, Casie Aviles, Ryan Luke Daque, Sahana Asokan, Connor Fenn
WEBVTT
1 00:00:31.910 ⇒ 00:00:32.880 Uttam Kumaran: Hey, guys.
2 00:00:35.020 ⇒ 00:00:35.779 Ryan Luke Daque: Hello! Hello!
3 00:00:36.420 ⇒ 00:00:37.250 Uttam Kumaran: Hey?
4 00:00:38.430 ⇒ 00:00:40.880 Uttam Kumaran: Just give me one sec. I’m gonna go get a coffee.
5 00:02:11.430 ⇒ 00:02:15.250 Uttam Kumaran: You can kick things off. Luke. Feel free.
6 00:02:15.770 ⇒ 00:02:18.019 Ryan Luke Daque: Yeah. Hello, yeah. Hello. Everyone.
7 00:02:18.200 ⇒ 00:02:21.209 Ryan Luke Daque: Happy Friday. Is everyone here? By the way,
8 00:02:23.030 ⇒ 00:02:25.149 Ryan Luke Daque: are we waiting for anybody else?
9 00:02:25.750 ⇒ 00:02:28.200 Ryan Luke Daque: I think maybe Ryan.
10 00:02:30.150 ⇒ 00:02:33.529 Ryan Luke Daque: But yeah, I guess we can start. So yeah, good
11 00:02:33.850 ⇒ 00:02:39.870 Ryan Luke Daque: Friday, everyone happy. Friday. I’ve been tasked by Utam to lead today’s
12 00:02:41.260 ⇒ 00:02:48.450 Ryan Luke Daque: Friday session. Basically, yeah. So maybe before we start we can do
13 00:02:48.902 ⇒ 00:02:52.577 Ryan Luke Daque: an icebreaker. I know it’s been a busy week for everyone. So let’s
14 00:02:52.990 ⇒ 00:02:54.950 Ryan Luke Daque: maybe lighten up the mood a bit.
15 00:02:56.600 ⇒ 00:03:01.010 Ryan Luke Daque: Yeah, let me share a link. This is basically a poll.
16 00:03:03.850 ⇒ 00:03:08.390 Ryan Luke Daque: And this is like a mini like.
17 00:03:08.510 ⇒ 00:03:15.110 Ryan Luke Daque: I. I don’t know if it’s a game. It’s just like, basically, it’s a poll where we would be
18 00:03:16.990 ⇒ 00:03:18.950 Ryan Luke Daque: putting emojis.
19 00:03:19.490 ⇒ 00:03:23.520 Ryan Luke Daque: So we’ll be sharing emojis that describe best describe our week.
20 00:03:25.340 ⇒ 00:03:32.190 Ryan Luke Daque: Based on the based on these 3 categories, like maybe one Emoji that describes your
21 00:03:32.410 ⇒ 00:03:35.969 Ryan Luke Daque: week at work, and then another outside work.
22 00:03:36.250 ⇒ 00:03:43.039 Ryan Luke Daque: and then a 3rd one that’s like a wild card, like something random or like unexpected. That happened
23 00:03:43.893 ⇒ 00:03:46.439 Ryan Luke Daque: to your week. I think this would be great.
24 00:03:46.650 ⇒ 00:03:48.739 Uttam Kumaran: Just in order like 1, 2, 3.
25 00:03:49.140 ⇒ 00:03:53.450 Ryan Luke Daque: Yeah, in any order. Just 3 emojis that describe your week.
26 00:03:54.440 ⇒ 00:03:56.979 Uttam Kumaran: I’ll I can. I can maybe start it.
27 00:03:57.140 ⇒ 00:03:58.000 Ryan Luke Daque: See?
28 00:03:59.110 ⇒ 00:04:02.239 Ryan Luke Daque: Can you see the link? By the way, like, are you able to.
29 00:04:02.660 ⇒ 00:04:03.220 Uttam Kumaran: Yeah, yeah.
30 00:04:04.320 ⇒ 00:04:15.140 Ryan Luke Daque: Cool, and maybe we can describe it after, like, once we complete 3 emojis.
31 00:04:21.490 ⇒ 00:04:24.270 Ryan Luke Daque: Let’s give ourselves. Let me maybe a minute or 2,
32 00:04:32.240 ⇒ 00:04:37.640 Ryan Luke Daque: and once you add you can like add a response after.
33 00:04:39.690 ⇒ 00:04:40.400 Mariane Cequina: Oregon.
34 00:04:43.640 ⇒ 00:04:45.829 Uttam Kumaran: Outside work. What is that.
35 00:04:59.080 ⇒ 00:05:00.510 Ryan Luke Daque: Let’s see.
36 00:05:02.040 ⇒ 00:05:05.090 Ryan Luke Daque: Yeah, I made this up. I I didn’t even prepare
37 00:05:05.560 ⇒ 00:05:08.020 Ryan Luke Daque: what emoji I was just adding.
38 00:05:19.340 ⇒ 00:05:20.110 Uttam Kumaran: Okay.
39 00:05:36.010 ⇒ 00:05:40.059 Ryan Luke Daque: And let’s see if, like anybody, take the same emoji or something.
40 00:05:45.980 ⇒ 00:05:50.020 Robert Tseng: Oh, I didn’t put all of them in the same row. I did 3 separate rows.
41 00:05:50.635 ⇒ 00:05:52.470 Ryan Luke Daque: That’s fine. Yeah, I did 3.
42 00:05:52.470 ⇒ 00:05:53.380 Uttam Kumaran: Come on!
43 00:05:55.340 ⇒ 00:05:59.397 Robert Tseng: I thought that was the I mean. Why would they give you the option to break it up more than.
44 00:06:05.820 ⇒ 00:06:15.166 Uttam Kumaran: Why, it’s a lot of like sick and like throw up and brain exploding.
45 00:06:19.010 ⇒ 00:06:22.899 Ryan Luke Daque: Yeah. Has everyone like added their emojis.
46 00:06:23.500 ⇒ 00:06:28.980 Ryan Luke Daque: Looks like nobody picked the same emoji for something cool.
47 00:06:37.980 ⇒ 00:06:38.520 Miguel de Veyra: Hmm!
48 00:06:38.710 ⇒ 00:06:39.920 Miguel de Veyra: Who’s the spy?
49 00:06:42.420 ⇒ 00:06:47.889 Nicolas Sucari: It was me, but it was not like a spy. It was my most more kind of a detective.
50 00:06:48.300 ⇒ 00:06:49.109 Miguel de Veyra: Oh, I see!
51 00:06:54.010 ⇒ 00:06:58.419 Robert Tseng: Dang! Who’s who’s sipping cocktails? I wanna be I wanna be over there.
52 00:07:01.000 ⇒ 00:07:01.609 Ryan Luke Daque: And there’s a.
53 00:07:01.830 ⇒ 00:07:02.690 Nicolas Sucari: If you could.
54 00:07:02.690 ⇒ 00:07:06.910 Uttam Kumaran: Oh, it’s a year. Yeah, I guess. What is a yerba gourd?
55 00:07:07.830 ⇒ 00:07:08.580 Robert Tseng: Yeah.
56 00:07:11.690 ⇒ 00:07:15.010 Ryan Luke Daque: I guess everyone already provided their emojis.
57 00:07:15.460 ⇒ 00:07:20.650 Ryan Luke Daque: Anybody care to like share like first, st like which.
58 00:07:21.000 ⇒ 00:07:22.820 Nicolas Sucari: Emojis. They chose.
59 00:07:24.800 ⇒ 00:07:26.159 Ryan Luke Daque: I can start, I guess.
60 00:07:26.160 ⇒ 00:07:27.290 Uttam Kumaran: Okay, yeah. Go ahead.
61 00:07:27.787 ⇒ 00:07:29.280 Ryan Luke Daque: So I did.
62 00:07:30.413 ⇒ 00:07:34.526 Ryan Luke Daque: I I did send in 3 separate emojis. So 1st one would be the
63 00:07:34.930 ⇒ 00:07:37.150 Ryan Luke Daque: the one, the exploding brain.
64 00:07:37.750 ⇒ 00:07:45.290 Ryan Luke Daque: Yeah, that’s that’s the work. One. Because, I believe this was the the busiest week ever in Brain Forge for at least for me, like I’ve been.
65 00:07:45.560 ⇒ 00:07:46.280 Uttam Kumaran: For me, too.
66 00:07:48.520 ⇒ 00:07:51.889 Uttam Kumaran: Yeah, one of the top weeks. I feel like, definitely in terms of data.
67 00:07:52.520 ⇒ 00:07:58.430 Ryan Luke Daque: Yeah, so pretty much a lot of meetings did a lot of work for data for
68 00:07:58.660 ⇒ 00:08:11.019 Ryan Luke Daque: different clients like, I, I guess 5 clients, right? And a lot of data modeling. So yeah, pretty much head explode. So like, yeah, for this week at least for work. It’s pretty fun, though.
69 00:08:11.710 ⇒ 00:08:17.239 Uttam Kumaran: Dude. I don’t know about you. I have like so much like I have like couple of Vs code windows
70 00:08:17.380 ⇒ 00:08:22.860 Uttam Kumaran: I log in. I’m like which warehouse like what? What client.
71 00:08:22.860 ⇒ 00:08:33.609 Ryan Luke Daque: It’s the same like I. I gotta jump from one client to the to the the other. I do like need to sign up and sign in again to a different account and stuff like that. So yeah.
72 00:08:35.729 ⇒ 00:08:40.670 Ryan Luke Daque: next, Emoji, I did. Was the running man at the very right, I believe.
73 00:08:41.080 ⇒ 00:08:49.139 Ryan Luke Daque: Yeah, I did a lot of exercising this week like physical activity. I would. I went back and played like table tennis
74 00:08:50.818 ⇒ 00:08:55.099 Ryan Luke Daque: with, with like the a community here in our city.
75 00:08:55.240 ⇒ 00:08:56.560 Ryan Luke Daque: So yeah, it’s pretty fun.
76 00:08:57.130 ⇒ 00:09:00.549 Ryan Luke Daque: 3rd one is like this, drum emoji.
77 00:09:00.986 ⇒ 00:09:08.039 Ryan Luke Daque: This is because I had my, this is like a wild card, Emoji cause I had my 4 year old kid.
78 00:09:08.593 ⇒ 00:09:15.550 Ryan Luke Daque: Going to music school, and of all the instruments he chose drum. So it’s pretty pretty funny.
79 00:09:15.830 ⇒ 00:09:16.940 Uttam Kumaran: Gonna be loud.
80 00:09:17.120 ⇒ 00:09:18.160 Uttam Kumaran: Yes.
81 00:09:21.420 ⇒ 00:09:27.039 Ryan Luke Daque: Yeah. Who who’s who wants to share next? Maybe anybody wanna share next.
82 00:09:30.050 ⇒ 00:09:30.750 Nicolas Sucari: I can go.
83 00:09:30.750 ⇒ 00:09:31.669 Uttam Kumaran: People, oh, yeah.
84 00:09:31.670 ⇒ 00:09:32.640 Nicolas Sucari: Yeah, yeah.
85 00:09:34.046 ⇒ 00:09:41.959 Nicolas Sucari: My 1st one is the detective one or the spy, as we all said. I found like this past week
86 00:09:42.613 ⇒ 00:09:49.290 Nicolas Sucari: crazy like a lot of work on the data side and me doing some research on everything, trying to dig
87 00:09:49.724 ⇒ 00:09:53.286 Nicolas Sucari: all of the tasks. And that so yeah, it’s been a lot of
88 00:09:53.850 ⇒ 00:10:21.540 Nicolas Sucari: kind of detective work, because I needed to find out a lot of different things to help look. And that were doing all of the modeling and engineering work. So that was kind of my 1st emoji outside work. I would the one the demand lifting weights I started going back to the gym. I am trying to be fit right now, so I’m starting to eat in
89 00:10:21.690 ⇒ 00:10:44.670 Nicolas Sucari: boring stuff and going to the gym and the wild card. Emoji is the one, the mate one. I don’t know. If you guys know this the brown stuff with this like a straw there. It’s like a beverage here in Argentina. I don’t usually drink a lot of mate, but this week was kind of I don’t know why, but I’ve been drinking a lot of a lot of it
90 00:10:44.720 ⇒ 00:10:52.619 Nicolas Sucari: kind of a T, but it’s with German knows what I’m talking about, but I don’t know guys if you know it, but it’s yeah something to be called.
91 00:10:52.620 ⇒ 00:10:56.500 Uttam Kumaran: Yeah, I feel like in the in the States. Here they have, like yerba mate, the gas, like at
92 00:10:56.900 ⇒ 00:10:58.740 Uttam Kumaran: they have yerba mate drinks
93 00:10:58.880 ⇒ 00:11:03.869 Uttam Kumaran: like. They don’t serve that often like with the actual mate, but they do serve like
94 00:11:04.840 ⇒ 00:11:06.570 Uttam Kumaran: Lord, like sodas and stuff.
95 00:11:08.800 ⇒ 00:11:12.210 Nicolas Sucari: But yeah, that was my kind of week in emojis.
96 00:11:13.960 ⇒ 00:11:14.450 Ryan Luke Daque: Whoa!
97 00:11:14.450 ⇒ 00:11:15.100 Uttam Kumaran: Next.
98 00:11:15.100 ⇒ 00:11:17.520 Ryan Luke Daque: Nice. Yeah. Who wants to share next?
99 00:11:20.660 ⇒ 00:11:23.430 Ryan Luke Daque: Maybe, Connor, you wanna, you know, share yours.
100 00:11:24.050 ⇒ 00:11:31.580 Connor Fenn: Yeah, I can go. I my emojis are the 3 at the bottom. So the 1st one’s kind of the the guy rolling his eyes.
101 00:11:32.720 ⇒ 00:11:40.379 Connor Fenn: it’s just kinda how I feel about some of the responses I’ve gotten from our proposals that are out right now, just
102 00:11:41.683 ⇒ 00:11:45.626 Connor Fenn: yeah. People’s excuses are annoying at times.
103 00:11:46.670 ⇒ 00:11:54.530 Connor Fenn: the running emoji is actually kind of the same I ran for like the 1st time, and
104 00:11:54.790 ⇒ 00:12:00.930 Connor Fenn: honestly, I couldn’t even tell you how long the other day in the morning. I am not a runner.
105 00:12:01.090 ⇒ 00:12:07.600 Connor Fenn: I I like to swim and go to the gym and do all of that. But like, if someone’s not chasing me, I’m not normally running.
106 00:12:07.740 ⇒ 00:12:14.172 Connor Fenn: And so I did that the other day. So that was a nice little morning routine, and then
107 00:12:14.770 ⇒ 00:12:31.630 Connor Fenn: the frog is, I’m a big Pepe, the Pepe, the frog crypto trader, Guy and and one of the group chats that I’m in is, I actually found out, like one of the main devs, for the project I work on is in Austin. I was talking to him for a while today. So that was pretty cool.
108 00:12:32.170 ⇒ 00:12:33.690 Connor Fenn: But yeah.
109 00:12:34.060 ⇒ 00:12:35.219 Ryan Luke Daque: It’s about an 8th
110 00:12:36.460 ⇒ 00:12:47.355 Ryan Luke Daque: cool. Thanks for sharing. Looks like a lot of us are like going to like physical, at least like Nico Nico, you and me, like we’ve done like some some sort of physical activity for the week.
111 00:12:49.440 ⇒ 00:12:50.340 Nicolas Sucari: Yeah.
112 00:12:50.630 ⇒ 00:12:51.610 Ryan Luke Daque: Cool.
113 00:12:52.330 ⇒ 00:12:57.680 Ryan Luke Daque: Yeah, how about like someone from the AI side? Maybe, Casey, you want to share next.
114 00:12:59.670 ⇒ 00:13:00.949 Casie Aviles: Yeah, sure.
115 00:13:01.370 ⇒ 00:13:07.050 Casie Aviles: So I guess mine is. I think someone guessed it already. The one with the robot.
116 00:13:08.642 ⇒ 00:13:14.190 Casie Aviles: Yeah. So yeah, it’s just, you know, talking to a lot of agents. So we’ve been working on
117 00:13:14.990 ⇒ 00:13:22.410 Casie Aviles: a lot of agents this week. So it’s just mostly just talking to them all day testing them.
118 00:13:23.721 ⇒ 00:13:28.089 Casie Aviles: Yeah. So and then for the second emoji, which is just the game controller. And
119 00:13:28.680 ⇒ 00:13:37.850 Casie Aviles: I guess the interesting thing is, I’m not just playing a game anymore. But I I gave it gave the game development a shot like
120 00:13:38.160 ⇒ 00:13:44.949 Casie Aviles: tried looking into a game engine so I could make my own like. I don’t know just something to learn in my free time.
121 00:13:45.510 ⇒ 00:13:48.130 Uttam Kumaran: I know it’s very nerdy.
122 00:13:48.480 ⇒ 00:13:52.960 Casie Aviles: And now I’m just making a clone of this old game called Doom.
123 00:13:53.150 ⇒ 00:13:53.810 Casie Aviles: It’s kind of.
124 00:13:53.810 ⇒ 00:13:54.670 Uttam Kumaran: Oh, yeah.
125 00:13:55.010 ⇒ 00:13:55.425 Casie Aviles: Yeah.
126 00:13:57.070 ⇒ 00:14:03.039 Casie Aviles: And they are just having fun with, you know, making the sprites the art assets, you know, stuff like that.
127 00:14:03.360 ⇒ 00:14:05.969 Uttam Kumaran: Are you using AI to to build any of it?
128 00:14:06.980 ⇒ 00:14:10.270 Casie Aviles: Yeah, I mean the the visual. But
129 00:14:10.450 ⇒ 00:14:19.979 Casie Aviles: I’m using like the the worst model, because I wanted it to like the to have a pretty, you know, it’s kind of like a, I guess. Horror, in a way. Yeah.
130 00:14:20.090 ⇒ 00:14:20.880 Casie Aviles: yeah.
131 00:14:21.650 ⇒ 00:14:26.369 Casie Aviles: But yeah, I guess the last emojis just
132 00:14:26.670 ⇒ 00:14:29.399 Casie Aviles: I don’t. A sleeping Emoji, I guess there’s
133 00:14:29.620 ⇒ 00:14:31.719 Casie Aviles: I just met with a friend with
134 00:14:32.600 ⇒ 00:14:41.450 Casie Aviles: who I haven’t seen for a long time, and I guess I I skipped sleeping because I had to see him in sometime in the morning.
135 00:14:41.740 ⇒ 00:14:46.530 Casie Aviles: Yeah. So I mean, we only meet once, almost once a year. So
136 00:14:47.390 ⇒ 00:14:50.259 Casie Aviles: yeah, I guess that was the wild card.
137 00:14:54.020 ⇒ 00:14:55.460 Ryan Luke Daque: Yeah, thanks for sharing.
138 00:14:55.870 ⇒ 00:14:58.859 Ryan Luke Daque: Yeah, I want to go next. Maybe you, Tom, you want to go next.
139 00:14:59.430 ⇒ 00:15:08.299 Uttam Kumaran: Sure. Yeah, mine. The 1st one was upside down. Smiley face like it’s usually how I feel most days.
140 00:15:08.460 ⇒ 00:15:14.720 Uttam Kumaran: I really like that, Emoji, though I feel like every year I pick another emoji that I sort of like use all the time.
141 00:15:14.860 ⇒ 00:15:17.009 Uttam Kumaran: I think that’s usually how I feel.
142 00:15:17.260 ⇒ 00:15:18.750 Uttam Kumaran: Kind of bipolar
143 00:15:19.249 ⇒ 00:15:35.979 Uttam Kumaran: depending on the day the next one snow. Yeah, it’s like very cold in Austin, which is kind of a bummer. Have I really do like how it’s usually hot all the time, and I spend a bunch of time outside. It’s pretty cold, and I’ve had to run the heater a lot
144 00:15:37.890 ⇒ 00:16:03.619 Uttam Kumaran: But it won’t last that long. So it’s okay. And then, yeah, I’ve been because of, I’ve been doing a lot more data work in the last like few weeks I’ve been able to listen to a lot more music. It’s not something that I typically get a lot of time to listen to these days, because I’m either meetings or I’m in between meetings. So there’s not much time to like. Listen to anything. But if I’m doing data work, I can like blast music, and I’m listening to a lot of drum and bass.
145 00:16:04.040 ⇒ 00:16:12.499 Uttam Kumaran: and like getting it really into drum and bass music. It’s a little bit like spazz music like it’s really high. Bpm, like, it’s kind of like all over the place.
146 00:16:12.740 ⇒ 00:16:33.850 Uttam Kumaran: But I don’t know. It’s really nice to listen to. I’m listening to a lot of house sets and like, Edm. And yeah, I remember my 1st job when I was just like doing data work. I could. I was listening to like 8 h of music every day. So some days I would go to work and just pick an artist and say, cool, I’m gonna listen to like everything they’ve made in one day, because I would maybe have one stand up meeting, and then I would just be like
147 00:16:34.420 ⇒ 00:16:45.110 Uttam Kumaran: like I’d be just drinking like 4 cold brews every day and like, just just writing Dvt, so I’m like, I’m glad to be back writing some Dvt code. So yeah, that’s me.
148 00:16:47.440 ⇒ 00:16:48.350 Ryan Luke Daque: Cool.
149 00:16:48.460 ⇒ 00:16:49.470 Ryan Luke Daque: Thanks.
150 00:16:49.890 ⇒ 00:16:51.659 Ryan Luke Daque: Yeah. I want to go next.
151 00:16:55.160 ⇒ 00:16:57.330 Ryan Luke Daque: Maybe Ann, you wanna go next.
152 00:17:00.384 ⇒ 00:17:04.640 Anne: Yeah. So mine was the one with the smiling face and sweat.
153 00:17:05.365 ⇒ 00:17:12.300 Anne: Same with you, though. I have this emoji also, like I use a lot. So for me
154 00:17:12.470 ⇒ 00:17:16.760 Anne: that one for this week. For work.
155 00:17:17.434 ⇒ 00:17:23.250 Anne: Yeah, for AI demo. I have a bunch of like screens flows
156 00:17:23.390 ⇒ 00:17:25.509 Anne: I have in my head so they’re just
157 00:17:25.780 ⇒ 00:17:33.910 Anne: they’re juggling, and I can’t. I couldn’t put it in Figma, like all all the screens that I
158 00:17:34.400 ⇒ 00:17:37.935 Anne: wanted to do so, maybe just probably
159 00:17:38.720 ⇒ 00:17:41.903 Anne: procrastinating, procrastinating myself. And then
160 00:17:43.160 ⇒ 00:17:45.079 Anne: for the last 2, I think
161 00:17:45.840 ⇒ 00:17:47.240 Anne: I don’t know. I just want
162 00:17:47.990 ⇒ 00:17:55.940 Anne: this January to end, cause it feel like 2 months already. It’s it’s January is too long.
163 00:17:56.730 ⇒ 00:18:02.340 Anne: I can’t believe it’s just January 25, th and we still have one full week.
164 00:18:02.680 ⇒ 00:18:05.600 Anne: So, yeah, thanks.
165 00:18:06.860 ⇒ 00:18:07.920 Ryan Luke Daque: Oh, thanks.
166 00:18:08.520 ⇒ 00:18:09.165 Ryan Luke Daque: Yeah.
167 00:18:10.490 ⇒ 00:18:19.270 Ryan Luke Daque: yeah. I didn’t expect us to be taking so much time. But maybe maybe do, let’s do one more, and then maybe we can go go ahead with the
168 00:18:19.970 ⇒ 00:18:21.110 Ryan Luke Daque: with updates.
169 00:18:21.240 ⇒ 00:18:21.940 Ryan Luke Daque: Is that okay?
170 00:18:21.940 ⇒ 00:18:25.559 Uttam Kumaran: Go through everybody, but maybe everyone. Just just pick one Emoji to explain.
171 00:18:25.660 ⇒ 00:18:26.350 Uttam Kumaran: Yeah.
172 00:18:26.350 ⇒ 00:18:26.840 Ryan Luke Daque: Okay.
173 00:18:26.840 ⇒ 00:18:30.630 Uttam Kumaran: Maybe, Brian, you could quickly call. You can just quickly call on everybody, and then.
174 00:18:30.960 ⇒ 00:18:35.410 Ryan Luke Daque: Cool. Yeah. Maybe, Robert, do you want to go next? Just maybe pick one. Emoji.
175 00:18:37.053 ⇒ 00:18:38.565 Robert Tseng: Yeah, sure I
176 00:18:39.930 ⇒ 00:18:46.689 Robert Tseng: I I picked the embarrassed emoji. I actually didn’t know. That’s what it meant. So I think it’s a misinterpretation.
177 00:18:47.130 ⇒ 00:18:48.580 Uttam Kumaran: Wait, which one is that.
178 00:18:48.770 ⇒ 00:18:50.650 Robert Tseng: It’s the one where you’re covering your mouth.
179 00:18:52.090 ⇒ 00:18:55.779 Uttam Kumaran: Oh, I thought, that’s like a you’re like giggling about something.
180 00:18:55.780 ⇒ 00:19:06.349 Robert Tseng: Yeah, yeah, well, I just, I think outside life outside of work has been humorous. So that counterbalances is the stress that from from the work week. But yeah, there have just been some
181 00:19:06.990 ⇒ 00:19:13.200 Robert Tseng: random, funny moments from things, whether just like walking around and slipping on the ice on the sidewalk, or
182 00:19:13.340 ⇒ 00:19:18.119 Robert Tseng: I don’t know just remembering that, like like, there’s more to life than outside of my
183 00:19:18.630 ⇒ 00:19:22.340 Robert Tseng: outside of my apartment. I guess where I do most of my work.
184 00:19:23.700 ⇒ 00:19:24.640 Robert Tseng: Yeah.
185 00:19:25.410 ⇒ 00:19:26.110 Uttam Kumaran: Nice.
186 00:19:27.940 ⇒ 00:19:32.989 Ryan Luke Daque: Thanks. Yeah. Who wants to go next? Maybe Miguel, you wanna go next.
187 00:19:34.360 ⇒ 00:19:39.140 Miguel de Veyra: Hey, guys? Yeah. So mine is. Honestly, I forgot already what was mine.
188 00:19:39.590 ⇒ 00:19:45.539 Miguel de Veyra: It’s probably it’s probably the one with the crazy face, like the one with the circle.
189 00:19:45.540 ⇒ 00:19:46.779 Uttam Kumaran: I love the eyes.
190 00:19:46.780 ⇒ 00:19:53.130 Miguel de Veyra: Yeah, yeah, the googly. I don’t know what you call it. But yeah, cause my sleep schedule has been like messed up this week. I don’t know why.
191 00:19:54.300 ⇒ 00:19:56.879 Uttam Kumaran: Dude you. You’re like.
192 00:19:56.880 ⇒ 00:19:58.069 Miguel de Veyra: I always loved it.
193 00:19:58.070 ⇒ 00:19:58.640 Uttam Kumaran: Yeah.
194 00:19:58.640 ⇒ 00:19:59.030 Miguel de Veyra: Yeah.
195 00:19:59.030 ⇒ 00:20:01.810 Uttam Kumaran: Every day. Yeah, it’s like, yeah.
196 00:20:02.681 ⇒ 00:20:04.839 Miguel de Veyra: But yeah, so yeah, that’s
197 00:20:05.860 ⇒ 00:20:07.860 Miguel de Veyra: that’s pretty much it for me. I guess
198 00:20:08.010 ⇒ 00:20:12.790 Miguel de Veyra: that’s the one Emoji. I think it’s all the same. By the way, all 3 of my emojis are about that.
199 00:20:14.750 ⇒ 00:20:16.950 Miguel de Veyra: Thank you for 7. Yeah.
200 00:20:16.950 ⇒ 00:20:22.159 Uttam Kumaran: I know sometimes I’ll I’ll just be like, Send Miguel something like a random time. And he’s like.
201 00:20:22.330 ⇒ 00:20:36.790 Uttam Kumaran: Yeah, I got it. I’m like dude. Isn’t it? Like like I just. It’s just random anytime during the day. Stay with Ryan. Sometimes I like think, Ryan, I think Ryan on the content side. I feel like he like wakes up and responds, cause like he’ll be offline.
202 00:20:36.920 ⇒ 00:20:42.989 Uttam Kumaran: and I know sometimes I have something in my head, and I’m like I’ll just like, don’t respond. But here’s this, he’s like, Okay, God, I’ll work on it tomorrow. I’m like
203 00:20:43.110 ⇒ 00:20:51.779 Uttam Kumaran: you, just like in the middle of sleep. And you just like I imagine his room like gets a blight flashing like notification, and he wakes up and responds.
204 00:20:52.070 ⇒ 00:20:54.660 Uttam Kumaran: oh, yeah.
205 00:20:58.980 ⇒ 00:21:01.780 Ryan Luke Daque: Yeah, we’ll watch for Ryan. By the way, he’s not here.
206 00:21:01.780 ⇒ 00:21:04.209 Uttam Kumaran: Yeah, he said he was feeling a little sick today.
207 00:21:04.760 ⇒ 00:21:09.990 Uttam Kumaran: Apparently he his family. They got a new dog and a new cat, and I think he’s having an allergy.
208 00:21:09.990 ⇒ 00:21:10.810 Miguel de Veyra: Shared it.
209 00:21:11.120 ⇒ 00:21:13.220 Uttam Kumaran: He’s having allergies or something.
210 00:21:15.170 ⇒ 00:21:19.379 Uttam Kumaran: Yeah. And then I told him he said, my house is a barn right now.
211 00:21:19.560 ⇒ 00:21:24.339 Uttam Kumaran: and I was like dude. Send send pictures of your house. And yeah.
212 00:21:24.340 ⇒ 00:21:26.830 Ryan Luke Daque: Yeah, I think, yeah. He just sent some pictures in.
213 00:21:26.980 ⇒ 00:21:29.230 Uttam Kumaran: The the slack channel.
214 00:21:30.000 ⇒ 00:21:30.400 Uttam Kumaran: Yeah.
215 00:21:30.400 ⇒ 00:21:31.070 Ryan Luke Daque: Cool.
216 00:21:31.490 ⇒ 00:21:36.749 Ryan Luke Daque: Yeah. We have, like, maybe, Sahana, you wanna go next.
217 00:21:37.780 ⇒ 00:21:44.059 Sahana Asokan: Yeah, sure, 5 min late, so I didn’t understand. I didn’t see the game. But I’ll pick an emoji
218 00:21:44.390 ⇒ 00:21:44.890 Ryan Luke Daque: Okay.
219 00:21:44.890 ⇒ 00:21:58.369 Sahana Asokan: I will pick the melting place. I I’m moving. I’m moving apartments in a week, so I’m just getting ready for the move. So I have like a bunch of stuff going on and moving in New York is actually the worst thing ever.
220 00:21:58.860 ⇒ 00:22:01.457 Sahana Asokan: So let’s see how it goes.
221 00:22:01.890 ⇒ 00:22:03.760 Uttam Kumaran: Where are you moving to?
222 00:22:03.760 ⇒ 00:22:08.749 Sahana Asokan: So I was in Williamsburg before I’m moving to Fort Green. So it’s more like South.
223 00:22:08.750 ⇒ 00:22:09.410 Uttam Kumaran: Nice.
224 00:22:09.410 ⇒ 00:22:11.030 Sahana Asokan: I’m excited about it. Yeah.
225 00:22:11.360 ⇒ 00:22:13.180 Uttam Kumaran: Great. That’s awesome.
226 00:22:13.530 ⇒ 00:22:13.860 Sahana Asokan: Yeah.
227 00:22:13.860 ⇒ 00:22:18.620 Uttam Kumaran: IA lot of my friends are near their park slope, basically. And they’re like
228 00:22:18.730 ⇒ 00:22:20.379 Uttam Kumaran: they don’t go anywhere now.
229 00:22:20.380 ⇒ 00:22:21.165 Uttam Kumaran: No, there.
230 00:22:21.590 ⇒ 00:22:26.368 Sahana Asokan: I think that’s kind of my I’m in that era as well. I I just kinda want my space.
231 00:22:26.620 ⇒ 00:22:31.920 Uttam Kumaran: Yeah, good luck with the move, like I moved every year when I was in New York. And
232 00:22:33.090 ⇒ 00:22:37.640 Uttam Kumaran: yeah, I like it shaved like years off my life in terms of stress.
233 00:22:37.850 ⇒ 00:22:39.090 Uttam Kumaran: Yeah, so.
234 00:22:39.280 ⇒ 00:22:41.159 Sahana Asokan: Movers are worth. It is all.
235 00:22:41.160 ⇒ 00:22:47.809 Uttam Kumaran: Yes, yes, cool. Maybe, Marianne, I think you’re the last one.
236 00:22:48.140 ⇒ 00:22:48.880 Ryan Luke Daque: Yeah.
237 00:22:49.510 ⇒ 00:22:54.100 Mariane Cequina: Yeah, I think I think mine will be just like a smiley face with close eyes.
238 00:22:54.300 ⇒ 00:23:02.089 Mariane Cequina: because it’s in my case this week I’m pretty chill and as well as I stopped drinking coffee. So yeah, so I don’t.
239 00:23:02.090 ⇒ 00:23:03.090 Uttam Kumaran: Wow!
240 00:23:03.090 ⇒ 00:23:14.040 Mariane Cequina: Anymore. And I stopped drinking sugar. So I mean adding sugar to my drink. So I just use honey just in case. But I switched the matcha or tea. So yeah, I don’t.
241 00:23:14.040 ⇒ 00:23:15.540 Uttam Kumaran: That’s caffeine, though.
242 00:23:15.970 ⇒ 00:23:23.170 Mariane Cequina: Yes, that sounds like it’s not as hard. Yeah, it’s just more like chill, definitely. So I that’s me.
243 00:23:23.360 ⇒ 00:23:29.650 Uttam Kumaran: I’m not sure we can afford for me to quit coffee right now, but I would like to sometime
244 00:23:30.020 ⇒ 00:23:33.325 Uttam Kumaran: this year, but that’s good.
245 00:23:33.860 ⇒ 00:23:38.179 Uttam Kumaran: I heard. Matcha is like really good for your health, too, like ceremonial grade. Matcha, apparently.
246 00:23:38.180 ⇒ 00:23:40.230 Mariane Cequina: Yeah, yeah, that’s what I’m doing.
247 00:23:40.510 ⇒ 00:23:41.870 Uttam Kumaran: Oh, nice. Okay.
248 00:23:42.450 ⇒ 00:23:46.248 Mariane Cequina: It’s actually tastes nice with soy milk and as well as honey, just sharing.
249 00:23:46.520 ⇒ 00:23:50.130 Uttam Kumaran: I’ve been doing. Lattes. Yeah. Nice.
250 00:23:50.260 ⇒ 00:23:51.130 Uttam Kumaran: Great.
251 00:23:52.250 ⇒ 00:23:52.980 Ryan Luke Daque: Cool.
252 00:23:53.910 ⇒ 00:23:59.700 Ryan Luke Daque: Yeah, I guess that’s everyone. So yeah, thanks everyone for sharing like, how your week is
253 00:23:59.970 ⇒ 00:24:01.420 Ryan Luke Daque: through. Emojis.
254 00:24:01.890 ⇒ 00:24:02.959 Uttam Kumaran: Yeah. Great game.
255 00:24:03.190 ⇒ 00:24:03.900 Ryan Luke Daque: Cool.
256 00:24:04.710 ⇒ 00:24:10.710 Ryan Luke Daque: Yeah, I guess we can go ahead and start with the the updates for each like group.
257 00:24:14.040 ⇒ 00:24:16.200 Ryan Luke Daque: I guess we can start with data.
258 00:24:19.180 ⇒ 00:24:26.999 Ryan Luke Daque: I can provide a couple of updates for data. So yeah, like, I mentioned earlier. And Nico mentioned earlier, we’ve been working with a lot of
259 00:24:27.631 ⇒ 00:24:31.850 Ryan Luke Daque: clients this week. Doing a lot of data modeling
260 00:24:33.860 ⇒ 00:24:41.150 Ryan Luke Daque: like migration for Javi, basically from 5 grand to portable and like for cool parts. We did.
261 00:24:42.480 ⇒ 00:24:44.039 Ryan Luke Daque: Yeah, like a couple of
262 00:24:44.440 ⇒ 00:24:47.289 Ryan Luke Daque: tasks that were in the backlog.
263 00:24:48.469 ⇒ 00:24:54.340 Ryan Luke Daque: So yeah. Still, still, a lot of backlog to do for next week, though, and for the coming weeks as well.
264 00:24:54.820 ⇒ 00:24:58.789 Ryan Luke Daque: and also still a lot of data modeling for Javi.
265 00:25:00.430 ⇒ 00:25:00.950 Ryan Luke Daque: Yeah.
266 00:25:00.950 ⇒ 00:25:01.300 Nicolas Sucari: Yeah.
267 00:25:01.300 ⇒ 00:25:03.229 Ryan Luke Daque: Anything else to add. Nico.
268 00:25:03.530 ⇒ 00:25:09.259 Nicolas Sucari: Since. Since I’ve been working in Brainforge, I think this is the week where we
269 00:25:09.490 ⇒ 00:25:15.029 Nicolas Sucari: been working with more more clients. At the same time. I don’t remember that we’ve
270 00:25:15.390 ⇒ 00:25:18.150 Nicolas Sucari: worked with 5 or more clients
271 00:25:18.330 ⇒ 00:25:29.699 Nicolas Sucari: at the same time, maybe 3 with them. I don’t know. 2 or 3 was total. And right now we’re working at 5 clients at the same time. So that’s a big win and also a big challenge for us.
272 00:25:30.188 ⇒ 00:25:38.089 Nicolas Sucari: I think we’ve we’ve been able to do to to deliver stuff for every client but we
273 00:25:38.120 ⇒ 00:26:07.688 Nicolas Sucari: have a long way to go yet. We still need to work on some processes and try to identify how we can be more like accurate on what we need to do, what is the priorities, and how to to deliver that for each client and try to organize ourselves a little bit better on, so that we don’t need to be rushing all of the work every week. But yeah, I mean, it’s it’s a big win to be working with so many clients at the same time.
274 00:26:08.070 ⇒ 00:26:13.690 Nicolas Sucari: we it’s a it’s as I said, it’s a big win and a big challenge. I think we
275 00:26:13.870 ⇒ 00:26:17.090 Nicolas Sucari: we are gonna be able to to be a bit
276 00:26:17.230 ⇒ 00:26:20.880 Nicolas Sucari: yeah, better. On managing all of the clients at the same time. But yeah.
277 00:26:21.040 ⇒ 00:26:28.349 Nicolas Sucari: this week’s gone. Was was crazy, crazy amount of work. But we’re gonna yeah nail it next week. I think.
278 00:26:29.620 ⇒ 00:26:43.866 Uttam Kumaran: Yeah, I agree. And you know, I I feel really excited. One of our clients, you know, they just closed a huge funding round. And they’re they just raised at a 700 million dollar evaluation. They’re one of the fastest growing clients in the AI space.
279 00:26:44.450 ⇒ 00:26:57.410 Uttam Kumaran: you know, and we’re, you know, looking at all of our clients, we’re sort of tapped in on 2 really big areas, one in AI, and also we’re working with a large glp, one retailer. So you know, it’s really great to be able to work with all these different clients and
280 00:26:57.910 ⇒ 00:27:02.180 Uttam Kumaran: sort of produce data work. So yeah, I totally double click on everything you said and go.
281 00:27:06.950 ⇒ 00:27:14.279 Uttam Kumaran: And then, broadly, I think we’ll we have a data retro later, and we’ll talk a little bit about sort of the development process as well. Now that we have
282 00:27:14.550 ⇒ 00:27:20.679 Uttam Kumaran: sort of everyone working on the full stack, we’ll talk about how we delivering work and things like that in that meeting. But yeah, I agree.
283 00:27:24.370 ⇒ 00:27:26.700 Ryan Luke Daque: Oh, thanks.
284 00:27:27.120 ⇒ 00:27:30.299 Ryan Luke Daque: Yeah. We can proceed with the with the AI updates.
285 00:27:32.740 ⇒ 00:27:46.209 Uttam Kumaran: Yeah, I don’t know Miguel or Casey, like, maybe I can just share my screen. I I mean, I and maybe you guys wanna give a little bit of commentary about the the agents we’ve been working on. I’ll just share. And then you guys
286 00:27:47.420 ⇒ 00:27:51.460 Uttam Kumaran: do the talking. Cause I it’s really, really exciting some of the stuff we’re working on.
287 00:28:01.860 ⇒ 00:28:07.210 Uttam Kumaran: Okay, so yeah, maybe we want to talk about tickets here. The Zoom Meeting agents.
288 00:28:09.260 ⇒ 00:28:10.210 Uttam Kumaran: Anything.
289 00:28:11.320 ⇒ 00:28:39.149 Miguel de Veyra: Okay, yeah. So for the ticket here, it’s basically the idea here is that we give it like a transcript, or like some notes about something that we talked to, and then it’ll automatically decide like which tickets to create, and then which you know, assigned to the titles and everything. And then it’ll ask us for basically, hey, are you sure that this is the details you want? And then it’ll automatically create everything based on the inputs that you give it to, and also assign it to the right person.
290 00:28:41.330 ⇒ 00:28:42.140 Miguel de Veyra: Oh, yeah.
291 00:28:43.540 ⇒ 00:28:52.123 Uttam Kumaran: So the ticket creation process, we’re streamlining. And we’re we built an agent, basically that allows us to do that straight from slack. We’re also working on updating tickets as well.
292 00:28:53.056 ⇒ 00:28:58.179 Uttam Kumaran: And then maybe, Casey, do you want to talk about the meeting summarization work?
293 00:28:59.150 ⇒ 00:29:00.259 Casie Aviles: Oh, yeah, sure.
294 00:29:00.600 ⇒ 00:29:03.399 Casie Aviles: So yeah, for the summarizer. We. We
295 00:29:03.690 ⇒ 00:29:06.470 Casie Aviles: added it to a couple of channels already. And
296 00:29:07.230 ⇒ 00:29:14.619 Casie Aviles: yeah, not. Sure if you’ve noticed already. But yeah, some of the channels like for the data team, sales.
297 00:29:15.360 ⇒ 00:29:18.449 Casie Aviles: also design and operations. We have some
298 00:29:18.700 ⇒ 00:29:22.120 Casie Aviles: agents there are sending the summaries. So
299 00:29:23.040 ⇒ 00:29:28.459 Casie Aviles: yeah, I mean, I guess the idea is, you know, for the meetings to have like
300 00:29:29.613 ⇒ 00:29:34.480 Casie Aviles: like, we don’t have to like take notes we just let the agent for now to do it, and
301 00:29:35.350 ⇒ 00:29:40.818 Casie Aviles: not frantic note taking, I mean, something like that. And also we could review the
302 00:29:41.650 ⇒ 00:29:43.330 Casie Aviles: the action points needed.
303 00:29:44.450 ⇒ 00:29:46.999 Casie Aviles: So yeah, I guess that’s
304 00:29:47.140 ⇒ 00:29:49.510 Casie Aviles: the gist of the Zoom agent.
305 00:29:51.340 ⇒ 00:30:18.680 Uttam Kumaran: Yeah. And I think, just to go one step beyond on that. So we’re gonna start with summaries. And I think everybody’s probably used to using fathom or something, or seeing zoom summaries. We’re going one step beyond. In that you’ll not only get the summary, but then the agent will actually suggest tickets to get created, or actions to be taken. Summary sort of the 1st piece of like connecting the pipes. So I’m very excited that the notion creation agent will basically start to leverage the summary. Say, we talked about
306 00:30:18.720 ⇒ 00:30:21.729 Uttam Kumaran: these Xyz things. You want to go ahead and create a ticket for that.
307 00:30:21.800 ⇒ 00:30:30.220 Uttam Kumaran: So ideally, we’re sort of starting to automate, you know, a lot of the typical information transfer work that happens. And allowing us to focus more on
308 00:30:30.340 ⇒ 00:30:36.020 Uttam Kumaran: just the work needed. So small quality of life updates, I think mainly so.
309 00:30:36.410 ⇒ 00:30:40.599 Ryan Luke Daque: Can we interact with it or not? Or is it like.
310 00:30:40.600 ⇒ 00:30:43.669 Uttam Kumaran: You guys want to talk about what interactions are available. Now.
311 00:30:45.130 ⇒ 00:30:50.834 Casie Aviles: At the moment. You don’t. Really. You can’t really talk to the agent right now. Just spits out the
312 00:30:51.650 ⇒ 00:30:53.820 Casie Aviles: automation, either. Summary. Yeah.
313 00:30:56.356 ⇒ 00:30:59.110 Miguel de Veyra: For the ticket here you could interact with it.
314 00:30:59.660 ⇒ 00:31:11.039 Miguel de Veyra: Cause it’s gonna ask you, you know, it’s gonna clarify the tickets that it’s suggesting. And then you can make changes and stuff, you know, give more information. Just so the ticket is more accurate, and then also assign the
315 00:31:11.430 ⇒ 00:31:18.659 Miguel de Veyra: the person like, clarify, basically. But what you can’t do is update tickets. That’s still something that we’re working on.
316 00:31:22.090 ⇒ 00:31:26.640 Uttam Kumaran: Casey, do you want to also talk about the work we did for Craig this week?
317 00:31:27.866 ⇒ 00:31:35.699 Casie Aviles: Yeah, we can also talk about this. So yeah, basically, what this is for is to just create like, automated
318 00:31:36.380 ⇒ 00:31:43.010 Casie Aviles: outreach emails. So for Craig, what he does is he will read an article, and then he
319 00:31:44.059 ⇒ 00:31:50.210 Casie Aviles: inserts the art. Yeah, this thing, this information like article
320 00:31:50.660 ⇒ 00:31:55.750 Casie Aviles: the name, and we mainly use the name. And they’re
321 00:31:57.822 ⇒ 00:32:02.510 Casie Aviles: the company name. And then we get the email we use clay for that.
322 00:32:03.220 ⇒ 00:32:09.479 Casie Aviles: So we retrieve the email addresses. And then, yeah, we create the draft.
323 00:32:09.640 ⇒ 00:32:18.560 Casie Aviles: We pass it to the AI, and then they will. And then it will, you know, generate this email draft in Craig’s style. So he provided some examples.
324 00:32:18.870 ⇒ 00:32:23.530 Casie Aviles: And we basically fed that to the Ais instructions.
325 00:32:24.220 ⇒ 00:32:31.159 Casie Aviles: And the last thing is that these drafts should also be created automatically on his Gmail account.
326 00:32:32.070 ⇒ 00:32:34.289 Casie Aviles: So yeah, that’s pretty much it.
327 00:32:34.980 ⇒ 00:33:02.199 Uttam Kumaran: Yeah. So one thing for Craig is, he’s like, he’s basically like networking with a lot of people in AI right now. And he, Craig, is like a referral partner of ours and leads AI crocs. And one of the things he asked was like, Hey, I’m I’m sending these networking emails out, can you guys build something for me that helps automate? And so actually, what he can do is he just goes to a spreadsheet, puts an article and a person from the article. And automatically the email gets written and it gets put into his Gmail draft.
328 00:33:02.932 ⇒ 00:33:05.847 Uttam Kumaran: Probably in like 30 seconds.
329 00:33:06.710 ⇒ 00:33:20.480 Uttam Kumaran: So I think, for the folks on the sales side and just operations wise. These are the sort of stuff that we can do in AI. That’s like very, very, very convenient. You know. So just to open your mind to like what we can do.
330 00:33:20.540 ⇒ 00:33:42.699 Uttam Kumaran: like writing gmail drafts, doing things in sheets. This is like really, really great work from casey and Craig was most like mo most likely become a customer of ours. As well as like. He’s also sending us like a ton of leads. Connor, in particular. Craig is probably connected with every
331 00:33:43.200 ⇒ 00:33:49.969 Uttam Kumaran: like. Probably the top 500 Ecom companies. So if we need, if we have one, if we have a couple where we’re like.
332 00:33:50.580 ⇒ 00:33:57.150 Uttam Kumaran: you know, these ones like we have there, they seem really strong or like we already have a way in, or they’ve they’ve sent. They’ve like.
333 00:33:57.300 ⇒ 00:34:04.930 Uttam Kumaran: basically been like cool. We’re interested. I can get us a leg up and he and he owes us he owes us one for this. So
334 00:34:08.389 ⇒ 00:34:09.190 Uttam Kumaran: cool.
335 00:34:09.420 ⇒ 00:34:11.070 Uttam Kumaran: All right back to you, Ryan.
336 00:34:11.440 ⇒ 00:34:14.760 Ryan Luke Daque: Well, yeah, thanks. Thanks for the updates.
337 00:34:15.159 ⇒ 00:34:20.420 Ryan Luke Daque: Yeah. Next, we have the sales. Anybody from sales here.
338 00:34:22.090 ⇒ 00:34:23.500 Connor Fenn: Yeah. So
339 00:34:23.679 ⇒ 00:34:31.950 Connor Fenn: for sales. We have a couple of proposals out that I got some updates on yesterday. Hopefully.
340 00:34:32.705 ⇒ 00:34:45.389 Connor Fenn: we’ll get some more answers on next week, and then I was able to schedule a handful of demo calls and kind of discovery calls for next week’s with some new weeds. So we’re gonna see how those go
341 00:34:46.191 ⇒ 00:34:54.219 Connor Fenn: and then also, I’m just putting together kind of a new weed list based off some E-com brands that
342 00:34:54.489 ⇒ 00:34:57.079 Connor Fenn: Utah and I’ve been kind of speaking about. But
343 00:34:57.760 ⇒ 00:34:59.850 Connor Fenn: that’s it for the most part, on my end.
344 00:35:00.140 ⇒ 00:35:02.739 Connor Fenn: You guys wanna talk about some more. But.
345 00:35:04.220 ⇒ 00:35:06.910 Uttam Kumaran: Yeah. Anything else, Robert, on sales stuff.
346 00:35:11.430 ⇒ 00:35:16.880 Robert Tseng: No, I think that’s- that’s pretty much it. I mean, there may be more stuff coming in the next week.
347 00:35:17.780 ⇒ 00:35:22.660 Uttam Kumaran: Okay, yeah, I know, we kicked off like a legal campaign. And
348 00:35:22.980 ⇒ 00:35:30.260 Uttam Kumaran: yeah, maybe at some point we’ll have, we can talk, probably about how that stuff works. I think we’re we can kick off a lot of automated campaigns soon.
349 00:35:30.606 ⇒ 00:35:36.280 Uttam Kumaran: And then, Connor, are you in? Is hey? Reach stuff working for you like, are you as messages coming out? Yet from your account.
350 00:35:38.880 ⇒ 00:35:44.280 Connor Fenn: I don’t think so. I I honestly don’t even check my own Linkedin. I have like.
351 00:35:45.040 ⇒ 00:35:48.500 Uttam Kumaran: And auto saved. So, okay, okay.
352 00:35:48.500 ⇒ 00:35:52.369 Connor Fenn: But looking on my phone real quick.
353 00:35:52.480 ⇒ 00:35:53.490 Connor Fenn: No.
354 00:35:53.710 ⇒ 00:35:54.710 Uttam Kumaran: Okay. Okay.
355 00:35:54.710 ⇒ 00:35:57.379 Connor Fenn: I have them in my messages, I assume right.
356 00:35:58.020 ⇒ 00:36:03.850 Uttam Kumaran: Yeah, it would. Yeah, it would show up in your messages. I don’t know whether they turned it all on or not, but maybe we could follow up with Eric, since.
357 00:36:04.350 ⇒ 00:36:05.050 Uttam Kumaran: Okay.
358 00:36:10.110 ⇒ 00:36:12.729 Ryan Luke Daque: Yeah. Next, we have operations.
359 00:36:18.050 ⇒ 00:36:20.309 Mariane Cequina: Hello, so hold on.
360 00:36:25.440 ⇒ 00:36:35.720 Mariane Cequina: Yeah. So I actually did some tasks. I completed all the tasks that would give me, except from one, which is the
361 00:36:37.070 ⇒ 00:36:43.599 Mariane Cequina: which is the data view for the task ticket, like the number of task tickets, something like that.
362 00:36:43.980 ⇒ 00:36:45.770 Mariane Cequina: But basically I did the
363 00:36:45.870 ⇒ 00:37:14.099 Mariane Cequina: the personal dashboard for Connor, and I did some edit as well with design team. If I’m not mistaken. And I finished the user guide from Team dashboard, the personal navigation tips because we’re planning to create like a well documented for each step for future use. If we will be onboarding new team members and then, yeah, completing the stack leads onboarding as well, and then building the project team. So that’s pretty much it.
364 00:37:14.220 ⇒ 00:37:18.490 Mariane Cequina: and I’ll be waiting for your for
365 00:37:18.690 ⇒ 00:37:26.310 Mariane Cequina: for the requirements for consolidate the pogo notion in the brain forge. Yeah. So I think that’s pretty much it for the operation side.
366 00:37:28.080 ⇒ 00:37:28.640 Uttam Kumaran: Okay.
367 00:37:31.160 ⇒ 00:37:36.260 Ryan Luke Daque: Thanks. Next we have designed from Anne, I believe.
368 00:37:38.167 ⇒ 00:37:41.430 Anne: Yep. So for design wins. We had
369 00:37:41.670 ⇒ 00:37:49.529 Anne: 2 mock up version of AI Demo showcase. Then Halim is done working with the resources pages. And
370 00:37:49.940 ⇒ 00:37:52.340 Anne: I’ve updated the website’s client
371 00:37:52.630 ⇒ 00:37:58.730 Anne: Logos. And for this week I think the challenges would be still for the copies. And then
372 00:37:59.837 ⇒ 00:38:10.590 Anne: in my end tracking the illustration design task in notion. Because, currently, I’m just manually tracking it in notifications. And then
373 00:38:12.585 ⇒ 00:38:19.269 Anne: for next week, I think we’ll be having user interview for AI Demo showcase
374 00:38:19.630 ⇒ 00:38:25.519 Anne: and then for Halim, there will be more research on improving the website. Search experience.
375 00:38:26.350 ⇒ 00:38:27.310 Anne: That’s all from.
376 00:38:27.310 ⇒ 00:38:27.650 Uttam Kumaran: Yeah.
377 00:38:27.650 ⇒ 00:38:28.350 Anne: Saying.
378 00:38:30.220 ⇒ 00:38:53.169 Uttam Kumaran: Yeah, right now, the search experience isn’t like so solid. So we’re doing that. And this is the logo update. So we added a lot of the stuff from Robert side on past clients as well. And then there’s the new careers at the bottom which goes to our notion. Sort of Careers Page, which is really great. And then in terms of new things
379 00:38:53.670 ⇒ 00:38:56.249 Uttam Kumaran: here, I just wanted to show
380 00:38:56.830 ⇒ 00:39:15.080 Uttam Kumaran: a flash of 2 things. One is we’re working on the basically how the AI Demos are going to get put up on the site. So we presented this in the in the design team meeting today. But we’ll end up looking something sort of like this. Which is really, really exciting to get out
381 00:39:15.685 ⇒ 00:39:19.569 Uttam Kumaran: and then we’re also basically working on our
382 00:39:19.740 ⇒ 00:39:25.780 Uttam Kumaran: like final pricing page like copy. And how like, content is gonna work?
383 00:39:27.560 ⇒ 00:39:29.800 Uttam Kumaran: I think we just have some follow ups on that.
384 00:39:31.630 ⇒ 00:39:32.310 Uttam Kumaran: Yeah.
385 00:39:33.200 ⇒ 00:39:34.850 Connor Fenn: Did you see that note I put in there.
386 00:39:37.760 ⇒ 00:39:40.290 Uttam Kumaran: I did not yet. I’ll have to. I can look.
387 00:39:40.670 ⇒ 00:39:44.989 Connor Fenn: I just I don’t think I actually tagged you. Remember that after oh.
388 00:39:44.990 ⇒ 00:39:48.790 Uttam Kumaran: Oh, I probably got yeah, I I’ll look at it. Okay.
389 00:39:53.980 ⇒ 00:40:01.520 Ryan Luke Daque: Nice. Yeah. Next, we have marketing any updates from marketing.
390 00:40:03.443 ⇒ 00:40:31.909 Uttam Kumaran: I don’t know if I think I’ll just probably give an update. So yeah, we’re working on new copy for the services pages right now, if you go to Brainforge, the services pages suck they just don’t reflect everything that we’re we do. So it’s a long time coming that we updated to reference all the data engineering, all the product analytics work and the audit work. So all that copy is mostly done is with Ryan. He’s working on that. We have the pricing updates. And then video. So we actually released
391 00:40:32.550 ⇒ 00:40:48.210 Uttam Kumaran: ryan went ahead and did a great job and didn’t wait for me and just went ahead and released videos and me talking about stuff, and they’re actually like doing well like they’re getting views. And it looks pretty good. I don’t like watching them, but they look okay. So if you follow the Brainforge account on Instagram.
392 00:40:48.613 ⇒ 00:40:58.249 Uttam Kumaran: You’ll see it there and we’ll start. We’re gonna start to push it on Youtube as well. And I think next week we’re gonna work on some more videos, particularly with Connor on some sales, steps.
393 00:40:59.280 ⇒ 00:41:04.440 Connor Fenn: Are we putting those videos on Linkedin? It’s got like a Instagram reels kind of thing.
394 00:41:04.520 ⇒ 00:41:07.130 Uttam Kumaran: Yeah, we’re gonna I think we’re gonna
395 00:41:07.480 ⇒ 00:41:09.890 Uttam Kumaran: try to blast them out everywhere. Basically,
396 00:41:11.510 ⇒ 00:41:14.640 Uttam Kumaran: I’ve been hesitant. Cause like, I don’t like.
397 00:41:15.090 ⇒ 00:41:24.718 Uttam Kumaran: I don’t know. I I’m like, just do it without me knowing. Don’t ask me for feedback, because I’m like, yeah, I don’t know. I’m not a great like on video. But
398 00:41:25.240 ⇒ 00:41:25.850 Uttam Kumaran: I also want.
399 00:41:25.850 ⇒ 00:41:26.389 Nicolas Sucari: And it’s not.
400 00:41:26.390 ⇒ 00:41:30.320 Uttam Kumaran: I think pious would be good to do videos as well. Yeah, he has a really good
401 00:41:31.410 ⇒ 00:41:32.060 Uttam Kumaran: good at like.
402 00:41:32.060 ⇒ 00:41:32.640 Nicolas Sucari: I know.
403 00:41:32.640 ⇒ 00:41:34.110 Uttam Kumaran: Energy, high energy.
404 00:41:34.880 ⇒ 00:41:39.710 Nicolas Sucari: I know Tiktok has been kind of yeah. I don’t know if it’s banned or not in the Us. Right now.
405 00:41:39.710 ⇒ 00:41:41.239 Uttam Kumaran: It’s back, it’s back.
406 00:41:41.240 ⇒ 00:41:42.260 Nicolas Sucari: He’s back. Okay.
407 00:41:42.260 ⇒ 00:41:42.670 Uttam Kumaran: Yeah.
408 00:41:42.670 ⇒ 00:41:45.170 Nicolas Sucari: Maybe we can use that videos on Tiktok, too.
409 00:41:46.970 ⇒ 00:41:49.550 Uttam Kumaran: Yeah, Ryan has a lot of ideas there. So.
410 00:41:50.400 ⇒ 00:41:51.470 Nicolas Sucari: Yeah, okay.
411 00:41:53.300 ⇒ 00:41:59.690 Uttam Kumaran: And then on people. So yeah, we are. We had one conversation with a
412 00:42:00.581 ⇒ 00:42:23.050 Uttam Kumaran: friend of the company who’s interested in helping out with like technical marketing. Basically, her background is in engineering. But she’s trying to get more into marketing, and I think she would fit really well with our marketing team to basically help give more instruction. Sort of like, take take a lot of stuff that’s in my head, and spend more time with design and marketing and the website to sort of get out more assets.
413 00:42:23.374 ⇒ 00:42:35.890 Uttam Kumaran: So we’re gonna be, you know, bringing her in and giving her a couple of things to to test her skill, set out. So I’m really excited for that. And then we’re basically talking to a few people. For like a senior
414 00:42:36.360 ⇒ 00:42:54.240 Uttam Kumaran: product manager, like a like, almost like an engagement leader. Type of role, someone who’s like at like me or Robert’s level in terms of understanding like how to put together data teams and the higher analytics strategy. And then also coming in to help us understand? Like, how do we run project management, for, like
415 00:42:54.600 ⇒ 00:43:02.370 Uttam Kumaran: 5 or 6 simultaneous clients, and still be effective. So I’m excited to kind of like start to bring those people in the fold
416 00:43:02.998 ⇒ 00:43:06.509 Uttam Kumaran: and we most likely will have one more
417 00:43:06.760 ⇒ 00:43:12.877 Uttam Kumaran: analytics. Engineer. Come on and sort of take over some of the stuff that I’m doing.
418 00:43:15.200 ⇒ 00:43:16.750 Uttam Kumaran: Yeah, so.
419 00:43:18.670 ⇒ 00:43:19.270 Ryan Luke Daque: Nice
420 00:43:21.260 ⇒ 00:43:30.160 Ryan Luke Daque: cool. Yeah, I think that’s about it for all everyone, anything else you wanna discuss like Tom or anybody else want to talk about something else.
421 00:43:36.960 ⇒ 00:43:37.780 Uttam Kumaran: Think we’re good.
422 00:43:38.210 ⇒ 00:43:41.760 Ryan Luke Daque: Yeah, cool and thanks. Everyone. Have a nice rest of your week.
423 00:43:41.760 ⇒ 00:43:44.930 Uttam Kumaran: Thanks for hosting dude. It’s 1st time guest host.
424 00:43:45.704 ⇒ 00:43:47.600 Uttam Kumaran: But I guess like
425 00:43:47.770 ⇒ 00:43:52.330 Uttam Kumaran: I’ll I guess I don’t know. You should pick someone. Maybe you can pick someone new to host next week.
426 00:43:54.180 ⇒ 00:43:58.899 Uttam Kumaran: and yes, I think this is good, like having the rest of the team host, and we’ll continue to evolve these. So
427 00:43:59.310 ⇒ 00:44:01.950 Uttam Kumaran: maybe, Ryan, do you want to pick someone now.
428 00:44:03.266 ⇒ 00:44:07.950 Ryan Luke Daque: I don’t know anybody. Wanna nominate themselves.
429 00:44:10.670 ⇒ 00:44:13.750 Ryan Luke Daque: I guess nobody wants I could do.
430 00:44:14.880 ⇒ 00:44:17.190 Uttam Kumaran: Oh, yeah, cool.
431 00:44:17.540 ⇒ 00:44:18.160 Ryan Luke Daque: Cool.
432 00:44:18.160 ⇒ 00:44:26.724 Uttam Kumaran: Okay, cool. So that I think that the really, the bare minimum is, think about the games need to get more fun and more captivating. That’s the real like test.
433 00:44:27.530 ⇒ 00:44:28.830 Uttam Kumaran: Today’s was good.
434 00:44:29.820 ⇒ 00:44:30.520 Ryan Luke Daque: Cool.
435 00:44:31.390 ⇒ 00:44:31.900 Uttam Kumaran: Thanks guys.
436 00:44:31.900 ⇒ 00:44:32.730 Ryan Luke Daque: Sounds good.
437 00:44:33.840 ⇒ 00:44:34.910 Ryan Luke Daque: Yeah, that’s Connor.
438 00:44:35.480 ⇒ 00:44:35.990 Connor Fenn: Yep.
439 00:44:35.990 ⇒ 00:44:36.520 Uttam Kumaran: Thanks. Everyone.
440 00:44:37.130 ⇒ 00:44:39.040 Ryan Luke Daque: Thanks. Have a nice rest of your day.
441 00:44:39.350 ⇒ 00:44:41.230 Anne: Thanks. Guys. Have a weekend.
442 00:44:41.230 ⇒ 00:44:41.790 Uttam Kumaran: Bye.