Meeting Title: Friday Brainforge Demos & Retro Date: 2025-06-06 Meeting participants: Uttam Kumaran, Annie Yu, Ryan Brosas, Amber Lin, Mustafa Raja, luke, Anne, Demilade Agboola, Hannah Wang, Raymund Verzosa, Luke Daque
WEBVTT
1 00:00:36.500 ⇒ 00:00:37.390 Ryan Brosas: Hi guys.
2 00:00:37.390 ⇒ 00:00:38.420 Uttam Kumaran: Hey!
3 00:00:44.170 ⇒ 00:00:45.360 Uttam Kumaran: How are you?
4 00:00:46.380 ⇒ 00:00:51.558 Ryan Brosas: Doing fine doing fine this getting my coffee.
5 00:00:52.030 ⇒ 00:00:56.960 Uttam Kumaran: Yeah, I’m I’m just gonna get a coffee. I’ll be. I mean, I’ll be on. But okay.
6 00:00:56.990 ⇒ 00:00:58.650 Amber Lin: Hi.
7 00:01:16.940 ⇒ 00:01:18.319 Ryan Brosas: I don’t like
8 00:01:48.000 ⇒ 00:01:52.780 Ryan Brosas: nice Pio la Pedro.
9 00:02:07.410 ⇒ 00:02:13.069 Uttam Kumaran: Give me one second, I’ll be right back. Just gonna get this coffee, cause I’m I’m really, really tired.
10 00:02:13.610 ⇒ 00:02:15.599 Uttam Kumaran: This meeting will be very lame.
11 00:02:17.610 ⇒ 00:02:18.580 Ryan Brosas: Yeah.
12 00:03:00.210 ⇒ 00:03:01.080 luke: Hold on!
13 00:03:11.950 ⇒ 00:03:12.720 luke: Join.
14 00:03:51.110 ⇒ 00:03:59.432 Uttam Kumaran: Okay, cool, I think. It is just this crew. I think Hannah is
15 00:04:00.000 ⇒ 00:04:02.040 Uttam Kumaran: dealing with a power outage right now
16 00:04:08.980 ⇒ 00:04:10.190 Uttam Kumaran: in Greece.
17 00:04:13.230 ⇒ 00:04:26.359 Uttam Kumaran: Alright. I can get started. If anyone else wants to go camera on to give me a name or some company that’d be great. But no problem. Otherwise. I will.
18 00:04:29.360 ⇒ 00:04:31.019 Uttam Kumaran: Let’s kick this off.
19 00:04:33.490 ⇒ 00:04:35.559 Uttam Kumaran: And it’s small group. So
20 00:04:35.930 ⇒ 00:04:43.279 Uttam Kumaran: like, ask some questions. And I wanna I’ll probably spend a bunch of time on AI stuff, too. So let me know what people are thinking.
21 00:04:44.256 ⇒ 00:04:50.369 Uttam Kumaran: Cool. So I don’t think we do. We have any new team members?
22 00:04:52.660 ⇒ 00:04:53.460 Uttam Kumaran: Okay?
23 00:04:55.600 ⇒ 00:05:02.100 Uttam Kumaran: Yeah, maybe we could. I mean, I guess we could all do this like in this group.
24 00:05:02.792 ⇒ 00:05:06.409 Uttam Kumaran: So pick one person to draw, using zoom whiteboard.
25 00:05:06.540 ⇒ 00:05:10.844 Uttam Kumaran: And then, okay, so who wants to?
26 00:05:12.090 ⇒ 00:05:16.119 Uttam Kumaran: Does anyone wanna do the drawing?
27 00:05:20.770 ⇒ 00:05:31.379 Amber Lin: I think we can all just draw on this screen. I think we can annotate. I’m not sure where we do that, but I feel like we couldn’t just draw on.
28 00:05:31.930 ⇒ 00:05:33.010 Uttam Kumaran: So if I go share.
29 00:05:33.010 ⇒ 00:05:34.680 Amber Lin: Sure. Too. Yeah.
30 00:05:34.890 ⇒ 00:05:36.730 Uttam Kumaran: Documents, whiteboard.
31 00:05:37.340 ⇒ 00:05:38.030 Amber Lin: Hmm.
32 00:05:42.300 ⇒ 00:05:43.410 Uttam Kumaran: It’s working right?
33 00:05:45.100 ⇒ 00:05:47.910 Amber Lin: Can we also draw? Oh, yay, okay.
34 00:05:48.260 ⇒ 00:05:53.140 Uttam Kumaran: Okay. So maybe I’ll nominate. I guess I wanna nominate probably the best
35 00:05:53.530 ⇒ 00:05:57.239 Uttam Kumaran: drawer on here, which is Ann.
36 00:05:58.910 ⇒ 00:06:02.190 Uttam Kumaran: and you are you? Alright, do you? Wanna
37 00:06:03.220 ⇒ 00:06:05.640 Uttam Kumaran: run like the 1st round of pictionary.
38 00:06:06.270 ⇒ 00:06:09.809 Anne: Sorry I’m late, so can you share the link again?
39 00:06:10.210 ⇒ 00:06:11.390 Uttam Kumaran: You’re good.
40 00:06:11.590 ⇒ 00:06:13.740 Uttam Kumaran: Huh? Oh, wait! Share! What?
41 00:06:15.020 ⇒ 00:06:17.109 Anne: The offing jam.
42 00:06:17.570 ⇒ 00:06:21.359 Uttam Kumaran: Oh, yeah, no, no. So we’re we’re actually here in this whiteboard.
43 00:06:23.220 ⇒ 00:06:24.490 Anne: Where can I got it?
44 00:06:24.740 ⇒ 00:06:26.126 Anne: Oh, okay.
45 00:06:27.040 ⇒ 00:06:32.230 Uttam Kumaran: And so if you go, if you go to this link I sent in the chat.
46 00:06:36.890 ⇒ 00:06:38.000 Anne: Okay.
47 00:06:38.980 ⇒ 00:06:41.080 Uttam Kumaran: So it’ll generate you a word.
48 00:06:41.890 ⇒ 00:06:44.309 Uttam Kumaran: and then we have to guess it.
49 00:06:45.410 ⇒ 00:06:47.799 Uttam Kumaran: And 1st person who guesses wins.
50 00:06:49.270 ⇒ 00:06:52.258 Uttam Kumaran: We have to also agree on what the prices still be.
51 00:06:54.480 ⇒ 00:06:56.949 Uttam Kumaran: Prize is a short meeting.
52 00:07:01.640 ⇒ 00:07:02.360 Anne: Okay.
53 00:07:09.310 ⇒ 00:07:10.609 Uttam Kumaran: I’m gonna erase all this.
54 00:07:10.990 ⇒ 00:07:11.780 Uttam Kumaran: Hello, someone.
55 00:07:12.770 ⇒ 00:07:15.456 Amber Lin: Oh, someone’s drawing is very good also.
56 00:07:17.580 ⇒ 00:07:20.209 Uttam Kumaran: Okay, wait. And where where are you gonna be drawing.
57 00:07:21.680 ⇒ 00:07:23.020 Anne: Oh, maybe here.
58 00:07:23.270 ⇒ 00:07:24.940 Uttam Kumaran: You want to draw like in this square.
59 00:07:25.570 ⇒ 00:07:26.540 Anne: Oh, okay.
60 00:07:32.300 ⇒ 00:07:34.490 Anne: Yup. I’ll generate one.
61 00:07:35.560 ⇒ 00:07:36.110 Uttam Kumaran: Okay.
62 00:07:37.720 ⇒ 00:07:38.980 Anne: What’s the category?
63 00:07:41.450 ⇒ 00:07:42.310 Anne: Preview.
64 00:07:42.510 ⇒ 00:07:43.750 Uttam Kumaran: Let’s do medium.
65 00:08:16.810 ⇒ 00:08:18.000 Anne: I know.
66 00:08:31.060 ⇒ 00:08:31.930 Amber Lin: Barrel.
67 00:08:32.740 ⇒ 00:08:33.500 Anne: Yeah, yeah.
68 00:08:33.500 ⇒ 00:08:36.065 Uttam Kumaran: Wine barrel. Yay.
69 00:08:42.024 ⇒ 00:08:44.220 Uttam Kumaran: does someone else? Someone want to go next?
70 00:08:46.470 ⇒ 00:08:48.239 Uttam Kumaran: You might just work down the list.
71 00:08:48.839 ⇒ 00:08:51.040 Uttam Kumaran: Amber. Do you wanna go.
72 00:08:51.150 ⇒ 00:08:52.970 Amber Lin: Oh, okay, I can go.
73 00:08:55.300 ⇒ 00:08:57.969 Amber Lin: You can give me another square if you want.
74 00:08:57.970 ⇒ 00:08:58.839 Uttam Kumaran: That’s fine!
75 00:09:00.240 ⇒ 00:09:01.080 Amber Lin: Okay.
76 00:09:05.070 ⇒ 00:09:08.189 Amber Lin: Oh, fling my bad. Okay.
77 00:09:08.530 ⇒ 00:09:10.460 Amber Lin: Dictionary generator.
78 00:09:10.980 ⇒ 00:09:13.489 Amber Lin: Y’all, I’m gonna do hard.
79 00:09:14.370 ⇒ 00:09:18.259 Amber Lin: You guys have figured. Oh, there’s a really hard oh, he did.
80 00:09:19.060 ⇒ 00:09:21.048 Anne: It’s not even the hardest one.
81 00:09:22.450 ⇒ 00:09:27.097 Amber Lin: I don’t even know what that word means. Okay, next word.
82 00:09:35.650 ⇒ 00:09:38.290 Amber Lin: Oh, oh, my gosh, this is hard. Okay,
83 00:10:20.480 ⇒ 00:10:25.900 Amber Lin: also, I am drawing on my computer trackpad. I, this is a little hard.
84 00:10:26.360 ⇒ 00:10:33.020 Amber Lin: close, close, close, are very close.
85 00:10:33.580 ⇒ 00:10:38.000 Amber Lin: It’s 2 words, is it? Few words?
86 00:10:54.840 ⇒ 00:10:56.100 Amber Lin: Hmm.
87 00:10:58.450 ⇒ 00:11:00.780 Amber Lin: I’m drawing texture.
88 00:11:03.486 ⇒ 00:11:04.420 Ryan Brosas: Nice one.
89 00:11:08.150 ⇒ 00:11:09.730 Amber Lin: No!
90 00:11:17.415 ⇒ 00:11:20.040 Amber Lin: Do I reveal.
91 00:11:20.930 ⇒ 00:11:23.110 Uttam Kumaran: Really vast.
92 00:11:24.470 ⇒ 00:11:26.910 Amber Lin: It’s a sweater vest.
93 00:11:27.520 ⇒ 00:11:30.900 Uttam Kumaran: Oh, yeah, that’s good.
94 00:11:32.930 ⇒ 00:11:37.239 Uttam Kumaran: You should have drawn like a business dude, or something like who wears sweater vests.
95 00:11:38.140 ⇒ 00:11:41.519 Amber Lin: I know I don’t even know who wears, though I couldn’t.
96 00:11:41.520 ⇒ 00:11:44.382 Uttam Kumaran: Should have wrote like Mba or like business.
97 00:11:47.877 ⇒ 00:11:52.720 Uttam Kumaran: Ryan, you wanna go, Ryan Brosas.
98 00:11:53.500 ⇒ 00:11:54.360 Ryan Brosas: Sure, sure.
99 00:12:30.020 ⇒ 00:12:31.200 Amber Lin: Furniture.
100 00:12:31.650 ⇒ 00:12:37.189 Amber Lin: Well, outlet! Oh.
101 00:13:42.146 ⇒ 00:13:43.273 Uttam Kumaran: I don’t know.
102 00:13:44.285 ⇒ 00:13:45.555 Ryan Brosas: Almost there.
103 00:13:53.650 ⇒ 00:13:56.620 Ryan Brosas: Only a little bit can do meeting plan.
104 00:13:59.760 ⇒ 00:14:00.069 Amber Lin: Oh!
105 00:14:00.070 ⇒ 00:14:01.550 Amber Lin: Is it 2 words?
106 00:14:02.380 ⇒ 00:14:04.470 Ryan Brosas: Yeah. One word only
107 00:14:20.990 ⇒ 00:14:24.990 Ryan Brosas: I think connected is fine. I guess.
108 00:14:25.628 ⇒ 00:14:27.119 Uttam Kumaran: What was? What is it?
109 00:14:27.650 ⇒ 00:14:28.710 Ryan Brosas: Connect.
110 00:14:29.560 ⇒ 00:14:34.030 Uttam Kumaran: Oh, wait! Well, people have said, connect right?
111 00:14:35.020 ⇒ 00:14:35.930 Ryan Brosas: The.
112 00:14:36.580 ⇒ 00:14:39.451 Uttam Kumaran: Oh, no one said literally the word Connect!
113 00:14:39.810 ⇒ 00:14:40.545 Ryan Brosas: Connection.
114 00:14:43.380 ⇒ 00:14:47.429 Uttam Kumaran: Okay, okay, cool. Do we have 1, 1 more.
115 00:14:47.640 ⇒ 00:14:50.450 Uttam Kumaran: Someone before was drawing like, really
116 00:14:51.240 ⇒ 00:14:55.870 Uttam Kumaran: like good shit. Actually. So who was drawing? Who’s drawing down here?
117 00:14:57.270 ⇒ 00:14:58.210 Amber Lin: Huh!
118 00:14:58.840 ⇒ 00:15:01.329 Uttam Kumaran: Who is, who is drawing down here.
119 00:15:03.013 ⇒ 00:15:04.139 Amber Lin: The cat.
120 00:15:04.140 ⇒ 00:15:10.509 Uttam Kumaran: It was Demo latte, or Annie, like someone was drawing like, actually something really, really nice. And I erased it.
121 00:15:13.930 ⇒ 00:15:15.860 Annie Yu: Definitely wouldn’t be me.
122 00:15:16.559 ⇒ 00:15:21.350 Uttam Kumaran: Wait, Demolan, is it you? Someone is like sneaky good at drawing.
123 00:15:21.850 ⇒ 00:15:23.180 Demilade Agboola: That’s definitely not me.
124 00:15:25.310 ⇒ 00:15:27.040 Uttam Kumaran: Okay, I don’t know.
125 00:15:28.180 ⇒ 00:15:32.500 Uttam Kumaran: Does anyone does anyone else want to do last? Oh, is that Luke?
126 00:15:36.060 ⇒ 00:15:40.539 Luke Daque: I don’t know. I was on my cell phone earlier, so it wouldn’t have been me.
127 00:15:40.960 ⇒ 00:15:44.740 Uttam Kumaran: Okay, does anyone want to do the last round.
128 00:15:46.700 ⇒ 00:15:49.474 Amber Lin: I mean. Luke guessed it.
129 00:15:50.030 ⇒ 00:15:52.699 Uttam Kumaran: Okay, Luke. Look you did. You did last round.
130 00:15:52.790 ⇒ 00:15:53.730 Amber Lin: T.
131 00:15:55.050 ⇒ 00:15:58.750 Luke Daque: Okay? Sure. What’s the link again? Can you? Can you send it to me?
132 00:15:58.750 ⇒ 00:15:59.890 Amber Lin: In the chat.
133 00:16:00.459 ⇒ 00:16:02.979 Amber Lin: Scroll up. Oh, never mind. Okay.
134 00:16:02.980 ⇒ 00:16:05.090 Luke Daque: Because I just rejoined.
135 00:16:08.630 ⇒ 00:16:09.510 Amber Lin: Sent.
136 00:16:14.250 ⇒ 00:16:15.590 Luke Daque: Wait what? I can’t.
137 00:16:15.860 ⇒ 00:16:16.570 Luke Daque: I have them.
138 00:16:17.360 ⇒ 00:16:21.930 Luke Daque: I’m getting access, denied what.
139 00:16:23.610 ⇒ 00:16:28.009 Amber Lin: Somebody generate a way to send it to Luke or Luke. You just come up with a word.
140 00:16:28.010 ⇒ 00:16:30.480 Uttam Kumaran: Or just ask chat. Should Betty real quick.
141 00:16:31.740 ⇒ 00:16:33.000 Luke Daque: Yeah, okay.
142 00:16:48.290 ⇒ 00:16:54.070 Luke Daque: these are pretty easy, I guess. Wait, let me try again.
143 00:16:56.440 ⇒ 00:16:58.149 Luke Daque: Okay, this one.
144 00:17:01.960 ⇒ 00:17:03.380 Luke Daque: How do I draw?
145 00:17:25.990 ⇒ 00:17:28.160 Luke Daque: I don’t know what to draw a person.
146 00:17:29.110 ⇒ 00:17:32.100 Uttam Kumaran: I don’t know. It looks like that’s not bad.
147 00:17:40.370 ⇒ 00:17:42.720 Luke Daque: Yeah. Boxing. Yeah. Somebody already got it.
148 00:17:43.360 ⇒ 00:17:44.640 Uttam Kumaran: Nice. Alright.
149 00:17:44.910 ⇒ 00:17:46.880 Uttam Kumaran: Yeah.
150 00:17:46.880 ⇒ 00:17:48.720 Amber Lin: Good person drawing Luke.
151 00:17:49.810 ⇒ 00:17:53.150 Uttam Kumaran: Yeah, like you got the. You have the knees bent and everything
152 00:17:57.980 ⇒ 00:17:58.910 Uttam Kumaran: nice.
153 00:17:58.910 ⇒ 00:18:00.709 Demilade Agboola: I wish the word was Ping Pong.
154 00:18:01.250 ⇒ 00:18:01.909 Uttam Kumaran: Being fine.
155 00:18:06.110 ⇒ 00:18:09.090 Uttam Kumaran: cool. Hannah, are you back.
156 00:18:09.240 ⇒ 00:18:14.809 Hannah Wang: Yeah, I I’m like in my car. I drove out to like the street and has better connection.
157 00:18:15.310 ⇒ 00:18:17.510 Hannah Wang: I’m from my hotspot. So I’m literally like.
158 00:18:17.510 ⇒ 00:18:20.320 Uttam Kumaran: Is there a we work near you? You could go there.
159 00:18:20.320 ⇒ 00:18:25.849 Hannah Wang: It was too early for my brain to to do that and make that effort. But.
160 00:18:26.183 ⇒ 00:18:26.770 Uttam Kumaran: I think.
161 00:18:26.770 ⇒ 00:18:31.180 Hannah Wang: It’ll be fine for the next hour, so I can do my lab share.
162 00:18:31.180 ⇒ 00:18:32.480 Uttam Kumaran: Yeah, please.
163 00:18:33.700 ⇒ 00:18:36.810 Hannah Wang: Okay, let’s see.
164 00:18:42.670 ⇒ 00:18:48.110 Hannah Wang: alright. So my lab share. Can you guys see my screen.
165 00:18:48.730 ⇒ 00:18:49.450 Uttam Kumaran: Yes.
166 00:18:49.770 ⇒ 00:18:50.120 Hannah Wang: Okay.
167 00:18:50.120 ⇒ 00:18:51.530 Amber Lin: Wow!
168 00:18:51.530 ⇒ 00:18:54.339 Hannah Wang: Like super random but.
169 00:18:54.510 ⇒ 00:18:55.980 Amber Lin: The nice slide.
170 00:18:55.980 ⇒ 00:18:56.920 Hannah Wang: Thank you.
171 00:18:57.040 ⇒ 00:19:00.189 Hannah Wang: As a designer, I had to, I guess. So.
172 00:19:02.380 ⇒ 00:19:17.439 Hannah Wang: okay, so this is super random, but like in elementary school, I remember there was like a game that we used to play like on my laptop where you were given a bunch of Logos, and you have to see how many you can recognize, and for some reason that like stuck with me
173 00:19:17.590 ⇒ 00:19:27.839 Hannah Wang: until many years later. So I kinda knew the science behind. Why, good logos are good and why they stick. So I just kinda wanted to share
174 00:19:27.860 ⇒ 00:19:56.460 Hannah Wang: that today. So yeah, obviously, like these 3 iconic logos, you can clearly guess within a split second and you like, yeah, immediately recognize them. But like, why? Why do we remember these Logos instantly? And there’s 3 kind of reasons that I kind of simplified it, too. And the 1st one is because it’s simple. So I think
175 00:19:56.890 ⇒ 00:20:06.090 Hannah Wang: why companies create Logos in the 1st place, because is because it works as like a shorthand for their brand.
176 00:20:06.680 ⇒ 00:20:29.920 Hannah Wang: I feel like names are harder to memorize. A lot of companies have like really intricate names, so I feel like creating a logo will help it stick a little bit better. And the reason why simplicity works is because it sticks better in our brains. Our brains, like love simple things because it reduces cognitive load. I feel like our brain wants to preserve as many
177 00:20:30.270 ⇒ 00:20:37.819 Hannah Wang: calories as possible, and not exert that much energy, so the easier it is to store.
178 00:20:38.100 ⇒ 00:20:50.630 Hannah Wang: and the easier it is to retrieve like the better it’ll stick in our brains. So classic example, is the nike logo, the the swoosh. It’s like so simple, but so iconic and like everywhere in our world.
179 00:20:51.435 ⇒ 00:20:54.989 Hannah Wang: The second reason why it’s sticky is because of
180 00:20:55.080 ⇒ 00:21:23.369 Hannah Wang: psychology. So there’s psychology behind colors and shapes. So certain colors evoke certain emotions. So, for example, red, if you think about red things, it’s like bold. And out there, if you think about blue, blue is typically used to represent trust. So I use Facebook’s Meta’s old logo Facebook, whatever. But Facebook is like a platform for connection and trust. So that’s why it’s blue
181 00:21:23.768 ⇒ 00:21:28.550 Hannah Wang: shapes. Also matter a lot. So if you think about a circle.
182 00:21:28.960 ⇒ 00:21:49.439 Hannah Wang: it kind of gives like a friendly vibe versus a square is more like stable and grounded, so you can see the Pepsi Logo is a circle, and then Microsoft’s old logo, or I feel like this is an older version of their logo. It’s a square to kind of represent like, oh, I’m I can be trusted. I’m steady. I’m here.
183 00:21:50.900 ⇒ 00:21:55.130 Hannah Wang: And a little fun fact, I feel like, maybe probably.
184 00:21:55.130 ⇒ 00:21:57.080 Uttam Kumaran: Wow, yeah, I didn’t even think about this.
185 00:21:57.080 ⇒ 00:22:08.019 Hannah Wang: Okay, yeah. So yeah, fast food brands love to use these colors because they make you hungry. So I feel like some of these Logos are a bit older, but I think it’s still.
186 00:22:08.020 ⇒ 00:22:10.610 Uttam Kumaran: Why is that cause? It looks like ketchup and stuff, or.
187 00:22:10.610 ⇒ 00:22:14.434 Hannah Wang: I don’t. I don’t know. Ketchup and mustard. Maybe.
188 00:22:14.860 ⇒ 00:22:15.630 Amber Lin: So.
189 00:22:15.780 ⇒ 00:22:16.500 Uttam Kumaran: I am hungry.
190 00:22:16.500 ⇒ 00:22:16.930 Uttam Kumaran: Wait.
191 00:22:18.740 ⇒ 00:22:24.139 Amber Lin: I went across a study where people wanted to eat less, and then they died, all their food
192 00:22:24.430 ⇒ 00:22:28.030 Amber Lin: like blue, and then it appetite.
193 00:22:28.030 ⇒ 00:22:28.590 Hannah Wang: Yeah.
194 00:22:28.590 ⇒ 00:22:31.109 Amber Lin: Color is not something you want to.
195 00:22:33.200 ⇒ 00:22:44.348 Hannah Wang: Yeah, like blue, green, purple. I guess not. A lot of foods have that color, I don’t know, but like red and yellow, not a lot of foods have that color, either. So it’s like very interesting.
196 00:22:44.650 ⇒ 00:22:59.590 Amber Lin: I guess red is mostly the color of fruits. Right? If you have fruit that is ripe, that humans can’t probably eat like an apples, right, or tomatoes are red, like the fruits of things. The ones that have more calories are usually red.
197 00:22:59.915 ⇒ 00:23:00.240 Hannah Wang: Yeah.
198 00:23:00.240 ⇒ 00:23:02.890 Amber Lin: And then if you look at the vegetables and leaves and
199 00:23:03.010 ⇒ 00:23:06.070 Amber Lin: like, if it’s blue, maybe it’s poisonous. I don’t know.
200 00:23:06.070 ⇒ 00:23:09.560 Hannah Wang: Oh, like our very primitive brains will.
201 00:23:10.710 ⇒ 00:23:11.540 Hannah Wang: Yeah.
202 00:23:12.130 ⇒ 00:23:14.770 Hannah Wang: Well, anyway, that’s why, when you go
203 00:23:15.200 ⇒ 00:23:23.860 Hannah Wang: to places with food, it’s just red and yellow and orange everywhere, except cinnabon. Cinnabon’s like blue, but
204 00:23:24.020 ⇒ 00:23:27.080 Hannah Wang: either way it it still stands
205 00:23:28.040 ⇒ 00:23:43.260 Hannah Wang: and the 3rd one of white sticks is because of clever designs. I don’t know if you guys know there’s like hidden designs in these Logos. So Fedex, if you look at the E and the X like, it forms an arrow in that white space.
206 00:23:43.660 ⇒ 00:23:47.323 Hannah Wang: I didn’t know that until like probably 2 years ago,
207 00:23:47.690 ⇒ 00:23:49.300 Uttam Kumaran: Hey? Say that one more time.
208 00:23:49.300 ⇒ 00:23:55.399 Hannah Wang: So you see that the E and the x, like the white space between the letters.
209 00:23:55.400 ⇒ 00:23:58.800 Uttam Kumaran: Oh, no way, I didn’t. Even I couldn’t even see what you’re talking about.
210 00:23:59.350 ⇒ 00:23:59.900 Hannah Wang: Yeah.
211 00:24:00.856 ⇒ 00:24:21.750 Hannah Wang: And then Amazon. The the arrow here is supposed to represent a smile, and that equals like the happiness of customers. And then it points from A to Z. So that shows like, Oh, the range of products, like, we have a lot of large range of products that we sell so that’s kind of the meaning behind that logo
212 00:24:22.220 ⇒ 00:24:28.060 Hannah Wang: tostitos is like a popular tortilla chip brand here in the States. And if you look at the.
213 00:24:28.060 ⇒ 00:24:30.419 Uttam Kumaran: Oh, my God! I didn’t even know that either.
214 00:24:30.420 ⇒ 00:24:34.589 Hannah Wang: Sharing a a tortilla chip, and there’s a salsa on top of that.
215 00:24:34.590 ⇒ 00:24:35.650 Uttam Kumaran: What?
216 00:24:35.810 ⇒ 00:24:48.730 Hannah Wang: Toblerone is a popular like chocolate company here, but it was made in Switzerland. So if you look at the mountain, you can kind of see the outline of a bear standing up.
217 00:24:49.290 ⇒ 00:24:50.350 Uttam Kumaran: Where.
218 00:24:51.208 ⇒ 00:24:53.519 Hannah Wang: Do you see my cursor? How do I.
219 00:24:53.520 ⇒ 00:24:54.200 Uttam Kumaran: Yeah.
220 00:24:54.550 ⇒ 00:24:57.630 Hannah Wang: Okay, that.
221 00:24:57.630 ⇒ 00:25:00.000 Uttam Kumaran: Oh! What!
222 00:25:00.000 ⇒ 00:25:00.800 Hannah Wang: Right there.
223 00:25:02.970 ⇒ 00:25:09.469 Uttam Kumaran: My mind is being blown. I have no idea I’m so many fun facts for people. Now, when I see these in the airport.
224 00:25:09.740 ⇒ 00:25:15.570 Hannah Wang: Yeah, toblerone is actually made in Bern, Switzerland, and there’s a lot of bears in Bern. So that’s why that bear
225 00:25:15.570 ⇒ 00:25:16.200 Hannah Wang: ow.
226 00:25:16.200 ⇒ 00:25:17.249 Uttam Kumaran: Bear? Is there.
227 00:25:17.826 ⇒ 00:25:26.770 Hannah Wang: And then how do I clear my annotation? Clear all my drawings? Okay? And then the last one baskin Robbins, the pink
228 00:25:26.930 ⇒ 00:25:33.779 Hannah Wang: parts 31. That’s a 3 and a 1, and it represents like the variety of flavors that they have.
229 00:25:34.020 ⇒ 00:25:36.610 Hannah Wang: I did not know this until yesterday, so
230 00:25:36.710 ⇒ 00:25:43.400 Hannah Wang: I thought that was that was cool. I just thought it was like a cool pink and blue design. But actually it’s a 3 and a 1.
231 00:25:45.090 ⇒ 00:25:46.249 Hannah Wang: Alright. So
232 00:25:47.040 ⇒ 00:26:02.269 Hannah Wang: conclusion. Tldr, a good logo isn’t just pretty. There’s a lot of psychology behind it, as you can see from all these 3 points, and that’s why you know good Logos are good because they stick in our brain and you can recognize it immediately.
233 00:26:02.640 ⇒ 00:26:11.150 Hannah Wang: So yeah, that was my lab share and Amber said Brainstorm. Our logo is a brain too complicated.
234 00:26:11.150 ⇒ 00:26:20.589 Uttam Kumaran: I’m also. I’m also curious about it. If, reviewing this, you you have any thoughts on our well, I mean I and I don’t know if I’ve told everyone. This, like
235 00:26:20.760 ⇒ 00:26:23.600 Uttam Kumaran: the name of the company, is not like
236 00:26:23.740 ⇒ 00:26:41.179 Uttam Kumaran: very like meaningful, you know. I think it’s become more meaningful, as the company now means something, but when we started it was just like what I needed to put on documents, you know, and so, in fact, we didn’t spend much time thinking about it. Which.
237 00:26:41.600 ⇒ 00:27:01.640 Uttam Kumaran: like in hindsight like, I couldn’t have spent more time doing that. It was wasn’t as important as getting clients. But now that we have a brand, I think it is unique, and I do see the value in it. I do wonder, though, like our our logo now, is just the word we don’t have like a specific thing. This is, this is really really cool to sort of think about.
238 00:27:03.250 ⇒ 00:27:05.969 Hannah Wang: Yeah, I mean, I feel like a bee
239 00:27:06.640 ⇒ 00:27:09.000 Hannah Wang: is good cause it’s like simple.
240 00:27:09.745 ⇒ 00:27:25.180 Hannah Wang: I feel like a brain is like, kind of ugly, or it’s like kind of gross, or it has like a lot of grooves and stuff. So I feel like you have to make it really cartoonified. I don’t know. I thought about it. Should we change our logo? But then that’s like a whole rebrand.
241 00:27:25.630 ⇒ 00:27:33.389 Hannah Wang: And then like, do we want to do that to our current clients and potential customers. It’s like, I don’t really know.
242 00:27:33.841 ⇒ 00:27:42.139 Hannah Wang: I feel like A, B is okay. And we also don’t want to like change the name of our entire company. I don’t think so.
243 00:27:43.530 ⇒ 00:27:46.199 Hannah Wang: I don’t know anyone else else have any thoughts.
244 00:27:49.070 ⇒ 00:27:54.320 Hannah Wang: Yeah, people can think about it. A circle of brains forging into one
245 00:27:54.650 ⇒ 00:28:00.800 Hannah Wang: that do we have a logo? We do. It’s that bee, the bee logo that you see everywhere.
246 00:28:00.970 ⇒ 00:28:03.959 Amber Lin: Oh, I thought that was just like
247 00:28:04.090 ⇒ 00:28:07.220 Amber Lin: design for the decks and stuff.
248 00:28:07.220 ⇒ 00:28:07.760 Hannah Wang: Oh!
249 00:28:07.760 ⇒ 00:28:08.220 Amber Lin: Okay.
250 00:28:09.950 ⇒ 00:28:12.909 Hannah Wang: I mean, if we go into branding.
251 00:28:20.170 ⇒ 00:28:21.040 Hannah Wang: Yeah, I mean.
252 00:28:21.040 ⇒ 00:28:21.640 Amber Lin: Oh, wow!
253 00:28:21.640 ⇒ 00:28:31.170 Hannah Wang: Our like full logo. This is our like icon, so I don’t know.
254 00:28:34.660 ⇒ 00:28:36.520 Amber Lin: It could be 2 brains.
255 00:28:38.180 ⇒ 00:28:39.604 Hannah Wang: Coming to one.
256 00:28:41.620 ⇒ 00:28:47.910 Uttam Kumaran: Yeah, I don’t know. I feel like Forge is sort of like for forging something like you’re creating something
257 00:28:48.620 ⇒ 00:28:50.740 Uttam Kumaran: I like the with the brain.
258 00:28:51.250 ⇒ 00:28:57.369 Uttam Kumaran: The B is good, I think. Just like everything. We’ll sort of think of some evolution.
259 00:29:00.970 ⇒ 00:29:04.960 Uttam Kumaran: It is kind of nice, though, to have a logo like
260 00:29:05.490 ⇒ 00:29:12.780 Uttam Kumaran: that is meaningful. But I will say, if we do one, we should think about some Easter egg thing for sure. Now that we know that we have to.
261 00:29:12.780 ⇒ 00:29:13.170 Amber Lin: Oh, yeah.
262 00:29:15.670 ⇒ 00:29:22.180 Hannah Wang: It makes the logo more fun if there’s like a hidden visual or something in it.
263 00:29:22.360 ⇒ 00:29:22.980 Uttam Kumaran: Yeah.
264 00:29:22.980 ⇒ 00:29:29.679 Amber Lin: Be the blanks. Yeah, like the space. You know, the blank space in the B that could be a shape of A,
265 00:29:31.000 ⇒ 00:29:34.399 Amber Lin: you know, the brain does have 2 parts, right? So.
266 00:29:34.400 ⇒ 00:29:34.830 Hannah Wang: No.
267 00:29:34.830 ⇒ 00:29:41.909 Uttam Kumaran: I I did. I did. I wrote something down about having 2 parts of the brain as like the theme, because we do data and AI work.
268 00:29:42.220 ⇒ 00:29:43.190 Amber Lin: Yeah.
269 00:29:45.710 ⇒ 00:29:53.721 Uttam Kumaran: but I don’t know it’s and and everybody is familiar with this concept of left brain, right brain, although I think it’s been debunked. There are many parts of the brain.
270 00:29:54.350 ⇒ 00:29:58.090 Uttam Kumaran: but like that is a common like understood concept.
271 00:29:58.360 ⇒ 00:30:00.620 Uttam Kumaran: But I don’t know. I’m not a designer, so.
272 00:30:02.710 ⇒ 00:30:12.940 Hannah Wang: Yeah, we can think about it more. But it is. I mean, that’s why people are paid to make Logos, because it’s like hard. And it. It makes a big difference on the brand. So
273 00:30:14.720 ⇒ 00:30:21.830 Hannah Wang: anyway, that was my lab share. Also, I was like sharing my whole screen. I didn’t know you could see like the notes pad I had.
274 00:30:21.830 ⇒ 00:30:24.279 Uttam Kumaran: Oh, I thought you were! That was just helpful to read also.
275 00:30:24.280 ⇒ 00:30:34.789 Hannah Wang: Oh, okay, okay, cool. I was like, oh, cause I cannot present without like something in my hand, or like flash or whatever. No cards
276 00:30:35.275 ⇒ 00:30:42.820 Hannah Wang: so that I just exposed myself there. But hopefully it was, expose yourself. It’s totally fine.
277 00:30:42.820 ⇒ 00:30:50.409 Hannah Wang: I’m like so jealous of people who can just like, go up there and talk, and not like have a note in their hand, or like note cards. But.
278 00:30:50.410 ⇒ 00:30:55.229 Uttam Kumaran: But sometimes you get jammed. You don’t know what meeting you’re in or what you’re talking about.
279 00:30:55.230 ⇒ 00:30:56.219 Hannah Wang: That’s true.
280 00:30:57.370 ⇒ 00:30:58.500 Hannah Wang: Okay, cool.
281 00:30:59.447 ⇒ 00:31:00.842 Uttam Kumaran: Let’s continue
282 00:31:03.310 ⇒ 00:31:14.989 Uttam Kumaran: great. So I wanted to just give a couple of updates as part of exact updates. So I know. Last time I went through accomplishments. I think I’m gonna try to do this once a month.
283 00:31:15.736 ⇒ 00:31:35.070 Uttam Kumaran: Although I can talk a little bit about how sales are going. I think, most importantly, every week. I do want to talk about the collective clients that we have and maybe work on some group takeaways. So I’ve we’ve kind of broken it out into now into sort of strategic clients and what we’re gonna call developing clients.
284 00:31:35.832 ⇒ 00:31:58.410 Uttam Kumaran: I didn’t really like large and small. It doesn’t indicate, like how we see them, how we want them to grow. So I think, you know, this is our current list of what we would consider as you know, strategic clients. We have urban stems, Eden, ABC and matter more. They’re strategic both because of our potential to grow revenue with them. Our potential impact
285 00:31:58.884 ⇒ 00:32:07.835 Uttam Kumaran: and the amount of resources that we we put onto the project and so I feel like across the 4 of these I I feel
286 00:32:08.380 ⇒ 00:32:29.639 Uttam Kumaran: pretty good. I think. Eden is probably the only one where maybe I’ll I’ll pick on somebody from that project like. And maybe, Annie, I can ask you, since you’re going to be sharing a little bit later, like, how do you think the the project is going so far, maybe, compared to last month or 2 months ago. Versus. Now do you just have a short reflection.
287 00:32:31.715 ⇒ 00:32:51.604 Annie Yu: I would say they are definitely coming to us. More as like seeing us as a partner, and they used to more like sticking to their previous versions. Things like in whatever things that’s in looker. And now they’re like can see, they are actually transitioning things
288 00:32:52.800 ⇒ 00:32:56.490 Annie Yu: to whatever bi tool we’re using now and then.
289 00:32:57.585 ⇒ 00:33:08.000 Annie Yu: They also like talk to our engineers a lot asking things. So I think overall definitely, we’re making more like
290 00:33:09.360 ⇒ 00:33:13.270 Annie Yu: we’re having, like higher visibility there and then more trust.
291 00:33:14.990 ⇒ 00:33:17.559 Annie Yu: Yeah. But on the other hand, it’s like
292 00:33:18.093 ⇒ 00:33:21.240 Annie Yu: we’re serving lots of the teams there. So
293 00:33:21.500 ⇒ 00:33:27.960 Annie Yu: even within like one client, you have to like context switching multiple times, too.
294 00:33:28.360 ⇒ 00:33:29.130 Uttam Kumaran: I see.
295 00:33:33.060 ⇒ 00:33:39.509 Uttam Kumaran: What would you say? You know I’m just maybe just to kind of go one step like, what would you say is like something we can improve on there
296 00:33:39.620 ⇒ 00:33:40.699 Uttam Kumaran: with Eden.
297 00:33:45.800 ⇒ 00:33:46.630 Annie Yu: Hmm.
298 00:33:46.630 ⇒ 00:33:52.119 Uttam Kumaran: It’s not super. If if it’s fine, if it’s you’re like, Yeah, we’re cruising, then that’s it, like there doesn’t have to be.
299 00:33:53.750 ⇒ 00:34:06.109 Annie Yu: This is not like a huge deal, but for me I like. I also like shared this with them all day, too, like I. I get distracted very easily, and so to like respond to different
300 00:34:06.280 ⇒ 00:34:13.840 Annie Yu: stakeholders on different teams. Sometimes it’s it’s hard, and it like takes much time than I would. I would like
301 00:34:14.050 ⇒ 00:34:15.909 Annie Yu: to focus on like my work.
302 00:34:16.690 ⇒ 00:34:20.310 Uttam Kumaran: Yeah, I I feel really similar. I think one thing we can talk about
303 00:34:23.960 ⇒ 00:34:30.610 Uttam Kumaran: is yes, I think not. Only the fact that in within a client like Eden. There’s a lot of context switching. And
304 00:34:30.900 ⇒ 00:34:40.420 Uttam Kumaran: of course, like across other accounts. And like me, sending something in the channel like there’s all types of context switching. So I think that’s something we can talk about.
305 00:34:40.999 ⇒ 00:34:51.990 Uttam Kumaran: So I’ll just note that down, I think for urban stems. Yeah, I think we’re we’re in this next phase. I think we’re in what we’re sort of deeming this like 1st month of this next project where
306 00:34:52.179 ⇒ 00:35:00.020 Uttam Kumaran: amber demalade and I talked this morning about sort of being full court press, so that we could establish the pace upfront.
307 00:35:00.370 ⇒ 00:35:15.119 Uttam Kumaran: And so we’re sort of in that mode right now, I think I think the work has been really good. I think credit to Kyle for a lot of the organizational work. And we, you know we’ve been working with them for almost 2 and a half months now. So this is really a strategic client. I think we’re going to be able to do a lot for them.
308 00:35:15.644 ⇒ 00:35:22.540 Uttam Kumaran: Abc. Home. I think it’s getting better compared to last week where we talked, I think.
309 00:35:22.910 ⇒ 00:35:25.910 Uttam Kumaran: like I don’t know amber. If you would agree, we have groomed.
310 00:35:25.910 ⇒ 00:35:27.590 Amber Lin: Grooming helped.
311 00:35:27.590 ⇒ 00:35:29.889 Uttam Kumaran: Yeah, broom more tickets.
312 00:35:30.380 ⇒ 00:35:37.769 Uttam Kumaran: I think the client is still slow. But like, I guess my feedback for amber today was, that’s okay.
313 00:35:38.248 ⇒ 00:35:44.881 Uttam Kumaran: As long as like our ability to make them succeed isn’t blocked, and we still have stuff on our side. We can do.
314 00:35:46.110 ⇒ 00:35:49.559 Amber Lin: And I think an update here is that now.
315 00:35:50.027 ⇒ 00:36:00.030 Amber Lin: We are after we groomed the tickets. Now we’re getting back up to speed in terms of our development, because engineering has slowed before. But we’re getting back.
316 00:36:00.680 ⇒ 00:36:01.170 Uttam Kumaran: How bad.
317 00:36:01.170 ⇒ 00:36:11.364 Amber Lin: I think, for these clients. It doesn’t. Even if they’re slow. What we’ve learned is that we can still guide them a lot of times. They just want it. Want us to tell them what to do.
318 00:36:11.970 ⇒ 00:36:13.840 Uttam Kumaran: Okay, yeah, I agree.
319 00:36:14.562 ⇒ 00:36:20.910 Uttam Kumaran: The other thing is for ABC home, where our our pricing model is based on their usage of our AI tools.
320 00:36:21.050 ⇒ 00:36:39.590 Uttam Kumaran: So you know, although I’m I’m actually fine if we don’t have development work. But we need the adoption to pick up right? So if our engineers need to go sit with people, or we need to. Many somehow figure that out. That’s what we need to do. So I still think here that there’s room to improve usage that we have to crack
321 00:36:40.111 ⇒ 00:36:49.839 Uttam Kumaran: and we need to. We need to meet and and talk about how we, how we do that more frequently. The last piece. I think this is probably the most out of all the clients this week. I think this is the every week
322 00:36:50.100 ⇒ 00:36:52.340 Uttam Kumaran: one of these has a problem. So
323 00:36:52.767 ⇒ 00:37:03.609 Uttam Kumaran: as you guys know, like, I stay pretty stoic. And I’m like, well, we’re gonna full. Figure it out. Think this one matter more this week was the was the week where we’re like we’re super misaligned and like
324 00:37:04.359 ⇒ 00:37:15.529 Uttam Kumaran: like, they think we’re not doing anything. And blah blah like fine, I think, where we sort of arrived is on 2 big, you know. I think a couple of big takeaways here one is.
325 00:37:15.640 ⇒ 00:37:28.979 Uttam Kumaran: We actually did everything that they wanted. Like we have, we have, like SQL view set up, we have business logic, we have synthetic data. We have the data like everything they actually want. I just don’t think we
326 00:37:29.503 ⇒ 00:37:38.989 Uttam Kumaran: when we translated to them, it wasn’t until I came in the meeting of like we actually have. We have that we have, that we have that and was confident that they were like, Okay, cool
327 00:37:39.469 ⇒ 00:37:52.300 Uttam Kumaran: so I think there’s something to be said here about like confidence and showing I think the way we fix this is a way she’s now be like tech leading on this project, which is, gonna be helpful.
328 00:37:53.680 ⇒ 00:37:57.339 Uttam Kumaran: I think the other piece on matter more, too, is they’re a startup
329 00:37:57.929 ⇒ 00:38:07.639 Uttam Kumaran: and part of the reason we don’t work with many startups is because of what you see here. You know, my career has all been in startups
330 00:38:08.098 ⇒ 00:38:29.429 Uttam Kumaran: but like, I don’t even consider our company a startup just because startup comes with connotations of like crazy. Everything’s breaking. There’s no plans. That’s not how we work, and because we can’t engineer success for our clients in that way. And so part of the reason that these clients are always hard is that they are startups, which means they want things. Tomorrow
331 00:38:29.620 ⇒ 00:38:32.659 Uttam Kumaran: they want it fast, and they want it to be like unplanned.
332 00:38:32.980 ⇒ 00:38:36.900 Uttam Kumaran: It’s hard. It’s not easy to do that. And so.
333 00:38:37.150 ⇒ 00:38:45.240 Uttam Kumaran: you know, I think while we’re in this stage. It’s helpful for me, on the sales side, to, to sort of get a better grasp of the types of clients we work with.
334 00:38:45.250 ⇒ 00:39:09.010 Uttam Kumaran: Matter. More happens to Matthew, happens to just be a good friend of the company, and has has helped us in different ways. So when he started his company, I was like, let’s let’s try to assist but we will also see like if if it’s continues to be tough to work with them, we can decide what we want to do. But I’m glad that we’re seeing this. And I think, Annie, you did a good job of giving really really great feedback. I think, amber.
335 00:39:09.030 ⇒ 00:39:24.130 Uttam Kumaran: I think I think it’s fine. I think we all we all have these like, oh, shit, is it gonna like, what’s gonna happen with these guys. But I think what you saw is the is the the meeting, and how we actually found resolution. So hopefully, that will give you some indication about how to do that.
336 00:39:24.340 ⇒ 00:39:26.498 Uttam Kumaran: you know, in the future.
337 00:39:27.080 ⇒ 00:39:31.156 Uttam Kumaran: so yeah, I think that’s those are the main things about these clients.
338 00:39:31.590 ⇒ 00:39:48.335 Uttam Kumaran: and then on the developing side. So we have these 4 clients right now. It’s actually crazy, because I have to make sure every week that we’re like not missing anybody. But we have cool parts off the record. Read me and Spark plug. Read me and spark, plug our newest 2 clients.
339 00:39:48.650 ⇒ 00:40:07.050 Uttam Kumaran: and we are currently in like an audit phase with them. Typically, our audits are like 2 weeks, and then we propose a longer engagement, pull parts and off the record. We’re in the engagement phase. The reason they’re here on the developing side is because a we’ve slimmed down the resources that we’re providing to them just because
340 00:40:07.080 ⇒ 00:40:20.049 Uttam Kumaran: there may be a slower path towards becoming a larger client. Or second, they’re just in the growth phase, and they don’t need a large sprint planning, grooming all of that. So for these we try to run with at least
341 00:40:20.170 ⇒ 00:40:34.330 Uttam Kumaran: 2 people. I know on. Read me and spark plug. I think oat and demalade are involved somewhat in the channels on both of those. So I think these are also the change help because we can run these leaner, have more direct line of communication.
342 00:40:34.767 ⇒ 00:40:54.170 Uttam Kumaran: Shift from like having sprints to like. Let’s just execute on a week to week basis. They get one meeting a week with me, or or whoever so I think this is getting better. Actually, I think we’re gonna start to categorize them in 2 ways that way. Not. Everybody gets the whole package, and ideally, if they want the whole package, they should grow with us, you know.
343 00:40:55.520 ⇒ 00:41:16.229 Uttam Kumaran: So maybe I’ll just say one thing about this context switching and slack demand. So one, please set up slack. Don’t have slack notifications on your phone. Also, I would probably not have slack notifications at all. If you’re like me, you’re probably checking slack. You always have slack open anyways, so it’s kind of you would rather be in charge of checking
344 00:41:16.637 ⇒ 00:41:39.539 Uttam Kumaran: and I don’t have any notifications. So I feel like it’s it’s not the worst. And also we don’t have clients that are typically like, I need this right now, so give yourself some grace. Second thing is block off hours where you’re going to be in deep work mode, especially if you’re an engineer on the team. I highly highly recommend
345 00:41:39.640 ⇒ 00:42:01.309 Uttam Kumaran: you. Do that where you block off time you can indicate to your client team that you are going to be working. I know how hard it is to get work done in an environment where slack is pinging, and I’m probably pinging. I’m pinging every day, so probably half the reason for this, but nothing I would say is going to be urgent, and if it is, it’ll find a way to you. So leverage slack
346 00:42:01.780 ⇒ 00:42:06.889 Uttam Kumaran: mutes or leverage slack notifications and try to just block off time where you can
347 00:42:07.090 ⇒ 00:42:11.270 Uttam Kumaran: like turn off email and slack, and just like blast
348 00:42:11.670 ⇒ 00:42:15.089 Uttam Kumaran: lo-fi or house, and like, get like a couple of things done.
349 00:42:15.743 ⇒ 00:42:19.976 Uttam Kumaran: So like definitely, I’ll I’ll keep trying to remind that
350 00:42:21.210 ⇒ 00:42:24.390 Uttam Kumaran: any feedback here. I’m gonna try to just get through the next couple of things
351 00:42:29.130 ⇒ 00:42:37.000 Uttam Kumaran: cool. Okay. So I just wanna walk through a couple of things. I’m I may not get to everything. But regardless I will
352 00:42:37.560 ⇒ 00:42:46.220 Uttam Kumaran: try. So one thing I wanted to show is something that the AI team has been working on which is the Zoom Platform that we built
353 00:42:48.870 ⇒ 00:43:09.144 Uttam Kumaran: and basically what it is is our version of a fireflies, our version of a otter or a granola, where, as you guys know, we record a lot of our meetings primarily for the use case of having it speed up our operations and our project management, for example.
354 00:43:10.470 ⇒ 00:43:17.630 Uttam Kumaran: let’s take a meeting like me and Amber and Mustafa just met. Actually, this was 30 min ago.
355 00:43:18.172 ⇒ 00:43:29.470 Uttam Kumaran: We now have access to them to a couple of things here. You have access to the video, right? So I can go ahead and press play on the video. We should see
356 00:43:29.610 ⇒ 00:43:35.154 Uttam Kumaran: because they are corrupted us talking and sharing screen and stuff. You can also
357 00:43:35.780 ⇒ 00:43:47.840 Uttam Kumaran: we have different videos. We have a shared screen share screen, the speaker view and the audio. So you can actually just click here and listen to the audio, too. The other thing is, we have the transcript here. So, and this is cleaned up so you can see
358 00:43:48.200 ⇒ 00:43:59.769 Uttam Kumaran: everything we talked about quickly. Copy this into Chat Gpt. So one of one of the things, and I’ll just try to kill 2 birds with one stone. Not a great analogy, but
359 00:44:02.060 ⇒ 00:44:06.469 Uttam Kumaran: quickly. What you what I kind of come here and do is I go into? I have, like a
360 00:44:06.630 ⇒ 00:44:21.489 Uttam Kumaran: I have a linear here, or I. For example, I have, like a transcript to tickets sort of project here, where sometimes I’ll be in a meeting. I’m like cool. I want to take this transcript. I want to put it in here, and I just press enter, and then chat. Gpt will give me
361 00:44:21.620 ⇒ 00:44:23.769 Uttam Kumaran: like what the tickets are
362 00:44:24.050 ⇒ 00:44:36.239 Uttam Kumaran: cool like. We need to migrate something to slack. Blah blah but I was like cool. I could do this now we we should make this available to everybody. So what you can actually do here is you can go down, click, generate tickets.
363 00:44:38.220 ⇒ 00:44:46.200 Uttam Kumaran: What you’ll see here is the AI is is looking through the meeting transcript. It’s pulling out the things that are
364 00:44:46.450 ⇒ 00:44:52.289 Uttam Kumaran: that we mentioned. Things like this should be done, we should update something like, it’s basically grabbing the tasks.
365 00:44:52.470 ⇒ 00:44:59.859 Uttam Kumaran: And then what you’ll get is basically cards that will that will allow you to create tickets directly in linear
366 00:45:00.347 ⇒ 00:45:15.080 Uttam Kumaran: so let me wait for that. So cool. So what you see here is basically a couple of things. So, for example, I asked, we found some environment variables in one of the repos. I said, Let’s fix that so you can go here. And AI just propose all these tickets.
367 00:45:15.280 ⇒ 00:45:28.070 Uttam Kumaran: Right? So you can go in here. Press edit. You can change these. You can select the team you want to create it in who you want to assign it to, and then go ahead and and click, save. And so if I go ahead and assign this to
368 00:45:28.925 ⇒ 00:45:31.999 Uttam Kumaran: Miguel and I click save
369 00:45:32.774 ⇒ 00:45:41.590 Uttam Kumaran: and then I go ahead and click the check here. It’s gonna be approved. And then I can now open this directly in linear and the tickets there
370 00:45:41.970 ⇒ 00:45:43.160 Uttam Kumaran: in the AI team
371 00:45:44.159 ⇒ 00:45:53.140 Uttam Kumaran: for anyone who’s on project management side. This is saving like at least 1 h per meeting, I would guess.
372 00:45:53.890 ⇒ 00:46:06.760 Uttam Kumaran: So this is an example of how we went from something that I was doing individually, or people were doing to. Now this is available right here. The other thing you do is like you could talk directly to the meeting. Say, summarize this meeting.
373 00:46:06.970 ⇒ 00:46:10.199 Uttam Kumaran: There’s a direct chat Bot that has access to the transcript here.
374 00:46:10.718 ⇒ 00:46:33.430 Uttam Kumaran: And you can talk to it and ask questions. We’ll be adding a couple of other things here, so we’ll be adding a couple of more quick buttons like generate scope of work. Generate. Follow up emails. We’ll be cleaning this up a bit. Other thing is, this chat will will actually have access to who you are and what you do on the team. And so things will be tailored that way.
375 00:46:33.780 ⇒ 00:46:36.770 Uttam Kumaran: But this is sort of the platform that we’re starting to work on.
376 00:46:38.770 ⇒ 00:46:47.416 Uttam Kumaran: trying to do anything else. So the other piece that I wanted to share is on notion. If you go into
377 00:46:48.080 ⇒ 00:46:48.970 Uttam Kumaran: up
378 00:46:49.260 ⇒ 00:47:17.160 Uttam Kumaran: this prompt library. A couple of us are using this now, but if you’re using Chat Gbt, and you’re like, Hey, I want to just like I need a bunch of prompts, and I’m tired of writing them. We’ve written a lot of them. A couple of us. So in case you’re like, Hey, I want something to help me write sales, copy, help me write emails, help me write case studies. Help me take a transcript to tickets. We have a lot of prompts here. Another thing we do is we have a a prompt
379 00:47:17.230 ⇒ 00:47:25.400 Uttam Kumaran: writing prompt that actually just helps you create better prompts where you would just tell it everything you need. For example.
380 00:47:26.091 ⇒ 00:47:36.669 Uttam Kumaran: I worked on the linear ticket creation prompt here, where I literally said, I want a help creating a prompt that will help our team create linear tickets from transcripts.
381 00:47:36.800 ⇒ 00:47:41.899 Uttam Kumaran: Just basic details about the meeting or other information. And this is all it is. And I created
382 00:47:42.010 ⇒ 00:47:48.690 Uttam Kumaran: a project from that. So there’s a lot of helpful stuff here, just ping me or anyone on the team. If you need help setting this up.
383 00:47:49.161 ⇒ 00:47:54.130 Uttam Kumaran: It’s probably all I’ll go through, because I want to make sure everyone else can go through Demos. But
384 00:47:54.543 ⇒ 00:48:01.419 Uttam Kumaran: yeah, credit to Mustafa, to Casey, and for Miguel, for all the zoom AI stuff. So
385 00:48:05.858 ⇒ 00:48:08.489 Uttam Kumaran: cool, who, I think? Who’s next
386 00:48:08.970 ⇒ 00:48:11.419 Uttam Kumaran: let me get back to the slides.
387 00:48:13.750 ⇒ 00:48:15.350 Uttam Kumaran: Oh.
388 00:48:24.506 ⇒ 00:48:40.279 Uttam Kumaran: yes, I guess maybe we can wait until next week to present the tech lead work, guys, because I think we’re like halfway through. Cool. Annie, do you want to present some of the Eden reports? I just saw glimpses of this in the channel. But I selfishly like I wanted to see
389 00:48:40.810 ⇒ 00:48:45.540 Uttam Kumaran: what you guys are producing for them, because it looked really cool.
390 00:48:47.131 ⇒ 00:48:56.400 Annie Yu: Yeah, I think I’ll probably just share one report today. Or I guess if we have more questions or time we can.
391 00:48:56.900 ⇒ 00:49:20.519 Annie Yu: we can go through others. So this is just one example of a tableau dashboard that would build for Eden team. And this is for like a product team. So they for this specific report they usually will like to see like just one product and then drill down on what’s the performance there, and who are buying them things of that sort.
392 00:49:21.271 ⇒ 00:49:27.750 Annie Yu: So starting with the top line, of course, we have the selected timeframe and product
393 00:49:27.980 ⇒ 00:49:38.359 Annie Yu: and the top line kpis here. And one thing about tableau, though, and I think this is also like why some people are intimidated by like tableau. Is that
394 00:49:38.740 ⇒ 00:49:58.840 Annie Yu: in like looker? Usually you can just grab a metric, and then this Delta gross rate will just come automatically. But in tablet something that you’ll have to like manually set up, which takes more time, but it’s doable. So when we like select a different time, frame this, all numbers should change from there.
395 00:49:58.840 ⇒ 00:49:59.570 Uttam Kumaran: Nice.
396 00:50:00.090 ⇒ 00:50:03.130 Annie Yu: Yeah. And then.
397 00:50:03.430 ⇒ 00:50:12.900 Annie Yu: in my opinion, this is like way too many kpis on the top line. But I guess these are all important to our stakeholders. And then the trend line.
398 00:50:13.525 ⇒ 00:50:32.110 Annie Yu: So we all know, like kind of like revenue and order trend line are, the lines are pretty pretty standard and stable everywhere in every company, but our stakeholders wanted to understand. Kind of the underlying longer, longer term trend, not just week to week fluctuations.
399 00:50:32.550 ⇒ 00:50:51.159 Annie Yu: So we were able to add the 14 day and 30 day moving average to help smooth that noise and give better context. And this part is also if you can see. So if we, this is more like your typical revenue trend line, and then, if we look at
400 00:50:51.380 ⇒ 00:50:59.600 Annie Yu: the 14 days. So the trend is actually like kind of trending up. So from there you can see, like the longer term trend
401 00:51:00.240 ⇒ 00:51:16.149 Annie Yu: and I would say this part was also a bit tricky to implement, because normally, if you think of a date filter, at least in tableau, when we filter on May 1st all the data prior to that date just got cut off.
402 00:51:16.280 ⇒ 00:51:20.319 Annie Yu: So then, in that case, for the first, st at least 1st
403 00:51:20.700 ⇒ 00:51:24.239 Annie Yu: column, everything should be the same, because we didn’t have
404 00:51:24.590 ⇒ 00:51:27.539 Annie Yu: prior data to calculate the moving average.
405 00:51:27.700 ⇒ 00:51:50.740 Annie Yu: So then we to solve that, we will have to make some engineering. But behind the scene, so think of this date range filter. Actually, it’s like a like a fake fake filter. Because when we select this time, I actually had more data prior to this state back behind the scene to calculate all these numbers.
406 00:51:52.347 ⇒ 00:52:05.019 Annie Yu: And down here. These are also pretty self exploratory. They wanted to see the orders by new customers versus returning customers, also the breakdown by gender.
407 00:52:07.000 ⇒ 00:52:32.219 Annie Yu: Probably this part also, at least, like I know they’ve been using it. So our stakeholder launched new kind of newer personalized plans, and the goal was to get at least 80% of the customers on that. On those personalized plans versus just conventional plan. So they on the left hand side they could see
408 00:52:32.860 ⇒ 00:52:42.079 Annie Yu: the breakdown by personalized versus conventional, and by each month, and by each pharmacy. So when they see
409 00:52:42.250 ⇒ 00:52:52.009 Annie Yu: maybe a specific pharmacy is not really converting that many people on personalized plan, they could. They can phone them and then have a discussion from there
410 00:52:52.210 ⇒ 00:52:58.119 Annie Yu: on the right hand side this one so to read this bar chart.
411 00:52:58.420 ⇒ 00:53:03.740 Annie Yu: Think of the great bars as like the total customers of each week.
412 00:53:03.850 ⇒ 00:53:06.329 Annie Yu: And then we also have this blue
413 00:53:06.710 ⇒ 00:53:13.850 Annie Yu: once. So they are. They represents the number, the percent of the customers who are on the
414 00:53:14.110 ⇒ 00:53:26.569 Annie Yu: personalized plan. So we can more easily see, okay, this is 45%. And this week we we’ve got almost 45%. And then eventually, we wanna hit the goal. 80%.
415 00:53:29.712 ⇒ 00:53:34.469 Annie Yu: Yeah. And these are also more
416 00:53:34.680 ⇒ 00:53:39.069 Annie Yu: line charts to see the Cac versus profit.
417 00:53:40.830 ⇒ 00:53:44.140 Annie Yu: So that’s about it. For this report.
418 00:53:45.010 ⇒ 00:53:46.150 Uttam Kumaran: This looks great.
419 00:53:46.310 ⇒ 00:53:50.859 Uttam Kumaran: I feel like this is the best dashboard that our company has produced to date. For sure.
420 00:53:52.580 ⇒ 00:54:09.929 Annie Yu: I I would say, though, like I’m I’m a tableau gal. So I would say, like lots of the things that I’ve done with tableau like even these things that I would not have a clue how to do with other bi tools, or even like, if they are doable without a modeling support.
421 00:54:09.930 ⇒ 00:54:10.550 Uttam Kumaran: Yeah.
422 00:54:12.760 ⇒ 00:54:13.510 Annie Yu: Yeah.
423 00:54:13.510 ⇒ 00:54:16.890 Uttam Kumaran: Yeah, I think you could. You sort of convinced me that if
424 00:54:18.070 ⇒ 00:54:25.080 Uttam Kumaran: like, if real sophisticated dashboarding is a requirement, then it has to be in tableau.
425 00:54:25.490 ⇒ 00:54:28.479 Uttam Kumaran: I also just think some of our clients are not
426 00:54:29.440 ⇒ 00:54:34.549 Uttam Kumaran: ready to like invest in the time it takes to do that, because I know how long this takes
427 00:54:36.031 ⇒ 00:54:39.259 Uttam Kumaran: and their requirements aren’t like as specific
428 00:54:39.747 ⇒ 00:54:46.720 Uttam Kumaran: but I also know that you all this. This takes a full team effort like you. I know you need the modeling to be a certain way to accomplish some of these.
429 00:54:48.760 ⇒ 00:55:00.380 Uttam Kumaran: and so yeah, I’m really excited, I think. And I think you know, one of the certifications we’ll try to go for is something around tableau where everyone on the data team and more can learn a little bit about how to do real data visualization.
430 00:55:01.070 ⇒ 00:55:03.150 Uttam Kumaran: But this is really really nice to see.
431 00:55:05.880 ⇒ 00:55:06.710 Annie Yu: Whoa!
432 00:55:09.100 ⇒ 00:55:09.840 Uttam Kumaran: Cool.
433 00:55:10.701 ⇒ 00:55:15.038 Uttam Kumaran: Yeah. The only other piece I had today was
434 00:55:16.860 ⇒ 00:55:29.760 Uttam Kumaran: probably just about hiring. So we’re I would say, the only role. That we’re looking for right now is we’re currently looking to hire like a Pmp certified project manager.
435 00:55:31.320 ⇒ 00:55:44.799 Uttam Kumaran: we are interviewing a few people for that role, I would say on across data. And AI, I feel pretty good on our engineering support. So that’s probably the only thing. If there’s anyone in anyone’s network that’s interested in that.
436 00:55:45.221 ⇒ 00:55:51.920 Uttam Kumaran: We are in interview process for someone on the web flow development side as well, but sort of towards the final stages there.
437 00:55:54.590 ⇒ 00:55:56.760 Uttam Kumaran: But yeah, and then I maybe I’ll give a quick
438 00:55:57.216 ⇒ 00:55:59.990 Uttam Kumaran: sort of flash of sales. Since we have
439 00:56:00.680 ⇒ 00:56:03.099 Uttam Kumaran: 60 seconds. And hopefully, this will.
440 00:56:03.400 ⇒ 00:56:12.290 Uttam Kumaran: I like looking at the leads list because we now it’s now we have a lot there versus when it was less less impressive.
441 00:56:15.040 ⇒ 00:56:23.400 Uttam Kumaran: let me share this. So this is our leads. And notion and so
442 00:56:23.510 ⇒ 00:56:25.019 Uttam Kumaran: I did some.
443 00:56:25.450 ⇒ 00:56:28.659 Uttam Kumaran: I did some database myself last week.
444 00:56:29.407 ⇒ 00:56:47.429 Uttam Kumaran: And I, Annie, what if we use the we should use notion as our bi tool for clients? But I did so. What you can see here is, you can just see. This is new leads created. And this is not just leads meaning like.
445 00:56:48.090 ⇒ 00:57:08.190 Uttam Kumaran: Oh, I I know someone at Apple. This is someone where we’d actually like have an intro into. So you can see that it’s growing over time. Like. And this is cumulative. But I ideally, I can create another chart. That sort of shows a net new. But roughly, I would say, we’re we’re adding about like 3 or 4 leads per week, which is really really strong.
446 00:57:08.190 ⇒ 00:57:26.649 Uttam Kumaran: You can also see here, the amount of value in pipeline meaning. Every lead is worth a certain amount of contract value. For example, if a client plays as 5 K for 6 months, that’s 30 k, right? So we assume something for every client we assume, it’s least gonna be like 5 K for a month.
447 00:57:26.680 ⇒ 00:57:32.549 Uttam Kumaran: And then, based on when we talk, we can expand that. So you can see we have a fair bit in
448 00:57:32.590 ⇒ 00:57:41.440 Uttam Kumaran: the pipeline. Of course, some percentage of this closes and our goal for this quarter was to get to like 400 k, so we’re not there yet, but
449 00:57:41.640 ⇒ 00:57:50.570 Uttam Kumaran: this is looking pretty good. We have several automation set up on here as well, but you can see that actually, what I like to see is that a lot of things are in blue or green
450 00:57:50.750 ⇒ 00:57:58.980 Uttam Kumaran: that gives you any indication, which means we’re at the proposal stage. For all of these we’ve been pretty steadily sending at least one proposal
451 00:57:59.100 ⇒ 00:58:02.350 Uttam Kumaran: every day for the last, like 2 weeks.
452 00:58:02.500 ⇒ 00:58:08.670 Uttam Kumaran: which is really really crazy like, last year I was sending 2 proposals a month.
453 00:58:09.310 ⇒ 00:58:26.500 Uttam Kumaran: So we are in a completely different world. And so all of these we’re moving towards trying to get them to to close and so, yeah, very excited. All these companies on the list are doing really, really important stuff, and excited to hopefully close a couple of them.
454 00:58:27.064 ⇒ 00:58:29.610 Uttam Kumaran: Yeah. So I’ll probably leave. That
455 00:58:30.410 ⇒ 00:58:38.870 Uttam Kumaran: has a message going into the end of the week. So any questions or anything else I can answer.
456 00:58:40.580 ⇒ 00:58:42.860 Annie Yu: I have one question, and.
457 00:58:42.860 ⇒ 00:58:43.610 Uttam Kumaran: Sure.
458 00:58:43.610 ⇒ 00:58:50.660 Annie Yu: This is less so, I guess less so about one specific client. But just so as we grow
459 00:58:51.050 ⇒ 00:58:53.719 Annie Yu: as like, we have more clients.
460 00:58:54.180 ⇒ 00:59:01.579 Annie Yu: and is there like any opportunity for anyone to kind of rotate on different projects? If there are interested in others.
461 00:59:03.400 ⇒ 00:59:04.944 Uttam Kumaran: That’s a good question.
462 00:59:06.630 ⇒ 00:59:11.116 Uttam Kumaran: yeah, I guess I haven’t thought about that. But it’s a really good question. I guess.
463 00:59:13.180 ⇒ 00:59:22.850 Uttam Kumaran: sort of the way that we’ve been doing things now is just that like, we sort of have people stick with clients based on the need. I do think that
464 00:59:23.050 ⇒ 00:59:42.820 Uttam Kumaran: for some of our strategic clients, I do think that there is opportunities for people to move on and move off of one I do think that there, as you know, there’s probably something to say about like clients having someone that they recognize. So it may be on a
465 00:59:43.180 ⇒ 00:59:53.560 Uttam Kumaran: sort of 6 month basis or something. But certainly I I would be interested to see like if we can move people off to to new clients. I think maybe it should correlate with
466 00:59:54.418 ⇒ 00:59:57.771 Uttam Kumaran: renewals, or like some sort of milestone.
467 00:59:58.470 ⇒ 01:00:01.506 Uttam Kumaran: But yeah, that’s actually a really good question. I I
468 01:00:02.350 ⇒ 01:00:08.989 Uttam Kumaran: yeah, that’s great. I haven’t even thought about that but it shows because some of our clients now are sticking with us for for enough time
469 01:00:09.090 ⇒ 01:00:11.819 Uttam Kumaran: for us to move people around right? So
470 01:00:12.386 ⇒ 01:00:17.063 Uttam Kumaran: yeah, let me let me let me think about that a little bit more with the team. And and
471 01:00:17.910 ⇒ 01:00:25.729 Uttam Kumaran: certainly, of course, as we get new clients, people can get access to that. But I do think that for some of our larger clients it would be nice to kind of move people around.
472 01:00:27.820 ⇒ 01:00:36.008 Annie Yu: Yeah, sounds good. Yeah. I don’t think that’s something we like have to worry about like as soon as now. But eventually, hopefully.
473 01:00:36.350 ⇒ 01:00:39.439 Uttam Kumaran: Yeah, yeah, yeah, yeah, no. It’s a really really good question.
474 01:00:40.258 ⇒ 01:00:42.840 Uttam Kumaran: I think we can plan some rotations.
475 01:00:43.475 ⇒ 01:00:44.600 Uttam Kumaran: Like, maybe it’s
476 01:00:45.230 ⇒ 01:00:53.508 Uttam Kumaran: I think ideally, it’s lined up with the contract renewals. And then, as we plan out the next contract, we can move people in there.
477 01:00:55.930 ⇒ 01:00:58.210 Uttam Kumaran: yeah, yeah, cool.
478 01:01:02.410 ⇒ 01:01:13.308 Uttam Kumaran: Okay. Great. Well, thanks everyone. I appreciate. The time next week I think we’ll try to present on sort of some work we’ve been doing about Pm’s and tech leads.
479 01:01:13.890 ⇒ 01:01:15.130 Uttam Kumaran: and
480 01:01:15.360 ⇒ 01:01:21.580 Uttam Kumaran: yeah, hopefully, we have a few more clients closed. We have a bunch of contracts, bunch of proposals out for signing right now. So
481 01:01:21.780 ⇒ 01:01:22.840 Uttam Kumaran: let’s see.
482 01:01:24.640 ⇒ 01:01:28.600 Uttam Kumaran: Okay, thanks. Everyone. Have a great weekend. Get some rest.
483 01:01:29.490 ⇒ 01:01:29.980 Anne: Thanks.
484 01:01:29.980 ⇒ 01:01:31.020 Anne: Guys, I believe.
485 01:01:31.020 ⇒ 01:01:33.189 Demilade Agboola: Thanks. Everyone have a good weekend. Bye.
486 01:01:33.190 ⇒ 01:01:33.830 Uttam Kumaran: And.