2025-06-06_bf_managers_meeting_-_sprint_retros_315ec03b

Meeting Title: BF Managers Meeting - Sprint Retros Date: 2025-06-06 Meeting participants: Fireflies.ai Notetaker Awaish, Robert Tseng, Hannah Wang, robert, Amber Lin, Uttam Kumaran

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WEBVTT

1 00:05:35.830 ⇒ 00:05:37.790 Hannah Wang: Oh, you’re alive!

2 00:05:39.740 ⇒ 00:05:41.220 Robert Tseng: Oh, yeah. Hello.

3 00:05:41.430 ⇒ 00:05:42.470 Hannah Wang: Oh!

4 00:05:45.130 ⇒ 00:05:46.490 Robert Tseng: How was that call?

5 00:05:47.620 ⇒ 00:05:52.760 Hannah Wang: It’s good. I think I’m literally taking the call in the car cause?

6 00:05:54.040 ⇒ 00:05:58.470 Hannah Wang: Oh, yeah, Wi-fi is down. My hotspot wasn’t working in the apartment.

7 00:05:59.800 ⇒ 00:06:00.830 Hannah Wang: So I’m just like.

8 00:06:00.830 ⇒ 00:06:03.320 Robert Tseng: I thought you guys came back from your trip. Your trip.

9 00:06:05.810 ⇒ 00:06:08.330 Hannah Wang: Oh, that was last week! It! What.

10 00:06:09.980 ⇒ 00:06:10.720 Robert Tseng: Oh, no, no!

11 00:06:10.720 ⇒ 00:06:12.609 Robert Tseng: Your car in Culver City.

12 00:06:12.610 ⇒ 00:06:20.200 Hannah Wang: Yeah. Cause at home, like Wi-fi wasn’t working. So I try to go somewhere with different, like

13 00:06:21.500 ⇒ 00:06:30.440 Hannah Wang: Internet capacity somewhere not near my apartment. I’m just parked on the side in Doverwood.

14 00:06:32.730 ⇒ 00:06:42.172 Hannah Wang: Sorry does that make sense like cause my Wi-fi is down? Yeah. My hotspot wasn’t working, so I just moved somewhere else where my hotspot would be better. So I’m just in my car

15 00:06:42.700 ⇒ 00:06:43.980 Hannah Wang: with my laptop.

16 00:06:45.940 ⇒ 00:06:46.610 Robert Tseng: Okay.

17 00:06:57.480 ⇒ 00:07:01.090 Hannah Wang: Are you at the the hotel, or something working.

18 00:07:01.090 ⇒ 00:07:03.031 Robert Tseng: Yeah, we’re at a hotel.

19 00:07:04.890 ⇒ 00:07:07.600 Robert Tseng: we’re actually leaving tomorrow morning. So we have one more night here.

20 00:07:07.600 ⇒ 00:07:09.100 Hannah Wang: Yeah, yeah.

21 00:07:10.030 ⇒ 00:07:10.660 Robert Tseng: Yeah.

22 00:07:13.020 ⇒ 00:07:16.649 Hannah Wang: It’s been hard, huh? Working and trying to working in different.

23 00:07:16.650 ⇒ 00:07:18.320 Robert Tseng: It’s the worst.

24 00:07:18.320 ⇒ 00:07:18.990 Hannah Wang: Yeah.

25 00:07:20.310 ⇒ 00:07:27.359 Robert Tseng: Each last week was good. Kenya was great, but then time in Amsterdam has been pretty pretty tough.

26 00:07:27.640 ⇒ 00:07:28.380 Hannah Wang: Yeah.

27 00:07:29.310 ⇒ 00:07:30.829 Robert Tseng: I also don’t like answer.

28 00:07:31.670 ⇒ 00:07:33.349 Robert Tseng: I’m not gonna say it, too. Well, okay.

29 00:07:53.830 ⇒ 00:08:00.639 Hannah Wang: Oh, people are asking if we can start the meeting later. I don’t know if you want to chill and come back on, but.

30 00:08:05.560 ⇒ 00:08:06.909 Robert Tseng: Yeah, I’m just kinda

31 00:08:07.070 ⇒ 00:08:14.435 Robert Tseng: looking at other stuff. Anyway, I feel like if I hang up I’m not gonna come back. And I just I missed the other meeting.

32 00:08:14.730 ⇒ 00:08:15.270 Hannah Wang: Okay.

33 00:08:15.590 ⇒ 00:08:16.480 Hannah Wang: No worries.

34 00:08:26.280 ⇒ 00:08:30.930 Robert Tseng: Because, like the hours are so like random. And I know my time is limited.

35 00:08:31.340 ⇒ 00:08:37.229 Robert Tseng: I every day I just accept that I’m not gonna get to everything. I just try to get to what I can.

36 00:08:37.530 ⇒ 00:08:40.500 Robert Tseng: And then I just I just move on.

37 00:08:40.940 ⇒ 00:08:41.280 Hannah Wang: So.

38 00:08:41.659 ⇒ 00:08:43.920 Robert Tseng: I know it’s not a good feeling.

39 00:08:44.650 ⇒ 00:08:48.069 Robert Tseng: Never feel like anything is is done like, I just

40 00:08:49.520 ⇒ 00:08:54.150 Robert Tseng: yeah, I I leave everything like unfinished. It’s kind of how it feels this week.

41 00:08:55.290 ⇒ 00:09:06.999 Hannah Wang: And it’s probably like in the back of your mind, too, like as you’re trying to do it like, go out and stuff you’re like. Oh, but this work thing at least that’s for me. I don’t know how you are.

42 00:09:09.750 ⇒ 00:09:13.779 Hannah Wang: Yeah, I work outside of work all the time. Sometimes.

43 00:09:16.070 ⇒ 00:09:22.399 Robert Tseng: Yeah, I I feel like I was pretty good about it in Kenya. But yeah, definitely, this week I’ve been just thinking about it.

44 00:09:22.710 ⇒ 00:09:23.520 Hannah Wang: Yeah.

45 00:09:48.610 ⇒ 00:09:52.539 Robert Tseng: Alright. We’ll cover it more later. I’m just like doing other things.

46 00:09:52.780 ⇒ 00:09:53.850 Hannah Wang: Oh, it’s fine!

47 00:09:54.250 ⇒ 00:09:54.880 Robert Tseng: Okay.

48 00:17:29.120 ⇒ 00:17:33.490 Robert Tseng: I think I’m gonna take this call while I’m walking around.

49 00:17:35.190 ⇒ 00:17:36.690 Hannah Wang: Okay, that sounds good.

50 00:17:36.690 ⇒ 00:17:37.240 Robert Tseng: Yeah.

51 00:17:37.750 ⇒ 00:17:39.090 Robert Tseng: Alright. Bye.

52 00:17:39.280 ⇒ 00:17:39.930 Hannah Wang: Bye.

53 00:22:14.410 ⇒ 00:22:15.260 robert: Hello!

54 00:22:18.960 ⇒ 00:22:19.989 robert: And money here.

55 00:22:20.620 ⇒ 00:22:21.850 robert: Nobody’s here.

56 00:24:21.940 ⇒ 00:24:22.870 robert: Amber.

57 00:24:22.870 ⇒ 00:24:24.150 Amber Lin: Hi.

58 00:24:25.620 ⇒ 00:24:26.379 robert: Where are you?

59 00:24:26.750 ⇒ 00:24:36.820 Amber Lin: I’m now in Chicago. I flew here on Tuesday, and I just checked out of my hostel. I’m sitting in their lobby. It’s a really nice.

60 00:24:37.400 ⇒ 00:24:40.870 Amber Lin: Oh, that’s a really nice place. Yeah.

61 00:24:40.990 ⇒ 00:24:43.947 robert: Doing some solo travel? Or did

62 00:24:45.440 ⇒ 00:24:56.810 Amber Lin: Half half and half. My partner was in New York for the weekend, and then I’m solo traveling until this weekend, too. One of Nico’s

63 00:24:57.230 ⇒ 00:25:04.940 Amber Lin: best friends is graduating from Uchicago, and then we’re going to his graduation. So oh, cool.

64 00:25:04.940 ⇒ 00:25:07.710 Amber Lin: Yeah, but still no traveling until they come here.

65 00:25:07.830 ⇒ 00:25:09.610 robert: Yeah. Where are you at? Right now?

66 00:25:10.360 ⇒ 00:25:12.130 robert: I’m in Amsterdam.

67 00:25:15.290 ⇒ 00:25:16.190 Amber Lin: Wow!

68 00:25:16.690 ⇒ 00:25:19.490 Amber Lin: That sounds so fun. I miss it.

69 00:25:20.360 ⇒ 00:25:22.789 robert: You you’ve been when you were living in Milan.

70 00:25:22.970 ⇒ 00:25:27.460 Amber Lin: Yeah, I liked it. I was scared by the prices.

71 00:25:27.870 ⇒ 00:25:31.299 robert: Yeah, I didn’t realize how expensive it is here. It’s crazy.

72 00:25:31.740 ⇒ 00:25:33.060 Amber Lin: Know more expensive.

73 00:25:33.060 ⇒ 00:25:35.193 robert: New York would not recommend.

74 00:25:35.620 ⇒ 00:25:39.120 Amber Lin: I don’t know how they make a living there.

75 00:25:40.530 ⇒ 00:25:41.320 robert: Yeah.

76 00:25:42.360 ⇒ 00:25:45.300 Amber Lin: Most of them don’t seem like they work too much.

77 00:25:45.900 ⇒ 00:25:48.530 Amber Lin: Yeah, they do not working.

78 00:25:48.530 ⇒ 00:25:49.150 robert: Yeah.

79 00:25:51.280 ⇒ 00:25:53.480 Amber Lin: Makes me wonder what time is that for you?

80 00:25:55.260 ⇒ 00:25:57.550 robert: It’s like 6 30 Pm.

81 00:25:58.173 ⇒ 00:25:59.940 Amber Lin: Okay, okay, not that bad.

82 00:26:00.790 ⇒ 00:26:05.760 robert: Yeah, no, I’ve been working weird hour. I mean, I’m just. I’m just ready to be back

83 00:26:06.050 ⇒ 00:26:07.220 robert: in New York.

84 00:26:07.420 ⇒ 00:26:09.800 Amber Lin: When are you telling back? Oh, Fred! Oh, today.

85 00:26:10.830 ⇒ 00:26:11.560 robert: Yeah, today.

86 00:26:11.970 ⇒ 00:26:14.910 Amber Lin: Oh, long as flight.

87 00:26:15.240 ⇒ 00:26:15.875 robert: Yeah.

88 00:26:17.680 ⇒ 00:26:24.270 Amber Lin: I worked on the plane and I realized that it’s the same as working on a car. I just get car sick. I get plane sick.

89 00:26:24.760 ⇒ 00:26:27.560 robert: Yeah. Not not ideal.

90 00:26:28.080 ⇒ 00:26:31.550 robert: But at least he had. Wi-fi. I feel like the plane sent me here. I didn’t have Wi-fi.

91 00:26:31.800 ⇒ 00:26:37.910 Amber Lin: Oh, really! Oh, oh, cause it’s international! It’s harder to have that.

92 00:26:38.990 ⇒ 00:26:44.379 robert: Yeah, maybe I just flew like an older plane. So they also didn’t have Wi-fi.

93 00:26:44.780 ⇒ 00:26:45.860 Amber Lin: I see.

94 00:26:51.100 ⇒ 00:27:02.190 Amber Lin: Okay, let me ping the others to join, I think. Mostly I don’t know what’s on the agenda to discuss, but I know that we do want to talk about allocations for June.

95 00:27:03.300 ⇒ 00:27:04.070 robert: Yeah.

96 00:27:04.820 ⇒ 00:27:05.540 Amber Lin: Anything.

97 00:27:16.690 ⇒ 00:27:18.455 Amber Lin: Oh, you also had a cafe.

98 00:27:19.850 ⇒ 00:27:22.419 robert: I’m also in the lobby of a hotel I checked out of.

99 00:27:23.149 ⇒ 00:27:31.559 Amber Lin: It’s like I wanna, I wanna go outside. But then I can’t, because I need to do a meeting. And so I I feel trapped in this lot.

100 00:27:32.560 ⇒ 00:27:33.789 robert: That’s how I felt.

101 00:27:33.790 ⇒ 00:27:34.800 robert: But yeah.

102 00:27:35.260 ⇒ 00:27:38.803 Amber Lin: And it’s hard to balance traveling and

103 00:27:40.160 ⇒ 00:27:43.430 Amber Lin: and working. So yesterday I walked

104 00:27:44.301 ⇒ 00:27:57.640 Amber Lin: walk from Lakeview in Chicago all the way down the coastline of the Lake down to my hotel, and it was an hour, and I must say that 20 min in I was already regretting my choices.

105 00:27:58.750 ⇒ 00:27:59.449 robert: The hot.

106 00:27:59.640 ⇒ 00:28:08.820 Amber Lin: Oh, no, it’s perfect weather. It’s a little bit chilly, a little bit windy, but it’s it’s good like. If you have a sweater, it’s fine. I just don’t think I’ve walked more than an hour

107 00:28:08.990 ⇒ 00:28:13.170 Amber Lin: to walk in a while, so.

108 00:28:13.170 ⇒ 00:28:16.359 robert: Yeah, there’s a lot of walking here. I also walked for

109 00:28:17.300 ⇒ 00:28:20.251 robert: over an hour. Today I’m I’m done with walking.

110 00:28:21.303 ⇒ 00:28:24.199 Amber Lin: Hannah, how’s your wi-fi.

111 00:28:26.010 ⇒ 00:28:27.360 Hannah Wang: Back. I think.

112 00:28:27.360 ⇒ 00:28:28.100 Amber Lin: Yay!

113 00:28:28.100 ⇒ 00:28:32.449 Hannah Wang: That’s yeah. It was not fun taking a meeting in a car. I

114 00:28:32.850 ⇒ 00:28:35.730 Hannah Wang: felt trapped. But it’s better now.

115 00:28:35.970 ⇒ 00:28:36.590 robert: Oh, good!

116 00:28:36.590 ⇒ 00:28:38.899 robert: We were just talking about feeling trapped.

117 00:28:39.120 ⇒ 00:28:47.779 Amber Lin: I know, cause you can’t like. I think it’s a different type of trap like I feel trapped. If I have to take meetings in the same space. Again and again.

118 00:28:49.190 ⇒ 00:28:56.880 Hannah Wang: Yeah, I just need space. Well, I don’t mind taking meetings at the same spot, but like.

119 00:28:57.980 ⇒ 00:29:06.370 Hannah Wang: I feel like everyone’s so different. Everyone works best in like some people work well in coffee shops like I cannot cause. It’s so noisy in there.

120 00:29:06.370 ⇒ 00:29:06.950 Amber Lin: Hmm.

121 00:29:06.950 ⇒ 00:29:08.669 Hannah Wang: Like absolute silence.

122 00:29:10.010 ⇒ 00:29:15.970 Hannah Wang: So I prefer working like by myself, not in like a corporate space, or like a we work.

123 00:29:16.320 ⇒ 00:29:16.940 Amber Lin: Oh!

124 00:29:16.940 ⇒ 00:29:21.740 Hannah Wang: The other people need that to focus. But to me it’s distracting. So.

125 00:29:22.110 ⇒ 00:29:22.760 Amber Lin: See.

126 00:29:22.760 ⇒ 00:29:25.400 Hannah Wang: Yeah, anyway.

127 00:29:25.400 ⇒ 00:29:27.600 Uttam Kumaran: Yeah, I just sit. I sit here for like.

128 00:29:28.010 ⇒ 00:29:33.119 Uttam Kumaran: when you sit here for like 12 HI start to be like Yo, I just like need to be somewhere else like.

129 00:29:33.900 ⇒ 00:29:37.049 Uttam Kumaran: So I just try to go somewhere. But I also like.

130 00:29:37.180 ⇒ 00:29:42.180 Uttam Kumaran: since I was a kid, I’ve been. I usually work. And I watch Youtube or podcasts like my whole life.

131 00:29:42.470 ⇒ 00:29:43.420 Uttam Kumaran: So.

132 00:29:43.630 ⇒ 00:29:45.600 robert: Yeah, I noticed that when I was in office.

133 00:29:45.600 ⇒ 00:29:46.490 Uttam Kumaran: I usually have, like a.

134 00:29:46.490 ⇒ 00:29:48.760 robert: 3 monitors. And you have a Youtube monitor.

135 00:29:49.124 ⇒ 00:29:56.040 Uttam Kumaran: Yeah, dude. That’s the joy of not not having any shame is cause yeah, I don’t even like just

136 00:29:56.280 ⇒ 00:30:00.660 Uttam Kumaran: I. I kinda just need something going on in the background. Otherwise it’s

137 00:30:01.540 ⇒ 00:30:06.089 Uttam Kumaran: tough. I have like different. If I’m doing something very serious, then I it’s like music

138 00:30:07.030 ⇒ 00:30:09.780 Uttam Kumaran: super serious than I really have to think otherwise.

139 00:30:10.020 ⇒ 00:30:12.740 Uttam Kumaran: gotta be podcasts or something happening.

140 00:30:12.740 ⇒ 00:30:13.820 Amber Lin: Wow!

141 00:30:13.820 ⇒ 00:30:14.240 Uttam Kumaran: Yeah.

142 00:30:14.240 ⇒ 00:30:26.700 Amber Lin: It’s really interesting how brains work. But I just I can’t like. If something’s taught, someone is talking to me, I need to pause this and listen. I cannot multitask. It’s really bad for my brain.

143 00:30:28.930 ⇒ 00:30:29.460 robert: I’m.

144 00:30:29.460 ⇒ 00:30:31.010 Uttam Kumaran: All the noise. Yeah.

145 00:30:31.060 ⇒ 00:30:36.880 robert: I I do need noise sometimes, so I’ll play music of a language I don’t understand, so that my brain doesn’t really get distracted.

146 00:30:36.880 ⇒ 00:30:45.339 Amber Lin: Yeah, last time I was I was in the airport working on a project plan, and it was announcing the flight to Chicago. And then I typed in Chicago.

147 00:30:45.560 ⇒ 00:30:47.170 Amber Lin: in my, in my document.

148 00:30:48.230 ⇒ 00:30:50.420 Hannah Wang: I do that, too. I do that, too.

149 00:30:50.630 ⇒ 00:30:58.189 Hannah Wang: Yeah, I need like instrumental music. If I have lyrics, I can’t, even if it’s another language. I can’t like focus.

150 00:30:58.190 ⇒ 00:31:05.050 Uttam Kumaran: If there’s if I don’t have that, then I start to do like 2 things that like I start to think about like something else

151 00:31:05.350 ⇒ 00:31:08.120 Uttam Kumaran: while I’m doing whatever I’m doing, like I can’t.

152 00:31:08.410 ⇒ 00:31:10.970 Uttam Kumaran: And I don’t have add, like, I don’t think that’s add.

153 00:31:10.970 ⇒ 00:31:12.369 robert: Think that’s add Bro.

154 00:31:12.810 ⇒ 00:31:14.580 Uttam Kumaran: No, no, no, it’s not 8, 80, because.

155 00:31:14.580 ⇒ 00:31:15.577 Amber Lin: Yeah, there’s 80.

156 00:31:15.910 ⇒ 00:31:19.709 Uttam Kumaran: No, no, no, because add the common symptoms, for add is like

157 00:31:19.920 ⇒ 00:31:34.319 Uttam Kumaran: you can’t do. You can’t like focus on one thing. It’s not a focus problem, like I’m also do. I’m doing the thing. It’s more about like, I just need to be like, kind of occupied like, fully occupied like there has to be like multiple.

158 00:31:34.580 ⇒ 00:31:35.240 Uttam Kumaran: Thanks.

159 00:31:35.240 ⇒ 00:31:37.509 Amber Lin: So that’s because I said so. We’re.

160 00:31:37.510 ⇒ 00:31:45.070 Uttam Kumaran: For 8 HI sit here. I work for 8 h straight, like nobody with add is gonna fucking. Sit in one chair and like.

161 00:31:45.210 ⇒ 00:31:47.410 Uttam Kumaran: take 8 meetings in a row and like.

162 00:31:47.620 ⇒ 00:31:50.089 Uttam Kumaran: be like, okay. Another 4 h of business.

163 00:31:50.090 ⇒ 00:31:51.160 Amber Lin: Much capacity.

164 00:31:51.160 ⇒ 00:31:54.650 Hannah Wang: Yeah, your brain is too big. Like.

165 00:31:54.650 ⇒ 00:31:59.009 Hannah Wang: yeah, I don’t think it’s actually I don’t think it’s that I don’t know. I just like, can’t.

166 00:31:59.300 ⇒ 00:32:10.030 Uttam Kumaran: Whatever it is about, I just have to have something on. I think I’ve trained myself since I was in like high school. I would always watch like Minecraft videos, or let’s plays, or which.

167 00:32:10.260 ⇒ 00:32:17.850 Uttam Kumaran: or like podcasts or sports podcasts. While I was working, I mean, but I was a bad student so like it’s a bad habit.

168 00:32:18.070 ⇒ 00:32:19.060 Uttam Kumaran: I don’t know.

169 00:32:19.390 ⇒ 00:32:24.020 Hannah Wang: Oh, interesting!

170 00:32:24.580 ⇒ 00:32:27.879 robert: We would all not work well in the same environment.

171 00:32:27.880 ⇒ 00:32:31.130 robert: Sounds like we can’t all be in the same office clear.

172 00:32:31.520 ⇒ 00:32:35.679 Uttam Kumaran: I love what we work. I go to the very I go to love coffee shop

173 00:32:36.170 ⇒ 00:32:40.939 Uttam Kumaran: like. The only problem with coffee shop is that I have. I’m in like meeting hell. So

174 00:32:41.340 ⇒ 00:32:45.259 Uttam Kumaran: otherwise I live. I live in there. It’s just the perfect.

175 00:32:45.460 ⇒ 00:32:47.499 Uttam Kumaran: And there’s like all this ambient

176 00:32:47.680 ⇒ 00:32:53.589 Uttam Kumaran: shit going on, and it’s great for me for me.

177 00:32:53.870 ⇒ 00:32:54.580 Hannah Wang: Yeah.

178 00:32:56.350 ⇒ 00:32:57.010 robert: Thanks.

179 00:32:58.060 ⇒ 00:32:59.730 robert: Alright! Shall we jump into it?

180 00:32:59.730 ⇒ 00:33:01.449 Amber Lin: Yeah. Any

181 00:33:02.710 ⇒ 00:33:10.590 Amber Lin: sorry, any agenda items that you guys want to discuss? I’m just pulling a clock. If I to get the hours from the previous weeks. So if you guys have any.

182 00:33:10.590 ⇒ 00:33:12.549 robert: I’m just here for the allocations.

183 00:33:12.550 ⇒ 00:33:14.169 Uttam Kumaran: Yeah, I wanna do the allocation.

184 00:33:14.170 ⇒ 00:33:25.269 Amber Lin: Okay, never mind. Let’s just go straight into. Let’s straight go straight into that. Can someone share their screen about operating? Because I’m pulling up the clock if I report, so we can just do 2 at the same time.

185 00:33:25.870 ⇒ 00:33:28.579 Uttam Kumaran: And well, all the clockify hours are in.

186 00:33:29.330 ⇒ 00:33:31.750 Amber Lin: Is this checked?

187 00:33:32.650 ⇒ 00:33:35.939 Amber Lin: It’s oh, I’m in the wrong spreadsheet. But never mind

188 00:33:39.110 ⇒ 00:33:40.530 Amber Lin: What is the spreadsheet?

189 00:33:42.520 ⇒ 00:33:45.200 Uttam Kumaran: Clock if I export automation. So we have it all running.

190 00:33:45.200 ⇒ 00:33:54.440 Amber Lin: Oh, 4, 5, 6, 4. 0, yay, love it all right.

191 00:33:58.060 ⇒ 00:33:59.260 Amber Lin: Okay.

192 00:33:59.910 ⇒ 00:34:03.100 Uttam Kumaran: Yeah. And it looks it. I see the stuff from June 5.th

193 00:34:03.330 ⇒ 00:34:04.409 Uttam Kumaran: So, yeah.

194 00:34:05.430 ⇒ 00:34:09.939 robert: Can you share your screen? Amber? My computer is dead and charging. So I’m just on my phone.

195 00:34:10.090 ⇒ 00:34:12.730 Amber Lin: Don’t you worry.

196 00:34:13.340 ⇒ 00:34:23.200 Amber Lin: Here is this, I would like to. Oh, gosh! I need a pivot. I cannot look at this.

197 00:34:23.870 ⇒ 00:34:26.299 Amber Lin: Is this the this is the sheet I assume.

198 00:34:26.300 ⇒ 00:34:31.379 Uttam Kumaran: Well, you can go to sheet. Yeah, you can. You can look at what I did in sheet 4. I basically brought it up.

199 00:34:31.389 ⇒ 00:34:35.730 Uttam Kumaran: Oh, or you can do whatever you want with that I would just don’t touch time entries.

200 00:34:35.739 ⇒ 00:34:42.389 Amber Lin: Yeah, okay, so this is, oh, okay.

201 00:34:43.040 ⇒ 00:34:46.109 robert: You just filter by like this month, this past month.

202 00:34:47.620 ⇒ 00:34:48.650 Amber Lin: Alright.

203 00:34:49.350 ⇒ 00:34:50.199 Amber Lin: Yeah.

204 00:34:51.300 ⇒ 00:34:54.809 Amber Lin: I’ll make a new pivot.

205 00:34:55.830 ⇒ 00:35:00.988 Amber Lin: Insert the table new sheet. Create.

206 00:35:01.740 ⇒ 00:35:02.710 Amber Lin: Okay.

207 00:35:05.250 ⇒ 00:35:10.600 Uttam Kumaran: If I had a free week I could do all the data for our whole company so fast.

208 00:35:10.810 ⇒ 00:35:11.750 Amber Lin: Yeah, but we just.

209 00:35:11.750 ⇒ 00:35:19.549 Uttam Kumaran: Like cannot get around. I spent 4 h last night, and I got finally. We can do all of our.

210 00:35:20.180 ⇒ 00:35:21.870 Uttam Kumaran: Financial, like.

211 00:35:21.870 ⇒ 00:35:22.820 Amber Lin: Oh! Lovely!

212 00:35:22.820 ⇒ 00:35:23.770 Uttam Kumaran: And stuff I buy.

213 00:35:24.320 ⇒ 00:35:26.739 Amber Lin: What’s Marianne doing? Is she back.

214 00:35:27.080 ⇒ 00:35:30.840 Uttam Kumaran: No, I don’t. I don’t have any contact with her, so I have no idea.

215 00:35:32.340 ⇒ 00:35:33.069 Uttam Kumaran: I have no idea.

216 00:35:33.070 ⇒ 00:35:36.630 Amber Lin: Projects by end time.

217 00:35:39.240 ⇒ 00:35:48.058 Amber Lin: Oh, gosh! Never mind, let me just do this from trash anyways.

218 00:35:49.277 ⇒ 00:35:55.000 robert: Sheet, and then just filter on column B. By June, or whatever May 2025.

219 00:35:55.780 ⇒ 00:36:01.729 Amber Lin: Okay, cause I don’t. This is an aggregate. I don’t know how much of it is it is in each project, but.

220 00:36:01.900 ⇒ 00:36:03.160 robert: Oh, well.

221 00:36:03.340 ⇒ 00:36:08.239 robert: are we trying to just look at overall utilization, and then we can look at it by project afterwards.

222 00:36:17.000 ⇒ 00:36:19.429 Amber Lin: All right, let’s look at this.

223 00:36:21.607 ⇒ 00:36:24.939 robert: Or doing it by week. Are we doing it like last month?

224 00:36:24.940 ⇒ 00:36:28.580 Amber Lin: Well, this only has month data with shapes.

225 00:36:28.580 ⇒ 00:36:30.530 robert: Yes, maybe we just look at May.

226 00:36:30.780 ⇒ 00:36:32.940 Amber Lin: Yeah, let’s go. Look at that.

227 00:36:35.220 ⇒ 00:36:36.180 robert: Yeah. Okay.

228 00:36:48.900 ⇒ 00:36:49.830 robert: Dang.

229 00:36:51.390 ⇒ 00:36:56.339 robert: are there even 200 h in a month? I’m just kidding. Of course there are. But like that he is a.

230 00:36:56.640 ⇒ 00:36:57.720 Amber Lin: It’s gorgeous.

231 00:36:57.720 ⇒ 00:36:58.440 Amber Lin: Adrian.

232 00:37:03.810 ⇒ 00:37:04.680 Amber Lin: Okay.

233 00:37:05.070 ⇒ 00:37:05.850 robert: Okay.

234 00:37:06.710 ⇒ 00:37:09.640 Amber Lin: I’m gonna sort right.

235 00:37:12.770 ⇒ 00:37:15.089 Uttam Kumaran: I’ll get us the project view. Just give me a sec.

236 00:37:15.300 ⇒ 00:37:16.989 Uttam Kumaran: Okay, going on this.

237 00:37:16.990 ⇒ 00:37:17.880 Amber Lin: Thank you.

238 00:37:18.000 ⇒ 00:37:22.029 Amber Lin: So let’s look at why is this.

239 00:37:23.780 ⇒ 00:37:29.400 robert: We have a new web flow, Guy, cause our workflow. Guy is not doing anymore, or what.

240 00:37:30.010 ⇒ 00:37:30.660 Amber Lin: We’re trying.

241 00:37:30.660 ⇒ 00:37:36.210 Uttam Kumaran: No, we’re just not his fucking priority, and he screwed us ridiculous.

242 00:37:40.240 ⇒ 00:37:45.539 Uttam Kumaran: and I literally was like, Yo, what what can you? What can I pay you to make it our priorities like

243 00:37:45.700 ⇒ 00:37:49.360 Uttam Kumaran: dude. I can’t. I like up. I’m like, I’ll bug you with other people I’m like.

244 00:37:50.530 ⇒ 00:37:55.500 Uttam Kumaran: and I was like, can you refer us to someone he’s like? No, I don’t have anybody. I’m like Dude. You are like.

245 00:37:55.870 ⇒ 00:37:56.950 Amber Lin: Doing this.

246 00:37:56.950 ⇒ 00:37:59.709 Uttam Kumaran: Really getting on my nerves. But whatever.

247 00:38:04.950 ⇒ 00:38:08.119 Amber Lin: So let me just do one by week.

248 00:38:15.380 ⇒ 00:38:17.110 Amber Lin: That may have.

249 00:38:24.100 ⇒ 00:38:26.910 Amber Lin: Oh, gosh, okay. So

250 00:38:27.090 ⇒ 00:38:37.017 Amber Lin: oh, what I feel like, Miguel. Just logs 8 h per day period. Nobody ends up at 160 flat.

251 00:38:37.490 ⇒ 00:38:38.250 Uttam Kumaran: Yeah.

252 00:38:38.550 ⇒ 00:38:39.130 Hannah Wang: Fascinating.

253 00:38:39.130 ⇒ 00:38:42.980 Uttam Kumaran: He also like he just didn’t sleep for like the last 2 days. So this is where, like.

254 00:38:42.980 ⇒ 00:38:43.610 Amber Lin: Oh!

255 00:38:43.610 ⇒ 00:38:50.440 Uttam Kumaran: The thing with Miguel. What was happening before is, if you don’t give them enough work, they don’t work.

256 00:38:50.720 ⇒ 00:38:53.940 Uttam Kumaran: Now. We have like so much AI work.

257 00:38:54.280 ⇒ 00:38:57.170 Uttam Kumaran: and that all the work is actually like, really, really cool.

258 00:38:57.640 ⇒ 00:39:01.330 Uttam Kumaran: They just like they’re just blasting it out.

259 00:39:01.580 ⇒ 00:39:03.719 Uttam Kumaran: They need like a full plate of stuff

260 00:39:04.650 ⇒ 00:39:10.729 Uttam Kumaran: like when it was just floating between ABC and then maybe, and then like kind of like the the go to market stuff like

261 00:39:11.680 ⇒ 00:39:17.370 Uttam Kumaran: now, cause now I’m dude. But also now I’m like on their ass, like every day. I’m like, we’re getting stuff out. So it’s.

262 00:39:17.370 ⇒ 00:39:17.960 Amber Lin: Okay.

263 00:39:18.210 ⇒ 00:39:18.810 Uttam Kumaran: Good.

264 00:39:19.220 ⇒ 00:39:19.990 Amber Lin: Okay.

265 00:39:20.360 ⇒ 00:39:26.230 Amber Lin: So what I see here is that overload problem.

266 00:39:28.220 ⇒ 00:39:34.899 Amber Lin: I don’t want Ryan to burn out. I think it’s better this month this this month.

267 00:39:34.900 ⇒ 00:39:37.869 Uttam Kumaran: Are there even wait? Oh, is that even 2 h? What is that.

268 00:39:38.090 ⇒ 00:39:38.730 Amber Lin: Huh!

269 00:39:39.080 ⇒ 00:39:42.040 Uttam Kumaran: There’s 4.3 weeks a month. So how many hours is that? A week.

270 00:39:42.520 ⇒ 00:39:45.540 Amber Lin: Oh, 4.3. Okay, let me.

271 00:39:46.660 ⇒ 00:39:48.380 Uttam Kumaran: Okay. So he’s working about. 5.th

272 00:39:49.600 ⇒ 00:39:50.300 Amber Lin: Yeah, it’s about that.

273 00:39:50.300 ⇒ 00:39:52.080 Amber Lin: Divide by 4 or divide. Okay?

274 00:39:52.080 ⇒ 00:39:54.839 Uttam Kumaran: There’s on average 4.3. Sorry. I literally was just doing it.

275 00:39:54.840 ⇒ 00:39:55.370 Amber Lin: Swimming.

276 00:39:55.370 ⇒ 00:39:56.330 Uttam Kumaran: This yesterday, so.

277 00:39:56.330 ⇒ 00:40:00.360 Amber Lin: Yeah, just for may, cause may might be a little bit longer, anyway.

278 00:40:00.360 ⇒ 00:40:03.400 robert: Can we? Can we hide the the digits.

279 00:40:03.660 ⇒ 00:40:06.460 Amber Lin: Yes, I will. I’m trying to find.

280 00:40:09.810 ⇒ 00:40:11.340 robert: I’ll give you one digit.

281 00:40:12.920 ⇒ 00:40:13.240 Uttam Kumaran: Okay.

282 00:40:13.240 ⇒ 00:40:15.213 Amber Lin: Yeah, okay.

283 00:40:16.200 ⇒ 00:40:19.459 robert: I’m shocked that Dave. A Lotta is just chilling.

284 00:40:22.280 ⇒ 00:40:23.530 Amber Lin: Yeah. But does he?

285 00:40:25.070 ⇒ 00:40:27.209 Amber Lin: Does he have another job? Hello!

286 00:40:27.440 ⇒ 00:40:29.790 robert: Am. I like not logging everything.

287 00:40:30.050 ⇒ 00:40:30.739 robert: You should check his.

288 00:40:30.740 ⇒ 00:40:31.750 Uttam Kumaran: Logging.

289 00:40:31.750 ⇒ 00:40:33.540 robert: Everything in Eden is going to him.

290 00:40:33.870 ⇒ 00:40:34.360 Amber Lin: Okay.

291 00:40:34.360 ⇒ 00:40:35.270 robert: Going on.

292 00:40:36.210 ⇒ 00:40:40.929 Uttam Kumaran: You should check his. You should check what he’s logging if he’s if he’s logged urban stems.

293 00:40:41.090 ⇒ 00:40:46.140 Uttam Kumaran: and if he’s logged. But also we haven’t done anything on urban stems in

294 00:40:46.530 ⇒ 00:40:50.529 Uttam Kumaran: 2 weeks, and that’s that was my question today, cause I was like, what did he do

295 00:40:50.970 ⇒ 00:40:52.909 Uttam Kumaran: on urban stems this week. I don’t know.

296 00:40:54.050 ⇒ 00:40:54.750 Amber Lin: Oh!

297 00:40:55.850 ⇒ 00:40:59.759 Uttam Kumaran: But that’s a question for you. Amber like I don’t know, cause that’s what I asked. I was like I said, Kai.

298 00:40:59.760 ⇒ 00:41:01.170 Uttam Kumaran: Oh, I see, I see.

299 00:41:01.170 ⇒ 00:41:06.210 Amber Lin: I see, so Kyle’s very great on messaging. Let me quickly check.

300 00:41:06.540 ⇒ 00:41:13.129 Amber Lin: I know I know he did some stuff, but he’s a it’ll be slower.

301 00:41:15.450 ⇒ 00:41:20.020 Amber Lin: Oh, let me see his Don tickets.

302 00:41:21.660 ⇒ 00:41:25.709 Amber Lin: He looked at the accuracy of the explorers and Dbt models.

303 00:41:26.871 ⇒ 00:41:35.139 Amber Lin: Investigated the redshi brands and then audited the mother days patches

304 00:41:35.420 ⇒ 00:41:40.920 Amber Lin: so I could say that his workload has been lighter than Kyle’s.

305 00:41:41.900 ⇒ 00:41:43.470 Amber Lin: My estimate.

306 00:41:44.940 ⇒ 00:41:49.009 Uttam Kumaran: We expect Kyle to be bumped up right. He’s at 15.

307 00:41:49.010 ⇒ 00:41:50.710 Uttam Kumaran: An assignment problem like

308 00:41:51.130 ⇒ 00:41:55.460 Uttam Kumaran: this is where, like I’m I just don’t know what was assigned to him to get done this week.

309 00:41:57.750 ⇒ 00:42:04.190 Amber Lin: So we assigned to him the to start looking at the remodeling, but it was taking a bit longer, so it.

310 00:42:04.830 ⇒ 00:42:07.950 Uttam Kumaran: So what like? What is, what is the outcome of of that.

311 00:42:08.170 ⇒ 00:42:13.860 Amber Lin: He. He was supposed to design the remodeling, and, if possible, to

312 00:42:14.100 ⇒ 00:42:31.319 Amber Lin: like cause. I relied on him to do the he was the tech lead right? So he told me. This is what he needed by the end of this week, essentially end of this cycle to audit through it, and then give us a refractive plan plan for inventory.

313 00:42:31.820 ⇒ 00:42:32.490 Amber Lin: So.

314 00:42:32.490 ⇒ 00:42:42.500 Uttam Kumaran: But let so let me just walk through a couple. So I see 2 tickets on Demote’s plate that’s in progress past shopify, template, core objects for Emily.

315 00:42:44.550 ⇒ 00:42:47.579 Uttam Kumaran: Okay, did he? He responded. 3 h ago.

316 00:42:50.410 ⇒ 00:42:51.840 Uttam Kumaran: Fine! This isn’t.

317 00:42:52.180 ⇒ 00:42:53.330 Uttam Kumaran: Looks like this, is it?

318 00:42:53.330 ⇒ 00:42:53.910 Amber Lin: Nice.

319 00:42:54.150 ⇒ 00:42:55.390 Uttam Kumaran: This is in review.

320 00:42:55.500 ⇒ 00:43:00.160 Uttam Kumaran: The next thing is, investigate what tables are being touched by Dvt. This is a 2 point thing.

321 00:43:00.160 ⇒ 00:43:01.360 Amber Lin: Here, now.

322 00:43:02.710 ⇒ 00:43:04.529 Uttam Kumaran: I don’t know why this is not done.

323 00:43:05.690 ⇒ 00:43:10.980 Uttam Kumaran: There’s also create analysis of snapshots of amount of time.

324 00:43:12.020 ⇒ 00:43:15.389 Uttam Kumaran: So there’s all these things. But again, like none of them.

325 00:43:15.720 ⇒ 00:43:18.040 Uttam Kumaran: none of them have due dates and.

326 00:43:18.040 ⇒ 00:43:26.510 Amber Lin: I think those 4 he just created because I haven’t seen them yet on a power.

327 00:43:26.510 ⇒ 00:43:28.060 Uttam Kumaran: But like that’s not enough.

328 00:43:28.060 ⇒ 00:43:29.019 Amber Lin: Hey! How are you?

329 00:43:29.020 ⇒ 00:43:36.050 Uttam Kumaran: Like this is, what’s this is what the problem is, if if the engineers create stuff and there’s no clear like what happens.

330 00:43:36.260 ⇒ 00:43:37.900 Uttam Kumaran: and nobody’s on the hook.

331 00:43:39.300 ⇒ 00:43:42.030 Uttam Kumaran: This is what happens like this is what’s gonna happen.

332 00:43:42.030 ⇒ 00:43:42.590 Amber Lin: I know.

333 00:43:42.590 ⇒ 00:43:48.029 Uttam Kumaran: Right? So for investigation data issues with with inventory mart.

334 00:43:48.280 ⇒ 00:43:52.889 Uttam Kumaran: is this a meeting? Is this a document? Is this like, what is this?

335 00:43:53.040 ⇒ 00:43:55.849 Uttam Kumaran: What is gonna happen? And when is it gonna happen?

336 00:43:56.150 ⇒ 00:43:58.020 Uttam Kumaran: And because the due date says today.

337 00:44:04.430 ⇒ 00:44:06.240 Amber Lin: Okay. How are you looking at.

338 00:44:06.870 ⇒ 00:44:12.819 Uttam Kumaran: I’m asking open ended questions. Cause? I probably know the answer. But like this is the questions that I’m asking, because.

339 00:44:13.330 ⇒ 00:44:16.340 Uttam Kumaran: If we have no accountability, and, like the due dates, don’t matter.

340 00:44:16.400 ⇒ 00:44:17.000 Amber Lin: And.

341 00:44:17.000 ⇒ 00:44:22.210 Uttam Kumaran: The tickets don’t have like what? That? What is actually supposed to happen then, like

342 00:44:22.650 ⇒ 00:44:26.690 Uttam Kumaran: cause? My recommendation would for you to be like, hey? That we set the due date for this is today.

343 00:44:26.900 ⇒ 00:44:34.550 Uttam Kumaran: This doesn’t have clear, doesn’t have a clear acceptance. Criteria doesn’t have an outcome. There’s 4 sub issues

344 00:44:34.830 ⇒ 00:44:41.800 Uttam Kumaran: which, like we shouldn’t use sub issues. I I don’t know how to turn. I don’t even know how to turn that off in linear. But I’ll turn off sub issues

345 00:44:42.820 ⇒ 00:44:45.869 Uttam Kumaran: like. But like these are all, when are these? Gonna get done?

346 00:44:46.190 ⇒ 00:44:46.930 Amber Lin: Well.

347 00:44:47.120 ⇒ 00:45:00.530 Amber Lin: my, okay, I know you’re looking at this ticket called investigated investigation data issues with inventory mark. Last time we had a stand of this was done. This was Npr. Review. There was no sub issues. It was a. It was a resolved ticket

348 00:45:00.730 ⇒ 00:45:01.570 Amber Lin: today.

349 00:45:01.570 ⇒ 00:45:03.410 Uttam Kumaran: What what is done, mean.

350 00:45:06.210 ⇒ 00:45:19.069 Amber Lin: There was an issue that was brought up by one of the stakeholders, and then we investigated it. Nothing was. There was nothing indicating that will happen again. We resolve that issue so just to look clarify what happened.

351 00:45:21.070 ⇒ 00:45:27.370 Uttam Kumaran: But, Emily, it looks like Emily is creating these sub issues right now

352 00:45:27.720 ⇒ 00:45:29.570 Uttam Kumaran: and assigning it to Demo Ade.

353 00:45:29.870 ⇒ 00:45:30.450 Amber Lin: Hmm.

354 00:45:30.450 ⇒ 00:45:32.169 Uttam Kumaran: Okay, so maybe this is something else.

355 00:45:32.650 ⇒ 00:45:36.969 Amber Lin: This is something. I think this is something else that I don’t have. Context of what this is.

356 00:45:36.970 ⇒ 00:45:39.740 Uttam Kumaran: Oh, okay. So then he has like, no. So then

357 00:45:40.110 ⇒ 00:45:43.510 Uttam Kumaran: the only thing that he has to do this cycle is.

358 00:45:44.010 ⇒ 00:45:51.400 Uttam Kumaran: I guess this is what I’m saying is like I had a feeling that like he’s not doing anything on this client because he didn’t say anything for a week.

359 00:45:52.540 ⇒ 00:45:53.290 Amber Lin: Oh!

360 00:45:54.730 ⇒ 00:45:56.579 Uttam Kumaran: But again I have to ask you first.st

361 00:45:57.830 ⇒ 00:46:01.040 Amber Lin: Let’s see no

362 00:46:02.820 ⇒ 00:46:07.800 Uttam Kumaran: But like we have to have that, we have to have these tickets clear, right? Like

363 00:46:08.460 ⇒ 00:46:13.169 Uttam Kumaran: the only source of truth we have for what people are working on? Are these tickets.

364 00:46:14.630 ⇒ 00:46:16.700 Uttam Kumaran: No other way for me to know

365 00:46:17.010 ⇒ 00:46:20.130 Uttam Kumaran: for us to go through and look what what people are doing.

366 00:46:29.310 ⇒ 00:46:31.570 Amber Lin: I think most of this week was.

367 00:46:31.820 ⇒ 00:46:39.949 Amber Lin: Kyle did heavy lifting on, getting all the usage data, and he was doing mostly audits of things which

368 00:46:40.730 ⇒ 00:46:43.190 Amber Lin: I don’t know how long those take.

369 00:46:44.700 ⇒ 00:46:49.870 Uttam Kumaran: But what but but like I’m looking at the I’m looking at the ticket breakdown. There’s 3 tickets.

370 00:46:49.870 ⇒ 00:46:55.109 Uttam Kumaran: Kyle. There’s 2 for Emily that have been that have been started

371 00:46:59.610 ⇒ 00:47:04.149 Uttam Kumaran: And then there’s these are all sub tickets that just got added right now. But

372 00:47:06.500 ⇒ 00:47:12.430 Uttam Kumaran: I I guess this is what I’m saying is like, I’m not happy with the way these tickets are looking, because there’s nothing in here. There’s no like

373 00:47:13.730 ⇒ 00:47:17.400 Uttam Kumaran: like this is a common thing in engineering is investigate.

374 00:47:17.890 ⇒ 00:47:27.149 Uttam Kumaran: Nothing happens in investigation. Like, Who’s the who’s the murderer? Tell me, like, you know, that’s what we gotta find out here is like.

375 00:47:27.430 ⇒ 00:47:33.670 Uttam Kumaran: what, isn’t it? What is being investigated? And what is the outcome? I I don’t have a meeting to to review anything.

376 00:47:34.000 ⇒ 00:47:37.470 Uttam Kumaran: and it’s clear he didn’t work this week, so.

377 00:47:37.470 ⇒ 00:47:38.160 Amber Lin: Hmm.

378 00:47:39.750 ⇒ 00:47:40.819 Uttam Kumaran: What’s the like?

379 00:47:41.250 ⇒ 00:47:43.530 Uttam Kumaran: It’s either me or you know. The answer to this.

380 00:47:43.770 ⇒ 00:47:45.489 Amber Lin: We’re talking hold on

381 00:47:45.910 ⇒ 00:47:52.619 Amber Lin: the investigation tickets and the sub issues. That’s a separate problem that we need to ask Emily to not to create.

382 00:47:52.620 ⇒ 00:47:54.850 Uttam Kumaran: I’m not. No, I’m not talking about not talking about those.

383 00:47:54.850 ⇒ 00:47:55.460 Amber Lin: Okay. Good.

384 00:47:55.460 ⇒ 00:47:57.489 Uttam Kumaran: But even so, the other those out, yeah.

385 00:47:57.490 ⇒ 00:48:06.450 Amber Lin: Yeah. So overall, I think to remove that issue. Talking about the question of we feel like Demo is not working enough. And then we need proof to prove that he’s not working

386 00:48:07.250 ⇒ 00:48:11.959 Amber Lin: so based on. Now that I look at the tickets.

387 00:48:12.280 ⇒ 00:48:17.470 Amber Lin: I would agree with you that he is not working enough, and I.

388 00:48:17.470 ⇒ 00:48:18.579 Uttam Kumaran: Guess my question would be like.

389 00:48:18.580 ⇒ 00:48:19.035 Amber Lin: Right

390 00:48:19.490 ⇒ 00:48:22.050 Uttam Kumaran: Grooming with him. Why didn’t we assign him?

391 00:48:22.980 ⇒ 00:48:24.670 Uttam Kumaran: Why didn’t he get more tickets.

392 00:48:27.200 ⇒ 00:48:29.269 Amber Lin: When we were. Hmm.

393 00:48:32.190 ⇒ 00:48:35.440 Uttam Kumaran: Because I can’t I can’t. I can’t ask him anything, because he doesn’t have anything assigned to him

394 00:48:35.960 ⇒ 00:48:39.319 Uttam Kumaran: right? Because and I mean the only thing we should ask him is like.

395 00:48:40.600 ⇒ 00:48:47.290 Uttam Kumaran: verify usage. Approach with team was due was due June 3, rd and then

396 00:48:48.300 ⇒ 00:48:51.880 Uttam Kumaran: there was investigate what tables are being touched by Dvt.

397 00:48:53.950 ⇒ 00:48:58.090 Uttam Kumaran: so those we those should be done. And now we should ask like, why aren’t those done

398 00:49:02.120 ⇒ 00:49:04.499 Uttam Kumaran: so, I guess. Like maybe to just put a bow on this.

399 00:49:05.140 ⇒ 00:49:09.380 Uttam Kumaran: We really need groom tickets and to

400 00:49:09.940 ⇒ 00:49:12.930 Uttam Kumaran: like we really need everybody to have a full

401 00:49:13.450 ⇒ 00:49:17.939 Uttam Kumaran: prints workload on their plate as a ticket. Otherwise I can’t

402 00:49:18.300 ⇒ 00:49:21.099 Uttam Kumaran: like we can’t hold anybody accountable for anything, you know.

403 00:49:22.120 ⇒ 00:49:29.609 Uttam Kumaran: Look that that really needs to happen, because I don’t blame. I I don’t think he’s like gaming or anything. I I think he just doesn’t have.

404 00:49:30.480 ⇒ 00:49:33.689 Uttam Kumaran: He’s working on something for sure, and he’s working on Eden stuff.

405 00:49:37.990 ⇒ 00:49:46.320 Amber Lin: Since is to improve on this next cycle of when I assign tickets, demolan is assigning it with me.

406 00:49:47.250 ⇒ 00:49:54.729 Amber Lin: So what should I ask? How much work capacity you have? Or is this something that we.

407 00:49:54.850 ⇒ 00:49:56.820 Amber Lin: what we’re working on right now.

408 00:49:57.610 ⇒ 00:50:00.839 Uttam Kumaran: Yeah, we’re working on it now is like he has. He should have.

409 00:50:01.040 ⇒ 00:50:06.140 Uttam Kumaran: you know, around 40 HI would say we’re aiming for 80%. Right? So.

410 00:50:06.890 ⇒ 00:50:10.119 Uttam Kumaran: But like, when I look at when I look at this.

411 00:50:10.910 ⇒ 00:50:14.740 Uttam Kumaran: I think I think there’s kind of 2 ways to look at it from the client side.

412 00:50:15.450 ⇒ 00:50:18.099 Uttam Kumaran: We want to make sure that we’re hovering between

413 00:50:18.670 ⇒ 00:50:21.250 Uttam Kumaran: 10 to 15 HA week or so.

414 00:50:21.914 ⇒ 00:50:25.809 Uttam Kumaran: and but like, I would say, like, that’s probably secondary to like

415 00:50:27.560 ⇒ 00:50:30.660 Uttam Kumaran: like, Hey, you agreed to get this done at this point

416 00:50:30.840 ⇒ 00:50:33.500 Uttam Kumaran: and like, why are they gonna get done

417 00:50:33.750 ⇒ 00:50:35.680 Uttam Kumaran: like, when are they gonna get done.

418 00:50:36.140 ⇒ 00:50:40.970 Uttam Kumaran: Those types of check-ins need to happen, like, you know, during whatever the stand ups are.

419 00:50:42.860 ⇒ 00:50:56.509 Amber Lin: I think, for this cycle, I think. Well, why it fell apart was also because a lot of these tickets was created during the cycle. We the planning, wasn’t. We weren’t able to have a lot of details to do the planning.

420 00:50:56.640 ⇒ 00:51:09.480 Amber Lin: and I think a lot of it fell through because we were assigning it and giving it due dates as we go through the cycle. And I think why done a lot of didn’t get enough was.

421 00:51:09.650 ⇒ 00:51:34.740 Amber Lin: I perceived that he had based on my perception, which is not based on any data that I thought he had more work, because that’s what I perceived when he told me, I asked, do you have capacity to do this? He says I’m busy, and I don’t think that’s a good measure I should base off of. So I think, starting that cycle it will just be based on. Okay. You have these many hours allocated.

422 00:51:36.350 ⇒ 00:51:42.430 Amber Lin: Everything is within your capacity. You can juggle if you have to do even work, if you have to do this. But

423 00:51:42.530 ⇒ 00:51:45.000 Amber Lin: this is the amount of tickets I can assign to you.

424 00:51:45.310 ⇒ 00:51:45.810 Amber Lin: Yeah.

425 00:51:45.810 ⇒ 00:51:46.410 Amber Lin: So.

426 00:51:46.590 ⇒ 00:51:58.990 Uttam Kumaran: But I I would also like, don’t wait for me to to do grooming and stuff, because I’m just not gonna be available. I wouldn’t. I wouldn’t meet with him and Kyle every day until you groom everything like why not?

427 00:51:59.630 ⇒ 00:52:04.679 Uttam Kumaran: You know I I wouldn’t wait for them. They’re not gonna come to us with anything.

428 00:52:05.020 ⇒ 00:52:13.530 Uttam Kumaran: The engineers are never gonna be like raising their hand for more work. It’s never gonna be like that. So it’s always going to be this crew that has to make sure that there are

429 00:52:14.170 ⇒ 00:52:16.309 Uttam Kumaran: tickets that they can take on, and that

430 00:52:16.550 ⇒ 00:52:22.150 Uttam Kumaran: you’ve attached them to that. You’ve agreed that, hey? You’re gonna work on this, and it’s going to be done at this point.

431 00:52:22.560 ⇒ 00:52:23.350 Uttam Kumaran: you know.

432 00:52:23.490 ⇒ 00:52:30.509 Uttam Kumaran: he should be working with you to write a lot of those out for what Kyle, Emily, and him are gonna do.

433 00:52:30.650 ⇒ 00:52:36.849 Uttam Kumaran: But looking at looking at what’s assigned on the sprint, I think he would agree that this is not enough.

434 00:52:36.990 ⇒ 00:52:40.320 Uttam Kumaran: or I mean, like I don’t know. I don’t know how we would think otherwise.

435 00:52:46.030 ⇒ 00:52:47.240 Amber Lin: Should I be?

436 00:52:47.690 ⇒ 00:52:53.370 Amber Lin: What I hear is that I should be more aggressive in terms of it. This is this is just what you need to do.

437 00:52:53.370 ⇒ 00:52:56.070 Uttam Kumaran: Not aggressive, but like.

438 00:52:56.070 ⇒ 00:52:58.840 Amber Lin: Here. They say they’re busy. So I was like, Okay, you’re busy. So.

439 00:52:58.840 ⇒ 00:53:04.879 Uttam Kumaran: Well, what the fuck like, busy with what? What like, that’s not a I don’t know. I don’t know what that means with what visa.

440 00:53:04.880 ⇒ 00:53:07.030 Amber Lin: I don’t know, either. He has eaten heart. Maybe.

441 00:53:08.340 ⇒ 00:53:14.740 Uttam Kumaran: Busy is not a but this is where I’m like. I’m surprised I’d be surprised if that’s literally where he’s like I’m busy. I can’t do anything

442 00:53:15.650 ⇒ 00:53:17.800 Uttam Kumaran: because he didn’t log any time.

443 00:53:21.470 ⇒ 00:53:28.940 Uttam Kumaran: So. But but this is where I don’t have the full context of that meeting which was like, Oh, I was it like, I can’t take on any more work, as I’m busy.

444 00:53:31.766 ⇒ 00:53:42.060 Amber Lin: It’s more of a i i asked about capacity and and said, I have other work

445 00:53:42.350 ⇒ 00:53:43.650 Amber Lin: that I need to do.

446 00:53:45.300 ⇒ 00:53:50.119 Uttam Kumaran: That seems valid like that seems accurate

447 00:53:50.360 ⇒ 00:53:58.310 Uttam Kumaran: like. I don’t think there’s anything wrong with the way he said that I mean, I think I think when you ask, what’s your? I don’t think you should ask people. I think that’s probably maybe the problem is like.

448 00:53:58.500 ⇒ 00:54:06.069 Uttam Kumaran: don’t ask people what their capacity is across projects. We should know that like coming in.

449 00:54:07.920 ⇒ 00:54:12.359 Uttam Kumaran: Because, again, this is, I think, probably what we talked about in the team, even in the last meeting which is like.

450 00:54:12.650 ⇒ 00:54:16.459 Uttam Kumaran: do you ask people too many questions, or it’s too democratic.

451 00:54:17.400 ⇒ 00:54:20.140 Uttam Kumaran: This is where you gotta turn into Robert a little bit and

452 00:54:20.270 ⇒ 00:54:23.740 Uttam Kumaran: like. Don’t, don’t ask. Don’t be so democratic.

453 00:54:24.130 ⇒ 00:54:33.889 Uttam Kumaran: I think. Do you have Cap? Do you have capacity, or like? Who wants to take this? We found how hard it is to do that right. So I think we should buy us more towards like.

454 00:54:34.510 ⇒ 00:54:39.989 Uttam Kumaran: put everything on the plate and say, Can you get all this done? If not, why, and then remove stuff from there.

455 00:54:40.670 ⇒ 00:54:46.499 Uttam Kumaran: you know, cause they’re the engineers are not. Gonna look at themselves the way we look at like a basket of resources.

456 00:54:46.980 ⇒ 00:54:53.979 Uttam Kumaran: They’re gonna say, I’m busy with other stuff here. But if you put it on, then, like, Okay, cool, this is all the stuff I have to do today. They look on a to do list right.

457 00:54:55.560 ⇒ 00:54:56.250 Amber Lin: I, see.

458 00:54:56.250 ⇒ 00:55:02.520 Uttam Kumaran: It’s not not that you’re not democratic, I think. You know, you’re less democratic.

459 00:55:03.244 ⇒ 00:55:08.760 Uttam Kumaran: You’re not actually not less democratic. I just think that, like you have a.

460 00:55:08.760 ⇒ 00:55:09.889 Amber Lin: Being nice.

461 00:55:09.890 ⇒ 00:55:18.639 Uttam Kumaran: No, I think I I feel like I’m wrong. I do this. I probably wait. I think I’m way too democratic. Frankly, I think it’s helped in a lot of ways, and it’s hurt in a lot of ways.

462 00:55:18.810 ⇒ 00:55:19.490 Amber Lin: Hmm.

463 00:55:19.905 ⇒ 00:55:22.964 Uttam Kumaran: And that’s that is, that is just the way it is, you know.

464 00:55:23.670 ⇒ 00:55:36.919 Uttam Kumaran: Okay, but yeah, I wouldn’t ask. I would say, maybe the thing is, don’t worry about people’s capacity. Make sure they have a a full workload, I think, for you. What we need to get out of operating is what

465 00:55:37.470 ⇒ 00:55:41.100 Uttam Kumaran: like how many hours people do have per client, you know.

466 00:55:43.390 ⇒ 00:55:49.130 Uttam Kumaran: Cause you should come to the table with that like, Hey, you have 20 h to to do this. So here’s 20 h of work.

467 00:55:51.240 ⇒ 00:55:52.100 Amber Lin: Great.

468 00:55:52.760 ⇒ 00:55:53.899 Uttam Kumaran: Does that seem fair.

469 00:55:53.900 ⇒ 00:56:02.289 Amber Lin: Yeah, that seems fair. That helps me. I think that was the core issue. I like, I like that. We identified that. That makes me more confident.

470 00:56:02.290 ⇒ 00:56:14.880 Uttam Kumaran: Cause the engineers and they’re not. They’re not malicious, like. I think you’ve learned now from the AI team like nobody is malicious or nobody is like actively sandbagging like in this current team

471 00:56:15.060 ⇒ 00:56:19.249 Uttam Kumaran: that it will happen again. And it has happened in the past. But it’s not happening right now.

472 00:56:19.430 ⇒ 00:56:22.029 Uttam Kumaran: cause. I I have a good sense of that.

473 00:56:22.240 ⇒ 00:56:27.930 Uttam Kumaran: But what you’re seeing is that the engineers and they’re not like it’s just not in their nature to be proactive and to like.

474 00:56:28.530 ⇒ 00:56:32.420 Uttam Kumaran: understand their workload. They’re just like, well, tell me what to do.

475 00:56:32.670 ⇒ 00:56:33.200 Uttam Kumaran: You know.

476 00:56:33.200 ⇒ 00:56:43.649 Amber Lin: I see. Okay, that’s a shift of perspective, because I think I expected download it to say, Hey, I have this this because I expected the tech lead to be this, but that’s the wrong expectation.

477 00:56:43.650 ⇒ 00:56:50.099 Uttam Kumaran: But he he’s probably he is pro definitely working on stuff, or there’s something to do. But like, I think your job is less of

478 00:56:52.350 ⇒ 00:56:55.680 Uttam Kumaran: who gets it done. It’s got to be that the fact that it gets done.

479 00:56:56.500 ⇒ 00:56:57.210 Uttam Kumaran: Right.

480 00:56:57.630 ⇒ 00:57:01.799 Uttam Kumaran: So if it’s not getting done, then you’re like, who’s gonna do it then, or like

481 00:57:02.060 ⇒ 00:57:03.740 Uttam Kumaran: we’re off track right.

482 00:57:04.130 ⇒ 00:57:05.999 Uttam Kumaran: We’re off track on our plan.

483 00:57:11.220 ⇒ 00:57:13.210 Uttam Kumaran: Cool. Sorry to derail this.

484 00:57:13.500 ⇒ 00:57:14.749 Amber Lin: Great. No, I.

485 00:57:15.720 ⇒ 00:57:20.120 Amber Lin: And I think that’s essentially why we’re doing these allocations

486 00:57:20.650 ⇒ 00:57:23.210 Amber Lin: essentially to know what’s going on.

487 00:57:23.440 ⇒ 00:57:25.579 Uttam Kumaran: Can you rank this bi-weekly.

488 00:57:27.480 ⇒ 00:57:30.150 Amber Lin: We only have months data here.

489 00:57:30.150 ⇒ 00:57:32.150 Uttam Kumaran: So by the weekly hours.

490 00:57:32.150 ⇒ 00:57:33.780 Amber Lin: Oh, sure! Let me.

491 00:57:42.051 ⇒ 00:57:43.260 Uttam Kumaran: You could just click, filter.

492 00:57:43.260 ⇒ 00:57:44.620 Amber Lin: Oh no!

493 00:57:46.120 ⇒ 00:57:47.950 Uttam Kumaran: You just click, you can just click this.

494 00:57:48.730 ⇒ 00:57:50.769 Uttam Kumaran: Just select this whole thing.

495 00:57:53.630 ⇒ 00:57:55.950 Uttam Kumaran: No, just select. Just select this whole area.

496 00:57:56.180 ⇒ 00:57:56.840 Amber Lin: Yeah.

497 00:57:57.340 ⇒ 00:57:58.850 Uttam Kumaran: And then click, filter.

498 00:58:01.770 ⇒ 00:58:02.970 Amber Lin: Top priority hub, right.

499 00:58:05.180 ⇒ 00:58:05.710 Uttam Kumaran: Oh!

500 00:58:06.680 ⇒ 00:58:07.120 Amber Lin: Oh, God!

501 00:58:07.120 ⇒ 00:58:07.590 Uttam Kumaran: Click, it again.

502 00:58:07.590 ⇒ 00:58:08.760 Amber Lin: Do you already have.

503 00:58:08.760 ⇒ 00:58:10.120 Uttam Kumaran: Click it again. Yeah.

504 00:58:10.340 ⇒ 00:58:10.920 Amber Lin: Yeah.

505 00:58:11.820 ⇒ 00:58:12.780 Uttam Kumaran: And now you can just.

506 00:58:12.780 ⇒ 00:58:14.460 Amber Lin: Filter by after this. And yeah.

507 00:58:28.520 ⇒ 00:58:31.100 Amber Lin: huh, why is this sorting like this?

508 00:58:31.350 ⇒ 00:58:34.469 Uttam Kumaran: Oh, you didn’t. You didn’t select the entire category for

509 00:58:35.810 ⇒ 00:58:38.149 Uttam Kumaran: like, just control Z out of this

510 00:58:40.290 ⇒ 00:58:46.379 Uttam Kumaran: and then click like click on each of like, just like shift click on a BCD,

511 00:58:49.550 ⇒ 00:58:55.750 Uttam Kumaran: no, no shift shift, no, no as exit out of this shift. Click on a BCD,

512 00:58:57.130 ⇒ 00:59:00.249 Uttam Kumaran: yeah, and then click on filter here.

513 00:59:00.440 ⇒ 00:59:02.659 Uttam Kumaran: click it and then click it again.

514 00:59:03.870 ⇒ 00:59:05.159 Amber Lin: Well, it’s already.

515 00:59:05.410 ⇒ 00:59:07.200 Uttam Kumaran: And then click it again. Yep, and then, now.

516 00:59:07.200 ⇒ 00:59:08.100 Amber Lin: I need to.

517 00:59:08.100 ⇒ 00:59:09.010 Uttam Kumaran: To May.

518 00:59:13.720 ⇒ 00:59:14.450 Uttam Kumaran: Yep.

519 00:59:19.140 ⇒ 00:59:20.920 Uttam Kumaran: and then just sort, yeah.

520 00:59:21.820 ⇒ 00:59:24.919 Amber Lin: Yeah, I think the problem was that this was not really, let me see.

521 00:59:24.920 ⇒ 00:59:27.810 Uttam Kumaran: No, this is just. It’s right. Now, it’s not a number call. Yeah. So just.

522 00:59:27.810 ⇒ 00:59:28.250 Amber Lin: This.

523 00:59:28.250 ⇒ 00:59:31.299 Uttam Kumaran: Click on, but don’t, don’t highlight just those click on d

524 00:59:33.140 ⇒ 00:59:38.690 Uttam Kumaran: click on the entire column and then now convert it to a number, click on 1, 2, 3.

525 00:59:41.713 ⇒ 00:59:43.139 Amber Lin: Oh, gosh.

526 00:59:43.140 ⇒ 00:59:44.000 Uttam Kumaran: And click on number.

527 00:59:44.000 ⇒ 00:59:47.740 Amber Lin: We don’t work in Google sheets.

528 00:59:47.740 ⇒ 00:59:49.780 Uttam Kumaran: And then click on yup. And now sort.

529 00:59:49.780 ⇒ 00:59:52.210 Amber Lin: Try still.

530 00:59:52.500 ⇒ 01:00:00.609 Amber Lin: Oh, number now, but I don’t know what the issue is.

531 01:00:00.610 ⇒ 01:00:02.940 Uttam Kumaran: Okay, you wait. Let me share. I can try to share.

532 01:00:02.940 ⇒ 01:00:03.670 Amber Lin: Yeah.

533 01:00:05.960 ⇒ 01:00:06.650 Uttam Kumaran: Okay.

534 01:00:07.220 ⇒ 01:00:08.470 Hannah Wang: Data.

535 01:00:08.680 ⇒ 01:00:12.309 Hannah Wang: I don’t know how to do any of this manipulation.

536 01:00:14.870 ⇒ 01:00:15.760 Uttam Kumaran: Okay.

537 01:00:28.290 ⇒ 01:00:29.100 Uttam Kumaran: Oh.

538 01:00:43.980 ⇒ 01:00:46.939 robert: Okay, I gotta run the Eden retro.

539 01:00:47.220 ⇒ 01:00:47.810 Uttam Kumaran: Okay.

540 01:00:50.320 ⇒ 01:00:56.309 robert: Oh, wait, that’s in alright. Never mind. I got 15 long minutes.

541 01:00:56.940 ⇒ 01:01:01.049 Uttam Kumaran: Okay, I don’t know why this isn’t really sorting. I don’t know what’s going on.

542 01:01:03.175 ⇒ 01:01:06.420 Uttam Kumaran: Okay? So I mean, it looks like, otherwise.

543 01:01:07.380 ⇒ 01:01:10.029 Uttam Kumaran: yeah, these guys don’t really matter.

544 01:01:11.120 ⇒ 01:01:12.500 Uttam Kumaran: Kyle,

545 01:01:17.280 ⇒ 01:01:21.199 Uttam Kumaran: yeah. So the change we’re making there is, Mustafa is gonna help on. ABC,

546 01:01:25.670 ⇒ 01:01:34.719 Uttam Kumaran: And Ryan has a bit of capacity, but, like Kyle and Ryan, have capacity.

547 01:01:35.810 ⇒ 01:01:36.440 Amber Lin: Hmm.

548 01:01:40.860 ⇒ 01:01:46.029 Uttam Kumaran: I think it is. I think we definitely like we’ll need to close on something on the Pm. Side.

549 01:01:47.090 ⇒ 01:01:51.349 Uttam Kumaran: And that way, amber, you can just focus on like the large strategic folks.

550 01:01:53.380 ⇒ 01:02:02.100 Uttam Kumaran: and really more more focused on like, like ABC, carbon stems.

551 01:02:02.100 ⇒ 01:02:05.249 Amber Lin: Yeah, I I think when I talked to these 2 candidates.

552 01:02:05.450 ⇒ 01:02:18.319 Amber Lin: I was realizing I don’t know how to grade them again, so will they be managing other bigger clients, or they’re gonna be managing other smaller clients. What what are they gonna be doing.

553 01:02:19.070 ⇒ 01:02:22.639 Uttam Kumaran: I guess it. Kinda I guess it depends. I’m not like.

554 01:02:23.010 ⇒ 01:02:27.370 Uttam Kumaran: yeah, they’re gonna be managing. They’re gonna be managing clients. I don’t know which ones

555 01:02:27.860 ⇒ 01:02:30.579 Uttam Kumaran: most likely it’ll be the easy ones, for now.

556 01:02:30.810 ⇒ 01:02:31.530 Amber Lin: Okay.

557 01:02:31.530 ⇒ 01:02:34.019 Uttam Kumaran: Because I can’t do what I’m doing on.

558 01:02:34.020 ⇒ 01:02:34.900 Amber Lin: I agree.

559 01:02:34.900 ⇒ 01:02:36.050 Uttam Kumaran: Yeah, I can’t. I can.

560 01:02:36.280 ⇒ 01:02:36.850 Amber Lin: Okay.

561 01:02:37.500 ⇒ 01:02:41.270 Uttam Kumaran: Yeah, I can’t manage the like pool parts and

562 01:02:41.993 ⇒ 01:02:44.920 Uttam Kumaran: off the record, like I need someone else to sort of help

563 01:02:45.120 ⇒ 01:02:48.689 Uttam Kumaran: like I can come in and and give direction for like probably an hour a week.

564 01:02:49.060 ⇒ 01:02:53.770 Uttam Kumaran: But someone needs to be following up with the engineers and like executing like.

565 01:02:53.770 ⇒ 01:02:55.120 Amber Lin: I see, I see.

566 01:02:55.120 ⇒ 01:02:59.490 Amber Lin: I think then they should be good for it. The thing I notice is, I don’t know how

567 01:03:01.380 ⇒ 01:03:14.440 Amber Lin: decisive. I don’t know if that’s the right word, because our clients sometimes need us to take the lead right? And that’s the characteristic that I was kind of looking for, but if they’re just managing smaller clients, I think that should be fine.

568 01:03:14.930 ⇒ 01:03:17.490 Uttam Kumaran: Yeah, like, I think this is a stopgap.

569 01:03:17.880 ⇒ 01:03:19.719 Uttam Kumaran: like the reason why I’m looking

570 01:03:20.010 ⇒ 01:03:23.180 Uttam Kumaran: like, I would rather have a delivery folks here in the States. But

571 01:03:23.530 ⇒ 01:03:31.319 Uttam Kumaran: we just need somebody, because I can’t like I it’s really really hard for me to manage projects like ideally. That person

572 01:03:31.710 ⇒ 01:03:37.609 Uttam Kumaran: I mean the AI team. They’re pretty good, like, I’m only doing like maybe 30 min a day.

573 01:03:40.630 ⇒ 01:03:44.800 Uttam Kumaran: but like it’s quickly gonna break. If the 2, if we get like 3 other clients that are in

574 01:03:48.590 ⇒ 01:03:55.664 Uttam Kumaran: cool. I don’t know. I don’t know how I did this. So I’ve had this new sheet with the projects. But I don’t. What did I even do here?

575 01:04:06.220 ⇒ 01:04:14.829 Uttam Kumaran: I like how I did this formula, but either way I can. I’ll I can look at. We can start to talk about what we had set up in operating.

576 01:04:16.220 ⇒ 01:04:25.950 Amber Lin: Okay, hmm, is, is Robert gonna be listening to on this or.

577 01:04:27.320 ⇒ 01:04:28.349 Uttam Kumaran: Yeah, he’s still here.

578 01:04:28.350 ⇒ 01:04:30.149 Amber Lin: Okay. Sounds good.

579 01:04:32.960 ⇒ 01:04:39.220 Amber Lin: I. I made a pivot for just the month of June.

580 01:04:39.590 ⇒ 01:04:42.940 Amber Lin: but don’t have people breakdowns yet.

581 01:04:43.460 ⇒ 01:04:46.600 Amber Lin: so let me go open offering.

582 01:05:23.780 ⇒ 01:05:30.220 Uttam Kumaran: I guess maybe, Robert, while you’re on this call, I think probably the biggest thing to understand. I think maybe the rest of this we can do. But

583 01:05:30.600 ⇒ 01:05:34.470 Uttam Kumaran: what do you think about the with readme and with

584 01:05:36.480 ⇒ 01:05:38.890 Uttam Kumaran: with spark plug like, what’s gonna happen.

585 01:05:40.513 ⇒ 01:05:47.760 robert: Yeah, so so far with readme, I think. They have a

586 01:05:48.710 ⇒ 01:05:58.119 robert: like, they don’t have tech. They don’t have deep technical talent. They just have, like a product designer doing some of their like event instrumentation stuff there. So

587 01:05:58.290 ⇒ 01:06:14.350 robert: now they’re like thinking about hooking up other stuff into like their reporting. That’s not like native to what amplitude can do out of the box. And so I think there’s that to me is opportunity for d work to come in.

588 01:06:15.714 ⇒ 01:06:22.119 robert: So yeah, I I think that one has potential to become like, to start to sell and to to be

589 01:06:24.470 ⇒ 01:06:31.290 robert: yeah, cause right now. It’s just like I’m just working with Ops people and and their product designer, who happens to be kind of technical.

590 01:06:31.510 ⇒ 01:06:32.190 robert: Oh.

591 01:06:32.650 ⇒ 01:06:43.920 robert: and then on the spark, plug side that they don’t have any. Well, they don’t have data people, either, but they have a bunch of engineers that are kind of like doing stuff so that one’s a bit more

592 01:06:44.290 ⇒ 01:06:52.819 robert: chaotic than read me, so I don’t have a good read on it yet, and then I’m just. I just did like the 1st analysis I submitted yesterday. Follow along on that

593 01:06:52.940 ⇒ 01:06:56.909 robert: slack channel. But but yeah, I don’t really have a good

594 01:06:57.390 ⇒ 01:06:59.219 robert: sense of of that one yet.

595 01:07:02.520 ⇒ 01:07:06.110 Uttam Kumaran: Okay, what I’m kind of trying to look at is like.

596 01:07:06.670 ⇒ 01:07:08.800 Uttam Kumaran: so these people are all fine.

597 01:07:08.910 ⇒ 01:07:18.320 Uttam Kumaran: I think Awaii is kind of Maxed Demo latte has time.

598 01:07:23.670 ⇒ 01:07:28.910 Uttam Kumaran: Oh, so like it looks like he didn’t even log his hours for May. So that’s probably what’s happening.

599 01:07:31.950 ⇒ 01:07:39.299 Uttam Kumaran: So I I guess my question would be if one of those converts to something bigger

600 01:07:40.390 ⇒ 01:07:46.559 Uttam Kumaran: default comes in, I’ll need a wish, and we get one more.

601 01:07:47.250 ⇒ 01:07:53.380 Uttam Kumaran: Can we handle that with our folks like Luke has capacity.

602 01:07:56.510 ⇒ 01:08:00.600 Uttam Kumaran: we’re out of sort of senior capacity. We’re almost out of senior capacity.

603 01:08:01.110 ⇒ 01:08:02.839 Amber Lin: Like I think Lotta can handle one more.

604 01:08:05.360 ⇒ 01:08:05.790 Uttam Kumaran: Who does?

605 01:08:05.920 ⇒ 01:08:09.960 Amber Lin: How’s Kyle’s capacity right now? I’m just checking on the.

606 01:08:10.900 ⇒ 01:08:12.740 Uttam Kumaran: He can definitely take on another one.

607 01:08:12.740 ⇒ 01:08:13.530 Amber Lin: Okay.

608 01:08:14.710 ⇒ 01:08:17.970 Uttam Kumaran: But he but but again, like he’s like a junior side.

609 01:08:19.760 ⇒ 01:08:23.889 Uttam Kumaran: But I don’t know like I think we can handle 3 more.

610 01:08:32.220 ⇒ 01:08:32.985 robert: Yeah.

611 01:08:37.510 ⇒ 01:08:40.380 robert: okay, I mean you. You have a better read than I do. But

612 01:08:40.660 ⇒ 01:08:46.610 robert: my sense of Luke, like Luke, can only do Dbt stuff right? So like

613 01:08:47.640 ⇒ 01:08:53.519 robert: he kind of needs someone to set the scaffolding for him, and then he can pick it up, and then he can like knock out models.

614 01:08:54.279 ⇒ 01:08:57.570 robert: so I don’t think he’s like an early

615 01:08:57.800 ⇒ 01:09:03.240 robert: early stage client. Kind of like person.

616 01:09:04.180 ⇒ 01:09:04.830 Uttam Kumaran: Yeah.

617 01:09:05.720 ⇒ 01:09:10.549 robert: Yeah. So I think if we’re gonna bring, yeah, I think.

618 01:09:11.550 ⇒ 01:09:16.450 robert: yeah, if we if we become something bigger, we get 2 more to more life

619 01:09:17.240 ⇒ 01:09:21.780 robert: clients. Then I think, Dave, a lot and Oashi will be will be slammed.

620 01:09:21.930 ⇒ 01:09:28.910 robert: and it’s like who can, who can really be, or who can be an early de not sure.

621 01:09:30.760 ⇒ 01:09:31.990 Uttam Kumaran: Okay. But like.

622 01:09:31.990 ⇒ 01:09:33.280 robert: As a new. Yeah.

623 01:09:33.750 ⇒ 01:09:42.130 Uttam Kumaran: That’s still more of like like deu like this is like setting up data pipelines and just setting up like the environments.

624 01:09:44.670 ⇒ 01:09:46.080 Uttam Kumaran: and like owning that.

625 01:09:48.850 ⇒ 01:09:56.980 robert: Yeah, or just like doing some integrations doing people wanting to know, like, get some reporting off of a source that they’ve never seen before.

626 01:09:57.556 ⇒ 01:10:03.379 robert: So it I don’t know. I do think that that’s I’m just talking out loud, like different early.

627 01:10:03.770 ⇒ 01:10:08.974 robert: like what what our clients are expecting from des, I think, yeah, like,

628 01:10:09.960 ⇒ 01:10:10.540 Uttam Kumaran: Yeah.

629 01:10:12.310 ⇒ 01:10:23.980 robert: Yeah, like Eden has. They’re always experimenting with different, like media networks or new tools. They want to be able to like, bring it in quickly into like the paid reporting that we already have, and so

630 01:10:24.180 ⇒ 01:10:48.599 robert: like that may not mean, like automatically putting it into our whole Dbt process and getting that channel model because we may switch it off after 2 weeks or to a month. But it does mean like someone being able to run the query, be able to join to the model? Maybe it lives in Google sheets, or like something that’s like half baked. That’s but it it like they’re able to go and work with that vendor specifically.

631 01:10:49.020 ⇒ 01:10:51.910 robert: that’s just like Tiktok influencers, or whatever like

632 01:10:52.190 ⇒ 01:10:57.630 robert: something like that is, it’s kind of ambiguous like what the needs are there. And like.

633 01:10:58.770 ⇒ 01:10:59.209 Uttam Kumaran: Yeah, yeah.

634 01:10:59.210 ⇒ 01:10:59.970 robert: I, yeah.

635 01:10:59.970 ⇒ 01:11:02.040 Uttam Kumaran: No, you need a senior, more senior person.

636 01:11:02.290 ⇒ 01:11:02.840 robert: Yeah.

637 01:11:07.990 ⇒ 01:11:15.850 Uttam Kumaran: Okay, I think, yeah, I mean, we’ll I think we’ll. I think we’re okay.

638 01:11:16.780 ⇒ 01:11:20.149 Uttam Kumaran: like, if almost Friday and default close.

639 01:11:22.810 ⇒ 01:11:26.349 Uttam Kumaran: Then we’re in a jam. But I can. I can get someone in pretty quickly.

640 01:11:27.520 ⇒ 01:11:32.249 Uttam Kumaran: I will just bias more towards someone more senior. But this is helpful to see this.

641 01:11:32.710 ⇒ 01:11:38.899 robert: Okay, yeah. I mean, I think it. The seniors need to know how to like. Bring the juniors into their work

642 01:11:39.210 ⇒ 01:11:44.159 robert: is what it what it seems like. It seems like they’re maxed out. But then the juniors were like

643 01:11:44.860 ⇒ 01:11:46.990 robert: really underutilized. So

644 01:11:47.750 ⇒ 01:11:51.409 robert: kind of like, how do we? How do we get them to to pull on the juniors more.

645 01:11:53.990 ⇒ 01:11:56.220 Uttam Kumaran: Yeah, so, okay.

646 01:11:57.520 ⇒ 01:12:01.520 robert: Yeah, I mean, there is a bit of like a chain of command where, like, I

647 01:12:01.690 ⇒ 01:12:10.410 robert: only really feel comfortable assigning things and a wish. But maybe they’re like they break it down further. And they’re like, okay, well, actually.

648 01:12:10.890 ⇒ 01:12:15.840 robert: you know, 70% of the stuff can, I think, end up giving to

649 01:12:16.210 ⇒ 01:12:19.409 robert: Kyle, and and a wish or not wish.

650 01:12:20.310 ⇒ 01:12:25.809 Uttam Kumaran: Ryan. Yeah, yeah, I think on any of these, whoever you bring in. You should bring Ryan with you.

651 01:12:26.080 ⇒ 01:12:31.959 Uttam Kumaran: and we can start having that person just basically hopefully like tech lead and give most of the work to Ryan.

652 01:12:36.110 ⇒ 01:12:42.590 Uttam Kumaran: Ryan’s really good. And I think he could learn under somebody like he’s definitely like more soft spoken and

653 01:12:42.780 ⇒ 01:12:46.859 Uttam Kumaran: needs the push, but is good.

654 01:12:47.070 ⇒ 01:12:49.500 Uttam Kumaran: So that’s how I would look at. It is like

655 01:12:49.800 ⇒ 01:12:52.879 Uttam Kumaran: for any of these where you’re bringing in demolati or wish.

656 01:12:53.190 ⇒ 01:13:01.490 Uttam Kumaran: Try to bring Ryan with you and have that person say, assign, try to assign 50 to 80% of the work to Ryan.

657 01:13:02.580 ⇒ 01:13:04.939 Uttam Kumaran: and then Ryan will become that person

658 01:13:05.270 ⇒ 01:13:08.389 Uttam Kumaran: because he has more space. Kyle, on the other hand,

659 01:13:14.590 ⇒ 01:13:15.530 Uttam Kumaran: tougher.

660 01:13:18.930 ⇒ 01:13:21.240 Uttam Kumaran: Yeah, to talk to a waste about.

661 01:13:21.550 ⇒ 01:13:22.610 Uttam Kumaran: But okay.

662 01:13:25.680 ⇒ 01:13:31.229 Uttam Kumaran: okay, but for but for if some of these turn into larger, larger clients like almost Friday, if it turns to a big thing.

663 01:13:32.060 ⇒ 01:13:34.540 Uttam Kumaran: then we can bring Kyle to that. So.

664 01:13:35.090 ⇒ 01:13:39.930 Uttam Kumaran: okay, yeah. So I think we have capacity for 3 more. I would like us to take on 3 more clients and not

665 01:13:40.160 ⇒ 01:13:41.680 Uttam Kumaran: on another data person.

666 01:13:41.880 ⇒ 01:13:47.319 Uttam Kumaran: I do think, Amber, we should. We have to bring on someone on the Pm. Side?

667 01:13:47.510 ⇒ 01:13:51.809 Uttam Kumaran: I do think that even if it is that junior person that’s offshore.

668 01:13:52.050 ⇒ 01:13:54.569 Uttam Kumaran: If it is Con. I’m okay with that.

669 01:13:55.103 ⇒ 01:14:01.240 Uttam Kumaran: I think we will limit him to either assisting like I’ll I’ll sort of find out like what his

670 01:14:01.480 ⇒ 01:14:04.509 Uttam Kumaran: like customer facing appetite and like ability is.

671 01:14:07.800 ⇒ 01:14:12.259 Uttam Kumaran: Cause. I do think that we have an ability to move ABC to 10 K.

672 01:14:12.390 ⇒ 01:14:17.800 Uttam Kumaran: We can. Also, we should. We should crush urban stems because they’ll stick with us for long time.

673 01:14:19.700 ⇒ 01:14:21.030 Uttam Kumaran: And then, like.

674 01:14:21.650 ⇒ 01:14:25.760 Uttam Kumaran: you know we still have all the strategy stuff and things like that. So I know that’s like, sort of dropped

675 01:14:27.720 ⇒ 01:14:28.420 Uttam Kumaran: cool.

676 01:14:29.180 ⇒ 01:14:35.810 Uttam Kumaran: Okay? So I think one thing on my plate is, I’ll go through and update operating.

677 01:14:37.010 ⇒ 01:14:43.530 Uttam Kumaran: I think I will send a list of like the splits. It’s actually helpful. We have this now, and I can sort of

678 01:14:43.640 ⇒ 01:14:46.829 Uttam Kumaran: I will. Actually, I’m going to build something that that will show.

679 01:14:50.410 ⇒ 01:14:55.730 Uttam Kumaran: I’ll get it in here. But I also think it’s helpful to see in here, like what percentage of times are going to, what projects.

680 01:14:55.860 ⇒ 01:14:58.209 Uttam Kumaran: And then we can then decide here. So

681 01:14:58.450 ⇒ 01:15:06.309 Uttam Kumaran: I I just asked a group of questions. So we’re aiming for 80% occupied, are we? Gonna assume 100% is is 40 is 40 h.

682 01:15:10.380 ⇒ 01:15:17.139 Amber Lin: If it’s 80% occupied that 20% also goes to our internal meetings and any other stuff.

683 01:15:17.410 ⇒ 01:15:17.760 Uttam Kumaran: Yeah.

684 01:15:17.760 ⇒ 01:15:20.289 robert: I mean, we should assume 50 is a hundred percent.

685 01:15:21.270 ⇒ 01:15:27.639 Uttam Kumaran: Oh, okay, well, what’s the what’s the what’s the math? There.

686 01:15:30.500 ⇒ 01:15:33.350 robert: So 40, 40 h to 80%.

687 01:15:35.190 ⇒ 01:15:35.669 Amber Lin: What it was.

688 01:15:35.670 ⇒ 01:15:36.289 Uttam Kumaran: Because we’re.

689 01:15:36.290 ⇒ 01:15:36.970 Amber Lin: Good.

690 01:15:37.480 ⇒ 01:15:38.260 Amber Lin: It worked.

691 01:15:40.560 ⇒ 01:15:46.339 robert: Because I think people can stretch beyond 40 like it’s not gonna hit 40 consistently all the time. But like.

692 01:15:47.520 ⇒ 01:15:55.470 robert: yeah, I mean, like somebody who’s at 50, who’s maxed out 100. Then we need to dial them back a bit that somebody who’s at 40 like they can. They can.

693 01:15:55.470 ⇒ 01:15:56.870 robert: Yeah, can take on more.

694 01:15:57.600 ⇒ 01:15:58.200 Uttam Kumaran: Yeah.

695 01:16:04.690 ⇒ 01:16:05.780 robert: I actually think that.

696 01:16:05.780 ⇒ 01:16:08.840 Amber Lin: Internal meeting times. Do we account for that as well.

697 01:16:09.620 ⇒ 01:16:11.880 Uttam Kumaran: Do we even have much these days.

698 01:16:12.250 ⇒ 01:16:13.300 Amber Lin: No, not really.

699 01:16:13.300 ⇒ 01:16:16.109 Uttam Kumaran: I mean for for us, for this crew, sure. But like

700 01:16:18.720 ⇒ 01:16:22.669 Uttam Kumaran: I don’t know, I feel like that. We don’t have. People aren’t in really like other stuff.

701 01:16:29.020 ⇒ 01:16:29.980 Uttam Kumaran: I think.

702 01:16:30.450 ⇒ 01:16:31.600 Uttam Kumaran: Let’s go with this.

703 01:16:31.600 ⇒ 01:16:32.430 robert: I got a job first.st

704 01:16:32.430 ⇒ 01:16:33.770 Uttam Kumaran: Okay. Okay. Okay, okay.

705 01:16:35.180 ⇒ 01:16:41.490 Uttam Kumaran: yeah. I think we’ll go with this. And I’ll sort of do the I’ll do a little bit of math and show what people are in different categories.

706 01:16:43.820 ⇒ 01:16:47.270 Uttam Kumaran: Think. The other thing we basically decided is like, I think.

707 01:16:47.850 ⇒ 01:16:50.800 Uttam Kumaran: figure out how many people are going to be across. How many clients?

708 01:17:00.170 ⇒ 01:17:01.050 Uttam Kumaran: yeah.

709 01:17:06.390 ⇒ 01:17:13.540 Hannah Wang: I have a question like, are we? Is there like a Max number of clients that

710 01:17:13.700 ⇒ 01:17:15.089 Hannah Wang: we’re taking on?

711 01:17:16.500 ⇒ 01:17:23.510 Hannah Wang: Or are we just gonna keep like I don’t know. Cause I I’m just wondering like, Oh, as you take on more clients, and we have limited

712 01:17:24.467 ⇒ 01:17:28.230 Hannah Wang: people and hours for those clients like.

713 01:17:28.460 ⇒ 01:17:35.910 Hannah Wang: Are we just gonna keep hiring or like, are we, gonna Max? Out on clients and say no to some people.

714 01:17:36.390 ⇒ 01:17:38.664 Uttam Kumaran: Yeah, it’s a good question. I mean,

715 01:17:39.570 ⇒ 01:17:44.799 Uttam Kumaran: Let’s take, for example, like everyone from here like that is billable.

716 01:17:45.170 ⇒ 01:17:49.079 Uttam Kumaran: Right? So I’m just gonna remove a couple of people.

717 01:18:02.450 ⇒ 01:18:06.580 Uttam Kumaran: right? That’s like all the core billable people.

718 01:18:14.960 ⇒ 01:18:20.100 Uttam Kumaran: again it to just bring in what the things are.

719 01:18:30.840 ⇒ 01:18:32.199 Uttam Kumaran: oh, this is

720 01:18:41.710 ⇒ 01:18:43.600 Uttam Kumaran: this is potential.

721 01:19:44.910 ⇒ 01:19:47.390 Uttam Kumaran: Do you guys see the picture I’m putting together.

722 01:19:49.450 ⇒ 01:19:52.564 Amber Lin: Right now, we are at 60%.

723 01:19:54.090 ⇒ 01:19:55.959 Uttam Kumaran: This number is wrong, though.

724 01:19:56.390 ⇒ 01:19:57.050 Hannah Wang: Yeah.

725 01:19:58.660 ⇒ 01:20:04.799 Uttam Kumaran: So let’s I’m just gonna assume that this is closer to like.

726 01:20:05.290 ⇒ 01:20:08.580 Uttam Kumaran: Let’s just assume it’s 1 20.

727 01:20:16.790 ⇒ 01:20:20.930 Uttam Kumaran: The other thing is, I’ve only told Mustafa that he has 20 h.

728 01:20:22.880 ⇒ 01:20:25.509 Uttam Kumaran: Everyone else is like 40.

729 01:20:26.940 ⇒ 01:20:27.530 Hannah Wang: Hmm.

730 01:20:29.020 ⇒ 01:20:30.230 Uttam Kumaran: So

731 01:20:33.220 ⇒ 01:20:38.500 Uttam Kumaran: I guess what I’m trying to say is like, I will. We have to basically have like a freak out.

732 01:20:38.740 ⇒ 01:20:41.110 Uttam Kumaran: We have to have like a worried.

733 01:20:41.960 ⇒ 01:20:43.540 Uttam Kumaran: and then a freak out moment.

734 01:20:43.540 ⇒ 01:20:43.985 Hannah Wang: Yeah.

735 01:20:44.430 ⇒ 01:20:47.830 Uttam Kumaran: Right now. I’m not worried because.

736 01:20:47.830 ⇒ 01:20:48.290 Hannah Wang: Okay.

737 01:20:48.290 ⇒ 01:20:51.800 Uttam Kumaran: And I know this is AI and data people, and whatever. But like

738 01:20:52.680 ⇒ 01:20:56.420 Uttam Kumaran: even if I was gonna take out the

739 01:20:57.590 ⇒ 01:21:01.730 Uttam Kumaran: I mean the AI folks, I think we have a lot of time. So even if I was gonna take out

740 01:21:02.560 ⇒ 01:21:05.230 Uttam Kumaran: that AI people.

741 01:21:11.100 ⇒ 01:21:12.739 Uttam Kumaran: we’re still at 70%.

742 01:21:13.270 ⇒ 01:21:13.910 Hannah Wang: Oh!

743 01:21:15.120 ⇒ 01:21:22.500 Uttam Kumaran: So right now, we’re we’re at 70% of our 80% target. We’re at 55% of our 100 target.

744 01:21:24.390 ⇒ 01:21:28.960 Uttam Kumaran: Right? So ideally. What I want to see is that we’re somewhere in between this.

745 01:21:29.110 ⇒ 01:21:35.080 Uttam Kumaran: And, as I mentioned, we want to be slightly understaffed at all times.

746 01:21:35.080 ⇒ 01:21:35.860 Hannah Wang: Okay.

747 01:21:35.970 ⇒ 01:21:36.730 Hannah Wang: Yeah.

748 01:21:38.200 ⇒ 01:21:43.771 Uttam Kumaran: To give you a sense of of like what happens if we’re able to out. Do 3 more clients.

749 01:21:44.590 ⇒ 01:21:49.750 Uttam Kumaran: I get to put basically start to build up runway for the company.

750 01:21:50.450 ⇒ 01:21:55.219 Uttam Kumaran: Build runway for the company and then basically start to build.

751 01:21:55.220 ⇒ 01:21:56.499 Amber Lin: What’s wrong with.

752 01:21:57.100 ⇒ 01:22:01.269 Uttam Kumaran: Runway is, do you think about a plane taking off?

753 01:22:04.230 ⇒ 01:22:06.770 Uttam Kumaran: What happens when the runway is very short?

754 01:22:07.200 ⇒ 01:22:11.949 Uttam Kumaran: The plane, just like goes off the the thing so runway is just

755 01:22:12.190 ⇒ 01:22:14.210 Uttam Kumaran: giving us more and more space.

756 01:22:14.690 ⇒ 01:22:18.250 Uttam Kumaran: Our goal for runway is 6 months of cash.

757 01:22:19.610 ⇒ 01:22:26.050 Uttam Kumaran: 6 months of of of cash to cover expense.

758 01:22:28.560 ⇒ 01:22:30.290 Uttam Kumaran: That would be like

759 01:22:30.840 ⇒ 01:22:36.030 Uttam Kumaran: this is not something we’re gonna hit easily, but that’s gotta be like, probably like 500 k. Or

760 01:22:36.270 ⇒ 01:22:41.769 Uttam Kumaran: 50 or 60 times 6. But it’d be like, probably like 3 50 k.

761 01:22:42.130 ⇒ 01:22:50.470 Uttam Kumaran: The other thing we want to do is start to hire mid senior level delivery folks, right?

762 01:22:50.690 ⇒ 01:22:56.694 Uttam Kumaran: People that have actually done some of this agency stuff before, who are like gonna come in and just like dominate.

763 01:22:57.550 ⇒ 01:23:04.080 Uttam Kumaran: We probably want to bring in like one or 2 of those people we need.

764 01:23:04.080 ⇒ 01:23:04.510 Hannah Wang: See.

765 01:23:04.510 ⇒ 01:23:07.709 Uttam Kumaran: We need like to be able to afford

766 01:23:08.920 ⇒ 01:23:15.460 Uttam Kumaran: their salary. And then, of course, like we want to promote incentives.

767 01:23:17.770 ⇒ 01:23:19.220 Uttam Kumaran: Bonus, all that stuff.

768 01:23:19.220 ⇒ 01:23:20.640 Hannah Wang: Hmm, okay.

769 01:23:21.290 ⇒ 01:23:26.560 Uttam Kumaran: So right now to give you a sense like we’re running like

770 01:23:26.980 ⇒ 01:23:32.180 Uttam Kumaran: on any given month. We have like 40 to 50 K. In.

771 01:23:35.150 ⇒ 01:23:38.619 Uttam Kumaran: Let’s put it here. We have 40 to 50 k. In payroll.

772 01:23:40.970 ⇒ 01:23:48.639 Uttam Kumaran: We have another 10 to 15 K. And like general expense for all of our stuff.

773 01:23:49.840 ⇒ 01:23:54.609 Uttam Kumaran: This is just rough numbers. I it’s probably not like it’s it’s generally accurate.

774 01:23:58.026 ⇒ 01:23:59.759 Uttam Kumaran: This is actually probably less.

775 01:24:01.730 ⇒ 01:24:09.330 Uttam Kumaran: It’s probably 10 K, and then our income fluctuates. So this is the thing is like, last month

776 01:24:10.100 ⇒ 01:24:12.149 Uttam Kumaran: this was on the high end.

777 01:24:12.870 ⇒ 01:24:23.089 Uttam Kumaran: and we lost Javi, and we didn’t replace Javi with urban stems until this this past close month.

778 01:24:23.700 ⇒ 01:24:26.289 Uttam Kumaran: So this one we want to try to get.

779 01:24:26.910 ⇒ 01:24:32.379 Uttam Kumaran: So if you, if you say like total like total expense.

780 01:24:34.960 ⇒ 01:24:38.760 Uttam Kumaran: this let’s say on the high end is 60 k.

781 01:24:39.080 ⇒ 01:24:43.189 Uttam Kumaran: Our like revenue. We want to try to get above a hundred k.

782 01:24:43.330 ⇒ 01:24:45.550 Uttam Kumaran: Right now. We’re like at 80 k.

783 01:24:46.260 ⇒ 01:24:46.960 Hannah Wang: Okay.

784 01:24:49.160 ⇒ 01:24:54.880 Uttam Kumaran: If we get a hundred k, then that’s 40 k in margin.

785 01:24:56.450 ⇒ 01:25:02.350 Uttam Kumaran: That’s 40 k in in in gross margin.

786 01:25:02.640 ⇒ 01:25:05.909 Uttam Kumaran: So then you have to take 15% for tax

787 01:25:07.359 ⇒ 01:25:09.649 Uttam Kumaran: and then there’s a bunch of other shit. But like.

788 01:25:09.650 ⇒ 01:25:10.510 Hannah Wang: Hmm.

789 01:25:10.510 ⇒ 01:25:11.300 Amber Lin: No.

790 01:25:11.750 ⇒ 01:25:16.089 Uttam Kumaran: Basically, that’s the cash that then we then put in into the bank.

791 01:25:16.440 ⇒ 01:25:17.280 Hannah Wang: I see.

792 01:25:18.030 ⇒ 01:25:21.550 Uttam Kumaran: And then, like, I also like.

793 01:25:21.690 ⇒ 01:25:25.589 Uttam Kumaran: I’m not taking any money out. So like I, I get paid.

794 01:25:25.780 ⇒ 01:25:32.020 Uttam Kumaran: Robert gets paid. And then this crew gets. Yeah, this crew will. And then we start to work on bonuses and stuff. So.

795 01:25:32.770 ⇒ 01:25:34.879 Uttam Kumaran: My math is.

796 01:25:35.010 ⇒ 01:25:40.710 Uttam Kumaran: if if we were to say cool, we can bring this to 80 K. But then, now this is going to go to 70 K.

797 01:25:41.460 ⇒ 01:25:43.100 Uttam Kumaran: We’re not doing any better.

798 01:25:44.430 ⇒ 01:25:45.110 Hannah Wang: Right.

799 01:25:45.110 ⇒ 01:25:49.769 Uttam Kumaran: The lines have to like. The lines have to start to diverge.

800 01:25:49.770 ⇒ 01:25:50.990 Hannah Wang: Right, yeah.

801 01:25:50.990 ⇒ 01:25:51.660 Uttam Kumaran: Right.

802 01:25:53.830 ⇒ 01:25:58.110 Uttam Kumaran: So every anytime we make a hire.

803 01:25:59.700 ⇒ 01:26:04.709 Uttam Kumaran: you know. I want it to feel like we’re pulling teeth like we really should not. We really should try not to.

804 01:26:04.710 ⇒ 01:26:05.600 Hannah Wang: Hmm, yeah.

805 01:26:05.760 ⇒ 01:26:14.020 Uttam Kumaran: If we’re if we’re like, if this is like 200 K, and we’re like, Yo, everybody’s dying like from work, then, yeah. But like. We’re not even

806 01:26:14.650 ⇒ 01:26:18.640 Uttam Kumaran: now. The numbers would not, wouldn’t prove that hypothesis.

807 01:26:19.790 ⇒ 01:26:20.430 Uttam Kumaran: Right.

808 01:26:21.200 ⇒ 01:26:25.980 Uttam Kumaran: So until this gets to like everybody is at 80%

809 01:26:27.880 ⇒ 01:26:36.689 Uttam Kumaran: and like honestly until it gets to like 90%, then I’m then I’m down.

810 01:26:36.800 ⇒ 01:26:44.340 Uttam Kumaran: But what I what I do look at is the people. So amber is

811 01:26:44.450 ⇒ 01:26:49.209 Uttam Kumaran: amber, and is only number one the only one in her role. And is that over?

812 01:26:49.570 ⇒ 01:26:54.159 Uttam Kumaran: I even think this is probably like not the. This is probably you’re probably on underestimating.

813 01:26:54.160 ⇒ 01:26:55.590 Hannah Wang: Under this meeting? Yeah.

814 01:26:55.590 ⇒ 01:27:00.849 Uttam Kumaran: And then I also think awaish is gonna feel the same way.

815 01:27:01.150 ⇒ 01:27:01.890 Hannah Wang: Oh!

816 01:27:01.890 ⇒ 01:27:05.699 Uttam Kumaran: The last thing I’ll mention is this doesn’t account for me or Robert’s hours either.

817 01:27:06.040 ⇒ 01:27:07.030 Hannah Wang: Yeah, right.

818 01:27:08.250 ⇒ 01:27:14.710 Uttam Kumaran: Meaning. There are client hours that I’m spending that should go to someone that should go to someone else. Billable.

819 01:27:15.460 ⇒ 01:27:23.350 Uttam Kumaran: That is even another transition point, where we probably I know Robert has his stuff for Eden and his clients. I have stuff.

820 01:27:23.580 ⇒ 01:27:28.530 Uttam Kumaran: so there’s probably 20 HA week. Each is probably an extra 40 here. That is not.

821 01:27:29.270 ⇒ 01:27:31.200 Uttam Kumaran: that needs to be stripped away from us.

822 01:27:31.470 ⇒ 01:27:32.200 Hannah Wang: Hmm.

823 01:27:32.360 ⇒ 01:27:32.990 Uttam Kumaran: Right.

824 01:27:33.900 ⇒ 01:27:41.939 Uttam Kumaran: But see the reason why we can’t do that is because that is some really senior work like, imagine. We try to bring on another account. Executive level person

825 01:27:42.320 ⇒ 01:27:47.449 Uttam Kumaran: I can’t, can’t get, can’t get like I can’t find. We have to really really search for that person.

826 01:27:47.450 ⇒ 01:27:48.290 Hannah Wang: Hmm.

827 01:27:48.610 ⇒ 01:27:51.540 Uttam Kumaran: And that person’s gonna want a lot of money.

828 01:27:51.540 ⇒ 01:27:52.580 Hannah Wang: Money. Yeah.

829 01:27:53.100 ⇒ 01:27:53.640 Uttam Kumaran: Yeah.

830 01:27:53.640 ⇒ 01:27:55.660 Hannah Wang: Okay, I see

831 01:27:58.890 ⇒ 01:27:59.929 Hannah Wang: this is helpful.

832 01:27:59.930 ⇒ 01:28:00.490 Uttam Kumaran: Little bit.

833 01:28:01.375 ⇒ 01:28:02.090 Hannah Wang: Yeah.

834 01:28:02.090 ⇒ 01:28:06.259 Amber Lin: This is really, this is really helpful for me to see. Okay.

835 01:28:06.750 ⇒ 01:28:12.120 Amber Lin: what? Because essentially, when I allocate these hours into the projects, this is what I’m

836 01:28:12.580 ⇒ 01:28:17.539 Amber Lin: this is the goal I’m trying to achieve. And this is really helpful to help me see the bigger picture

837 01:28:17.870 ⇒ 01:28:21.869 Amber Lin: cause. It’s been a little. It’s been a little bit tough, and I feel like I’ve been

838 01:28:23.600 ⇒ 01:28:27.709 Amber Lin: lagging the higher the higher level view. So this is really helpful.

839 01:28:28.160 ⇒ 01:28:35.529 Uttam Kumaran: Yeah, I I think, like, the reason why I haven’t shared is is not from anything. It’s just that, like, I want to share with you guys what’s helpful

840 01:28:36.090 ⇒ 01:28:41.760 Uttam Kumaran: for planning. But like, there’s a lot of stress that comes with knowing these numbers

841 01:28:41.960 ⇒ 01:28:45.729 Uttam Kumaran: every day and like, that’s not something you want to be a part of

842 01:28:46.500 ⇒ 01:28:48.349 Uttam Kumaran: I think more of like

843 01:28:48.620 ⇒ 01:28:52.430 Uttam Kumaran: more of this is to like, actually, I think each of you as individuals.

844 01:28:52.640 ⇒ 01:28:58.899 Uttam Kumaran: did not look at this number, amber, and this will be again in other companies. You may be like, Oh, cool! I can like take on all this stuff.

845 01:28:59.140 ⇒ 01:29:01.860 Uttam Kumaran: What you will find is like, this is not good.

846 01:29:02.030 ⇒ 01:29:04.649 Uttam Kumaran: I actually want you to be closer to 60%

847 01:29:04.910 ⇒ 01:29:10.596 Uttam Kumaran: build, because then you can spend the other hours on strategy, and like the company, right and.

848 01:29:10.950 ⇒ 01:29:12.550 Amber Lin: This is not

849 01:29:12.770 ⇒ 01:29:22.410 Amber Lin: not all of this is billable like not all of this is billable, like part of the hours here is spent on the company, but I would like to spend a little bit more.

850 01:29:23.230 ⇒ 01:29:23.920 Uttam Kumaran: Yeah.

851 01:29:33.560 ⇒ 01:29:35.770 Amber Lin: I really want you to be able

852 01:29:35.970 ⇒ 01:29:42.420 Amber Lin: able to spend less on fine work, especially the small projects that

853 01:29:43.370 ⇒ 01:29:48.779 Amber Lin: originally it’s the easier ones that you can hand off, but you kind of have to take them now, and

854 01:29:49.340 ⇒ 01:29:51.810 Amber Lin: I don’t think that’s the best stage for it to be.

855 01:29:52.340 ⇒ 01:29:52.990 Uttam Kumaran: Yeah.

856 01:29:55.320 ⇒ 01:30:06.730 Uttam Kumaran: no, I agree. And I and I also think that for yeah, even for me, it’s just we just need to take some hours off. It’s gonna it’s gonna be it’s hard to sustain. But I think what you’ll see is that part of the reason why

857 01:30:06.830 ⇒ 01:30:11.160 Uttam Kumaran: we have these is because of the push we made in the last month like very dedicated.

858 01:30:11.570 ⇒ 01:30:13.610 Uttam Kumaran: So the time that goes into this

859 01:30:14.710 ⇒ 01:30:21.880 Uttam Kumaran: like I will say, like the a lot of if these aren’t, if like, it looks like there’s currently like 30 K or so in signing.

860 01:30:22.780 ⇒ 01:30:26.950 Uttam Kumaran: And like some like this is all proposal, meaning like

861 01:30:29.340 ⇒ 01:30:34.940 Uttam Kumaran: the gaps. So we have between 60 K and this K. That means like these are all like, there’s a pretty good chance.

862 01:30:35.250 ⇒ 01:30:36.720 Uttam Kumaran: Half of these clothes

863 01:30:37.910 ⇒ 01:30:42.889 Uttam Kumaran: which is insane like this is this is like a hundred grand worth of stuff.

864 01:30:43.883 ⇒ 01:30:48.709 Uttam Kumaran: Which, again, if it’s a hundred grand over 6 months, it could look like anywhere from

865 01:30:48.830 ⇒ 01:30:51.139 Uttam Kumaran: 10 to 10 to 15 KA month.

866 01:30:51.930 ⇒ 01:30:55.049 Uttam Kumaran: Which would get us closer to that, like 100 K.

867 01:30:55.750 ⇒ 01:30:56.380 Hannah Wang: Hmm.

868 01:30:57.640 ⇒ 01:31:05.730 Uttam Kumaran: But I think what this crew needs to start to look at, and we’ll start to look at this more frequently as we get all this stuff automated is

869 01:31:06.730 ⇒ 01:31:12.860 Uttam Kumaran: we will start to see if, as this gets closer to 90%, you know.

870 01:31:12.860 ⇒ 01:31:16.499 Amber Lin: Okay, I think Friday meetings this meeting, if

871 01:31:16.810 ⇒ 01:31:21.859 Amber Lin: if it’ll be great, if by next Friday meeting, we at least at least have a view of, say.

872 01:31:22.920 ⇒ 01:31:24.150 Amber Lin: the week

873 01:31:24.270 ⇒ 01:31:32.209 Amber Lin: with the week before, because right now we’re looking at May, maybe next week, and look at this current week as people take some time to log their hours.

874 01:31:32.440 ⇒ 01:31:43.959 Amber Lin: and so if each Friday we can see, are we progressing to at least have that number to look at it? We’ll have a better sense of where we’re at, because if I don’t think it’s enough to look at it once a month.

875 01:31:44.390 ⇒ 01:31:45.890 Uttam Kumaran: Yeah, yeah, no, I agree.

876 01:31:48.050 ⇒ 01:31:51.320 Uttam Kumaran: But again, even the time to do this I did like

877 01:31:52.100 ⇒ 01:31:57.019 Uttam Kumaran: last night, like one am. So even finding time to do this stuff is a bit hard.

878 01:31:57.020 ⇒ 01:32:00.469 Amber Lin: I know I was trying to do the allocations the whole week. I just did it.

879 01:32:00.470 ⇒ 01:32:02.278 Uttam Kumaran: No, it’s hard. It’s hard.

880 01:32:03.070 ⇒ 01:32:07.680 Amber Lin: But if we automate it, and I think we just need to spend one time to.

881 01:32:08.330 ⇒ 01:32:12.859 Uttam Kumaran: But I’ll so even sitting with even sitting and staring at this, it’s like needs to happen.

882 01:32:15.130 ⇒ 01:32:16.459 Amber Lin: It’s a little painful, not gonna lie.

883 01:32:16.460 ⇒ 01:32:25.860 Uttam Kumaran: Yeah, which is again lean on a wish and damalade for grooming as much as you can, because I’m not gonna be able to be at every single one

884 01:32:26.810 ⇒ 01:32:35.529 Amber Lin: No, not at this point. I just assume you’re not gonna be there like I don’t. I didn’t schedule it in the afternoons, because I. I need my engineers to be in the meeting.

885 01:32:36.260 ⇒ 01:32:37.809 Amber Lin: I would, if it’s after.

886 01:32:37.810 ⇒ 01:32:42.050 Uttam Kumaran: Afternoon. I can do it because some of these I just need to be on. But like morning is really hard.

887 01:32:42.050 ⇒ 01:32:42.750 Amber Lin: -

888 01:32:42.910 ⇒ 01:32:43.510 Uttam Kumaran: Yeah.

889 01:32:43.890 ⇒ 01:32:46.260 Amber Lin: Yeah, if I want you to be there, I’ll move it to the afternoon.

890 01:32:46.510 ⇒ 01:32:47.110 Uttam Kumaran: Okay.

891 01:32:49.010 ⇒ 01:32:49.680 Uttam Kumaran: Okay.

892 01:32:50.690 ⇒ 01:32:58.730 Amber Lin: Great. I need to go work on some ABC stuff because we’re having a meeting later. I have no clue. Who’s gonna be there because they just came back from a retreat.

893 01:32:59.040 ⇒ 01:33:06.789 Amber Lin: And I also need to flesh out the stuff from Mustafa so that can get going. ABC did not get too much attention.

894 01:33:07.190 ⇒ 01:33:10.999 Amber Lin: It got attention in the start of the week, and in the middle of the week it was urban stem.

895 01:33:11.000 ⇒ 01:33:13.990 Amber Lin: But I think Casey got Casey got some stuff done?

896 01:33:13.990 ⇒ 01:33:19.440 Amber Lin: Yeah, yeah, we did some stuff. It’s just like middle of the week. I couldn’t like

897 01:33:19.890 ⇒ 01:33:21.999 Amber Lin: look at it as often. So.

898 01:33:22.250 ⇒ 01:33:22.900 Uttam Kumaran: Okay.

899 01:33:22.900 ⇒ 01:33:24.330 Amber Lin: I’m going to go. Do that now.

900 01:33:25.240 ⇒ 01:33:25.870 Uttam Kumaran: Okay. Cool.

901 01:33:25.870 ⇒ 01:33:31.599 Amber Lin: Yeah, maybe I’ll do a presentation. Maybe I’ll just use it to talk about usage problems

902 01:33:31.950 ⇒ 01:33:37.810 Amber Lin: and a few things that I did not get back to me on email. So I’ll just ask them in the meeting.

903 01:33:38.550 ⇒ 01:33:39.090 Uttam Kumaran: Okay, perfect.

904 01:33:39.090 ⇒ 01:33:43.850 Amber Lin: Okay, yeah. If you can join later. If not, okay, I can talk to them.

905 01:33:44.080 ⇒ 01:33:44.670 Uttam Kumaran: Okay.

906 01:33:44.840 ⇒ 01:33:46.669 Amber Lin: Alright! Bye, guys!

907 01:33:46.670 ⇒ 01:33:51.859 Uttam Kumaran: Thank you, guys Hannah, send me anything to review. By the way, I reviewed the stuff you ping me yesterday.

908 01:33:52.870 ⇒ 01:33:53.320 Hannah Wang: Hmm.

909 01:33:53.320 ⇒ 01:33:57.139 Uttam Kumaran: Send me anything else, and I I went through all my Sigma mentions.

910 01:33:57.570 ⇒ 01:34:12.182 Hannah Wang: Okay, yeah, I’ll probably tag you probably later tonight or later in the afternoon. And then I don’t think anything is super urgent except maybe the case study right? The AI Case study. I don’t know if you still want that today.

911 01:34:14.512 ⇒ 01:34:19.237 Uttam Kumaran: Yeah, I I made a couple of comments. Yeah, if we can ship that, we can ship it.

912 01:34:19.630 ⇒ 01:34:21.060 Uttam Kumaran: I would like to have it.

913 01:34:22.260 ⇒ 01:34:25.529 Hannah Wang: Okay, I’ll just yeah. I’ll prioritize that. Then.

914 01:34:25.530 ⇒ 01:34:26.140 Uttam Kumaran: Okay.

915 01:34:26.350 ⇒ 01:34:27.120 Hannah Wang: Okay.

916 01:34:27.370 ⇒ 01:34:30.050 Amber Lin: Okay, go on a walk. You are.

917 01:34:30.050 ⇒ 01:34:34.009 Uttam Kumaran: I gotta go meet. I gotta go meet. I gotta go meet superposition, David. He’s in town.

918 01:34:34.010 ⇒ 01:34:35.040 Hannah Wang: Oh!

919 01:34:35.040 ⇒ 01:34:35.410 Amber Lin: Okay.

920 01:34:35.410 ⇒ 01:34:36.249 Hannah Wang: Oh, my goodness!

921 01:34:36.540 ⇒ 01:34:38.459 Uttam Kumaran: I’m gonna go meet him. And then, yeah.

922 01:34:38.770 ⇒ 01:34:50.270 Amber Lin: Drink some coffee, get outside. I’m gonna go outside, I am. I’m dying. Somebody came into our dorm last night because I live in a I stay in a hostel.

923 01:34:50.450 ⇒ 01:34:58.500 Amber Lin: and and she turned on the light at 12 Am. And she was like, What is all of this? Why is there people stuffing things like Bro. This is a hostel.

924 01:34:59.700 ⇒ 01:35:02.473 Amber Lin: and so I I got very little sleep.

925 01:35:02.820 ⇒ 01:35:03.650 Uttam Kumaran: Oh, my God!

926 01:35:05.610 ⇒ 01:35:06.220 Amber Lin: But that’s.

927 01:35:06.220 ⇒ 01:35:10.110 Hannah Wang: Oh, you’re coming back! Are you coming back this week or next week?

928 01:35:10.850 ⇒ 01:35:12.390 Amber Lin: Okay, this. Sunday.

929 01:35:12.390 ⇒ 01:35:19.790 Hannah Wang: Okay, yeah, you should. Yeah. Your own bed, your own pillow. Your own room, look forward to it.

930 01:35:20.202 ⇒ 01:35:21.440 Amber Lin: Me, too. Okay.

931 01:35:22.260 ⇒ 01:35:23.230 Hannah Wang: Alright. We’ll catch up.

932 01:35:23.230 ⇒ 01:35:24.600 Amber Lin: Up when I’m in. La.

933 01:35:24.600 ⇒ 01:35:26.690 Hannah Wang: Bye, bye.