Meeting Title: Time Allocations Prep Date: 2025-07-01 Meeting participants: Awaish Kumar, Rico Rejoso, Amber Lin


WEBVTT

1 00:10:57.060 00:10:58.110 Amber Lin: Hi! There!

2 00:11:02.740 00:11:03.740 Rico Rejoso: I got it.

3 00:11:05.210 00:11:08.940 Amber Lin: Hi! Nice to meet you, Rico. It’s the 1st time we’re talking.

4 00:11:13.030 00:11:13.900 Rico Rejoso: Yeah.

5 00:11:16.815 00:11:30.000 Amber Lin: So in terms of time allocations, I think we can start with. The clockify, export, export spreadsheet.

6 00:11:30.620 00:11:38.169 Amber Lin: and then let’s start with looking at the actuals for the month of June.

7 00:11:38.710 00:11:54.030 Amber Lin: and then we can talk. We can look at the ideal rates we want for each project and talk about ideally how many hours we want on each project, and then we can do the allocations in operating so 3 steps.

8 00:11:58.080 00:12:00.449 Amber Lin: Like, do you have any ex like.

9 00:12:00.820 00:12:02.090 Rico Rejoso: Excel, sheet.

10 00:12:03.175 00:12:03.609 Awaish Kumar: Yes.

11 00:12:04.530 00:12:07.929 Awaish Kumar: Like the way, like, I I would like, like.

12 00:12:08.790 00:12:11.089 Amber Lin: Yeah, let me send that to.

13 00:12:11.430 00:12:16.020 Awaish Kumar: I have a suggestion, and like we go something like that.

14 00:12:16.500 00:12:17.710 Awaish Kumar: Oh, no!

15 00:12:20.730 00:12:21.390 Awaish Kumar: And

16 00:12:24.860 00:12:26.350 Awaish Kumar: who dialed.

17 00:12:35.570 00:12:40.280 Awaish Kumar: Time entry. Okay, you do have in some from it. But like.

18 00:12:43.290 00:12:50.090 Awaish Kumar: For saying that, for example, can I share my screen.

19 00:12:50.090 00:12:51.060 Amber Lin: Yeah. Totally.

20 00:12:52.540 00:12:53.930 Awaish Kumar: Okay, my plan.

21 00:13:02.270 00:13:03.509 Awaish Kumar: But come on.

22 00:13:07.220 00:13:10.409 Awaish Kumar: it’s like something like, maybe

23 00:13:10.780 00:13:15.319 Awaish Kumar: client name, like the things which are easy, the way easy

24 00:13:15.730 00:13:18.680 Awaish Kumar: for us to basically work on it.

25 00:13:21.070 00:13:24.750 Awaish Kumar: Like, I have.

26 00:13:24.970 00:13:28.039 Awaish Kumar: Well, for a I don’t know. Maybe we have a year.

27 00:13:28.420 00:13:32.740 Awaish Kumar: the hours we basically we get for the client.

28 00:13:35.540 00:13:39.380 Awaish Kumar: Like billable hours, or how click on saved.

29 00:13:39.910 00:13:47.920 Awaish Kumar: And then each individual’s questions always so like hidden, we have, maybe, for example, 80 h.

30 00:13:49.830 00:13:54.450 Awaish Kumar: 10 for me, maybe without it.

31 00:13:56.740 00:13:59.629 Awaish Kumar: or 20 for him, something like that.

32 00:13:59.800 00:14:11.669 Awaish Kumar: And okay, the like. This way, we can basically decide what are the total hours, how they are

33 00:14:12.130 00:14:14.220 Awaish Kumar: sign for each person.

34 00:14:15.090 00:14:21.799 Awaish Kumar: And like ideally like. And then on the side, we maybe have data from clock to 5,

35 00:14:23.800 00:14:28.189 Awaish Kumar: right? To help us decide on on these numbers.

36 00:14:33.130 00:14:39.640 Awaish Kumar: So like clock. If I data will help us define these 3 columns, and then when it’s done

37 00:14:40.244 00:14:45.130 Awaish Kumar: like, then we can just easily go in operating and come on.

38 00:14:45.470 00:14:51.591 Awaish Kumar: Put this for, for example, the month of July.

39 00:14:54.450 00:14:56.950 Amber Lin: Yeah, I agree. Actually, I think you’re

40 00:14:57.520 00:15:09.320 Amber Lin: similar to the one I suggested. And I think we could start by looking at clockify right? Because we do want to look at how many people, how many hours people actually worked.

41 00:15:09.430 00:15:22.890 Amber Lin: And then we can look at ideally like to. We will have a cap of how many hours per project, and then we can look at how far that is from the actual, from from the actuals. And then we can.

42 00:15:23.316 00:15:26.480 Amber Lin: Take those 2 numbers together, and we can decide, okay.

43 00:15:27.060 00:15:29.920 Amber Lin: what is something that I want to allocate for. July.

44 00:15:31.770 00:15:35.560 Awaish Kumar: Yeah, like, why, I’m saying that, like, it’s easy to

45 00:15:35.720 00:15:41.359 Awaish Kumar: actually like, we don’t all like all of us don’t have to spend one and a half hour.

46 00:15:42.022 00:15:47.219 Awaish Kumar: Working on operating right? Like, it’s a different Ui, and

47 00:15:47.320 00:15:52.550 Awaish Kumar: only one person can just fill in. In operating. We can decide here on.

48 00:15:52.550 00:15:53.440 Amber Lin: Yeah. Totally.

49 00:15:53.440 00:16:01.990 Awaish Kumar: Be on it. And then, like we can, maybe anyone like who wants to take on it can just go in operating and fill that hours.

50 00:16:02.390 00:16:10.119 Amber Lin: Yeah, I agree. That’s what that’s what I want to do as well. So I’m glad we’re aligned.

51 00:16:13.752 00:16:15.480 Awaish Kumar: So for this data.

52 00:16:18.240 00:16:25.290 Awaish Kumar: Like, for now we I don’t know how you fill it from crocify, but

53 00:16:26.480 00:16:30.690 Awaish Kumar: like, that’s something we can choose interns to like.

54 00:16:31.660 00:16:35.880 Awaish Kumar: Make something totally every month for us.

55 00:16:36.020 00:16:36.550 Awaish Kumar: Got it.

56 00:16:36.550 00:16:38.680 Amber Lin: Yes, occupied data for each trial.

57 00:16:38.680 00:16:56.680 Amber Lin: A. Yes, let me share my screen. I have this. I was working on this. So I have this ready. Actually, I just want to show you what it looks like here, so we can look at it together. I’ve already made the sheet with it. So if I share my screen

58 00:16:56.680 00:17:10.229 Amber Lin: here. So this is what we were just talking about, right? So to give you a sense of how we got here. So this is the exports from clockify. I believe this is automated. So this just continues.

59 00:17:11.013 00:17:13.869 Amber Lin: this will continue updating.

60 00:17:15.170 00:17:15.530 Awaish Kumar: Okay.

61 00:17:15.530 00:17:21.319 Amber Lin: Yeah, let me check let me check the date. The last entry.

62 00:17:21.760 00:17:23.649 Awaish Kumar: Okay, I actually have some.

63 00:17:25.359 00:17:30.119 Awaish Kumar: For me. We are missing entries for Zoom. Basically, I have to add it.

64 00:17:30.120 00:17:50.447 Amber Lin: Okay, okay, I see. So the I mean, there’s no way for us to capture whatever is not entered, but it like at least we know that for you it is. It is not entered yet. So that’s something else that we can ensure. And maybe something, Rico, that’s something you can help us with is to make sure people actually lock their hours

65 00:17:52.040 00:17:53.050 Amber Lin: So.

66 00:17:54.080 00:17:54.740 Rico Rejoso: Sure.

67 00:17:55.090 00:18:00.780 Rico Rejoso: Yeah, thank you. And then, so based on the raw entries from clockify, we have

68 00:18:01.330 00:18:06.760 Amber Lin: So I put in year and then month, and if it’s a billable project.

69 00:18:07.000 00:18:16.099 Amber Lin: and here is just a quick pivot table. So this is a wish. This is what you were talking about. This is for the month of June. And then we can see here, okay.

70 00:18:17.659 00:18:18.569 Amber Lin: for

71 00:18:18.910 00:18:26.649 Amber Lin: for pool parts. These are people’s hours for a matter more. These are people’s hours for the month of June.

72 00:18:26.850 00:18:37.730 Amber Lin: and then we can infer, like, how many hours per week are people spending? I think this is a really good point for us to start to, just to look at the month of June and to say, okay.

73 00:18:38.490 00:18:41.390 Amber Lin: how do? How are we? How are we doing.

74 00:18:42.380 00:18:48.880 Awaish Kumar: Hmm, okay, yeah. One like, does, we have total hours somewhere?

75 00:18:49.710 00:18:50.529 Awaish Kumar: Can you see it.

76 00:18:51.510 00:18:52.999 Amber Lin: Yeah matter more total.

77 00:18:53.110 00:18:58.769 Amber Lin: It’s the this one and then off the record total who puts who parts total.

78 00:18:59.910 00:19:06.420 Awaish Kumar: I’m in the I mean the like, not the one which which we have worked with the

79 00:19:06.550 00:19:09.180 Awaish Kumar: when we have decided with the client like, we will work.

80 00:19:10.150 00:19:12.219 Awaish Kumar: 60 h for you.

81 00:19:12.520 00:19:36.050 Amber Lin: Yeah, I think so. The 1st one. Let’s we can look at this sheet where I just showed, and the second one I’m looking at. This is the financial model that Utam is working on. So I’m thinking we can go find the rates for each project, and based on that. We can estimate like, how many hours total we want to work for

82 00:19:36.690 00:19:38.100 Amber Lin: each client.

83 00:19:38.390 00:19:41.480 Amber Lin: So let me just actually let me look.

84 00:19:41.480 00:19:48.429 Awaish Kumar: Is it like like? Do? We commit some hours with the client or is how it works.

85 00:19:50.260 00:20:00.559 Amber Lin: Well for monthly contracts, right monthly contracts. We didn’t commit any hours. We just receive a certain amount monthly, and then we try to

86 00:20:01.144 00:20:12.600 Amber Lin: do as little hours as possible while completing the work for hourly clients. Some of them have a cap. Some of them don’t have a cap.

87 00:20:12.980 00:20:16.069 Amber Lin: so those are more flexible.

88 00:20:18.940 00:20:23.361 Awaish Kumar: Okay? So like, hmm, like, just to simplify it like,

89 00:20:23.960 00:20:27.539 Awaish Kumar: for each of the client, can we have a process

90 00:20:27.970 00:20:31.310 Awaish Kumar: like when we jump into this meeting. We have a

91 00:20:31.933 00:20:38.510 Awaish Kumar: kind of a actual or estimated hours for each client. Right? That’s what we want to work for. This client.

92 00:20:38.510 00:20:59.259 Amber Lin: Awesome. We we should get that set up every time every app meeting. After this we will come in prepared. With that. I mean, this is the 1st time we’re meeting. So a lot of things are still floating on floating in the air. I don’t think we’re gonna take an hour and a half but I scheduled it just in case.

93 00:21:02.330 00:21:03.279 Awaish Kumar: So right now.

94 00:21:05.730 00:21:12.209 Awaish Kumar: Like. 1st of all, like, we can start with step one figuring out the

95 00:21:12.440 00:21:15.370 Awaish Kumar: covers based on rates, or whatever to decide.

96 00:21:15.370 00:21:16.040 Amber Lin: Yes.

97 00:21:16.280 00:21:17.700 Awaish Kumar: And we move from there.

98 00:21:19.053 00:21:20.940 Amber Lin: So let’s look at.

99 00:21:21.060 00:21:23.869 Amber Lin: I think this is a good page, too.

100 00:21:24.520 00:21:26.433 Amber Lin: Start with

101 00:21:28.480 00:21:29.450 Amber Lin: Oh.

102 00:21:31.240 00:21:32.390 Awaish Kumar: Okay.

103 00:21:36.504 00:21:45.759 Amber Lin: Okay. So ABC, fixed rate, urban stems fixed fixed rate off the record. Max, 20 h per month.

104 00:21:48.320 00:21:50.639 Amber Lin: Okay, let’s start with the

105 00:21:51.650 00:21:57.030 Amber Lin: yeah. Do you want me to copy this table somewhere, and then we can.

106 00:21:58.840 00:22:04.099 Rico Rejoso: Like like we. We don’t need this like table. We can just write down.

107 00:22:07.730 00:22:11.599 Amber Lin: Information on that. Yeah, let me go here.

108 00:22:13.730 00:22:17.039 Amber Lin: Pm, hours. Person capacity.

109 00:22:17.690 00:22:18.570 Amber Lin: Okay?

110 00:22:23.610 00:22:25.100 Amber Lin: Okay.

111 00:22:30.770 00:22:31.760 Amber Lin: okay.

112 00:22:32.060 00:22:34.419 Amber Lin: So we can.

113 00:22:38.550 00:22:41.539 Amber Lin: Let’s just we can have right here of

114 00:22:46.640 00:22:47.679 Amber Lin: Let me starting.

115 00:22:49.130 00:22:49.490 Amber Lin: Yeah.

116 00:22:49.490 00:22:49.820 Awaish Kumar: Like.

117 00:22:49.820 00:22:51.620 Awaish Kumar: So I think, let’s.

118 00:22:51.620 00:22:56.470 Amber Lin: These can be deleted. They don’t, they don’t exist, is not

119 00:22:56.900 00:22:59.279 Amber Lin: for us fiance. Do you have.

120 00:22:59.280 00:23:00.420 Awaish Kumar: Default is not there.

121 00:23:00.420 00:23:03.309 Amber Lin: Home. This is not there anymore.

122 00:23:03.580 00:23:06.319 Amber Lin: Nope, Javi’s not there.

123 00:23:06.560 00:23:10.770 Amber Lin: Full parts. I don’t know if they updated it yet.

124 00:23:11.810 00:23:12.799 Awaish Kumar: We’ll check on.

125 00:23:12.800 00:23:14.040 Amber Lin: 4 parts.

126 00:23:14.420 00:23:17.470 Amber Lin: Oh, is this? Okay? This is the

127 00:23:18.220 00:23:21.560 Amber Lin: it’s a new one. Yeah, let me go check. If there’s Eden.

128 00:23:27.840 00:23:31.659 Amber Lin: let’s go see? What’s the what’s the last invoice for Eden?

129 00:23:32.070 00:23:35.819 Amber Lin: Oh, that’s oh, I see. That’s Pungo.

130 00:23:36.510 00:23:37.440 Amber Lin: Huh?

131 00:23:37.920 00:23:46.660 Amber Lin: Okay, that’s 2 20 K, let’s go back here.

132 00:23:47.630 00:23:51.579 Amber Lin: Oh, okay, I’ll just write. Just write that down.

133 00:23:52.200 00:24:03.550 Amber Lin: Eden is great our cap.

134 00:24:04.900 00:24:07.910 Amber Lin: Alright. Then we can. We can work there.

135 00:24:21.290 00:24:22.490 Amber Lin: Alright.

136 00:24:23.880 00:24:25.279 Amber Lin: 4 parts.

137 00:24:29.380 00:24:30.970 Amber Lin: I don’t. Okay.

138 00:24:31.340 00:24:37.580 Amber Lin: If pool parts is 5 K. It’s a 5 k retainer.

139 00:24:45.420 00:24:47.550 Amber Lin: when I say, if we want to.

140 00:24:49.790 00:24:50.550 Awaish Kumar: Yeah. Then.

141 00:24:51.100 00:24:55.970 Amber Lin: Up 250 per hour, 200 per hour, 150.

142 00:24:56.260 00:24:59.249 Amber Lin: I just see what it’ll end up being

143 00:25:04.460 00:25:06.259 Amber Lin: as 20 h.

144 00:25:08.230 00:25:09.140 Amber Lin: Okay?

145 00:25:15.780 00:25:27.350 Amber Lin: So, depending on how, what rate, what actual rate we’re aiming for. These are the possible hour caps that we can add in. We don’t have default

146 00:25:27.530 00:25:30.750 Amber Lin: information information on default yet.

147 00:25:33.530 00:25:34.990 Amber Lin: Fixed rate.

148 00:25:36.510 00:25:37.510 Amber Lin: I don’t.

149 00:25:38.520 00:25:40.830 Amber Lin: Gosh, I don’t get what it?

150 00:25:42.690 00:25:43.550 Amber Lin: Okay?

151 00:25:44.920 00:25:51.790 Amber Lin: So that is 450.

152 00:25:55.100 00:25:57.160 Amber Lin: So that’s for Fan’s sake.

153 00:25:57.520 00:26:01.059 Amber Lin: These are the different hours depending on what rate you want.

154 00:26:02.114 00:26:05.049 Amber Lin: This is same thing.

155 00:26:10.490 00:26:12.239 Amber Lin: Oh, silly me!

156 00:26:16.660 00:26:23.590 Amber Lin: Monthly matter! More hour. It’s just hourly

157 00:26:31.130 00:26:38.450 Amber Lin: so that’s hourly urban stems eat. Okay.

158 00:26:48.360 00:26:50.550 Amber Lin: where’s that?

159 00:26:57.320 00:27:06.080 Amber Lin: And then Opt, the record is also flexible.

160 00:27:10.190 00:27:15.270 Amber Lin: That’s capped. Capped. Okay, matter. More is same thing.

161 00:27:16.100 00:27:17.719 Amber Lin: These 2.

162 00:27:18.400 00:27:19.200 Amber Lin: Okay?

163 00:27:19.540 00:27:21.890 Amber Lin: I think this is a good place to start right.

164 00:27:23.520 00:27:24.080 Awaish Kumar: Okay.

165 00:27:26.490 00:27:27.190 Rico Rejoso: Okay,

166 00:27:28.820 00:27:35.870 Awaish Kumar: We are seeing this like, okay and Canadian like, I think we.

167 00:27:36.440 00:27:39.709 Awaish Kumar: we could be spending a lot more than this.

168 00:27:40.420 00:27:45.209 Amber Lin: Yeah, let me. Let’s go. Look at Eden, then let’s go look at Eden, how it’s been doing.

169 00:27:49.420 00:27:50.899 Awaish Kumar: By all this matrices.

170 00:27:50.900 00:27:54.119 Amber Lin: Yeah. So let’s say.

171 00:27:54.120 00:27:56.395 Awaish Kumar: Oh, it looks like it is missing.

172 00:27:57.000 00:27:58.830 Awaish Kumar: yeah, but is, ours were like.

173 00:27:59.360 00:28:00.419 Amber Lin: I’m not.

174 00:28:00.740 00:28:02.720 Awaish Kumar: He said. 1 h like that’s not.

175 00:28:02.720 00:28:10.239 Amber Lin: Okay, okay, okay, let’s let’s write off the what the real hours might look like.

176 00:28:11.060 00:28:13.579 Amber Lin: There’s no Akash or Amber.

177 00:28:13.810 00:28:16.310 Amber Lin: Annie, I think, logged all of her hours.

178 00:28:17.706 00:28:21.120 Amber Lin: How much do you spend and quick estimate.

179 00:28:22.780 00:28:27.179 Awaish Kumar: Oh, I spend like 15 to 20 weekly, right?

180 00:28:27.720 00:28:28.240 Amber Lin: Hmm.

181 00:28:29.700 00:28:31.110 Awaish Kumar: There’s see the option.

182 00:28:33.080 00:28:35.560 Amber Lin: I would say 80 to 60.

183 00:28:35.560 00:28:37.030 Awaish Kumar: 70 h at least.

184 00:28:37.030 00:28:44.550 Amber Lin: Okay, 7, 70. If you say 70 at least, I’ll say 75, them a lot. I spent how many hours.

185 00:28:44.910 00:28:50.380 Awaish Kumar: Could be similar to me, right, maybe more than me, but at least equals to me.

186 00:28:50.830 00:28:57.220 Amber Lin: Okay, I’ll say 80, and then that will be some of oh.

187 00:28:57.680 00:28:58.370 Awaish Kumar: Then room.

188 00:28:58.370 00:28:58.859 Amber Lin: 1st time.

189 00:28:58.860 00:28:59.840 Awaish Kumar: Man, right.

190 00:29:00.340 00:29:02.370 Amber Lin: Oh, okay.

191 00:29:03.920 00:29:07.330 Awaish Kumar: Robert Spence, maybe at least 10,

192 00:29:07.800 00:29:09.980 Awaish Kumar: maybe 10 HA week, or something.

193 00:29:09.980 00:29:12.589 Amber Lin: Yeah, okay, so we’ll say, like.

194 00:29:13.160 00:29:15.450 Awaish Kumar: 4, 50, yeah.

195 00:29:15.980 00:29:37.290 Amber Lin: I’ll say 50. So goodness! We are spending 300 h on Eden, and then we are.

196 00:29:39.340 00:29:40.630 Awaish Kumar: So.

197 00:29:40.910 00:29:42.639 Amber Lin: Let me say.

198 00:29:42.640 00:29:42.960 Awaish Kumar: Little.

199 00:29:42.960 00:29:45.730 Amber Lin: Until June rates.

200 00:29:46.000 00:29:47.060 Amber Lin: How’s that.

201 00:29:47.720 00:29:55.730 Awaish Kumar: Yeah, but it could be small like, for we could take Mays, for example, rates to compare for June, like

202 00:29:56.386 00:30:08.600 Awaish Kumar: my hours could have been dropped, or like Demo nowadays as well, because we haven’t like actually logged. But for May we have the correct results right?

203 00:30:10.250 00:30:11.239 Amber Lin: Did do we.

204 00:30:11.240 00:30:12.710 Awaish Kumar: From me right.

205 00:30:13.910 00:30:19.489 Amber Lin: Are you? Are you sure he did he work 11 h in May?

206 00:30:21.510 00:30:22.459 Awaish Kumar: I had it.

207 00:30:24.470 00:30:27.409 Awaish Kumar: It’s not, either, but I don’t know.

208 00:30:29.200 00:30:34.140 Awaish Kumar: Yeah. But this is this report true? I I doubt that.

209 00:30:34.290 00:30:39.440 Amber Lin: Well, I mean let me. I can order.

210 00:30:39.440 00:30:40.190 Awaish Kumar: Every verified.

211 00:30:40.190 00:30:40.640 Amber Lin: Bye.

212 00:30:40.940 00:30:44.999 Awaish Kumar: Can we just go into crack if clockify and see the demo that is always.

213 00:30:45.570 00:30:53.597 Amber Lin: Okay. I don’t think you logged it. It’s my sense. Let me find where clock, if I is

214 00:30:56.800 00:31:05.050 Amber Lin: Oh, sorry back here reports Filter.

215 00:31:05.550 00:31:08.370 Awaish Kumar: I’m not a we are.

216 00:31:08.370 00:31:10.760 Amber Lin: There we go apply.

217 00:31:11.400 00:31:14.509 Amber Lin: So no hours logged.

218 00:31:15.490 00:31:16.590 Awaish Kumar: Or anyone.

219 00:31:17.950 00:31:21.500 Amber Lin: Okay. He logged for May.

220 00:31:24.230 00:31:24.800 Amber Lin: The.

221 00:31:24.800 00:31:26.950 Awaish Kumar: What like, which project is that?

222 00:31:27.880 00:31:29.749 Amber Lin: Let’s let’s see.

223 00:31:34.830 00:31:39.700 Amber Lin: Okay, a lot for Ermine’s times in Eden, or

224 00:31:40.800 00:31:46.070 Amber Lin: yeah, for I think for May. He logged everything for June. There’s nothing.

225 00:31:46.070 00:31:51.400 Awaish Kumar: I don’t think so like, for let’s select the full. May.

226 00:31:54.080 00:31:57.259 Amber Lin: Yeah, give me a quick. Second.

227 00:32:00.650 00:32:02.369 Awaish Kumar: And just select like.

228 00:32:02.370 00:32:04.510 Amber Lin: Oh, wrong, one.

229 00:32:05.670 00:32:06.729 Awaish Kumar: May want to.

230 00:32:07.159 00:32:07.589 Amber Lin: Smart.

231 00:32:08.880 00:32:09.740 Awaish Kumar: Criminal.

232 00:32:09.740 00:32:13.099 Amber Lin: That’s the only thing he had logged for June.

233 00:32:14.090 00:32:14.890 Awaish Kumar: From.

234 00:32:15.690 00:32:20.010 Awaish Kumar: Yeah. But I want to see from my traditionally, it may

235 00:32:20.270 00:32:23.270 Awaish Kumar: one to 31st of May.

236 00:32:24.420 00:32:27.180 Awaish Kumar: Okay, he he robbed 40 h.

237 00:32:28.170 00:32:29.699 Awaish Kumar: No, it’s for all.

238 00:32:31.270 00:32:35.780 Awaish Kumar: Okay. Let’s can we filter by also the client Eden health.

239 00:32:36.613 00:32:40.140 Amber Lin: Yeah. Give me a quick. Sec summary.

240 00:32:41.472 00:32:43.490 Rico Rejoso: Group by client.

241 00:32:43.930 00:32:45.489 Awaish Kumar: Okay. Now I see.

242 00:32:45.870 00:32:46.940 Awaish Kumar: Okay, he, he!

243 00:32:46.940 00:32:47.610 Amber Lin: I don’t think.

244 00:32:47.610 00:32:49.539 Awaish Kumar: That’s true. That’s not true.

245 00:32:49.660 00:32:50.290 Awaish Kumar: Just not.

246 00:32:50.290 00:32:50.930 Amber Lin: Yeah.

247 00:32:50.930 00:32:56.460 Awaish Kumar: So the okay, we need to nudge on that as well.

248 00:32:59.100 00:33:01.320 Awaish Kumar: Like people like this. They need to.

249 00:33:01.860 00:33:02.630 Amber Lin: Yeah.

250 00:33:02.640 00:33:03.325 Awaish Kumar: Properly

251 00:33:07.220 00:33:15.620 Awaish Kumar: Yeah, but that all let’s ask. Ask him like, if he thinks like 11 h per month, like 11 h per week might, I can agree is

252 00:33:15.780 00:33:18.630 Awaish Kumar: but 11 h per month. I I don’t.

253 00:33:19.743 00:33:25.616 Amber Lin: But this I mean that tells us at least this report is accurate but just people haven’t logged their hours.

254 00:33:26.090 00:33:32.480 Amber Lin: I think we could estimate this as a pretty like a pretty decent approximate.

255 00:33:34.320 00:33:40.669 Awaish Kumar: Yeah. But like for me, it’s not true either. So like how we what is the reference point.

256 00:33:41.360 00:33:41.910 Amber Lin: Hmm?

257 00:33:43.050 00:33:44.960 Amber Lin: Or do you mean a reference point.

258 00:33:45.640 00:33:48.040 Awaish Kumar: Like to estimate like, I want,

259 00:33:48.370 00:34:00.509 Awaish Kumar: how damn already spend? Like, I, maybe for June, he didn’t log, let’s see April, may, he didn’t log like. Let’s go to April. Then I can see some number like 50 h.

260 00:34:07.550 00:34:09.360 Awaish Kumar: I want to reference some to

261 00:34:09.710 00:34:16.900 Awaish Kumar: to real like, at least when he says 50 for April. Maybe you can say maybe 60 for June or.

262 00:34:16.900 00:34:17.750 Amber Lin: Okay.

263 00:34:18.250 00:34:18.690 Awaish Kumar: Yeah.

264 00:34:18.690 00:34:24.550 Amber Lin: I see, I see, I mean for June. I think your your estimates should be pretty accurate, isn’t it?

265 00:34:27.690 00:34:29.857 Awaish Kumar: Yeah, that’s my estimate

266 00:34:31.820 00:34:34.139 Awaish Kumar: But yeah, I

267 00:34:37.560 00:34:46.300 Awaish Kumar: that’s why I want the way. Because he he I don’t see any for all the months like

268 00:34:46.699 00:34:51.799 Awaish Kumar: starting from March. He never spent 80 h on reading, so I can’t say 80 right.

269 00:34:53.840 00:34:56.379 Awaish Kumar: So it’s it’s like 60.

270 00:34:56.389 00:35:04.349 Amber Lin: Okay, valid. Okay, let me document all the problems we identified, and then

271 00:35:04.459 00:35:11.749 Amber Lin: we can talk about it. But we’ll probably have a quick 30 meeting next week after people everyone has logged their hours.

272 00:35:11.939 00:35:13.019 Amber Lin: How’s that.

273 00:35:14.400 00:35:15.310 Awaish Kumar: Yeah.

274 00:35:15.440 00:35:17.159 Rico Rejoso: The data is insufficient.

275 00:35:17.740 00:35:18.540 Rico Rejoso: Okay?

276 00:35:19.150 00:35:25.000 Amber Lin: Sounds good problems identified.

277 00:35:29.110 00:35:35.840 Amber Lin: missing them a lot of the hours missing a wish hours.

278 00:35:37.060 00:35:45.210 Amber Lin: We’ll do the best we can, and whatever is rest left. We’ll do next time we meet.

279 00:35:46.420 00:35:46.980 Awaish Kumar: So

280 00:35:47.680 00:35:49.780 Amber Lin: Yeah, I mean, we can.

281 00:35:50.780 00:35:55.109 Awaish Kumar: We can say 60, we can keep it at 60 for W.

282 00:35:55.400 00:36:00.670 Awaish Kumar: And let’s let’s also say 60 for me, for for July.

283 00:36:01.190 00:36:02.190 Awaish Kumar: Oh.

284 00:36:02.880 00:36:03.300 Amber Lin: That’s fine!

285 00:36:03.300 00:36:04.020 Awaish Kumar: Okay.

286 00:36:07.700 00:36:13.763 Awaish Kumar: yeah, like, any is like 24, 5 h per week. That’s that’s true.

287 00:36:15.260 00:36:17.550 Awaish Kumar: it’s like 100 h for any.

288 00:36:17.550 00:36:26.099 Amber Lin: Any logs hours pretty accurately. So that should be good. So July and submit. So we’ll say a hundred

289 00:36:26.600 00:36:32.279 Amber Lin: for you. That is, that is not okay.

290 00:36:33.150 00:36:35.380 Amber Lin: Okay. Sorry. Robert.

291 00:36:36.540 00:36:39.300 Awaish Kumar: And Rob Robert could be same right, because.

292 00:36:42.240 00:36:47.170 Amber Lin: 50, a wish 60, 60.

293 00:36:47.170 00:36:47.770 Amber Lin: See?

294 00:36:49.768 00:36:58.170 Amber Lin: So that would give us 270, which will still keep us.

295 00:36:58.900 00:36:59.785 Amber Lin: Sorry.

296 00:37:02.270 00:37:03.680 Amber Lin: What happened.

297 00:37:07.410 00:37:09.430 Amber Lin: Oh, I’m so silly.

298 00:37:20.350 00:37:27.919 Amber Lin: Okay, I think it will. It will give us still about like closer to.

299 00:37:28.590 00:37:29.260 Awaish Kumar: Okay.

300 00:37:34.160 00:37:38.570 Amber Lin: Sorry these are not so.

301 00:37:43.976 00:37:47.090 Amber Lin: My bad numbers.

302 00:37:54.270 00:37:58.429 Amber Lin: yeah, so estimated July hours.

303 00:37:58.820 00:38:03.499 Amber Lin: So we’ll say it’s equal to that.

304 00:38:04.540 00:38:14.940 Amber Lin: And that is equal to this divided by this.

305 00:38:15.240 00:38:21.759 Amber Lin: Okay, that means we’re at 74.

306 00:38:24.270 00:38:32.690 Amber Lin: I do think we should eventually aim to see if there’s a way we can do less, or

307 00:38:33.430 00:38:38.374 Amber Lin: I mean increase the revenue.

308 00:38:39.980 00:38:42.890 Rico Rejoso: But that’s that’s what we have for. Now

309 00:38:46.530 00:38:48.969 Rico Rejoso: look at urban stems.

310 00:38:49.560 00:38:52.370 Amber Lin: -

311 00:38:54.310 00:38:55.060 Amber Lin: What.

312 00:39:15.200 00:39:17.349 Awaish Kumar: I understand it is.

313 00:39:23.000 00:39:24.250 Amber Lin: Urban stems.

314 00:39:26.110 00:39:27.410 Awaish Kumar: It’s Javi.

315 00:39:27.410 00:39:30.790 Amber Lin: Oh, that’s Joffy, my bad.

316 00:39:31.670 00:39:32.430 Awaish Kumar: Okay.

317 00:39:33.140 00:39:34.570 Awaish Kumar: So he has.

318 00:39:34.570 00:39:37.949 Amber Lin: Like Tim, and they did not log his hours.

319 00:39:38.680 00:39:39.530 Amber Lin: Okay.

320 00:39:39.530 00:39:40.730 Awaish Kumar: It is like.

321 00:39:41.448 00:39:47.490 Awaish Kumar: He said, like 2020

322 00:39:48.460 00:39:52.469 Awaish Kumar: like, how much is spending like 20 h per week or.

323 00:39:54.150 00:39:56.029 Amber Lin: Yeah, at least 20 h per week.

324 00:39:56.480 00:40:01.630 Amber Lin: Well, I’ll just say I’ll just say 20. So I’ll say that’s like 80.

325 00:40:07.200 00:40:11.620 Amber Lin: I think college hours are accurate because he gets paid by the hour.

326 00:40:18.440 00:40:21.810 Amber Lin: Okay, that gives us 200, which

327 00:40:21.990 00:40:27.609 Amber Lin: well, let us know that we’re at a not so great right.

328 00:40:29.900 00:40:32.030 Amber Lin: Project. That’s

329 00:40:39.160 00:40:42.360 Amber Lin: yeah, not not great.

330 00:40:43.934 00:40:47.150 Amber Lin: Let’s go look at.

331 00:40:51.250 00:40:55.490 Amber Lin: Okay. July. How should we allocate then?

332 00:41:00.100 00:41:04.299 Amber Lin: I can try to keep it lower.

333 00:41:05.730 00:41:11.980 Amber Lin: I mean it probably should, Cap. At least. Cap Kyle’s hours at 80

334 00:41:12.470 00:41:16.330 Amber Lin: cap down a lot is ours still, probably is going to be 80.

335 00:41:20.350 00:41:26.310 Amber Lin: Now spending. I’m spending around 7 HA week.

336 00:41:28.640 00:41:33.209 Amber Lin: I could try to go for 5. I don’t know if that’s possible.

337 00:41:33.930 00:41:40.040 Amber Lin: So if I say it’s still like 2720.8.

338 00:41:42.940 00:41:47.510 Amber Lin: So if it’s so.

339 00:41:47.510 00:41:49.559 Awaish Kumar: For urban stems, like.

340 00:41:50.590 00:41:57.799 Awaish Kumar: When assigning the points to tickets like you are getting help from Ramilade.

341 00:41:58.805 00:41:59.560 Amber Lin: Yes.

342 00:42:00.800 00:42:01.470 Awaish Kumar: Okay.

343 00:42:09.390 00:42:10.380 Amber Lin: Okay.

344 00:42:10.610 00:42:13.749 Amber Lin: If we can do that, it will slightly improve.

345 00:42:14.190 00:42:17.530 Amber Lin: slightly improved to 72 per hour.

346 00:42:18.020 00:42:22.820 Amber Lin: which I don’t know if we’re making even I think we we are.

347 00:42:23.740 00:42:26.660 Amber Lin: I don’t know what Kyle’s and demalades rates are.

348 00:42:27.660 00:42:29.980 Amber Lin: Okay for ABC,

349 00:42:34.700 00:42:41.280 Amber Lin: okay, I think we’re gonna see some ugly hours on. ABC, let’s let’s go look at that

350 00:42:43.690 00:42:48.660 Amber Lin: or ABC, so with June.

351 00:42:50.250 00:42:53.550 Amber Lin: But is there any?

352 00:42:54.050 00:42:56.620 Amber Lin: Okay? I’ll trust Casey’s hours.

353 00:42:57.020 00:42:59.319 Amber Lin: All sorts of stuff has hours.

354 00:43:13.820 00:43:19.190 Awaish Kumar: Like this is going to help us with, like how much hours we need from individuals.

355 00:43:19.440 00:43:20.520 Amber Lin: Yeah, I agree.

356 00:43:20.520 00:43:23.029 Awaish Kumar: Is not going to show us the

357 00:43:23.170 00:43:26.259 Awaish Kumar: correct profitability, but things like that.

358 00:43:27.550 00:43:28.590 Amber Lin: What do you mean?

359 00:43:28.590 00:43:31.279 Awaish Kumar: Each each individual have different rates.

360 00:43:34.320 00:43:41.220 Awaish Kumar: So like 270 h to like 13,000. Divided by this hour, we get 74,

361 00:43:41.760 00:43:42.880 Awaish Kumar: but, like

362 00:43:47.070 00:43:50.999 Awaish Kumar: Like every individual, is not working at the same pay rate.

363 00:43:51.430 00:43:57.340 Amber Lin: Yeah, but I think what Wujam is working on is he’s getting the total cost

364 00:43:57.560 00:44:17.560 Amber Lin: on this project per month. So he’s going to calculate, say, person, a spend 5 h. Their rates are 5, and then we times that. We add all of that together, and then we compare.

365 00:44:17.850 00:44:20.150 Amber Lin: when we compare overall margins.

366 00:44:26.090 00:44:29.279 Amber Lin: As for July, I think my hours would

367 00:44:29.670 00:44:35.179 Amber Lin: my hours would come less? I can aim for 4.

368 00:44:35.620 00:44:41.399 Amber Lin: I’ll aim for it like 40. So that’s 10 h per month per per week.

369 00:44:43.380 00:44:45.810 Amber Lin: Annie, probably.

370 00:44:46.580 00:44:48.750 Amber Lin: Hey, Casey? Hopefully.

371 00:44:48.750 00:44:52.199 Awaish Kumar: And for for these ABC. And matter more, how are you.

372 00:44:53.980 00:44:56.730 Awaish Kumar: Like, how should I log my hours? Because

373 00:44:57.040 00:44:59.920 Awaish Kumar: I’m not sure, like sometimes I’m needed.

374 00:45:00.040 00:45:01.509 Awaish Kumar: Can you start off in planning.

375 00:45:01.510 00:45:08.139 Amber Lin: Whenever I think, yeah, whenever you attend meetings, just log it. I think that will be helpful.

376 00:45:08.390 00:45:16.199 Amber Lin: So whenever we actually use your time, just just log it. And then that will tell me, okay, am I using too much of a waste of time.

377 00:45:17.610 00:45:18.560 Awaish Kumar: Okay,

378 00:45:20.580 00:45:25.119 Awaish Kumar: But apart from meetings also, like, if I’m looking at

379 00:45:25.270 00:45:28.740 Awaish Kumar: senior, if I’m following up on some ticket things.

380 00:45:29.069 00:45:32.690 Amber Lin: Same thing. I love all of them. It’s probably a little.

381 00:45:32.890 00:45:35.750 Amber Lin: It’s probably a bit granular

382 00:45:36.521 00:45:42.480 Amber Lin: and it’s takes a little bit of time to log that. But that’s how I log my time.

383 00:45:43.940 00:45:44.610 Awaish Kumar: Okay.

384 00:45:48.310 00:45:50.770 Awaish Kumar: okay, so we’ll try that.

385 00:45:51.150 00:45:51.710 Amber Lin: Hmm.

386 00:45:54.360 00:45:54.850 Awaish Kumar: Oh, yeah.

387 00:45:54.850 00:45:59.080 Amber Lin: So I think Annie would spend

388 00:46:11.790 00:46:14.830 Awaish Kumar: It’s 2025, though, and.

389 00:46:20.430 00:46:22.459 Awaish Kumar: And he spent like 4 h.

390 00:46:22.880 00:46:23.550 Amber Lin: Yeah, okay.

391 00:46:23.550 00:46:29.229 Amber Lin: there’s any. There wasn’t much dashboarding work she can do. But I think this time around she would.

392 00:46:29.390 00:46:32.160 Amber Lin: And I also need to. We also need to add Luke

393 00:46:32.490 00:46:36.300 Amber Lin: here as well, because I don’t think he’s logged his hours yet.

394 00:46:39.890 00:46:44.060 Amber Lin: Hopefully reducing Mustafa’s hours to like.

395 00:46:45.620 00:46:53.270 Amber Lin: I want to say 5 a week, but that’s not accomplishable at this point. So say 8. If it’s 8 a week.

396 00:46:57.560 00:47:08.800 Amber Lin: or 7, I can aim for 7 I think same for same for Casey, like between.

397 00:47:09.570 00:47:14.310 Amber Lin: Think I’ll aim for this. I don’t know if that that’s possible.

398 00:47:16.396 00:47:20.330 Amber Lin: And Annie probably 10 h.

399 00:47:22.550 00:47:25.900 Amber Lin: And then there’s also your time, right. There’s also.

400 00:47:28.700 00:47:32.750 Amber Lin: Alright, I’m gonna amber.

401 00:47:33.440 00:47:37.550 Amber Lin: How many KC.

402 00:47:38.030 00:47:42.580 Amber Lin: Luke awaii for July?

403 00:47:43.220 00:47:44.020 Amber Lin: Oh.

404 00:47:47.960 00:47:57.150 Amber Lin: and then Mustafa think Luke is at least gonna spend like 15

405 00:47:58.240 00:48:00.250 Amber Lin: to set it to set it up

406 00:48:01.130 00:48:05.310 Amber Lin: 10 to 15. I don’t know if we still need him later.

407 00:48:10.470 00:48:12.710 Amber Lin: Oh, hard choice.

408 00:48:12.890 00:48:16.209 Awaish Kumar: But you were saying about another Api endpoint as well.

409 00:48:16.680 00:48:24.050 Amber Lin: Yeah, that’s true. Oh, gosh, okay, think 15 is good. Right?

410 00:48:24.320 00:48:26.550 Awaish Kumar: But will you need me for that.

411 00:48:26.710 00:48:30.330 Amber Lin: Yes, it’s 5 h too little.

412 00:48:31.150 00:48:33.689 Awaish Kumar: Like 5 h per month.

413 00:48:34.440 00:48:38.190 Awaish Kumar: I definitely definitely think so, Mike.

414 00:48:39.530 00:48:43.179 Awaish Kumar: like, if you only, I guess like only attend like.

415 00:48:44.570 00:48:46.120 Amber Lin: Yeah, there’s.

416 00:48:46.120 00:48:49.479 Awaish Kumar: In only in meetings. Then it’s 5 h.

417 00:48:50.820 00:48:54.400 Amber Lin: I see. I mean, if you’re gonna be tech lead here.

418 00:48:54.600 00:48:59.060 Amber Lin: 5 meetings plus.

419 00:48:59.640 00:49:06.049 Amber Lin: I think you don’t have to come to stand up. I’ll check in with you for any grooming issues. They shouldn’t take too long.

420 00:49:09.170 00:49:14.380 Amber Lin: I’ll aim to keep it at 5, so.

421 00:49:15.750 00:49:17.629 Awaish Kumar: 1, 25.

422 00:49:31.185 00:49:34.279 Amber Lin: Yeah, it is 1, 25.

423 00:49:34.430 00:49:35.870 Amber Lin: Oh, dear!

424 00:49:42.830 00:49:43.500 Amber Lin: Oh.

425 00:49:56.230 00:49:58.480 Amber Lin: it’s gonna slightly improve.

426 00:49:58.910 00:50:01.260 Amber Lin: I don’t know much about fan steak

427 00:50:01.580 00:50:11.099 Amber Lin: I think they’re doing. I think they’re aiming to do 30 h sweep between Utam and Kyle.

428 00:50:11.780 00:50:19.579 Amber Lin: So well, that would be helpful. Wait, really.

429 00:50:20.490 00:50:24.780 Amber Lin: Yeah, that’s what they plan to do, and then

430 00:50:25.260 00:50:32.650 Amber Lin: pool parts is mostly just. Miguel miguel spent

431 00:50:35.830 00:50:44.540 Amber Lin: 54 h and June, so.

432 00:50:46.070 00:50:46.750 Awaish Kumar: Like.

433 00:50:49.320 00:50:54.140 Amber Lin: I think that it is like pool parts.

434 00:50:57.210 00:50:58.400 Amber Lin: Say.

435 00:51:01.400 00:51:17.119 Amber Lin: if it’s 5, 5 k. Retainer, then June he spent, is where is it?

436 00:51:18.274 00:51:25.000 Amber Lin: Here and equals okay?

437 00:51:30.560 00:51:34.169 Amber Lin: And I guess he’s probably also gonna spend like 5.

438 00:51:34.330 00:51:36.560 Amber Lin: Let’s say he spends 50 h.

439 00:51:38.690 00:51:41.280 Amber Lin: No, that’s a hundred.

440 00:51:41.760 00:51:47.810 Amber Lin: Okay default we have. I have no clue off the record. Let’s just say

441 00:51:48.280 00:51:50.400 Amber Lin: let’s see how much was spent.

442 00:52:00.250 00:52:01.070 Amber Lin: hmm!

443 00:52:07.260 00:52:08.769 Amber Lin: Oh, it’s here.

444 00:52:08.960 00:52:10.689 Amber Lin: Casey spent

445 00:52:11.340 00:52:18.070 Amber Lin: 17, and last one. He spent 21. So I would say, he’s still probably gonna spend a similar

446 00:52:18.670 00:52:20.260 Amber Lin: of the record.

447 00:52:25.220 00:52:27.040 Amber Lin: Max. Weekly.

448 00:52:28.790 00:52:35.290 Amber Lin: If it’s Max weekly rate, then oh, okay, hourly weekly cap.

449 00:52:36.460 00:52:37.430 Amber Lin: Okay.

450 00:52:37.740 00:52:39.689 Awaish Kumar: Yeah. 5 h. Per week.

451 00:52:40.100 00:52:40.860 Awaish Kumar: 4 cases.

452 00:52:41.580 00:52:49.760 Amber Lin: Okay, so that’s June hours was

453 00:52:53.890 00:52:55.420 Amber Lin: 17.

454 00:52:56.590 00:52:58.760 Amber Lin: Think it’s still under this cap?

455 00:52:59.180 00:53:02.470 Amber Lin: That’s that’s good revenue

456 00:53:07.420 00:53:09.450 Amber Lin: July estimate.

457 00:53:18.910 00:53:21.960 Amber Lin: So that’s also gonna be.

458 00:53:23.760 00:53:33.630 Amber Lin: Oh, sorry this times this. Okay, for

459 00:53:34.540 00:53:36.850 Amber Lin: let’s go look at madam. More.

460 00:53:39.140 00:53:43.509 Amber Lin: I wish I we should probably also add your hours here.

461 00:53:45.660 00:53:47.700 Amber Lin: Matter more.

462 00:53:58.870 00:54:01.200 Amber Lin: Let me clean this up a little bit.

463 00:54:38.490 00:54:44.470 Amber Lin: Okay? So for madam, More have these people.

464 00:54:47.530 00:54:49.300 Amber Lin: That’s June.

465 00:54:55.200 00:55:01.780 Amber Lin: I hope you logged it correctly. Let me also add a wish. How many hours do you estimate you spent.

466 00:55:07.420 00:55:09.809 Awaish Kumar: Are you still around.

467 00:55:13.730 00:55:16.219 Amber Lin: Like 3 a week, 5, 5 a week.

468 00:55:17.610 00:55:18.770 Awaish Kumar: Yes.

469 00:55:25.580 00:55:28.040 Amber Lin: 3, so I’ll say 12.

470 00:55:28.370 00:55:29.090 Amber Lin: Ish.

471 00:55:29.090 00:55:31.669 Awaish Kumar: Yeah, like 3 per week is.

472 00:55:42.380 00:55:43.080 Amber Lin: Okay.

473 00:55:44.230 00:55:48.810 Amber Lin: July, I’ll say, spend 25.

474 00:55:48.990 00:55:51.699 Amber Lin: Annie probably spends

475 00:55:54.260 00:55:57.840 Amber Lin: I don’t know, probably less, because I don’t know how much is left.

476 00:55:58.540 00:56:05.740 Amber Lin: Say 25. Luke probably still spends 35.

477 00:56:05.960 00:56:08.119 Amber Lin: You probably still spend 12.

478 00:56:11.120 00:56:11.910 Amber Lin: Okay.

479 00:56:18.500 00:56:26.789 Amber Lin: There was a cap, but they have accepted all our oh, that’s a weekly cap. Never mind. June hours

480 00:56:28.220 00:56:32.500 Amber Lin: equals this.

481 00:56:37.290 00:56:38.670 Amber Lin: Oh, dear!

482 00:56:39.190 00:56:40.790 Amber Lin: We earned a lot, huh!

483 00:56:41.230 00:56:50.229 Amber Lin: And then July hours there’s that.

484 00:56:52.550 00:56:59.020 Amber Lin: and then read me and off the record. I don’t think any of them has.

485 00:56:59.830 00:57:00.710 Amber Lin: Oh, sorry.

486 00:57:02.170 00:57:12.070 Amber Lin: And oh, oh, what? Wait 3 K.

487 00:57:12.620 00:57:14.439 Amber Lin: For May and June.

488 00:57:15.380 00:57:32.390 Amber Lin: Okay, an hourly after go back here, so that would be, oh, so we got July estimate.

489 00:57:37.380 00:57:39.710 Amber Lin: So June would be.

490 00:57:41.410 00:57:44.900 Awaish Kumar: Jonah spent 17 h, but got 3 K. Right.

491 00:57:45.410 00:57:46.110 Amber Lin: Yes.

492 00:57:48.730 00:57:50.849 Awaish Kumar: July will be weekly.

493 00:57:57.440 00:58:00.660 Awaish Kumar: and that’s only Casey spending time.

494 00:58:00.970 00:58:03.800 Amber Lin: And a bit of Utam’s time, too.

495 00:58:05.354 00:58:06.469 Amber Lin: Rate.

496 00:58:14.640 00:58:16.900 Amber Lin: That’s June’s rate.

497 00:58:26.610 00:58:28.740 Amber Lin: July revenue.

498 00:58:32.890 00:58:35.080 Amber Lin: July rates.

499 00:58:35.400 00:58:37.299 Amber Lin: Oh, July is hourly

500 00:58:40.720 00:58:46.439 Amber Lin: sorry. This spreadsheet is so ugly. I apologize for madam or

501 00:58:49.500 00:58:50.370 Amber Lin: Okay.

502 00:58:50.370 00:58:51.519 Awaish Kumar: I don’t know. Maybe.

503 00:58:51.520 00:58:56.190 Amber Lin: Read me, read me. We don’t have data off the record. I can delete that.

504 00:58:56.700 00:58:59.939 Amber Lin: Okay, that’s all.

505 00:59:00.790 00:59:04.439 Amber Lin: I think we have allocations over here.

506 00:59:06.069 00:59:07.099 Amber Lin: Even.

507 00:59:08.020 00:59:09.210 Awaish Kumar: But we.

508 00:59:13.330 00:59:16.210 Amber Lin: Right, Hall.

509 00:59:18.930 00:59:19.720 Awaish Kumar: Sorry.

510 00:59:21.075 00:59:26.459 Amber Lin: That’s okay. The way she’s here.

511 00:59:26.580 00:59:29.180 Amber Lin: That’s a lot of

512 00:59:46.490 00:59:47.300 Amber Lin: okay.

513 00:59:50.780 00:59:53.059 Amber Lin: Think that’s good.

514 00:59:56.010 00:59:58.500 Amber Lin: June, July.

515 01:00:02.720 01:00:04.510 Amber Lin: Think these are good.

516 01:00:05.420 01:00:07.880 Amber Lin: I think good to have 2 annual

517 01:00:07.880 01:00:09.970 Amber Lin: operating. What did you say?

518 01:00:10.690 01:00:18.512 Awaish Kumar: So do you remember for tool parts? And off the record?

519 01:00:19.950 01:00:23.170 Amber Lin: I also don’t see individual entries here.

520 01:00:24.800 01:00:28.270 Amber Lin: Cool parts pool parties is just Miguel.

521 01:00:28.940 01:00:29.880 Awaish Kumar: No.

522 01:00:30.370 01:00:35.600 Amber Lin: So that wouldn’t be anything. And off the records is just Casey.

523 01:00:36.170 01:00:36.730 Awaish Kumar: Okay.

524 01:00:37.230 01:00:42.980 Amber Lin: Yeah, off the record.

525 01:00:43.940 01:00:45.360 Amber Lin: I think that could help.

526 01:00:48.910 01:00:50.399 Awaish Kumar: You’ll see 20 h.

527 01:00:51.310 01:00:53.820 Amber Lin: Yeah, Casey.

528 01:00:57.240 01:01:00.550 Amber Lin: 4 parts. Just Miguel.

529 01:01:02.870 01:01:03.990 Amber Lin: July.

530 01:01:04.890 01:01:07.989 Amber Lin: So that was 54.

531 01:01:17.460 01:01:26.650 Amber Lin: Okay, how’s this? I think, we’re good here, Rico, do you want me to walk you through operating.

532 01:01:27.190 01:01:30.190 Rico Rejoso: I think I wish you are good to go here.

533 01:01:31.010 01:01:31.983 Rico Rejoso: Okay? And

534 01:01:33.025 01:01:38.270 Awaish Kumar: Are you available to discuss like in terms project like.

535 01:01:38.420 01:01:41.806 Awaish Kumar: I have looked at your project plan.

536 01:01:42.720 01:01:45.810 Awaish Kumar: now, I I wanted to create some tickets.

537 01:01:47.600 01:01:51.449 Awaish Kumar: So like like I, I need a

538 01:01:51.780 01:01:55.440 Awaish Kumar: like, I can create some tickets. But I need you

539 01:01:56.189 01:02:06.669 Awaish Kumar: like your help in deciding those tickets because some of the tickets can have larger scope and like done a project management thing. So

540 01:02:06.850 01:02:10.169 Awaish Kumar: on on multiple clients you can help me with like

541 01:02:14.670 01:02:15.420 Amber Lin: Totally

542 01:02:16.780 01:02:24.869 Amber Lin: Are you? Good with basing it off of like? We’ll have a meeting to discuss it later. But are you good at basing it off of the project management plan

543 01:02:25.430 01:02:30.320 Amber Lin: that I made? Or is there any changes that you wanna make.

544 01:02:31.490 01:02:40.459 Awaish Kumar: Yeah, like, this is kind of like is a good plan. But it’s an high level plan. I want to map it to linear with.

545 01:02:40.460 01:02:41.099 Amber Lin: Oh! Totally!

546 01:02:41.577 01:02:43.009 Awaish Kumar: Tickets and everything.

547 01:02:43.010 01:03:03.619 Amber Lin: Yeah, I I think what I meant is that is this breakdown a good breakdown. If it is, I can make tickets. For each of these I was just wondering like, Are these good granularities for tickets, or are there any additional tickets that we wanna add? If we have that I think I can help.

548 01:03:04.310 01:03:05.010 Amber Lin: I can help.

549 01:03:05.010 01:03:09.280 Awaish Kumar: What is that ticket? Okay, it’s ticket breakdown, is that?

550 01:03:09.280 01:03:10.220 Awaish Kumar: Oh, Smith.

551 01:03:10.410 01:03:36.419 Amber Lin: Sure. Yes. So if you can make a comments here, or just cause I don’t know how this project is gonna get done right? So if we I broke it down to 2 projects. So one is productivity analytics, and one is marketing analytics. And can you just help me add any more tickets as in like, what needs to get done to complete this project, if you can add all of them here.

552 01:03:38.080 01:03:41.049 Amber Lin: And then I can make them into linear tickets.

553 01:03:42.090 01:03:42.650 Awaish Kumar: Hey?

554 01:03:44.580 01:03:46.250 Awaish Kumar: Sure. So

555 01:03:46.990 01:04:02.249 Awaish Kumar: I will look at that like the initial things which you have put it there like for bringing the data and all of that. This looks good. Only thing I’m I’m concerned about actually writing tickets for the dashboards like.

556 01:04:03.280 01:04:06.369 Awaish Kumar: We need an actual like after we have data.

557 01:04:06.580 01:04:10.410 Awaish Kumar: what we want to see in the dashboard, and how you want to see it. And.

558 01:04:10.410 01:04:19.680 Amber Lin: Yeah, I mean, that could just be a ticket in itself, I think. Cause we probably need to interview the stakeholders to say, Okay, what does this dashboard needs to look like.

559 01:04:20.100 01:04:20.665 Awaish Kumar: Yes.

560 01:04:22.760 01:04:29.530 Awaish Kumar: okay, yeah, yeah, that could be. But I I okay, I wanted that if I can add some information already there.

561 01:04:29.650 01:04:35.510 Awaish Kumar: But if we just if we go directly to stakeholders, then it’s it will be.

562 01:04:35.510 01:04:55.709 Amber Lin: We can go to stakeholders. We’ll do the best we can, and then cause we’re making it for them probably has to define what he needs, or Robert has to define what he needs. So just add any comments, or just write bullet points, or you can just send me a voice, note just type it out, and so I can look at it, and then we can find a time to meet.

563 01:04:56.990 01:05:00.509 Awaish Kumar: Actually, I’ve created in the linear. I’ve created a team.

564 01:05:00.780 01:05:08.540 Awaish Kumar: It’s called Internship Program 2,025. And then I’ve added a to projects.

565 01:05:09.760 01:05:10.380 Amber Lin: Oh, awesome!

566 01:05:10.380 01:05:15.840 Awaish Kumar: Dashboard and marketing dashboard. Now, in the individual project, we want to add.

567 01:05:15.840 01:05:18.450 Amber Lin: Is it in here.

568 01:05:18.450 01:05:20.670 Awaish Kumar: Yeah, it should be here, but it’s a private.

569 01:05:20.670 01:05:21.225 Amber Lin: And

570 01:05:21.780 01:05:22.559 Awaish Kumar: At your end.

571 01:05:22.560 01:05:26.909 Amber Lin: Oh, okay, yeah, add me there, and I can help.

572 01:05:27.310 01:05:27.960 Awaish Kumar: Okay.

573 01:05:29.030 01:05:31.379 Amber Lin: Okay. Sounds good. Thanks. A wish.

574 01:05:31.830 01:05:32.790 Awaish Kumar: Thank you. Bye.

575 01:05:35.131 01:05:39.989 Amber Lin: Rico. Do you know how to how to use operating.

576 01:05:40.770 01:05:44.470 Rico Rejoso: I just got the loom video for it. I’m still, you know.

577 01:05:44.470 01:05:45.480 Rico Rejoso: Oh, okay.

578 01:05:46.060 01:05:52.769 Rico Rejoso: no worries let me walk you through at least the 1st part, because I would love your help.

579 01:05:53.060 01:06:04.389 Amber Lin: I would love your help to get the July once in there, it should be pretty easy. Let me just log in first.st

580 01:06:04.970 01:06:05.860 Amber Lin: Oh.

581 01:06:07.580 01:06:08.660 Rico Rejoso: Operating

582 01:06:09.360 01:06:17.500 Rico Rejoso: for July. We can get a I mean, once we got this raw data from this week, we can create another one for next week, right?

583 01:06:17.610 01:06:26.560 Rico Rejoso: So that we can come up with the average from the hours that they will be putting into clockify. Because right now, we’re just making assumptions from

584 01:06:26.870 01:06:30.299 Rico Rejoso: basically asking a wish how much they’re spending their time with.

585 01:06:30.890 01:06:31.490 Rico Rejoso: Right.

586 01:06:31.990 01:06:34.160 Amber Lin: Yeah, yeah, essentially.

587 01:06:36.020 01:06:55.419 Amber Lin: So I I think we should. As a team, we’ll book a. We’ll book a meeting next week. Before that. We’ll make sure whoever’s missing their hours have entered their hours. I don’t think we’re missing too many people. Let’s let’s check real quick. Who

588 01:06:55.930 01:06:57.450 Amber Lin: who was missing?

589 01:06:58.955 01:07:04.349 Amber Lin: Honestly, I think it’s just done a lot of misses hours. Luke misses

590 01:07:04.590 01:07:13.199 Amber Lin: probably only misses. He misses like part of the those hours Kyle logs everything. Miguel logged everything.

591 01:07:13.850 01:07:17.540 Amber Lin: Okay? So that will just be.

592 01:07:38.710 01:07:45.820 Amber Lin: For readme is what pluck think those are.

593 01:07:46.490 01:07:47.930 Amber Lin: Those are good.

594 01:07:48.790 01:07:53.190 Amber Lin: So going back to operating.

595 01:07:54.100 01:08:02.569 Amber Lin: I would. So I would go here to the timeline. You might not see the view as I’m seeing right now. But if you click on this

596 01:08:02.690 01:08:08.229 Amber Lin: this button right here, it will give you a split view. So between

597 01:08:08.850 01:08:16.210 Amber Lin: between different projects or between different people. So as, for example, let’s do.

598 01:08:16.800 01:08:19.620 Amber Lin: let’s do a simple one. Let’s do off the record.

599 01:08:19.859 01:08:26.420 Amber Lin: So I would go to here. I would find where that team is.

600 01:08:27.590 01:08:34.850 Amber Lin: So I would look at when that is so. We’re entering July

601 01:08:34.960 01:08:38.620 Amber Lin: first.st If any of those things extend far.

602 01:08:38.840 01:08:51.049 Amber Lin: I would just end that. So I stopped pming for off the record like, if you don’t know that they just ended as of today, or you can ask me, and just ping me, I’ll say I ended

603 01:08:51.510 01:08:57.189 Amber Lin: there and then I would say, Casey spends

604 01:09:05.649 01:09:12.289 Amber Lin: how do I wait? 12.5.

605 01:09:17.109 01:09:18.560 Amber Lin: Times 40.

606 01:09:19.100 01:09:21.070 Amber Lin: Okay, that’s about right.

607 01:09:21.819 01:09:26.990 Amber Lin: Okay, I think you can either do hours per day or do percent

608 01:09:27.200 01:09:32.969 Amber Lin: allocations, and this is dependent on how many total hours they have.

609 01:09:33.290 01:09:38.750 Amber Lin: So we’ll just say that Casey has spends that amount.

610 01:09:38.880 01:09:48.950 Amber Lin: and then you can also go here to the Casey. So currently, only we’ve only logged him for off the record.

611 01:09:49.260 01:09:56.800 Amber Lin: and we can go to the next team, which he’s also on ABC.

612 01:09:57.930 01:09:59.790 Amber Lin: Where is ABC,

613 01:10:03.630 01:10:04.520 Amber Lin: okay.

614 01:10:15.670 01:10:18.789 Amber Lin: Oh, I’m so confused where the ABC one went.

615 01:10:23.790 01:10:24.820 Amber Lin: Okay.

616 01:10:51.650 01:11:03.710 Amber Lin: yeah. If there’s any project that don’t yet exist, let me know. And so let’s go here.

617 01:11:05.871 01:11:14.520 Amber Lin: And we can say, choose the role. I’m gonna choose project manager, say, Amber.

618 01:11:15.000 01:11:20.569 Amber Lin: I spent probably 2 HA day on this

619 01:11:21.180 01:11:25.899 Amber Lin: starting. They say, this is gonna start July first.st

620 01:11:26.330 01:11:29.760 Amber Lin: I’m just gonna do one month for now.

621 01:11:30.980 01:11:37.169 Amber Lin: And there’s that I’m gonna add

622 01:11:37.640 01:11:40.940 Amber Lin: AI engineer. I’m gonna add Casey.

623 01:11:41.930 01:11:47.989 Amber Lin: and I would say he spends about an hour a day

624 01:11:50.660 01:11:54.719 Amber Lin: and say, Oh, I forgot to choose a start and end date oops.

625 01:11:55.490 01:12:01.600 Amber Lin: So that will be July 1st to end of July, save that.

626 01:12:02.650 01:12:08.329 Amber Lin: And, as you can see, once I added them under Casey.

627 01:12:08.740 01:12:10.539 Amber Lin: So now we have.

628 01:12:11.080 01:12:19.130 Amber Lin: We have 2 of these, and then overall. It’ll show you how much capacity is already allocated.

629 01:12:21.630 01:12:25.860 Amber Lin: Oh, I want to.

630 01:12:27.920 01:12:31.885 Amber Lin: I want to edit this one because I want it to be named

631 01:12:32.360 01:12:36.330 Amber Lin: ABC, but I’ll let you. I’ll let you figure that out.

632 01:12:37.070 01:12:42.879 Rico Rejoso: Got it. So I had this little video from. I don’t know

633 01:12:43.080 01:12:52.679 Rico Rejoso: who it was. It’s not utam, but what they were doing was basing it off from clock, if I from, you know exporting the reports from clockified and uploading it to

634 01:12:53.265 01:13:04.509 Rico Rejoso: operating. But since we’re trying to be efficient with that time and trying to lessen the hours. So we can assume that we can. We’ll be assigning the hours that they should be spending for each project right.

635 01:13:06.303 01:13:09.199 Amber Lin: Yes, we can assign it. I think

636 01:13:09.460 01:13:35.149 Amber Lin: the purpose. Why we uploaded the hours from Clockify is, I think, because we can look at reports and look at actual time versus. I think it’s here we can look at actual time versus allocated time, so it would still be nice. If you were able to upload it, I think, on a say

637 01:13:37.282 01:13:45.747 Amber Lin: depending on how long it would take. We could do it weekly, bi-weekly, or monthly. Would you be able to try and upload it?

638 01:13:48.180 01:13:55.109 Amber Lin: Within this week, and tell me how long it will take, and then we can as a team, we can decide on how frequent.

639 01:13:55.280 01:13:57.419 Amber Lin: how frequent we should.

640 01:13:58.090 01:14:00.359 Amber Lin: We should create upload that.

641 01:14:02.030 01:14:09.689 Rico Rejoso: Yeah, by end of week, like I can upload it by Monday if you want after this week, so we can track the 1st week of July.

642 01:14:10.380 01:14:11.890 Rico Rejoso: or from last month.

643 01:14:12.781 01:14:26.869 Amber Lin: I think from last month, because usually people take some time. I don’t think they log their hours the day of, I think, what we should do is to let me say, get people to log all June hours.

644 01:14:28.500 01:14:32.029 Amber Lin: Bye, let’s say to do.

645 01:14:35.270 01:14:38.860 Amber Lin: Wait! Did I invite you to this. Okay, I did.

646 01:14:39.180 01:14:47.250 Amber Lin: By end of this week I’m gonna copy what we said here.

647 01:14:55.140 01:14:59.819 Rico Rejoso: Do you think also that it would be possible that we can log those hours every end of week?

648 01:15:01.079 01:15:03.949 Rico Rejoso: I mean, that would be. That would be great.

649 01:15:04.893 01:15:06.059 Rico Rejoso: I mean.

650 01:15:06.060 01:15:07.640 Amber Lin: I don’t know if people will.

651 01:15:08.020 01:15:19.099 Rico Rejoso: We have to like cause. I was thinking like, after this week. We get the data by next week. Then we can come up with the raw data times 4, so we can make a.

652 01:15:19.843 01:15:29.760 Rico Rejoso: You know, an assumption for what we’re talking for July, instead of like assuming what every month.

653 01:15:29.760 01:15:38.640 Amber Lin: Okay. Sounds good. Have everyone log 1st week, July hours.

654 01:15:39.520 01:15:43.630 Amber Lin: So that would be quite a hard task.

655 01:15:43.810 01:15:45.580 Rico Rejoso: I trust you.

656 01:15:46.230 01:15:48.970 Rico Rejoso: End of this week. Okay, we.

657 01:15:48.970 01:15:49.320 Amber Lin: Yeah.

658 01:15:49.460 01:15:58.189 Rico Rejoso: Yeah, I’ll be exporting each week after, like by Monday, so that we can not notify each of those who will not be uploading their hours.

659 01:15:58.410 01:16:02.670 Amber Lin: Okay, upload. Clockify hours to operating.

660 01:16:03.936 01:16:21.079 Amber Lin: I would say by next Monday, do okay, and then I will, or make into recurring.

661 01:16:21.970 01:16:23.140 Amber Lin: Ask.

662 01:16:27.130 01:16:32.600 Amber Lin: think next Tuesday, Tuesday.

663 01:16:33.070 01:16:41.970 Amber Lin: Me check in to view allocations based on accurate hours.

664 01:16:43.030 01:16:52.380 Amber Lin: So I’ll say that is Tuesday. So next Tuesday we’ll meet again.

665 01:16:54.551 01:17:07.400 Amber Lin: I’ll make a ticket to assign July allocations in operating.

666 01:17:16.400 01:17:19.370 Amber Lin: I’ll say this bye.

667 01:17:19.970 01:17:22.760 Amber Lin: Do you think this is doable

668 01:17:24.240 01:17:30.089 Amber Lin: day, or what time this week would you be able to do that?

669 01:17:33.478 01:17:36.619 Rico Rejoso: The allocation per hours per team. Member.

670 01:17:36.850 01:17:45.140 Rico Rejoso: Yeah, I think after we gather the data from this week by next week, I think guess.

671 01:17:46.870 01:17:54.360 Rico Rejoso: Yeah, we can assign that for, because I think this will take like a month for us to implement. And by August we can have this

672 01:17:54.873 01:18:04.900 Rico Rejoso: pretty smoothly, since we will be using the 1st week of July as a raw data, then multiply it by 4, then come up with the allocation hours for the next.

673 01:18:04.900 01:18:05.380 Amber Lin: Hmm.

674 01:18:05.380 01:18:06.970 Rico Rejoso: 3 weeks.

675 01:18:06.970 01:18:08.139 Rico Rejoso: I see, I see.

676 01:18:08.330 01:18:19.180 Amber Lin: I see. Yeah, I think that will be very much more accurate estimate as as we have now, do you think? Because there’s not too much here. I think it’ll take around

677 01:18:19.744 01:18:32.109 Amber Lin: it’ll be a good chance for you to understand how operating works is just to add what we currently have estimated in there mostly because I just need to show that we did something. That’s all.

678 01:18:32.110 01:18:35.280 Rico Rejoso: Yeah, we can use that as a basis. For now, okay.

679 01:18:35.280 01:18:40.140 Rico Rejoso: those are based from our wish. Statement.

680 01:18:41.100 01:18:44.380 Amber Lin: Okay. So I’ll just say, this is roughly estimated.

681 01:18:46.220 01:18:48.110 Amber Lin: Okay, I’ll say

682 01:18:48.760 01:18:58.230 Amber Lin: this is for you, and I can say by tomorrow that should be pretty quick. For operating.

683 01:18:59.390 01:19:02.470 Amber Lin: Let me make sure we have all the

684 01:19:02.880 01:19:08.699 Amber Lin: the projects we have. ABC, okay, let’s just say as, oh.

685 01:19:12.930 01:19:14.660 Amber Lin: to operating

686 01:19:21.300 01:19:23.630 Amber Lin: think that will be also pretty fast.

687 01:19:25.380 01:19:27.360 Amber Lin: Yeah, I think that’s all I can think of.

688 01:19:27.470 01:19:34.330 Amber Lin: We’ll meet soon next week, and I still, there’s still a few things that Uton was saying.

689 01:19:34.430 01:19:36.049 Amber Lin: forgot what? He said.

690 01:19:39.960 01:19:43.080 Amber Lin: Oh, here, yeah, I will.

691 01:19:44.210 01:19:47.129 Amber Lin: Okay. Create new clients. Add new team members.

692 01:19:48.760 01:19:55.770 Amber Lin: Okay, I’ll create tickets for these. Does any of these items look confusing for you.

693 01:20:00.190 01:20:01.480 Rico Rejoso: I’m not sure, really.

694 01:20:01.900 01:20:05.329 Rico Rejoso: I’m just, you know, adapting with the system and everything as of the moment.

695 01:20:05.330 01:20:08.930 Rico Rejoso: Okay, okay, no worries. So.

696 01:20:09.160 01:20:16.520 Amber Lin: We can upload, clockify, create new clients, I think, is just what I did to create this.

697 01:20:17.070 01:20:17.799 Amber Lin: If we’re

698 01:20:17.800 01:20:30.200 Amber Lin: we’re missing the client. So I think you can create a new one just by typing in here. And then we create the client 1st and then add the project. If the same thing. We can just type that in

699 01:20:32.013 01:20:40.040 Amber Lin: and then archive old clients. I think you’ll have to look into how to do that.

700 01:20:41.250 01:20:46.300 Amber Lin: We have tentative allocations. We just need to type that add that in.

701 01:20:47.241 01:21:02.910 Rico Rejoso: I don’t know what creating reports mean. I think creating reports just was similar to what we did today, of how much was spent. How much was allocations. I think we can finalize that together next time we meet

702 01:21:02.910 01:21:04.730 Rico Rejoso: you said rates. Yeah.

703 01:21:05.110 01:21:12.052 Rico Rejoso: yeah. I mean, we figure out the pain points, which is the missing hours. That’s why we haven’t have enough data to come up with the

704 01:21:12.460 01:21:14.399 Rico Rejoso: hours allocations for now.

705 01:21:14.780 01:21:27.320 Rico Rejoso: so we can include it in the report next week. Then, compare what we have right now. As long as we have all the data input to qualify from last month, then the assumption.

706 01:21:27.600 01:21:32.890 Rico Rejoso: the time that we assume for everyone to spend for each client or each project.

707 01:21:34.560 01:21:37.079 Rico Rejoso: We can create that by next week. I think, yeah, we can finalize.

708 01:21:37.080 01:21:45.720 Amber Lin: Okay, awesome. I will book a meeting for us next Tuesday.

709 01:21:46.250 01:21:51.070 Amber Lin: I’ll just put a tentative, actually.

710 01:22:03.060 01:22:10.510 Amber Lin: Okay, we’ll meet next week. We’ll talk about allocations. Oh, we’ll talk about allocations again.

711 01:22:11.660 01:22:12.430 Rico Rejoso: Got it.

712 01:22:12.900 01:22:13.600 Rico Rejoso: Okay.

713 01:22:14.240 01:22:14.710 Amber Lin: Awesome.

714 01:22:15.400 01:22:16.640 Rico Rejoso: Nice to meet you.

715 01:22:17.090 01:22:17.950 Rico Rejoso: Yes. Nice to meet.

716 01:22:17.950 01:22:18.580 Amber Lin: There you go!

717 01:22:18.580 01:22:22.790 Rico Rejoso: I mean, I’m sorry if I’m gonna you know, message you throughout the day, or by.

718 01:22:22.790 01:22:39.649 Amber Lin: Oh, don’t, don’t worry, I I apologize if I respond late, cause I, as you can see all of these channels ping me but I will, if I don’t respond. Pm. Like DM, me, and then I will be able to see that faster.

719 01:22:39.950 01:22:41.890 Rico Rejoso: Got it. Thank you so much. Amber.

720 01:22:42.250 01:22:42.710 Amber Lin: Thanks.

721 01:22:43.210 01:22:44.249 Rico Rejoso: All right, have a good one.

722 01:22:44.250 01:22:45.759 Rico Rejoso: Alright, you, too, bye, bye.