Meeting Title: [Eden] Daily Standup Date: 2025-03-26 Meeting participants: Aakash Tandel, Demilade Agboola, Josh, Sahana Asokan, James Freire
WEBVTT
1 00:03:19.380 ⇒ 00:03:20.449 James Freire: How we doing
2 00:03:20.810 ⇒ 00:03:21.729 Aakash Tandel: Hey? How’s it going
3 00:03:22.340 ⇒ 00:03:23.190 James Freire: Going? Well.
4 00:03:28.970 ⇒ 00:03:38.730 Aakash Tandel: I know Robert has conflict. I don’t think so. Hannah’s joining, so we’ll keep this tight, if
5 00:03:39.000 ⇒ 00:03:40.950 Aakash Tandel: that’s the case.
6 00:03:55.690 ⇒ 00:03:56.839 James Freire: You have a good weekend
7 00:03:58.380 ⇒ 00:04:00.259 Aakash Tandel: Yeah, what did I do this weekend
8 00:04:02.910 ⇒ 00:04:03.580 James Freire: Kids
9 00:04:03.880 ⇒ 00:04:08.259 Aakash Tandel: I do. I have a actually have a young baby. He’s 6 months old, so he takes
10 00:04:08.260 ⇒ 00:04:10.760 James Freire: Oh, that’s awesome. Yeah, I have 3 daughters
11 00:04:10.760 ⇒ 00:04:11.390 Aakash Tandel: Nice
12 00:04:11.390 ⇒ 00:04:12.050 James Freire: Yeah.
13 00:04:12.830 ⇒ 00:04:22.820 James Freire: the 6 6 months is like, I don’t know. I mean everybody be different. I mean, I I lucked out super hard that my kids slept through the night from a really young age.
14 00:04:22.820 ⇒ 00:04:23.370 Aakash Tandel: Nice
15 00:04:23.370 ⇒ 00:04:25.749 James Freire: It’s like almost criminal. How? How?
16 00:04:26.300 ⇒ 00:04:28.125 James Freire: How I got with that? Yeah.
17 00:04:28.430 ⇒ 00:04:31.209 Aakash Tandel: Yeah. My son didn’t start doing that until maybe
18 00:04:31.390 ⇒ 00:04:35.500 Aakash Tandel: 4 months. So it’s been a huge change. Hey, Sana!
19 00:04:35.500 ⇒ 00:04:36.370 Sahana Asokan: Hey!
20 00:04:37.780 ⇒ 00:04:42.630 Aakash Tandel: But yeah, sanity wise. The sleeping through the night thing is, is very, very big. Help
21 00:04:43.190 ⇒ 00:04:54.172 James Freire: Yeah, I used to, I mean, especially Maria was in her crib I used to. I wouldn’t even pick. I just have the bottle. I just give it to right in the crib she’d drink and then roll over is hilarious
22 00:04:56.571 ⇒ 00:05:05.850 Aakash Tandel: Cool. Let me just check. I think this might be it for our crew this morning. I know Robert’s out. With
23 00:05:06.610 ⇒ 00:05:08.720 Aakash Tandel: another call.
24 00:05:10.674 ⇒ 00:05:16.255 Aakash Tandel: Okay, yeah, I think this is it. Okay? Let’s go through.
25 00:05:16.850 ⇒ 00:05:37.820 Aakash Tandel: the way I typically run stand up. I know Robert’s running them. But I just typically go through our board and just kind of get updates on the things that we’re working on. So I’ll filter on, Signee James, we can start with you. I see the 3 things that you’re working on in progress. Do you have any updates, anything that we can help get this through the finish line?
26 00:05:38.084 ⇒ 00:05:43.379 James Freire: Yeah. So I have the retention dashboard. If you go, I have a screenshot also. If you scroll down
27 00:05:43.620 ⇒ 00:05:44.180 Aakash Tandel: And
28 00:05:44.180 ⇒ 00:05:47.531 James Freire: So that’s I’m gonna check that out today.
29 00:05:48.210 ⇒ 00:06:11.590 James Freire: there’s those negative values. And the way felt, the funny thing is with filtering in tableau. I’ve noticed, if you set the minimum to one, it does an absolute. So it includes the negative ones. So I want to figure out a way to to get rid of that negative 2. I don’t know why that’s appearing. I have to look at maybe the the info in a you know, back of the database to see why it’s doing that
30 00:06:11.700 ⇒ 00:06:12.650 James Freire: welcome.
31 00:06:12.850 ⇒ 00:06:16.770 James Freire: I did try. I tried the mobile formats.
32 00:06:17.620 ⇒ 00:06:24.400 James Freire: I’m not too sure how well this is gonna work on a mobile format to look at this type of detailed data.
33 00:06:25.071 ⇒ 00:06:35.030 James Freire: For you know, I don’t know how relevant that’d be to look on the phone for like looking at churn that far back historically. I’m pretty sure like things like the
34 00:06:35.150 ⇒ 00:07:02.900 James Freire: those line charts of the bar charts are probably gonna work better. And what was the other thing with that? That’s pretty much the only thing. So turning the line to a bar chart. That is, gonna I could not find a way to do it with the existing models I have right now. So I think we gotta just go back and finish out those models to pivot them. So they look at. So I can have the the data there to do the bar charts
35 00:07:03.670 ⇒ 00:07:08.070 James Freire: like. Did the you know, with the other one? I had done okay.
36 00:07:08.340 ⇒ 00:07:10.370 James Freire: and that should be it
37 00:07:10.370 ⇒ 00:07:20.489 Aakash Tandel: Okay, cool. I did add a deadline because the one this one didn’t have a deadline at it for Friday. Let’s know if that’s not possible, and then we can readjust. But yeah.
38 00:07:20.490 ⇒ 00:07:24.750 James Freire: Yeah, it’s totally possible. There’s just a little couple of little things that to just round out
39 00:07:24.750 ⇒ 00:07:29.119 Aakash Tandel: Awesome. Okay, let’s move over to Sahana.
40 00:07:30.001 ⇒ 00:07:31.760 josh: I just jumped in. Okay.
41 00:07:31.760 ⇒ 00:07:32.660 Aakash Tandel: Josh, how’s it going
42 00:07:34.120 ⇒ 00:07:35.539 Sahana Asokan: Sorry. Give me one sec.
43 00:07:35.540 ⇒ 00:07:36.150 Aakash Tandel: Yep.
44 00:07:36.730 ⇒ 00:07:40.469 Sahana Asokan: Yeah, for me. I’m working on the marketing dash for I think
45 00:07:40.470 ⇒ 00:07:40.870 Aakash Tandel: Yeah.
46 00:07:40.870 ⇒ 00:07:49.569 Sahana Asokan: So. On 2 and 3 I’m noticing that ad spent in product by summary
47 00:07:49.750 ⇒ 00:08:00.059 Sahana Asokan: product, by sales, summary transactions. Some reason it was fine last night. But today, when I look at it, we’re not seeing data for ad spend since January. So
48 00:08:00.200 ⇒ 00:08:03.359 Sahana Asokan: can we please look into that, because that’s not
49 00:08:03.360 ⇒ 00:08:05.560 Aakash Tandel: Yeah, Demoto, do you have
50 00:08:06.940 ⇒ 00:08:08.130 Aakash Tandel: Do you have any
51 00:08:08.130 ⇒ 00:08:09.609 Sahana Asokan: Brought in the Channel too.
52 00:08:09.610 ⇒ 00:08:13.820 Aakash Tandel: In the in the slack channel. Yeah, then, what are you on
53 00:08:14.100 ⇒ 00:08:14.939 Demilade Agboola: Yes, I am
54 00:08:15.160 ⇒ 00:08:15.930 Aakash Tandel: Oh, awesome!
55 00:08:16.538 ⇒ 00:08:20.570 Aakash Tandel: Are you? Are you? Do you have bandwidth to look at that today?
56 00:08:21.226 ⇒ 00:08:24.819 Demilade Agboola: Yeah, I’m I just saw the screenshot, and I’m diving into it right now.
57 00:08:24.820 ⇒ 00:08:25.910 Aakash Tandel: Okay. Cool.
58 00:08:26.740 ⇒ 00:08:33.849 Sahana Asokan: That’s important, just because a lot of our tables are connected to that. So we need to figure out why that happened and
59 00:08:33.990 ⇒ 00:08:38.889 Sahana Asokan: how that cannot happen in the future, because I know we just ran into the issue with
60 00:08:39.240 ⇒ 00:08:42.540 Sahana Asokan: about Qc. So let’s make sure we get that fixed
61 00:08:45.508 ⇒ 00:08:56.699 Aakash Tandel: And I know you’re still working on this. Anything else there’s I know there’s a lot of chatter in slack on. You know how how you guys are doing this with. You’re talking with Robert and stuff anything else that you need to bring up with this group
62 00:08:57.356 ⇒ 00:09:00.790 Sahana Asokan: Do you mind? Just clicking on the ticket again? The marketing dashboard?
63 00:09:00.970 ⇒ 00:09:03.180 Sahana Asokan: Yeah. So once
64 00:09:03.783 ⇒ 00:09:18.989 Sahana Asokan: the fixes that 1, 2, 3 should be done. 4 is done, too, and to the only big piece remaining is 5, the funnel conversion rates, and that I need to scope scope it out a little to really see what our funnel really is.
65 00:09:18.990 ⇒ 00:09:19.380 Aakash Tandel: Yeah.
66 00:09:19.380 ⇒ 00:09:33.690 Sahana Asokan: And based on the results. I actually think mixed panel might be the better way to really understand a funnel versus tableau. So if that’s the case, and I’m just gonna kind of come up. Present that argument and the reasons why. But yeah.
67 00:09:33.690 ⇒ 00:09:43.010 Aakash Tandel: After looking at like mixed panel a little bit. I was like this honestly, might be the better bet, just because we have a lot of data in there. That’s, you know, related to the customer journey. So yeah.
68 00:09:43.010 ⇒ 00:09:50.059 Sahana Asokan: Yeah, I think it’s also just way, too manual and tableau. And we’re gonna be missing, maybe like more nuanced
69 00:09:50.300 ⇒ 00:10:00.100 Sahana Asokan: parts of that journey. So I just feel like mixed panel has better has more granularity around that because it actually supports
70 00:10:00.170 ⇒ 00:10:24.360 Sahana Asokan: more of our tracking plan like we don’t have a tracking plan in place. Right? So that’s my argument for the funnel. But after I we figure out the Qc. For the ad spend data, this dashboard is mostly done. It’ll be done by today. So I’ll probably kick off the other. The order journey dashboard for farm Ops and customer experience tomorrow
71 00:10:24.750 ⇒ 00:10:26.510 Aakash Tandel: Okay. Cool. Yeah.
72 00:10:26.510 ⇒ 00:10:49.879 josh: Hey? Real quick, just from again. My priorities are one system. For now, if you’re saying that it’s going to be multiple places, we’ll get slightly better data. Like I, truthfully, I just want to get good at one thing like we have way, too many Qa issues like, I’m gonna bring some stuff up today that like, I still just don’t have clarity and really basic basic data that we need
73 00:10:50.375 ⇒ 00:10:54.964 josh: like here, I can share my screen and I’ll show you what I’m talking about.
74 00:10:55.270 ⇒ 00:10:57.490 Sahana Asokan: 3. I yeah, go ahead.
75 00:10:57.710 ⇒ 00:10:59.640 josh: Yeah, let me share my screen real quick.
76 00:11:02.540 ⇒ 00:11:04.839 josh: Okay, let me see my screen
77 00:11:05.740 ⇒ 00:11:06.380 Aakash Tandel: Yep.
78 00:11:07.070 ⇒ 00:11:15.680 josh: Okay. So on my report, this is literally how I run my whole business guys to 100 million dollar business. And I run it off just these numbers.
79 00:11:16.407 ⇒ 00:11:36.990 josh: I don’t have these consistent. So it’s really, really challenging. So as of today, when I get my report for yesterday, it says, Hey, I had 633 orders, and the report that I get that I’ve been running my business on 125 new customers, 18 and pending, and then 143 total new, like counting the pending people. Right?
80 00:11:37.100 ⇒ 00:11:53.200 josh: I go to look at our dashboard, depending, like, you know, these these new customers and pending customers. I don’t even know how to read that versus like total new when you compare them. So like, does this number mean a hundred 65. Or does it mean 145?
81 00:11:53.340 ⇒ 00:12:02.129 josh: Is the 1st question I have. And then I look at the total orders, and we’re off by a massive number like, we’re off by this one says, 6, 33.
82 00:12:02.190 ⇒ 00:12:28.250 josh: This says 574, and this is just consistently inconsistent like. So I honestly am going off of like 4 different data sets like, how am I going to run marketing? How am I going to know my ad spend. How am I gonna know my Ltv to Calc ratios? I can’t know these things until these data points are consistent like. And if we think this is correct, cool, like someone. Explain to me how this is correct versus what we’ve been running our whole business. On. Which is this
83 00:12:30.950 ⇒ 00:12:37.989 Aakash Tandel: Yeah. Do. Does anyone on the call? Did anyone on the call work on the dashboard? That tableau dashboard
84 00:12:38.855 ⇒ 00:12:42.319 Sahana Asokan: I did not work on this, but I can
85 00:12:42.740 ⇒ 00:12:51.010 Sahana Asokan: look into why, the queue, like the Qc. Issues around it. I think Robert has the most context on this piece
86 00:12:51.010 ⇒ 00:12:53.790 josh: You can work directly with Rob.
87 00:12:53.790 ⇒ 00:13:00.580 Demilade Agboola: Yeah, so I, I do have a little bit of context to some of this. So so the
88 00:13:01.900 ⇒ 00:13:05.310 Demilade Agboola: part of the numbers in terms of like disparity.
89 00:13:06.030 ⇒ 00:13:12.669 Demilade Agboola: It comes from the like. So how we map our models of our
90 00:13:13.320 ⇒ 00:13:19.609 Demilade Agboola: products is a bit more accurate in terms of how we do it so like that creates more.
91 00:13:21.630 ⇒ 00:13:28.509 Demilade Agboola: how do I put how to explain this? So basically, we use Regex to be able to calculate the different product names and the different product types that we have.
92 00:13:29.210 ⇒ 00:13:41.440 Demilade Agboola: which means that we catch more of the orders that like, because the previous mapping was done like manually so that allows us to catch a bit more than what is currently there.
93 00:13:41.998 ⇒ 00:13:44.439 Demilade Agboola: And so that allows us to identify
94 00:13:44.440 ⇒ 00:13:48.240 josh: Hold on one sec. Hold on one sec. Because that’s refuted with the data.
95 00:13:48.580 ⇒ 00:13:53.150 josh: because we’re saying on one part, we’re capturing fewer orders.
96 00:13:53.360 ⇒ 00:14:18.210 josh: confirmed orders. But then, on the other part, we’re saying, we’re capturing more orders for new orders, so that logically doesn’t make sense to me to say that yeah, Regex is better, but that doesn’t make sense, because then they would both either be higher, both be lower. But the fact that one is higher and one is lower, and this happens daily. This happens every day, like, if I look at yesterday’s, it’s the same thing. I looked at yesterday’s, and I sent this to rob Robert.
97 00:14:18.400 ⇒ 00:14:27.730 josh: and like this number, yesterday wasn’t at 7 30 for you guys, it was in like the low sixes. And then but this number was in like the one forties.
98 00:14:28.300 ⇒ 00:14:32.449 josh: So it’s like we’re off both ways. So I just I have no trust in that
99 00:14:33.240 ⇒ 00:14:39.649 Aakash Tandel: So I guess I don’t know what. So where is the data for this screenshot coming from
100 00:14:39.650 ⇒ 00:14:41.389 josh: This is big. This is bigquery.
101 00:14:41.390 ⇒ 00:14:42.100 Aakash Tandel: That’s great. Okay.
102 00:14:42.100 ⇒ 00:15:04.909 josh: Rob, talk, to rob, please. This is like my most important things. I need to be able to make it so, cause I get this report every day. And I literally like this is getting sent to me. You guys, I think, are in this channel. You guys can look at the same data. I open this up. Today. I go right here. I look 6, 33, 1, 43. I go here, I open up this.
103 00:15:05.130 ⇒ 00:15:15.830 josh: I boom. I see this thing that open. I see 5, 74, and either 1, 65, or 1 45. You see what I’m saying doesn’t even make sense. I don’t know how I’m supposed to read this
104 00:15:16.440 ⇒ 00:15:25.270 Aakash Tandel: Sure. And I guess I guess the other question I have is, why are we doing this in 2 different systems? Why, why do we have a bigquery one, and why is there a tableau? One
105 00:15:25.700 ⇒ 00:15:52.970 josh: This is supposed to end up replacing everything I want to get out of bigquery. I want to get out of using multiple systems. That’s what I’m saying. Like, when we start talking about using mixed panel we start using. Like all these, I have 80 pieces of software, and I only need one. I want to get good at one. That’s what I want to hear that things like mix panel needs to be used. My alarm bells are starting to go off. Because I’m like guys, I just want one business intelligence system. Then we can add additional ones later. Like, right now, I’m at the point where I want to like close every account and start over
106 00:15:53.230 ⇒ 00:15:54.880 josh: because it’s such a mess
107 00:15:55.240 ⇒ 00:15:56.390 Aakash Tandel: Sure. Yeah.
108 00:15:57.220 ⇒ 00:16:23.099 Aakash Tandel: that makes sense. I think we will need to do some investigations into what the what the logic is in bigquery to aggregate these in this piece of information, and then compare that to what we’re doing. Tableau, because right now I don’t personally have a sense of what those 2 things are doing, and if it’s 1 to one, demo, is that something that’s currently in the works is that is like a ticket for that? Or is there
109 00:16:23.100 ⇒ 00:16:28.370 Demilade Agboola: Well, there isn’t a street. There’s a straight ticket for that. But like I’m investigating like our logic.
110 00:16:28.610 ⇒ 00:16:38.909 Demilade Agboola: and I am trying to compare against their logic, and trying to do like day by day, and figure out where that disparity comes from, like fully understand where all that disparity comes from
111 00:16:39.280 ⇒ 00:16:53.149 Aakash Tandel: And is, and this is a question, and I don’t. I’m not fully caught up on all this stuff. But is the issue that our orders. The product level information is something we’re parsing and not something. We’re getting directly from the source
112 00:16:55.010 ⇒ 00:17:05.620 Demilade Agboola: everything is gotten directly from the source. My guess with this like the screenshot that Josh has is that it’s order details that is being used for that
113 00:17:05.750 ⇒ 00:17:11.910 Demilade Agboola: I will need to confirm from Robert. And all of that gets is from the same source
114 00:17:12.589 ⇒ 00:17:13.859 Aakash Tandel: And what is that source?
115 00:17:15.248 ⇒ 00:17:18.829 Demilade Agboola: Give me one second. I’ll have to look at that
116 00:17:28.810 ⇒ 00:17:31.082 Aakash Tandel: And that’s fine if you have to do some digging
117 00:17:31.510 ⇒ 00:17:43.169 Aakash Tandel: for that. But yeah, I I think if we can find a way to hit the source. And then, if the source does not contain data that’s like parsable correctly, if there’s other ways we can
118 00:17:43.540 ⇒ 00:18:05.768 Aakash Tandel: like, if the if the data source data is giving us data that we have to then clean up to generate a report. That’s a problem. We should fix the source or get the source to give us better data so that it downstream. We don’t have to modify all the data to get an accurate report like, that’s typically how I would like to handle that system, but I don’t know if that’s possible yet, so I guess
119 00:18:06.590 ⇒ 00:18:13.000 Aakash Tandel: demote, I will write you a ticket to to look into this that’s like on your plate and on your like main to do
120 00:18:14.590 ⇒ 00:18:15.910 Demilade Agboola: Okay. Sounds good.
121 00:18:16.280 ⇒ 00:18:16.850 Aakash Tandel: Cool
122 00:18:18.750 ⇒ 00:18:23.550 Aakash Tandel: And then demo idea, I know you’re also working on.
123 00:18:24.180 ⇒ 00:18:25.980 Aakash Tandel: Actually, let me just go back to this thing
124 00:18:28.540 ⇒ 00:18:31.179 josh: Because like, unless we have like a basic
125 00:18:31.600 ⇒ 00:18:37.980 josh: fundamental alignment on like, what is the actual bi like, I feel like nothing else matters like. I don’t even know what else we’re doing
126 00:18:39.460 ⇒ 00:18:41.929 Aakash Tandel: Sure. No, that makes sense. I think
127 00:18:42.720 ⇒ 00:19:03.590 Aakash Tandel: we can drop other things that we basically deprioritize everything that’s not related to that. It’s the 2 things that we prioritized were the marketing dashboard and then the ship out information. And I know that was the other thing, demalada, you were working on. Let me just filter by your stuff. Oops.
128 00:19:13.220 ⇒ 00:19:16.429 Aakash Tandel: Do you have updates on this guy? Demote
129 00:19:17.711 ⇒ 00:19:26.280 Demilade Agboola: Yeah. So we’ve realized that the current a missing records are like a much smaller scope. Than
130 00:19:27.318 ⇒ 00:19:28.869 Demilade Agboola: we got off road.
131 00:19:29.010 ⇒ 00:19:36.759 Demilade Agboola: So we’re still looking at that and trying to confirm. But the missing record seem to be from like only in the month of June 2024,
132 00:19:36.980 ⇒ 00:19:37.380 Aakash Tandel: Okay.
133 00:19:37.520 ⇒ 00:19:45.710 Demilade Agboola: So with that logic, we’re trying to move ahead with what we already have. And I’m already talking to Hannah about what metrics we need to build out using the sheep or data
134 00:19:47.170 ⇒ 00:19:53.836 Aakash Tandel: Yeah, that sounds good. If it’s yeah. If it’s just from one month in 2024 let’s just try to.
135 00:19:54.640 ⇒ 00:20:07.379 Aakash Tandel: not make that a huge issue kind of move forward, and then we can flag that as a bug in the future, to to go back and reconcile when we have more bandwidth for this problem. But I wouldn’t. Yeah, let’s try to continue to push forward on that.
136 00:20:09.880 ⇒ 00:20:10.989 Aakash Tandel: It’s not cool.
137 00:20:11.320 ⇒ 00:20:31.659 Aakash Tandel: And then, okay, so that’s the other thing in part, so I’ll write you a ticket to. That’ll be like kind of your main thing for looking at bigquery and looking at tableau and making sure that the logic between those 2 is close, we need to get those basically closer in line so that they’re well. 1st of all, they need to be accurate, and then they need to be in line. So that’ll that’ll be the thing that
138 00:20:31.660 ⇒ 00:20:41.819 josh: This is Rob Wiley. He’s in our team. He’s the one that is the architect behind all this, and a lot of the things that he built. There’s a lot of reasons why he did them that certain way.
139 00:20:41.940 ⇒ 00:21:03.629 josh: So have questions. Please work with Rob Wiley directly you can message him. You can add me to a thread, and I’ll make sure that it’s getting addressed, because I feel like I’ve been at this point for 3 months. Guys like, I’m dying like, I, literally, how can I can’t run this business with with like these huge disparities like we’re talking like 1015%
140 00:21:04.000 ⇒ 00:21:05.950 josh: and sales like, it’s massive
141 00:21:06.810 ⇒ 00:21:14.780 Aakash Tandel: Yeah, no, we’ll definitely try to demo it. I think that should be your main main thing to sync up with Rob to to get this sorted out.
142 00:21:17.480 ⇒ 00:21:23.000 Aakash Tandel: I think that’s it for everyone on the call. There’s other stuff that we’re deprioritizing, that
143 00:21:23.210 ⇒ 00:21:40.860 Aakash Tandel: we’ll just talk with Mattesh, and like those folks who are involved with those and just say, Hey, we’re working on these other things first, st and we’ll get to your ask once these other things are corrected. But yeah, is there anything else? Anyone? 1st of all, is anyone blocked on anything, anything you need like someone else to do for you to continue working
144 00:21:40.860 ⇒ 00:21:49.259 Sahana Asokan: No, I think for me, the only blocker is on the data that’s not been refreshed for this dashboard before I publish it.
145 00:21:49.600 ⇒ 00:21:50.260 Aakash Tandel: Remarkable.
146 00:21:50.260 ⇒ 00:21:57.349 Sahana Asokan: The p, like the higher priority, is obviously understanding the discrepancy between the snapshot and
147 00:21:58.011 ⇒ 00:22:00.719 Sahana Asokan: what Josh is seeing. So yeah.
148 00:22:00.980 ⇒ 00:22:01.590 Aakash Tandel: Okay?
149 00:22:02.220 ⇒ 00:22:05.529 Aakash Tandel: Yeah, cool. That’ll be kind of your order of operations tomorrow.
150 00:22:06.920 ⇒ 00:22:07.750 Aakash Tandel: Cool.
151 00:22:08.320 ⇒ 00:22:11.529 Aakash Tandel: If you guys need anything from Robert, feel free to slack him.
152 00:22:12.000 ⇒ 00:22:14.760 Aakash Tandel: And then, yeah, if you need anything for me, too.
153 00:22:15.770 ⇒ 00:22:17.120 Aakash Tandel: But yeah, that’s it.
154 00:22:17.910 ⇒ 00:22:24.060 Aakash Tandel: Go forth. And good luck, and yeah, feel free to slack. And you know, hop on calls with where you’re needed.
155 00:22:24.560 ⇒ 00:22:26.780 Demilade Agboola: Okay. Sounds good.
156 00:22:26.780 ⇒ 00:22:28.630 Aakash Tandel: Thanks, all have a good day, bye.
157 00:22:29.450 ⇒ 00:22:29.935 James Freire: Bye.