Meeting Title: Brainforge Recruitment and Notion Sync Date: 2026-01-19 Meeting participants: Rico Rejoso, Uttam Kumaran, Clarence Stone, Sheshu Chandrasekar
WEBVTT
1 00:00:15.820 ⇒ 00:00:16.609 Uttam Kumaran: There we go.
2 00:00:20.110 ⇒ 00:00:21.280 Rico Rejoso: How you thumb, how are you?
3 00:00:21.880 ⇒ 00:00:22.530 Uttam Kumaran: income.
4 00:00:23.780 ⇒ 00:00:24.370 Rico Rejoso: Good.
5 00:00:24.750 ⇒ 00:00:25.660 Rico Rejoso: Alright.
6 00:00:37.260 ⇒ 00:00:40.130 Rico Rejoso: We just got off a call from the JTM.
7 00:00:40.440 ⇒ 00:00:43.990 Rico Rejoso: Shashu was there, so… Just message him.
8 00:00:44.620 ⇒ 00:00:45.360 Uttam Kumaran: No.
9 00:01:19.080 ⇒ 00:01:19.970 Sheshu Chandrasekar: Hey, everyone.
10 00:01:21.850 ⇒ 00:01:27.479 Uttam Kumaran: I mean… Probably mixed up today, but no worries.
11 00:01:29.940 ⇒ 00:01:34.070 Sheshu Chandrasekar: Yeah, I mean, I think… For this meeting.
12 00:01:34.360 ⇒ 00:01:40.550 Sheshu Chandrasekar: We will not be covering finance ops, just because Eliza is the one that was running point on that.
13 00:01:41.060 ⇒ 00:01:49.450 Sheshu Chandrasekar: But… We do have some updates regarding recruitment, and just, like, a plan of action.
14 00:01:49.700 ⇒ 00:01:55.639 Sheshu Chandrasekar: regarding, like, what to do with the Notion homepage, like we discussed last week, Utom, and then,
15 00:01:55.790 ⇒ 00:01:57.750 Sheshu Chandrasekar: Kind of want to get some thoughts on…
16 00:01:58.060 ⇒ 00:02:07.150 Sheshu Chandrasekar: the unified intake portion of, you know, kind of automating the Slack bot into linear that we discussed as well. So I’m gonna go ahead and share my screen here.
17 00:02:07.900 ⇒ 00:02:08.810 Uttam Kumaran: Great, thank you.
18 00:02:09.139 ⇒ 00:02:09.719 Sheshu Chandrasekar: Yeah.
19 00:02:10.289 ⇒ 00:02:19.229 Uttam Kumaran: This is great. Yeah, this is what I was… what I was saying… I was hoping for, so cool. Yeah, this is… we’re gonna always have, like, probably 100 things to talk about in this meeting, so as, like.
20 00:02:19.489 ⇒ 00:02:21.189 Uttam Kumaran: Streamlined as it could be.
21 00:02:21.719 ⇒ 00:02:23.829 Uttam Kumaran: You know, the more helpful I could be, so…
22 00:02:24.750 ⇒ 00:02:27.330 Sheshu Chandrasekar: Absolutely. So, Rico, I’ll…
23 00:02:27.520 ⇒ 00:02:31.710 Sheshu Chandrasekar: give you the floor here if you want to give some updates on the recruitment end.
24 00:02:31.990 ⇒ 00:02:34.249 Sheshu Chandrasekar: Just because you can paint an additional context here.
25 00:02:36.430 ⇒ 00:02:44.989 Rico Rejoso: Yep, for the recruitment, we created a bunch of docs, to also help guide our,
26 00:02:44.990 ⇒ 00:03:03.480 Rico Rejoso: interviewer when it comes to doing the interview, doing the interviews, but again, since we do have, like, different distincts, for example, we have VF chats and the formal interview itself, I kind of talked, like, creating a documentary show, since, we want to make sure that the one conducting the interviewer will also have a guide on how that is
27 00:03:03.780 ⇒ 00:03:14.409 Rico Rejoso: how the interview should be done, or the Brainforged chat with the candidate is done. At the same time, have them a view of how the recruitment process should be.
28 00:03:14.410 ⇒ 00:03:35.880 Rico Rejoso: So I created this one, so we’ll be rolling this out and be asking for feedback. I already sent this out on the operations channel for the initial review by the operations team, but again, further, we’ll be distributing this to the member of the team that will be conducting interview to get their feedbacks on if this process could work, and if we should be implementing this long-term.
29 00:03:36.280 ⇒ 00:03:44.310 Rico Rejoso: So we have, first up would be the recruitment Notion page, which would be, revamped as per the view, whereas…
30 00:03:44.570 ⇒ 00:04:04.329 Rico Rejoso: We’re still finalizing on how it will look. I mean, I already consulted Eliza in regards to it. I haven’t received any feedback yet. For Sheshu, we’ll be looking into it over during our operations sync, or operation stand-ups, and, created the hiring philosophy, the interview guide for BF Chats.
31 00:04:04.330 ⇒ 00:04:09.689 Rico Rejoso: and be in… interview SOP as well, as well as the revamped recruitment process.
32 00:04:11.070 ⇒ 00:04:17.859 Uttam Kumaran: Okay, great. So maybe one point here. Yeah, I guess I’m interested, especially in what you think, but, like, we have… almost have, like.
33 00:04:18.180 ⇒ 00:04:27.470 Uttam Kumaran: It’s sort of weird, we… some… we do formal… we have, kind of, some formal interviews, but we also have sort of these, like, informal, like, chats with Brainforged team. I…
34 00:04:27.590 ⇒ 00:04:30.979 Uttam Kumaran: consider them… I… I honestly think they’re…
35 00:04:31.190 ⇒ 00:04:38.230 Uttam Kumaran: the formal interviews, but it is… I know sometimes people internally and candidates want some clarity on, like.
36 00:04:38.490 ⇒ 00:04:42.140 Uttam Kumaran: How serious is this, you know, when they kind of go into conversations?
37 00:04:42.320 ⇒ 00:04:52.929 Uttam Kumaran: But, because sometimes we’re, like, when I… when I talk to candidates, sometimes it’s almost just like, hey, come learn a little bit more about what we do, and then we sort of, for some of the technical roles, we’re starting to move into more, like.
38 00:04:53.240 ⇒ 00:04:57.519 Uttam Kumaran: You know, we do, like, we’ll run, like, a technical interview or presentation-type portion.
39 00:04:57.660 ⇒ 00:04:58.560 Uttam Kumaran: So…
40 00:04:58.690 ⇒ 00:05:06.479 Uttam Kumaran: That’s kind of, like, what the thought is on, like, the pre-interview conversation. Certainly, I think it’s also really important is, is,
41 00:05:06.640 ⇒ 00:05:13.960 Uttam Kumaran: Having, like, a… Joint place for everybody to take notes, and then having a interview template.
42 00:05:14.290 ⇒ 00:05:21.780 Uttam Kumaran: Kind of like the… The best book that I read on this subject is this book called
43 00:05:21.930 ⇒ 00:05:23.180 Uttam Kumaran: Who?
44 00:05:23.350 ⇒ 00:05:28.280 Uttam Kumaran: It’s like, it’s sort of literally like WHO, it’s called a method for hiring.
45 00:05:28.430 ⇒ 00:05:32.980 Uttam Kumaran: I honestly think we should… like, they have a lot of stuff.
46 00:05:33.190 ⇒ 00:05:35.199 Uttam Kumaran: On their website, on, like.
47 00:05:35.500 ⇒ 00:05:43.069 Uttam Kumaran: Basically, how to run, like, kind of like a good interview process with, like, a kind of a collaborative scorecard.
48 00:05:43.320 ⇒ 00:06:00.130 Uttam Kumaran: I would urge us to consider leveraging that, if possible. I’ll send a link to the book in the operations chat, but if we’re interested in, like, you know, reading a couple portions of it together and, like, trying to adopt it, this is…
49 00:06:00.310 ⇒ 00:06:07.130 Uttam Kumaran: this is sort of the best book, one of the most highly recommended books on just, like, establishing the improvement process that I’ve read, so…
50 00:06:08.160 ⇒ 00:06:12.749 Sheshu Chandrasekar: So, yeah, I mean, I would love to read it. So I guess, like, to piggyback on that.
51 00:06:13.100 ⇒ 00:06:21.539 Sheshu Chandrasekar: Has, like, most of your conversations been kind of influenced by this book, in a way? Like, the informal conversation and the formal ones as well?
52 00:06:22.290 ⇒ 00:06:29.630 Uttam Kumaran: I wouldn’t say that, like, that’s sort of me coloring around the lines. This is… What this,
53 00:06:30.200 ⇒ 00:06:32.920 Uttam Kumaran: sort of talked to me a lot about is, like, why…
54 00:06:33.140 ⇒ 00:06:39.200 Uttam Kumaran: You know, kind of, like, what’s broken in typical, recruiting processes?
55 00:06:39.390 ⇒ 00:06:51.579 Uttam Kumaran: Where, like, multiple people are interviewing somebody, there’s no collaborative, like, there’s no… they’re not recruiting any under… they’re not interviewing with an understanding of, like, how the other people on the committee are interviewing.
56 00:06:51.680 ⇒ 00:06:56.369 Uttam Kumaran: Right? So there’s no joint principles or joint… joint, like, score… scorecard.
57 00:06:56.470 ⇒ 00:07:08.899 Uttam Kumaran: So really what this book is, is more tactically, like, every person on an interview gets a scorecard, they fill it out independently, they’re… they’re… they know what parts that they’re focused on, and then ultimately.
58 00:07:09.010 ⇒ 00:07:13.839 Uttam Kumaran: All of it comes together at the end for us to look at multiple scorecards and make a decision.
59 00:07:14.070 ⇒ 00:07:19.510 Uttam Kumaran: This is… this one, I would say, is less… it’s gonna be less about, like, the…
60 00:07:19.820 ⇒ 00:07:22.260 Uttam Kumaran: Steps, like, oh, informal…
61 00:07:22.400 ⇒ 00:07:27.170 Uttam Kumaran: like, coffee chat versus this, that’s sort of, like, us developing it. Does that make sense?
62 00:07:27.170 ⇒ 00:07:28.749 Sheshu Chandrasekar: Yeah, that makes sense now.
63 00:07:29.050 ⇒ 00:07:36.570 Uttam Kumaran: Yeah, so this is gonna be where I learned about interviews, scorecards, And, like, the system around
64 00:07:36.890 ⇒ 00:07:38.809 Uttam Kumaran: Like, committee-based interviewing.
65 00:07:39.470 ⇒ 00:07:40.350 Sheshu Chandrasekar: Gotcha.
66 00:07:40.520 ⇒ 00:07:41.699 Sheshu Chandrasekar: That makes sense.
67 00:07:45.840 ⇒ 00:07:54.629 Sheshu Chandrasekar: Yeah, so I guess your question was, like, should we have, like, some sort of, like, system where it’s, like, sort of informal, but also formal recruiting process? Is that kind of, like.
68 00:07:54.760 ⇒ 00:07:55.950 Sheshu Chandrasekar: what I’m getting at here.
69 00:07:57.180 ⇒ 00:08:01.289 Uttam Kumaran: Yeah, I guess more is, like, That is our process today.
70 00:08:02.630 ⇒ 00:08:05.370 Sheshu Chandrasekar: But how do we improve that? How do we make it more…
71 00:08:05.980 ⇒ 00:08:11.460 Uttam Kumaran: More as, like, how is everybody as part of that know their place and what they’re expected of, you know?
72 00:08:11.790 ⇒ 00:08:12.710 Sheshu Chandrasekar: Gotcha.
73 00:08:13.650 ⇒ 00:08:15.930 Uttam Kumaran: Because I’m often the first and last person.
74 00:08:17.560 ⇒ 00:08:32.719 Uttam Kumaran: But everybody in the middle, like, our team… for example, not everybody in our company is good at interviewing, and so we need to have a system by which they understand, like, what’s expected of them when they’re dealing with candidates, and a place where they can also
75 00:08:33.299 ⇒ 00:08:36.420 Uttam Kumaran: They can… they can put in notes, and, like, ideally.
76 00:08:36.809 ⇒ 00:08:48.349 Uttam Kumaran: the scorecard approach, I think, is helpful because we can start to, have metrics associated with different things. And then, to do what we always do is you think about, like, how AI gets involved.
77 00:08:48.460 ⇒ 00:08:54.820 Uttam Kumaran: more than likely, I think AI will be, like, a fourth… Like, an additional interviewer.
78 00:08:55.030 ⇒ 00:09:00.240 Uttam Kumaran: And we’ll leverage AI to actually support people as well, on several metrics.
79 00:09:02.880 ⇒ 00:09:08.740 Uttam Kumaran: In order to just, like, again, continue to get, like… because we’ll… we’re trying to record all the transcripts and things like that, right? So…
80 00:09:09.580 ⇒ 00:09:16.950 Sheshu Chandrasekar: No, I think the scorecard approach is very, very useful because, like you said, the AI piece is very, very important in that sense, like.
81 00:09:17.010 ⇒ 00:09:30.270 Sheshu Chandrasekar: we can use all that data, and when I mean by data, I mean, like, the write-ups and the scores, right? And, like, kind of run it through AI and ask, like, hey, like, what do you think this per- is this person, like, suited for our culture and, like, the work that…
82 00:09:30.680 ⇒ 00:09:32.810 Sheshu Chandrasekar: we will give them. So, like, yeah, we can.
83 00:09:32.810 ⇒ 00:09:37.200 Uttam Kumaran: We almost create a work… we almost create, like, a hub around that candidate.
84 00:09:38.600 ⇒ 00:09:39.439 Sheshu Chandrasekar: Yeah, like, I’m.
85 00:09:39.440 ⇒ 00:09:42.650 Uttam Kumaran: Put in their resumes, any relevant docs, their LinkedIn.
86 00:09:42.760 ⇒ 00:09:47.250 Uttam Kumaran: as people fill out… as people… they go through the interview process, I’m sure they…
87 00:09:47.600 ⇒ 00:09:51.030 Uttam Kumaran: people are taking, like, we’re gonna… right now, we’re doing Notion, but…
88 00:09:51.170 ⇒ 00:09:57.629 Uttam Kumaran: You know, again, to take it one step further, a lot of our processes, we’re probably gonna end up moving to the platform, where, like, for example.
89 00:09:57.760 ⇒ 00:10:01.920 Uttam Kumaran: Hey, you’re assigned an interview with… Joe Schmo.
90 00:10:02.270 ⇒ 00:10:06.819 Uttam Kumaran: Your, your, your, your part one in the five-part interview series?
91 00:10:07.440 ⇒ 00:10:08.170 Uttam Kumaran: when you…
92 00:10:08.300 ⇒ 00:10:16.790 Uttam Kumaran: When you turn… when you go on the interview, open up this page, it’s not only gonna have an opportunity for you to put in free text, but it’s also gonna have an opportunity to fill out the…
93 00:10:17.010 ⇒ 00:10:21.080 Uttam Kumaran: Scorecard, and then you just hit submit when you’re done, and that’s… your part’s done.
94 00:10:23.080 ⇒ 00:10:24.750 Sheshu Chandrasekar: Yeah, no, that sounds…
95 00:10:25.620 ⇒ 00:10:32.220 Uttam Kumaran: I don’t… I think we’re, like, probably, like, a week away from having something like that, but I guess more of my point is, like.
96 00:10:33.150 ⇒ 00:10:40.390 Uttam Kumaran: we have to… we have to do the manual version before we can loop in, like, the AI team to develop, like, a more polished system around it.
97 00:10:40.900 ⇒ 00:10:47.200 Uttam Kumaran: So I just want to nail what, like, the core tenets of, like, a… this sort of scorecard-based process is gonna look like.
98 00:10:47.450 ⇒ 00:10:51.809 Uttam Kumaran: And this book, like, is, like, the canonical book for us.
99 00:10:51.910 ⇒ 00:10:53.549 Uttam Kumaran: Probably the best in the industry.
100 00:10:54.990 ⇒ 00:10:55.740 Sheshu Chandrasekar: Got it.
101 00:10:56.000 ⇒ 00:11:07.280 Sheshu Chandrasekar: Yeah, no, that makes a lot of sense. Yeah, like… like I was gonna say, like, even when I was at Deloitte, we had this thing called Business Chemistry, and it… it’s so weird. It’s like… it’s like Harry Potter, where they kind of sort you into houses, in a way.
102 00:11:07.750 ⇒ 00:11:08.820 Uttam Kumaran: Oh, interesting, okay.
103 00:11:08.820 ⇒ 00:11:24.139 Sheshu Chandrasekar: Yeah, so it’s like… like, you’re a pioneer, that means, like, you like to try a new tech. Driver means, like, kind of, like, the person that wants to, like, run things and take it. Integrator’s, like, including everyone in your team, and then I forgot the fourth one, but we can… we… I don’t wanna… I don’t wanna, like…
104 00:11:24.240 ⇒ 00:11:27.530 Sheshu Chandrasekar: Take exactly those, kind of, like.
105 00:11:27.810 ⇒ 00:11:30.940 Sheshu Chandrasekar: buckets, but I feel like we can make something of our own that
106 00:11:31.140 ⇒ 00:11:38.419 Sheshu Chandrasekar: really, really integrates into the Brainforge culture that we’re trying to build here. So, I do like the idea of scorecards, and
107 00:11:38.680 ⇒ 00:11:44.839 Sheshu Chandrasekar: and the AI, but maybe we can, like, figure out, like, what kind of person are they at the core? At the core, right? So, like.
108 00:11:44.960 ⇒ 00:11:55.379 Sheshu Chandrasekar: if they… that kind of helps us understand, like, not from a role base, like, not the CSO or the EPs, but we understand who they are as, like, a team member to the… to the organization itself. I think…
109 00:11:55.510 ⇒ 00:11:59.220 Sheshu Chandrasekar: That’d be very neat, but like you said, you’re right, I think we need to…
110 00:11:59.620 ⇒ 00:12:11.110 Sheshu Chandrasekar: first, like, operationalize the scorecards and the recruitment philosophy and everything that goes into it, and then figure out how AI can… how we can leverage AI in that sense. And I would be very interested to see, like.
111 00:12:11.660 ⇒ 00:12:20.199 Sheshu Chandrasekar: exactly what the consensus would be when we finish all the interview process for a candidate, and then what everyone thinks about this candidate. Like, would he…
112 00:12:20.640 ⇒ 00:12:31.030 Sheshu Chandrasekar: is there a consensus that, you know, this person is good for Brainforge, good for, like, future potential? Like, in that sense, like, will he grow with the company, or he or she grow with the company, or not?
113 00:12:31.220 ⇒ 00:12:42.630 Sheshu Chandrasekar: like, hearing all those things would be very interesting, like a… kind of like a discussion at the end on why we want to hire, like, a case, why we want to hire this person, because I think that would be very useful when it comes to the AI piece, like…
114 00:12:43.080 ⇒ 00:12:48.909 Sheshu Chandrasekar: Yeah, you could even think about it, like, it’s even the… it’s even around their first 30 days.
115 00:12:49.140 ⇒ 00:13:00.229 Uttam Kumaran: Right? Like, for example, some people don’t work out here, and ultimately, what I… I don’t blame them, like, 98% of the time. It’s usually us. Like, we messed something up in the interview process.
116 00:13:00.350 ⇒ 00:13:07.800 Uttam Kumaran: And so it’s helpful for us as a retrospective to go back and look at, like, where did… where did our interview process fail us, right?
117 00:13:08.400 ⇒ 00:13:15.269 Uttam Kumaran: Before, it used to be very obvious, like, I would interview someone with one expectation for their job, Robert would do another, and then…
118 00:13:15.450 ⇒ 00:13:21.399 Uttam Kumaran: both of us would be, like, not aligned at all. Now, it’s maybe a little bit more challenging, because for the most part, we have job descriptions, and, like.
119 00:13:21.640 ⇒ 00:13:29.159 Uttam Kumaran: But even still, like, there’s gonna be obvious gaps, and this is the first part of, like, a Brave Forged team member’s profile with the company, right?
120 00:13:29.340 ⇒ 00:13:47.910 Uttam Kumaran: So their profile and their kind of whole engagement with us starts in the interview, and then carries on with them throughout their entire time here. You can think of, like, a multitude of different processes and products we could develop to support people and, like, allow us to use AI to, like, further understand, like, how we can, you know, enable people to grow.
121 00:13:47.960 ⇒ 00:13:51.630 Uttam Kumaran: All of that is great context that we, like, need to have on file.
122 00:13:52.690 ⇒ 00:13:53.330 Sheshu Chandrasekar: Yeah.
123 00:13:53.660 ⇒ 00:13:55.149 Sheshu Chandrasekar: No, that makes a lot of sense, and…
124 00:13:55.150 ⇒ 00:13:58.309 Uttam Kumaran: It’s like, it’s the equivalent of a Customer 360, but, like, a team member.
125 00:13:58.930 ⇒ 00:13:59.590 Sheshu Chandrasekar: Yeah.
126 00:13:59.750 ⇒ 00:14:05.210 Sheshu Chandrasekar: No, I totally hear you. It’s kind of like doing discovery and figuring out if, you know, our, like, what they’re well-suited for.
127 00:14:05.210 ⇒ 00:14:08.919 Uttam Kumaran: I mean, I’m just not gonna… I’m not gonna be surprised if, like.
128 00:14:09.390 ⇒ 00:14:14.940 Uttam Kumaran: Most of what the humans are gonna do in this process is just, like, collect the contacts, and then…
129 00:14:15.090 ⇒ 00:14:21.230 Uttam Kumaran: we may offload a healthy portion of this to AI to actually make the final determination to do all those inputs.
130 00:14:21.660 ⇒ 00:14:29.369 Sheshu Chandrasekar: Because that’s what, ultimately, the scorecard does, is it, like, standardizes the inputs into a score, and then you get the score at the end, and you compare scores.
131 00:14:29.660 ⇒ 00:14:32.580 Uttam Kumaran: Right? So, humans is more the input collection.
132 00:14:33.020 ⇒ 00:14:35.719 Uttam Kumaran: And we have structured inputs.
133 00:14:36.050 ⇒ 00:14:39.750 Uttam Kumaran: we leverage free text to get, like, a bunch of it, and then we have AI sort of give
134 00:14:40.010 ⇒ 00:14:46.779 Uttam Kumaran: Probably do big pros and cons analysis, and then it comes to, like, me or whoever’s the hiring manager for, like, a final decision.
135 00:14:48.170 ⇒ 00:14:50.750 Sheshu Chandrasekar: Yeah, and I’d be very curious to see what other, like.
136 00:14:51.880 ⇒ 00:14:59.369 Sheshu Chandrasekar: I don’t want to say psychological, maybe psychological, like, his profile, in a way, like, figure out, like, what else we can get from it, right? Yes.
137 00:15:00.490 ⇒ 00:15:03.599 Uttam Kumaran: And I don’t know too much, I know it is possible.
138 00:15:03.600 ⇒ 00:15:06.760 Sheshu Chandrasekar: Because I’ve seen it, but I just don’t know what goes into that.
139 00:15:06.760 ⇒ 00:15:11.370 Uttam Kumaran: What I want to avoid is AI, like…
140 00:15:11.620 ⇒ 00:15:17.610 Uttam Kumaran: I don’t want to use AI in, like, areas of the interview process where it’s unnecessary, right? Like, I don’t want you to talk to an AI.
141 00:15:17.710 ⇒ 00:15:18.729 Uttam Kumaran: Like, I don’t want to recruit.
142 00:15:18.730 ⇒ 00:15:19.580 Sheshu Chandrasekar: Yeah.
143 00:15:19.580 ⇒ 00:15:22.520 Uttam Kumaran: To talk to AI. That’s stupid. That’s like the… that’s like the…
144 00:15:22.720 ⇒ 00:15:25.520 Uttam Kumaran: That’s, like, when… that’s just, like, dumb people…
145 00:15:25.810 ⇒ 00:15:34.929 Uttam Kumaran: trying to, like, understand how to use AI in a process. Actually, humans are extremely valuable. I want people to talk to people. It’s everything around that that, like, we should hand off.
146 00:15:35.150 ⇒ 00:15:40.390 Uttam Kumaran: like, having… we’re not… we’re not like JP Morgan interviewing, like, 100,000 people at any moment, and, like.
147 00:15:40.800 ⇒ 00:15:45.600 Uttam Kumaran: it’s like, I want, I want, actually, like, better pre-qualification.
148 00:15:45.890 ⇒ 00:15:52.170 Uttam Kumaran: Right, like, can we hand a resume and, like, a JD,
149 00:15:52.390 ⇒ 00:15:56.879 Uttam Kumaran: to an AI to be like, should we even move forward, given what we know now?
150 00:15:57.180 ⇒ 00:16:03.410 Uttam Kumaran: Then, I actually want each person in the interview process to get prepared. Like, they should get a pre-read.
151 00:16:03.530 ⇒ 00:16:15.119 Uttam Kumaran: with, like, here’s some questions you should ask, here’s about this person. Like, that way, most people just, they’re jumping on an interview, like, with, like, 3 minutes to go. Like, if you have to read one paper.
152 00:16:15.280 ⇒ 00:16:17.159 Uttam Kumaran: In that 3 minutes, read this.
153 00:16:18.070 ⇒ 00:16:20.370 Uttam Kumaran: And then it’s sort of all the recruitment ops.
154 00:16:20.830 ⇒ 00:16:25.839 Uttam Kumaran: I don’t want to hand the call to Naya, I don’t want to also hand the final decision to Naya, certainly.
155 00:16:27.450 ⇒ 00:16:29.360 Sheshu Chandrasekar: Yeah, no, totally.
156 00:16:29.360 ⇒ 00:16:29.890 Uttam Kumaran: Yeah.
157 00:16:29.890 ⇒ 00:16:33.030 Sheshu Chandrasekar: That’d be… that’d feel so impersonal, as well, if we have an AI tool.
158 00:16:33.030 ⇒ 00:16:38.330 Uttam Kumaran: No, I know, but dude, I’m telling you, that’s, like, what people are doing now. They’re so… they’re just stupid. They’re morons.
159 00:16:38.330 ⇒ 00:16:40.320 Sheshu Chandrasekar: No way, I didn’t know that.
160 00:16:40.320 ⇒ 00:16:48.040 Uttam Kumaran: JP Morgan, you’ll… if you go through, like, a lot of finance companies, you’ll end up just, like, talking to an AI bot that’ll, you know…
161 00:16:48.430 ⇒ 00:16:54.089 Uttam Kumaran: Ashwini was telling me… I wish Sweeney or someone on our team was telling me about it. I did one of them years ago, but it was…
162 00:16:54.850 ⇒ 00:16:57.309 Uttam Kumaran: Yeah, it seems like… it’s horrible.
163 00:16:57.310 ⇒ 00:16:57.740 Sheshu Chandrasekar: Yes.
164 00:16:57.740 ⇒ 00:16:59.919 Uttam Kumaran: super dystopian.
165 00:16:59.920 ⇒ 00:17:06.819 Clarence Stone: There’s social media videos about, like, the AI agents bugging out.
166 00:17:06.829 ⇒ 00:17:08.709 Sheshu Chandrasekar: Oh, really? I did not know that at all.
167 00:17:08.710 ⇒ 00:17:09.199 Clarence Stone: what we’re.
168 00:17:09.200 ⇒ 00:17:10.040 Sheshu Chandrasekar: Crazy.
169 00:17:10.690 ⇒ 00:17:11.550 Sheshu Chandrasekar: Wow.
170 00:17:11.650 ⇒ 00:17:16.000 Sheshu Chandrasekar: Well, yeah, let’s not go on that path, then.
171 00:17:16.250 ⇒ 00:17:20.990 Sheshu Chandrasekar: Yeah, we’re not there yet, I feel, to end. It just seems, like, very inhuman.
172 00:17:21.280 ⇒ 00:17:22.260 Sheshu Chandrasekar: So…
173 00:17:23.230 ⇒ 00:17:32.179 Sheshu Chandrasekar: Cool, I guess, like, the next steps regarding recruitment would be just kind of, like, figuring out how to operationalize this, right? Like, from a manual process, like, create templates.
174 00:17:32.360 ⇒ 00:17:34.800 Uttam Kumaran: Like, when we have a new candidate inbound, like.
175 00:17:34.800 ⇒ 00:17:46.109 Sheshu Chandrasekar: maybe create, like, a simple, like, folder in Google Drive and have people, like, be the point of contact for each of the interview process. But I think the only thing we would want to clarify here is, like.
176 00:17:46.780 ⇒ 00:17:50.069 Sheshu Chandrasekar: Like, if a candidate’s technical.
177 00:17:50.240 ⇒ 00:17:59.639 Sheshu Chandrasekar: we would want a different template versus someone that’s not technical, right? So, maybe along those lines, we need to figure… figure out those intricacies, but apart from that, I think
178 00:17:59.910 ⇒ 00:18:05.800 Sheshu Chandrasekar: I think we have everything we need right now to just kind of operationalize this and figure out a standardized process, and then…
179 00:18:06.260 ⇒ 00:18:12.010 Sheshu Chandrasekar: Run it and see what happens, as we actually put this system out live and…
180 00:18:12.400 ⇒ 00:18:16.740 Sheshu Chandrasekar: And see what works and what doesn’t, and then see how AI can come in and help us out here.
181 00:18:17.750 ⇒ 00:18:26.549 Uttam Kumaran: Yeah, so what do you guys think about trying to read this… this book, and, like, maybe trying to crush it in, like, the next two weeks? I can get it for you, Shash, and I have one, I can get one.
182 00:18:26.820 ⇒ 00:18:28.819 Uttam Kumaran: for you, Rico, and for Eliza.
183 00:18:29.070 ⇒ 00:18:34.460 Uttam Kumaran: This’ll probably be the only book I’ll ever recommend on recruiting, but it’ll be the only book you’ll have to.
184 00:18:34.810 ⇒ 00:18:38.770 Uttam Kumaran: We should just basically doing soccer what it says.
185 00:18:42.500 ⇒ 00:18:47.469 Sheshu Chandrasekar: I’d be down to read it, but, I mean, is there, like, a Sparks version sometimes? Or is there not?
186 00:18:48.230 ⇒ 00:18:53.599 Uttam Kumaran: There is, like, there’s, like, a couple of… there’s a couple of chapters that are, like, the most…
187 00:18:54.270 ⇒ 00:19:04.039 Uttam Kumaran: urgent for us to read, like, the first couple chapters, so I can even earmark a couple chapters, but I would rather us just read it, it’s gonna save me. Otherwise, I’m gonna have to explain.
188 00:19:04.210 ⇒ 00:19:11.239 Uttam Kumaran: And they wrote a whole book about, like, this concept that I’m trying to explain, so we might as well just take a few hours from reading it.
189 00:19:11.860 ⇒ 00:19:12.230 Sheshu Chandrasekar: Yeah.
190 00:19:12.230 ⇒ 00:19:16.039 Uttam Kumaran: And Twitter page is probably the core… the core net of it is, like, probably, like, 100 pages.
191 00:19:16.840 ⇒ 00:19:17.659 Sheshu Chandrasekar: Sweet, tell us.
192 00:19:17.660 ⇒ 00:19:19.349 Uttam Kumaran: And most of it is just anecdotes.
193 00:19:20.220 ⇒ 00:19:23.949 Clarence Stone: Am I looking at the wrong client? I don’t see a book on updates.
194 00:19:24.600 ⇒ 00:19:28.119 Uttam Kumaran: Oh, no, no, no, I’m… I sent, this is a…
195 00:19:28.120 ⇒ 00:19:28.730 Clarence Stone: No.
196 00:19:28.730 ⇒ 00:19:30.070 Uttam Kumaran: a book I proposed.
197 00:19:32.060 ⇒ 00:19:32.549 Clarence Stone: What’.
198 00:19:32.550 ⇒ 00:19:33.869 Uttam Kumaran: I sent it to the ops channel.
199 00:19:33.870 ⇒ 00:19:34.840 Clarence Stone: Ops chat. It’s called…
200 00:19:34.840 ⇒ 00:19:35.750 Uttam Kumaran: Who?
201 00:19:37.000 ⇒ 00:19:39.530 Uttam Kumaran: It’s something called WHO, a method for hiring.
202 00:19:40.650 ⇒ 00:19:41.990 Uttam Kumaran: I’ve read this great book.
203 00:19:42.990 ⇒ 00:19:48.409 Uttam Kumaran: Yeah, like, years ago, when I first started getting involved in the hire process when I was young, I, like.
204 00:19:49.110 ⇒ 00:19:52.009 Uttam Kumaran: I did do what I usually do, which is I’m like, I googled
205 00:19:52.380 ⇒ 00:20:01.280 Uttam Kumaran: what’s the number one book I’m hiring? And this book showed up in the top 10 list, and I said, okay, this is it. I have one chance to get good at this, so I’ll just copy everything as they said.
206 00:20:02.120 ⇒ 00:20:05.800 Uttam Kumaran: Okay, cool. Sorry, I kind of derailed this, we can move on.
207 00:20:07.460 ⇒ 00:20:17.680 Sheshu Chandrasekar: Sweet. Yeah, I’m… I’m totally in on reading a book. I mean, I love reading, so I can definitely get on it, but I feel like I read too much, so I’m just like, wait, not another one to this channel.
208 00:20:21.550 ⇒ 00:20:22.700 Sheshu Chandrasekar: Cool,
209 00:20:23.100 ⇒ 00:20:32.899 Sheshu Chandrasekar: not for the fun part, the notion restructuring plan. So I think, Utam, we talked about it last week, and Clarence, just for context, like.
210 00:20:33.020 ⇒ 00:20:42.640 Sheshu Chandrasekar: I was on the Notion homepage, and the first thing, as someone that just joined, it was… it was a lot. It was a cognitive overload, in a way.
211 00:20:42.720 ⇒ 00:20:44.160 Clarence Stone: Yeah. Except for me.
212 00:20:44.180 ⇒ 00:20:48.420 Sheshu Chandrasekar: Even talking to Greg, like.
213 00:20:48.810 ⇒ 00:21:04.170 Sheshu Chandrasekar: He likes the homepage, but he also was in consensus that, you know, it is a lot to be thrown out. So, I think this is a very tentative plan I’ve put together for the next two weeks of what I want to do, that homepage.
214 00:21:04.280 ⇒ 00:21:15.190 Sheshu Chandrasekar: So, I want to start with Phase 1 here. And Phase 1 is really just auditing. Really just going through each document, each, kind of, like, binder, and figure out, you know.
215 00:21:15.580 ⇒ 00:21:24.680 Sheshu Chandrasekar: what files, or what documents, should be on this homepage, and what shouldn’t be. And we’re gonna categorize into 3 buckets here, so it’s a keep.
216 00:21:24.990 ⇒ 00:21:26.949 Sheshu Chandrasekar: You know, it’s basically, like.
217 00:21:27.210 ⇒ 00:21:38.149 Sheshu Chandrasekar: any documents that anyone needs at any point, right? It’s kind of like understanding the company philosophy, so that document will be on there. Anything that’s very much, like, global, in that sense, like.
218 00:21:38.700 ⇒ 00:21:42.670 Sheshu Chandrasekar: And that’s… Hot Brain Force related?
219 00:21:42.850 ⇒ 00:21:53.540 Sheshu Chandrasekar: on a broader… broader level will be on there, and then there’ll be role-specific ones, right? We’ll move certain documents that are very role-specific for CSOs, EPs, and SLs.
220 00:21:53.700 ⇒ 00:21:55.860 Sheshu Chandrasekar: So that’s, like, another…
221 00:21:56.330 ⇒ 00:22:05.180 Sheshu Chandrasekar: kind of bucket I want to categorize into, like, each document or file into, and then there’ll be the archive. I’ve noticed, even in the homepages, there’s a lot of
222 00:22:05.340 ⇒ 00:22:10.200 Sheshu Chandrasekar: Internal processes that are on there, but also there are a lot of incomplete drafts.
223 00:22:10.360 ⇒ 00:22:20.879 Sheshu Chandrasekar: some abandoned projects, some sort of zombie processes, and that way, like, I see someone, like, writing mid-dot, and then they just abandoned it, and it hasn’t been opened ever since, so…
224 00:22:21.240 ⇒ 00:22:37.589 Sheshu Chandrasekar: I want to do a clear audit, and, you know, by, like, day 3, give you guys, like, a spreadsheet of saying, like, okay, these are documents we’re gonna keep, things that we’re gonna move into, like, CSO, EPSL roles, and then everything else we’re archiving that no longer should be on the homepage.
225 00:22:39.240 ⇒ 00:22:43.939 Sheshu Chandrasekar: And yeah, that’s, like, the first phase, and I just want to stop there, see if, you guys have any thoughts here.
226 00:22:47.640 ⇒ 00:22:52.389 Uttam Kumaran: My only, piece would be, like, how are you using AI to, like, do the audit?
227 00:22:52.660 ⇒ 00:22:54.359 Uttam Kumaran: How big do you be on it?
228 00:22:55.880 ⇒ 00:22:57.500 Sheshu Chandrasekar: Oh,
229 00:22:58.490 ⇒ 00:23:10.450 Sheshu Chandrasekar: you know, I haven’t really thought about using AI, I was just gonna go through each one just to see, like, hey, like, what’s important and what’s not. Like, I was gonna ask Rico to help me out a little bit here, too, but I was gonna go through each one and see how…
230 00:23:10.890 ⇒ 00:23:13.909 Sheshu Chandrasekar: like, Important each document is.
231 00:23:14.030 ⇒ 00:23:14.850 Sheshu Chandrasekar: But…
232 00:23:14.850 ⇒ 00:23:21.629 Uttam Kumaran: Yeah, my suggestion… my suggestion would be… To take your plan.
233 00:23:21.880 ⇒ 00:23:25.500 Uttam Kumaran: and then take the documentation for the Notion MCP,
234 00:23:25.640 ⇒ 00:23:30.730 Uttam Kumaran: And then work with AI to give you suggestions on how to use AI to help you speed this up.
235 00:23:31.250 ⇒ 00:23:38.980 Uttam Kumaran: For example, one clear way is you can go tell Notion MCP To create you a…
236 00:23:39.190 ⇒ 00:23:45.699 Uttam Kumaran: a CSV with every single page, and the last time it was created or updated.
237 00:23:46.110 ⇒ 00:23:53.730 Uttam Kumaran: Right. That’s a good way. That’s one thing you can do. Second is, you can… basically, like.
238 00:23:54.180 ⇒ 00:23:57.830 Uttam Kumaran: You can kind of, like, help… immediately, at that point, you can also say, like.
239 00:23:58.040 ⇒ 00:24:07.989 Uttam Kumaran: just delete any blank pages, right? Or… so there’s also part of it where you should consider using the MCP for the actual, like, execution of the actions post.
240 00:24:08.170 ⇒ 00:24:09.560 Uttam Kumaran: Audit and triage.
241 00:24:09.820 ⇒ 00:24:18.090 Uttam Kumaran: Right, so audit is sort of, like, surface all the information. Triage is gonna be, like, work with me, Clarence, to, like, get decisions made. And then you’ll have to go execute, like.
242 00:24:18.360 ⇒ 00:24:22.259 Uttam Kumaran: And clicking through Notion to delete 50 pages is gonna make
243 00:24:22.840 ⇒ 00:24:24.780 Uttam Kumaran: You want to blow your brains out.
244 00:24:25.070 ⇒ 00:24:31.780 Uttam Kumaran: And it’s a bad use of company time, so I would just suggest, like, seeing if you can get the Notion MCP working in cursor.
245 00:24:32.000 ⇒ 00:24:36.549 Uttam Kumaran: If you need help, let me know, but, I think Gabe and…
246 00:24:36.770 ⇒ 00:24:42.430 Uttam Kumaran: Actually, anyone on the AI team should be able to help you get cursor and notion set up, actually, so I would just call that.
247 00:24:43.560 ⇒ 00:24:47.230 Sheshu Chandrasekar: Okay. Yeah, I’ll follow up with Gabe or someone else on the team.
248 00:24:47.640 ⇒ 00:24:49.289 Uttam Kumaran: Cool, I think Gabe’s out today, but yeah.
249 00:24:49.290 ⇒ 00:24:50.220 Clarence Stone: Good point.
250 00:24:50.690 ⇒ 00:24:51.509 Uttam Kumaran: Yeah, but…
251 00:24:51.840 ⇒ 00:25:03.469 Clarence Stone: Utam, this goes back to your point where everything’s this cursor, because, like, my most valuable way of looking through this Notion ended up being using Notion’s AI search function.
252 00:25:03.950 ⇒ 00:25:05.760 Clarence Stone: And…
253 00:25:05.760 ⇒ 00:25:09.310 Uttam Kumaran: And just favoriting the ones that I use most often.
254 00:25:11.010 ⇒ 00:25:21.490 Clarence Stone: So, maybe, Sesshu, that, like, on that homepage, we just provide some quick instructions on how they can leverage AI to find the exact document they’re looking for instead of trying to click around as well.
255 00:25:22.630 ⇒ 00:25:25.570 Sheshu Chandrasekar: Yeah, and that kind of brings to my next point.
256 00:25:26.510 ⇒ 00:25:40.699 Sheshu Chandrasekar: So, actually, that’s actually the third point. But, in the Phase 3 right here, like, I know I jumped Phase 2, but Phase 3, basically, we kind of create tags around each document, so when people want to search something, right, like, if…
257 00:25:40.820 ⇒ 00:25:54.790 Sheshu Chandrasekar: if an SL wants, like, a weekly, like, update deck, creating meta tags around that document can be very useful, so they don’t have to, like, remember the exact title of a deck of some sort. They use that metadata to kind of
258 00:25:55.090 ⇒ 00:25:56.109 Sheshu Chandrasekar: Pull it up.
259 00:25:56.280 ⇒ 00:26:07.809 Sheshu Chandrasekar: I don’t know if that’s useful, but something I thought that was very neat that I was reading up on, that you can attach a metadata and, you know, it’ll have context around a certain document when you type in the search.
260 00:26:11.300 ⇒ 00:26:11.930 Uttam Kumaran: Cool.
261 00:26:13.290 ⇒ 00:26:14.100 Uttam Kumaran: It’s coming in.
262 00:26:15.130 ⇒ 00:26:15.770 Sheshu Chandrasekar: Sweet.
263 00:26:16.430 ⇒ 00:26:28.199 Sheshu Chandrasekar: Yeah, and then going back to Phase 2, I know we were talking about this last week, you wanted to include Gabe in there, because we wanted to kind of have an MCP server up and running on the Notion side.
264 00:26:28.340 ⇒ 00:26:38.209 Sheshu Chandrasekar: So I thought one of the main things that would be very important is to create some sort of, like, hierarchy or an architecture of some sort of how the documents kind of interact with each other, right?
265 00:26:38.560 ⇒ 00:26:49.670 Sheshu Chandrasekar: Because a lot of them kind of has, like, what… like, there will be a document that says recruiting, and then under it, there’ll be, like, sub-documents of some sort, right? So we would want to create, like, some sort of hierarchy
266 00:26:50.010 ⇒ 00:27:05.639 Sheshu Chandrasekar: like a visual architecture that helps Gabe out in the future when he wants to fully deploy the MCP, or if someone else wants to get onboarded on the AI team or the platforms team, they can understand, like, oh, this is how the document structure works. So I’m kind of thinking a little bit ahead here.
267 00:27:05.800 ⇒ 00:27:08.400 Sheshu Chandrasekar: But, yeah, just wanted to figure out, like, if…
268 00:27:08.590 ⇒ 00:27:25.609 Sheshu Chandrasekar: that would be a good idea, but also, in this phase two, like, kind of creating, like, a day one onboarding document, like, what is needed when someone joins day one, and what documents they should read, and if they’re an SL, like, or EP, or a CSO, or even the internal ops team, like.
269 00:27:25.710 ⇒ 00:27:35.999 Sheshu Chandrasekar: which documents and path that they should take. It’s kind of like choosing your own journey, in a way. So, that’s what Facer’s all about, and yeah, again, I’ll stop here and see.
270 00:27:36.290 ⇒ 00:27:37.670 Sheshu Chandrasekar: collect any thoughts here.
271 00:27:43.780 ⇒ 00:27:50.560 Uttam Kumaran: It’s a little, yeah, I feel… I feel good with it.
272 00:27:52.160 ⇒ 00:27:55.490 Uttam Kumaran: I think once I see, like, a first version of stuff, I can give…
273 00:27:55.820 ⇒ 00:28:01.690 Uttam Kumaran: some probably more specific feedback, but I think you guys are on the right track. In particular, as I mentioned, like, I’m thinking about
274 00:28:01.800 ⇒ 00:28:03.070 Uttam Kumaran: Day one.
275 00:28:03.610 ⇒ 00:28:08.129 Uttam Kumaran: Day 7… Day 15, day 30, right? Like…
276 00:28:08.960 ⇒ 00:28:14.079 Uttam Kumaran: The first part of when people onboard is when it’s, like, the most shop, and that’s, like, what we want to focus on.
277 00:28:14.730 ⇒ 00:28:15.770 Uttam Kumaran: Yeah.
278 00:28:15.770 ⇒ 00:28:17.170 Sheshu Chandrasekar: It’s a reduced Goodnight.
279 00:28:17.710 ⇒ 00:28:21.550 Uttam Kumaran: Yeah, and… I also think about, like.
280 00:28:23.190 ⇒ 00:28:26.930 Uttam Kumaran: maybe we have people install Cursor on day one, and they’re starting to use
281 00:28:27.100 ⇒ 00:28:30.179 Uttam Kumaran: use it to, like, interact with Notion and, like, the Vault.
282 00:28:30.550 ⇒ 00:28:36.149 Uttam Kumaran: in our playbooks, and so I would probably encourage you guys to do the same, like.
283 00:28:36.300 ⇒ 00:28:38.650 Uttam Kumaran: Try to use cursor for as much as possible.
284 00:28:38.910 ⇒ 00:28:41.750 Uttam Kumaran: even interacting with Notion if you want.
285 00:28:42.940 ⇒ 00:28:45.599 Uttam Kumaran: But I would like that to be part of, like, our first…
286 00:28:46.160 ⇒ 00:28:49.840 Uttam Kumaran: You know, day one onboarding for every role, basically.
287 00:28:50.930 ⇒ 00:28:54.590 Uttam Kumaran: It’s gonna start to be basically the hub for doing a lot of work around here.
288 00:28:56.210 ⇒ 00:28:58.880 Sheshu Chandrasekar: Yeah, and I guess to just kind of…
289 00:28:59.570 ⇒ 00:29:08.100 Sheshu Chandrasekar: like, piggyback on that. For Gabe, would it be very useful if we created some sort of visual diagram, like, on how the documents interact, or is that…
290 00:29:08.340 ⇒ 00:29:09.220 Sheshu Chandrasekar: Is that pretty, like…
291 00:29:09.220 ⇒ 00:29:09.730 Uttam Kumaran: Yeah.
292 00:29:10.560 ⇒ 00:29:15.240 Uttam Kumaran: I… I think it’s… I think, ultimately, like, start with yourself.
293 00:29:15.530 ⇒ 00:29:18.610 Uttam Kumaran: Like, create anything that helps you understand.
294 00:29:18.960 ⇒ 00:29:24.070 Uttam Kumaran: And then… There’s a couple other stakeholders, right? Like… there’s me.
295 00:29:24.460 ⇒ 00:29:29.090 Uttam Kumaran: But I… I’m sort of not a good user of all this. Like, I’m probably the best, like.
296 00:29:29.770 ⇒ 00:29:33.860 Uttam Kumaran: I’m probably the best thought partner, and, like, I have to… I have to approve, but…
297 00:29:34.120 ⇒ 00:29:37.870 Uttam Kumaran: I’m not the best user, then I would also suggest, like.
298 00:29:38.100 ⇒ 00:29:44.509 Uttam Kumaran: maybe considering, like, partnering with, yeah, with Greg or another person internally who’s, like, gonna be a heavy user.
299 00:29:44.650 ⇒ 00:29:46.480 Uttam Kumaran: Getting their sign-off as well.
300 00:29:46.740 ⇒ 00:29:56.139 Uttam Kumaran: But first, I would start by just creating anything that you need, like, let’s say you were… put yourself in my shoes, what would you want to see? Start there.
301 00:29:58.260 ⇒ 00:29:58.880 Sheshu Chandrasekar: Perfect.
302 00:29:59.120 ⇒ 00:30:05.559 Sheshu Chandrasekar: Okay, that makes a lot of sense. I probably will be bugging you a lot then, in a way, to see what you think about it.
303 00:30:06.230 ⇒ 00:30:08.980 Uttam Kumaran: That’s fine, dude, I work for you, so I’m here.
304 00:30:09.710 ⇒ 00:30:10.300 Sheshu Chandrasekar: Sweet.
305 00:30:10.920 ⇒ 00:30:12.190 Sheshu Chandrasekar: Okay. Yeah, so…
306 00:30:12.190 ⇒ 00:30:28.260 Clarence Stone: Yeah, now that you’ve brought that up, something comes to mind. I would build this with end user in mind. I think the biggest demand we have on the project teams is that we’re asking CSOs and EPs to make sure that they’re maintaining up-to-date documentation on Notion, right?
307 00:30:28.260 ⇒ 00:30:35.169 Clarence Stone: That’s probably gonna be the most annoying heavy lift for them on a weekly basis. So, I…
308 00:30:35.190 ⇒ 00:30:46.539 Clarence Stone: you know, I would interview some of them and see what the best way to update their notion would be. Like, if you were to ask me, my ideal is I, you know, come out of a meeting.
309 00:30:46.790 ⇒ 00:31:00.570 Clarence Stone: And, the meeting notes system already pulls out everything I need to update, and then I type in all the other things that it didn’t catch, and I say, okay, send this to the Notion from Cursor.
310 00:31:01.040 ⇒ 00:31:11.250 Clarence Stone: You know, that’s how… one way to do it, but I’m not sure, you know, how everyone would want it done. But think about that end-to-end for your core user, and the most demanding Notion tasks.
311 00:31:12.970 ⇒ 00:31:14.560 Sheshu Chandrasekar: Got it. That makes sense.
312 00:31:15.080 ⇒ 00:31:21.549 Sheshu Chandrasekar: So anyone, basically, I can interview and just… Get that… get that feedback.
313 00:31:22.930 ⇒ 00:31:28.649 Clarence Stone: Yeah, CSOs and EPs, I think, would probably be your biggest consumers. FLs too, I guess, your project team.
314 00:31:29.690 ⇒ 00:31:32.230 Uttam Kumaran: And you don’t need to get… you don’t need to get feedback from all of them.
315 00:31:32.470 ⇒ 00:31:35.779 Uttam Kumaran: And not all of them we’re gonna have… are gonna have great feedback for you.
316 00:31:36.170 ⇒ 00:31:42.810 Uttam Kumaran: Right? So, Greg is interesting, because he’s pretty opinionated. Some of them are not gonna… they’re gonna be like.
317 00:31:42.950 ⇒ 00:31:53.020 Uttam Kumaran: never have thought of information architecture before, so just keeping you away. You don’t, don’t, like, expect everybody to be, like, super pumped to talk about that shit.
318 00:31:53.290 ⇒ 00:31:54.890 Uttam Kumaran: Sweet, yeah.
319 00:31:56.080 ⇒ 00:32:01.780 Sheshu Chandrasekar: I mean, there is a way to see who uses the Notion the most, right? That’s like an activity tracker of some sort?
320 00:32:02.480 ⇒ 00:32:07.479 Uttam Kumaran: I would like you to figure that out, how we get that data out, I guess. Yeah.
321 00:32:07.480 ⇒ 00:32:11.520 Sheshu Chandrasekar: Yeah, that is. Yeah, let me figure it out, and I’ll get back to you on it.
322 00:32:13.630 ⇒ 00:32:16.960 Sheshu Chandrasekar: Cool, I think we briefly touched on Phase 3.
323 00:32:17.120 ⇒ 00:32:23.720 Sheshu Chandrasekar: So… Yeah, I think, essentially, Phase 3 is just kind of, like, the launch,
324 00:32:24.760 ⇒ 00:32:30.530 Sheshu Chandrasekar: And kind of, like, creating some property tags around each document, the metadata tags I was referring to, and…
325 00:32:30.660 ⇒ 00:32:34.039 Sheshu Chandrasekar: Kind of building that front-end experience, which is basically, you know.
326 00:32:34.160 ⇒ 00:32:44.370 Sheshu Chandrasekar: the Notion homepage that we have, but make it a little bit more cleaner, and then kind of roll it out, hopefully by, you know, day 10 of some sort, and kind of
327 00:32:44.670 ⇒ 00:33:04.039 Sheshu Chandrasekar: get some feedback, some initial feedback to see what’s working, and I think what I would do is kind of… now that, Clarence, you mentioned that EPs and CSOs will be using this the most, like, kind of follow up with them, like, a couple days after, and be like, hey, what do you think about the current Notion homepage revamp, like, and kind of collect feedback and see where we can improve on it.
328 00:33:08.190 ⇒ 00:33:09.620 Clarence Stone: Yeah, that makes sense.
329 00:33:11.170 ⇒ 00:33:11.780 Sheshu Chandrasekar: Sweet.
330 00:33:12.030 ⇒ 00:33:19.640 Sheshu Chandrasekar: And now for the unified intake rollout. So the unified intake rollout’s kind of helped Rico and Eliza kind of
331 00:33:19.950 ⇒ 00:33:23.870 Sheshu Chandrasekar: eliminate ad hoc DMs, in essence, so…
332 00:33:24.990 ⇒ 00:33:31.089 Sheshu Chandrasekar: I’m still… I’m gonna work with Eliza a little bit tomorrow on this. I’m gonna try to identify, kind of, some sort of…
333 00:33:32.070 ⇒ 00:33:41.919 Sheshu Chandrasekar: I want to go through each… I want to go through the Slack, like, conversations and questions that they get on a frequent basis, and see which processes we can automatically
334 00:33:42.280 ⇒ 00:33:55.379 Sheshu Chandrasekar: enroll in the intake process, so if they ever have, like, a question, all they have to do is select, like, black… backslash, operations, and then there’d be, like, a form that pulls up, right? And it helps out understanding, like, okay.
335 00:33:55.600 ⇒ 00:33:58.479 Sheshu Chandrasekar: what do you need help with? And then, as soon as
336 00:33:58.750 ⇒ 00:34:15.090 Sheshu Chandrasekar: that form is filled out, it gets directed to linear, and then we can kind of auto-assign to either Rico, me, or Eliza. So that’s kind of the entire idea of the unified intake rollout. But right now, the first thing I need to do, personally, is go through the Slack conversations, kind of figure out, like, the top 10
337 00:34:15.270 ⇒ 00:34:23.290 Sheshu Chandrasekar: Like, biggest asks, and figure out, like, how do… how do we create a process around that when we roll out the form, when they, you know.
338 00:34:23.719 ⇒ 00:34:26.300 Sheshu Chandrasekar: kind of invoke the Slack… Slack bot here.
339 00:34:27.030 ⇒ 00:34:31.280 Sheshu Chandrasekar: Yeah, I’m gonna stop here, see if you guys have any thoughts here.
340 00:34:35.260 ⇒ 00:34:37.960 Uttam Kumaran: I, I think this is great as well.
341 00:34:40.090 ⇒ 00:34:45.539 Uttam Kumaran: My suggest is, like, for Slack history analysis, We’ve already ingested
342 00:34:45.730 ⇒ 00:34:48.869 Uttam Kumaran: And we’re continuing to ingest a ton of Slack data.
343 00:34:48.989 ⇒ 00:34:53.070 Uttam Kumaran: Casey can help you show where this information is. It’s in RHEL.
344 00:34:53.750 ⇒ 00:34:55.720 Uttam Kumaran: Which is a BI tool that we use.
345 00:34:56.090 ⇒ 00:34:56.840 Uttam Kumaran: Okay.
346 00:34:56.840 ⇒ 00:34:57.260 Sheshu Chandrasekar: Sweet.
347 00:34:57.300 ⇒ 00:35:01.609 Uttam Kumaran: So, additionally, we already have a lot of this data ingested.
348 00:35:01.910 ⇒ 00:35:06.510 Uttam Kumaran: And I would suggest… my sort of expectation is that
349 00:35:06.770 ⇒ 00:35:11.600 Uttam Kumaran: The audit that you produce leverages some of that data to sort of prove,
350 00:35:12.020 ⇒ 00:35:15.720 Uttam Kumaran: Prove, like, basically what questions are being asked.
351 00:35:16.210 ⇒ 00:35:20.500 Uttam Kumaran: In addition, I kind of hope that, like, we can start to use that Slack dashboard
352 00:35:21.290 ⇒ 00:35:23.700 Uttam Kumaran: On a weekly or monthly basis, to kind of show
353 00:35:24.160 ⇒ 00:35:28.029 Uttam Kumaran: Like, to kind of talk through, like, what kind of questions are being asked.
354 00:35:28.290 ⇒ 00:35:31.029 Uttam Kumaran: And then also, I want to start to see, like.
355 00:35:31.680 ⇒ 00:35:37.969 Uttam Kumaran: overall, a lot of… a lot of our job will also be able to see that more people are using Slack, you know, like, versus just a few of us.
356 00:35:38.290 ⇒ 00:35:42.550 Uttam Kumaran: So for the folks not using Slack, why aren’t they using it, things like that.
357 00:35:43.020 ⇒ 00:35:51.030 Uttam Kumaran: Yeah, I… if you… I think if you talk to a… Casey…
358 00:35:51.200 ⇒ 00:35:53.210 Uttam Kumaran: It’ll show you where that noise.
359 00:35:54.090 ⇒ 00:35:58.029 Sheshu Chandrasekar: Sweet. I will definitely follow up with Casey tomorrow and get that data.
360 00:35:58.250 ⇒ 00:36:00.079 Sheshu Chandrasekar: Because that’d be very useful for me.
361 00:36:00.230 ⇒ 00:36:06.369 Sheshu Chandrasekar: To understand, like, what’s… what are the heavy-hitter questions, and figure out… How to design that form.
362 00:36:06.760 ⇒ 00:36:15.299 Uttam Kumaran: Yeah, and then, like, this is something that we’re gonna, like, I really want to see, like, what people are talking about in Slack, and we’re gonna start to categorize, like.
363 00:36:15.590 ⇒ 00:36:21.409 Uttam Kumaran: any type of… every question is a good example of, like, I want to basically have a question category sort of taxonomy there.
364 00:36:23.780 ⇒ 00:36:29.099 Uttam Kumaran: that way, like, yeah, part of this is, like, you kind of take this again, like, one thing I’m gonna help…
365 00:36:29.420 ⇒ 00:36:33.980 Uttam Kumaran: you with this sort of seeing, like, how this gets into OpenAI world?
366 00:36:34.380 ⇒ 00:36:37.389 Uttam Kumaran: Primarily here, if you think about, like.
367 00:36:37.580 ⇒ 00:36:40.519 Uttam Kumaran: Once you start to have questions.
368 00:36:40.740 ⇒ 00:36:45.559 Uttam Kumaran: Ai can start to actually predict, like, in what way it should answer, right?
369 00:36:45.690 ⇒ 00:36:48.539 Uttam Kumaran: So as you start to build the routing matrix.
370 00:36:49.090 ⇒ 00:36:53.369 Uttam Kumaran: We’ll… we will potentially, in the future hand at the AI.
371 00:36:53.640 ⇒ 00:36:55.349 Uttam Kumaran: To start to auto-reply.
372 00:36:55.610 ⇒ 00:37:00.210 Uttam Kumaran: Given it has high confidence in where it should be routing the question to.
373 00:37:00.530 ⇒ 00:37:05.480 Uttam Kumaran: Additionally, we go one step further, Some people may be talking.
374 00:37:05.670 ⇒ 00:37:09.990 Uttam Kumaran: In a channel, and maybe not have framed the question well.
375 00:37:10.130 ⇒ 00:37:14.230 Uttam Kumaran: And AI can automatically take a look at that conversation, and then
376 00:37:14.760 ⇒ 00:37:17.789 Uttam Kumaran: flag to one of us, hey, I think I can reply with some help.
377 00:37:18.260 ⇒ 00:37:20.470 Uttam Kumaran: Would you like me to reply? Right?
378 00:37:21.110 ⇒ 00:37:29.820 Uttam Kumaran: So, it’ll… we’ll move… AI will start to become more, proactive in its use here, like… basically auto-replying.
379 00:37:29.980 ⇒ 00:37:37.130 Uttam Kumaran: Versus, like, having to wait to prompt it. But that’s, like, the long-term vision. Sort of something I wanted to do last year, but
380 00:37:37.880 ⇒ 00:37:39.819 Uttam Kumaran: Just got busy as usual, so…
381 00:37:41.110 ⇒ 00:37:46.759 Sheshu Chandrasekar: No, that’s very neat. I didn’t think about it that way, actually, so that’d be very exciting to work on.
382 00:37:47.280 ⇒ 00:37:50.750 Sheshu Chandrasekar: Cool, when I… when I work on the routing matrix, I’ll keep that in.
383 00:37:50.880 ⇒ 00:37:52.279 Sheshu Chandrasekar: How we design it.
384 00:37:55.090 ⇒ 00:37:59.279 Sheshu Chandrasekar: Sweet. Yeah, Phase 2 is just very much, like.
385 00:37:59.620 ⇒ 00:38:07.710 Sheshu Chandrasekar: building it in linear, like, I’ll work closely with Rico and Eliza and myself, and kind of build it out, the routing logic a little bit.
386 00:38:07.860 ⇒ 00:38:11.629 Sheshu Chandrasekar: Slack bot. I think…
387 00:38:14.620 ⇒ 00:38:18.840 Sheshu Chandrasekar: bots, if I’m correct, or if someone… if someone else is better, then I can kind of reach out to them.
388 00:38:18.840 ⇒ 00:38:23.480 Uttam Kumaran: Yeah, I would just… I would just hit up that, so that we have an AI ops channel.
389 00:38:24.460 ⇒ 00:38:28.010 Uttam Kumaran: Which is basically, like, AI and the ops team.
390 00:38:28.270 ⇒ 00:38:32.610 Uttam Kumaran: I think you’ll miss… Yeah.
391 00:38:32.810 ⇒ 00:38:37.730 Uttam Kumaran: So, like, I’ve created some channels that were, like, for example, there’s AI and data, AI and PM,
392 00:38:38.020 ⇒ 00:38:40.489 Uttam Kumaran: It’s basically, like, any question you have.
393 00:38:41.080 ⇒ 00:38:45.999 Uttam Kumaran: as a… that you want to ask the AI team, I would ask in AI-operations.
394 00:38:47.510 ⇒ 00:38:48.110 Sheshu Chandrasekar: Perfect.
395 00:38:48.110 ⇒ 00:38:48.660 Uttam Kumaran: Yeah.
396 00:38:49.130 ⇒ 00:38:54.059 Uttam Kumaran: That way, depending on who’s free, someone… they’re very accountable, someone will respond.
397 00:38:54.230 ⇒ 00:38:54.800 Sheshu Chandrasekar: Yeah.
398 00:38:55.590 ⇒ 00:39:10.930 Sheshu Chandrasekar: Yeah, and that makes sense. And then phase two, like, once I build that out, like, we’ll be testing it to see if it works, you know, run 10 test tickets, and figure out if they’re, you know, kind of landing in the right spots, the right tag. So that’s kind of phase two here.
399 00:39:11.270 ⇒ 00:39:16.040 Sheshu Chandrasekar: And then Phase 3, again, rolling it out, and kind of slowly…
400 00:39:16.250 ⇒ 00:39:30.440 Sheshu Chandrasekar: kind of implementing a behavioral change, right? So if anyone has RICO, it’s kind of like, hey, like, please use that command, instead of just asking me a question, because we want… it’s not that we build tools, it’s that it’s a behavior change that we really want to
401 00:39:30.990 ⇒ 00:39:32.360 Sheshu Chandrasekar: Really want to see.
402 00:39:32.570 ⇒ 00:39:34.869 Sheshu Chandrasekar: Rolling this unified intake out.
403 00:39:37.330 ⇒ 00:39:43.750 Sheshu Chandrasekar: And so these are the metrics that… they’re very ambitious, but I think it’s very possible, so… So by…
404 00:39:44.030 ⇒ 00:39:53.370 Sheshu Chandrasekar: Day 14, you know, we like to get close to 0% by DM, and every request gets originated using that Slack bot and gets landed into our linear, so…
405 00:39:53.750 ⇒ 00:39:58.960 Sheshu Chandrasekar: definitely a little… little ambitious, but I’m pretty confident we can get there, so…
406 00:40:00.030 ⇒ 00:40:03.350 Uttam Kumaran: Yeah, what you’re gonna find is I’m actually… me and Robert.
407 00:40:04.020 ⇒ 00:40:07.700 Uttam Kumaran: Are probably gonna be ask… most of the people asking a lot of questions.
408 00:40:08.110 ⇒ 00:40:11.249 Uttam Kumaran: So, you’ll be able to control our behavior pretty quickly.
409 00:40:11.790 ⇒ 00:40:15.800 Uttam Kumaran: But we’re also, like, tough customers in that, like, it has to… it’ll…
410 00:40:16.320 ⇒ 00:40:17.660 Clarence Stone: We don’t… we’re just gonna…
411 00:40:18.150 ⇒ 00:40:22.009 Uttam Kumaran: immediately, so… You tell me, because I’m probably… I think…
412 00:40:26.150 ⇒ 00:40:30.960 Sheshu Chandrasekar: Yeah, I think there’s a difference between asking, like, very ops-oriented questions, like, regarding.
413 00:40:30.960 ⇒ 00:40:31.429 Uttam Kumaran: Yeah. Finance.
414 00:40:31.430 ⇒ 00:40:46.479 Sheshu Chandrasekar: process, stuff like that, and then, like, asking, like, the details, right? That makes sense, like, why you would need to, like, DM someone, but when it comes to, like, the operations standpoint, like, hey, like, where do I find, like, the expenses for software, stuff like that? Like, that should be…
415 00:40:46.760 ⇒ 00:40:50.089 Uttam Kumaran: Yeah, you’re totally right. I actually think a lot of people aren’t asking…
416 00:40:50.260 ⇒ 00:40:52.819 Uttam Kumaran: I also think a lot of people aren’t asking the question.
417 00:40:52.930 ⇒ 00:40:58.799 Uttam Kumaran: Because they’re just, like, not sure where to ask or what to ask. The other thing, can I… if I can,
418 00:40:59.430 ⇒ 00:41:03.769 Uttam Kumaran: if I can ask you to add one piece here, is, like, how do we trigger
419 00:41:03.940 ⇒ 00:41:07.720 Uttam Kumaran: I want to be able to trigger, like…
420 00:41:08.810 ⇒ 00:41:15.480 Uttam Kumaran: I guess polite request is… makes sense, but I would rather just be like, can I just trigger…
421 00:41:15.910 ⇒ 00:41:18.719 Uttam Kumaran: The routing in a thread with somebody.
422 00:41:19.300 ⇒ 00:41:23.539 Uttam Kumaran: Or maybe, like, yes. Like, for example, if I’m going back and forth with someone, they’re like.
423 00:41:24.100 ⇒ 00:41:31.320 Uttam Kumaran: I usually am, like, ask ops. Instead, at that moment, I can either… And…
424 00:41:31.690 ⇒ 00:41:36.250 Uttam Kumaran: Or, like, instruct them to the help channel. Yeah.
425 00:41:36.830 ⇒ 00:41:39.680 Uttam Kumaran: Sort of, like, what is a pattern interrupt that I can use?
426 00:41:43.250 ⇒ 00:41:47.010 Sheshu Chandrasekar: Got it, that makes sense. So it’s like, if someone was asking, hey, how do I…
427 00:41:47.250 ⇒ 00:41:51.940 Sheshu Chandrasekar: you know, expensive software, right? Instead of, like, you answering it, it’s kind of like.
428 00:41:52.080 ⇒ 00:41:59.719 Sheshu Chandrasekar: Backslash operation slash software expense of some sort. And it gives you that form, or, that information.
429 00:42:00.400 ⇒ 00:42:01.030 Uttam Kumaran: Yeah.
430 00:42:01.180 ⇒ 00:42:02.800 Uttam Kumaran: Something like that, yeah.
431 00:42:04.010 ⇒ 00:42:04.660 Sheshu Chandrasekar: Okay.
432 00:42:05.040 ⇒ 00:42:10.100 Sheshu Chandrasekar: Yeah, I’ll take a look into that. It kind of sounds like an FAQ bot, in essence. Slack kind of.
433 00:42:10.100 ⇒ 00:42:14.149 Uttam Kumaran: Yeah, well, I think it’s gonna end up there, like… Yeah, basically.
434 00:42:16.130 ⇒ 00:42:19.970 Uttam Kumaran: Because, again, think about a world where, like,
435 00:42:20.400 ⇒ 00:42:26.649 Uttam Kumaran: one, like, we’re going back and forth, and it gets the question, I can tag something. Second, we’re going back and forth, and then…
436 00:42:27.790 ⇒ 00:42:29.929 Uttam Kumaran: Yeah, I’ve noticed is that you can answer.
437 00:42:30.150 ⇒ 00:42:33.709 Uttam Kumaran: He goes in and does so, because it’s every message.
438 00:42:34.300 ⇒ 00:42:37.830 Uttam Kumaran: That’s most likely, that’s, like, what the final form of this could be.
439 00:42:40.230 ⇒ 00:42:41.969 Sheshu Chandrasekar: Yeah, no, that makes a lot of sense.
440 00:42:42.960 ⇒ 00:42:43.410 Uttam Kumaran: Yeah.
441 00:42:43.410 ⇒ 00:42:45.259 Sheshu Chandrasekar: I think that’s a very unique way of…
442 00:42:45.550 ⇒ 00:42:52.389 Sheshu Chandrasekar: like, using the Slack bot, too, than the way I envisioned it. So, definitely we’ll take a look into it, figure out how we can implement that.
443 00:42:53.040 ⇒ 00:42:53.450 Uttam Kumaran: Cool.
444 00:42:53.450 ⇒ 00:42:57.750 Sheshu Chandrasekar: I think there’s gonna be two paths. It’s gonna be ones that’s, like, very ops-oriented requests, like.
445 00:42:57.880 ⇒ 00:43:13.650 Sheshu Chandrasekar: certain things that, you know, we need immediate action, so that would be routed to linear, and then certain things are kind of classified under that FAQ bucket, and then the answer’s, like, immediate, right? It gets you that answer right away. So, that’s how I’m kind of seeing it right now.
446 00:43:14.560 ⇒ 00:43:19.690 Uttam Kumaran: Yeah, and I’ll, I have a partner company of ours who’s already doing this.
447 00:43:19.870 ⇒ 00:43:26.600 Uttam Kumaran: So once we get closer to that, I can have you talk to… well, they’re doing it internally,
448 00:43:27.070 ⇒ 00:43:31.989 Uttam Kumaran: I’ll put us on the phone with the AI person at that company, we can chat with her about how they kind of thing.
449 00:43:32.410 ⇒ 00:43:34.690 Uttam Kumaran: But, like…
450 00:43:38.470 ⇒ 00:43:40.049 Uttam Kumaran: It’s visually nice.
451 00:43:42.030 ⇒ 00:43:52.460 Sheshu Chandrasekar: Perfect. Yeah, sounds good. Once we get closer to that point, I will definitely reach out. But one thing I do want to note here, I did say there’s a 10-day timeline to do all this.
452 00:43:52.710 ⇒ 00:43:59.760 Sheshu Chandrasekar: But realistically, this is gonna take 2 weeks, the 4 days that we’re missing out on here.
453 00:44:00.110 ⇒ 00:44:02.110 Sheshu Chandrasekar: Or… now I think about it.
454 00:44:02.360 ⇒ 00:44:05.629 Sheshu Chandrasekar: 8 to 10 days, yeah, so the 2-day lead time is kind of this…
455 00:44:05.870 ⇒ 00:44:10.479 Sheshu Chandrasekar: In case we miss something, or we need extra work, those two days will help us address
456 00:44:12.060 ⇒ 00:44:15.860 Sheshu Chandrasekar: bridging those gaps, or any of the problems that we need to put out, so…
457 00:44:16.070 ⇒ 00:44:17.699 Sheshu Chandrasekar: Just wanted to make a note there.
458 00:44:18.320 ⇒ 00:44:22.629 Uttam Kumaran: Yeah, no, that’s… no problem. That makes sense. Yeah, no, I’m pumped for this, and again.
459 00:44:22.840 ⇒ 00:44:29.120 Uttam Kumaran: there’s gonna be a lot that comes to interrupt, so I think the best way you can use me is to, like, ask for
460 00:44:29.240 ⇒ 00:44:32.120 Uttam Kumaran: Prioritization, and to push back.
461 00:44:32.660 ⇒ 00:44:39.770 Uttam Kumaran: The worst thing you can do is take on too much and then drop the ball, so… it’s not… that’s not gonna prevent people from asking you for stuff.
462 00:44:39.910 ⇒ 00:44:47.730 Uttam Kumaran: But consistently try to reprioritize, or get my help to help you reprioritize, or help you push back.
463 00:44:48.740 ⇒ 00:44:49.090 Sheshu Chandrasekar: Yeah.
464 00:44:49.090 ⇒ 00:44:51.930 Uttam Kumaran: Because Ops handles a lot around here, so…
465 00:44:52.290 ⇒ 00:44:54.770 Uttam Kumaran: Just keep that in… just keep that in mind.
466 00:44:56.100 ⇒ 00:44:58.139 Sheshu Chandrasekar: Absolutely, and I appreciate that.
467 00:45:00.610 ⇒ 00:45:05.180 Uttam Kumaran: Cool, okay. The CSO leads me, this is great. I think you should also send this to the…
468 00:45:06.720 ⇒ 00:45:12.339 Uttam Kumaran: Or you can even send us in the brand or theme channel in case other folks want to take a look at it.
469 00:45:12.820 ⇒ 00:45:16.890 Uttam Kumaran: I know Robert will certainly be interested. So, yeah, that’d be great.
470 00:45:17.440 ⇒ 00:45:21.310 Sheshu Chandrasekar: Yeah, I’ll definitely… I’ll definitely share a link to this, to this deck in that channel.
471 00:45:22.610 ⇒ 00:45:23.830 Uttam Kumaran: Okay, awesome.
472 00:45:24.250 ⇒ 00:45:30.640 Sheshu Chandrasekar: Sweet. Thanks, Clarence. Thanks, Utom. And Rico, I’ll chat with you soon. But thank you so much, guys.
473 00:45:30.800 ⇒ 00:45:31.269 Clarence Stone: Thanks, guys.
474 00:45:32.330 ⇒ 00:45:33.100 Uttam Kumaran: Have a good one.
475 00:45:33.450 ⇒ 00:45:34.130 Sheshu Chandrasekar: Bye.