Meeting Title: Brainforge_Hassan_Founder_Series_Automation Date: 2025-03-11 Meeting participants: Hassan, Uttam Kumaran, Amber Lin
WEBVTT
1 00:00:13.880 ⇒ 00:00:14.860 Uttam Kumaran: Hey! Amber.
2 00:00:17.590 ⇒ 00:00:18.470 Amber Lin: Hello!
3 00:00:19.940 ⇒ 00:00:21.380 Amber Lin: Who are we waiting?
4 00:00:24.172 ⇒ 00:00:27.099 Uttam Kumaran: We are waiting for.
5 00:00:29.510 ⇒ 00:00:30.610 Amber Lin: Hassan.
6 00:00:31.130 ⇒ 00:00:32.420 Amber Lin: Hmm, okay.
7 00:00:33.980 ⇒ 00:00:34.765 Uttam Kumaran: So.
8 00:00:35.610 ⇒ 00:01:01.319 Uttam Kumaran: So the founder series with Craig, which you’re on the email was a sort of a side project of a friend of mine. We’re helping on and it looks like he wants to keep working with us on a few things. So I think the big thing I I want to try to do is one. Of course, like, I don’t want anything to be sort of worked on on the side. So if it’s on the team. I think we should sort of run it through our typical process, I think, for this call. The biggest thing is just to find out
9 00:01:01.320 ⇒ 00:01:10.810 Uttam Kumaran: what’s the scope of work. And then, if we can basically break down what they need and get a estimate from Casey on how long things are. Gonna take.
10 00:01:11.520 ⇒ 00:01:15.519 Uttam Kumaran: That’s probably it. And then we can give them. Give them a quote. Basically.
11 00:01:16.030 ⇒ 00:01:21.950 Amber Lin: I see. So we’re converting it into a more serious project. Essentially.
12 00:01:22.200 ⇒ 00:01:25.369 Uttam Kumaran: Correct. Yes, Casey has done a little bit of work for
13 00:01:25.890 ⇒ 00:01:28.980 Uttam Kumaran: for Craig already randomly, but like nothing.
14 00:01:28.980 ⇒ 00:01:29.680 Amber Lin: No.
15 00:01:29.680 ⇒ 00:01:30.639 Uttam Kumaran: Super, formal.
16 00:01:31.530 ⇒ 00:01:33.419 Amber Lin: Oh, okay. Sounds good.
17 00:01:37.670 ⇒ 00:01:40.270 Uttam Kumaran: Looks like, he said. He’s running a couple of minutes late, so.
18 00:01:48.760 ⇒ 00:01:50.259 Amber Lin: How have you been today.
19 00:01:50.550 ⇒ 00:01:52.899 Uttam Kumaran: Oh, I’m good. Today’s been great
20 00:01:54.464 ⇒ 00:01:56.890 Uttam Kumaran: like 3 h, and no meetings.
21 00:01:57.280 ⇒ 00:02:12.230 Amber Lin: Well, that is so awesome, you know. I didn’t think I would really appreciate not being always on meetings until I’ve worked. Now. I would say 4 days. It’s just been 4 days, and I’m like no.
22 00:02:12.570 ⇒ 00:02:20.950 Uttam Kumaran: That’s because you’ve been calling everybody and meeting everyone. But yeah, the the enemy of productivity is meetings. Unfortunately.
23 00:02:21.250 ⇒ 00:02:29.840 Uttam Kumaran: because it’s tough, because we’re in the business of getting things done with people. But really, I think it’s just like, if we have clear process, clear expectations.
24 00:02:30.300 ⇒ 00:02:34.239 Uttam Kumaran: You don’t need to meet, you know, for everything. I think a lot of people.
25 00:02:34.870 ⇒ 00:02:36.080 Uttam Kumaran: They.
26 00:02:36.340 ⇒ 00:02:41.750 Uttam Kumaran: you know, they instead of writing, they’ll they’ll be like, let’s just hop on. And it takes time it takes effort, you know.
27 00:02:44.310 ⇒ 00:02:44.990 Uttam Kumaran: Let’s see.
28 00:02:45.770 ⇒ 00:02:50.919 Amber Lin: And honestly, today I’ve been in meetings since 5 30,
29 00:02:51.080 ⇒ 00:02:54.149 Amber Lin: so my brain power is a little bit.
30 00:02:54.450 ⇒ 00:02:58.310 Amber Lin: I’m a little bit clogged in my brain.
31 00:02:58.310 ⇒ 00:03:00.490 Uttam Kumaran: That’s what happens. I mean, that’s
32 00:03:00.490 ⇒ 00:03:06.409 Uttam Kumaran: that’s a problem. But hopefully, I mean, are these are these all like meetings for clients stuff, or just meeting people, or what is it.
33 00:03:06.774 ⇒ 00:03:30.440 Amber Lin: It’s I like them, I enjoy them. It’s just a lot. So I had a meeting with Janice. So the client at 5 30, and then the AI team met, which is pretty good. Patrick was there. So we kind of got things going on different fronts. It feel it’s it feels a lot more organized compared to Monday, so I’m very happy about that.
34 00:03:30.700 ⇒ 00:03:42.859 Amber Lin: And then what what happened? And then I ran to the gym. I did heavy lifting, and that did not go through, because I guess I was just tired. And then I met with Nico.
35 00:03:43.440 ⇒ 00:04:03.929 Amber Lin: and then I met with my other gig, and I went, and I met with another another gig, and and then I went back, and with went through all the different projects I know I’m getting on boarded to a lot of different projects. And there’s a lot of channels I’m in so figuring that out
36 00:04:04.540 ⇒ 00:04:06.500 Amber Lin: and then eating lunch.
37 00:04:07.240 ⇒ 00:04:08.620 Amber Lin: And I’m here.
38 00:04:15.600 ⇒ 00:04:16.970 Uttam Kumaran: Yeah, that makes sense.
39 00:04:18.320 ⇒ 00:04:19.390 Uttam Kumaran: I’m just kidding.
40 00:04:21.909 ⇒ 00:04:26.849 Uttam Kumaran: I mean, it’ll be a few days of sort of onboarding. But you know, I think
41 00:04:27.090 ⇒ 00:04:27.620 Amber Lin: Yeah.
42 00:04:28.100 ⇒ 00:04:33.350 Uttam Kumaran: That’ll that’ll slim down a little bit, as you sort of pick everything up, so.
43 00:05:17.400 ⇒ 00:05:18.270 Hassan: Hey folks.
44 00:05:20.760 ⇒ 00:05:21.330 Amber Lin: Hello!
45 00:05:21.330 ⇒ 00:05:22.550 Uttam Kumaran: Hey! How are you?
46 00:05:22.890 ⇒ 00:05:30.317 Hassan: Good good apologies for the delay. I had rice on the pot, and I I just couldn’t leave it. It was gonna burn.
47 00:05:30.640 ⇒ 00:05:36.250 Hassan: Oh, that’s that’s always a high priority. Yeah, I’ve been. I use a rice cooker, and I avoid.
48 00:05:36.350 ⇒ 00:05:37.939 Uttam Kumaran: Avoiding that issue.
49 00:05:39.220 ⇒ 00:05:46.029 Hassan: So I had this. It was a boiling pot of water, and like it was just a matter of like 4 min. So I was like, Okay.
50 00:05:46.330 ⇒ 00:05:48.699 Hassan: I don’t have a choice. I’m already too deep into this.
51 00:05:49.345 ⇒ 00:05:49.730 Uttam Kumaran: That’s.
52 00:05:49.730 ⇒ 00:05:50.160 Amber Lin: Funny.
53 00:05:50.550 ⇒ 00:05:56.720 Uttam Kumaran: No, it’s really really great to meet you. And I’m glad Craig put us in touch. How did you guys how’d you guys get connected.
54 00:05:57.130 ⇒ 00:06:03.760 Hassan: So I lead product at this company called Liquid Donate, where we match
55 00:06:04.060 ⇒ 00:06:06.880 Hassan: excess goods from retailers to nonprofits.
56 00:06:07.340 ⇒ 00:06:12.577 Hassan: And we were talking to Craig about it as well. And then
57 00:06:13.480 ⇒ 00:06:16.140 Hassan: Craig, just you know, you know, Craig, he’s a.
58 00:06:16.140 ⇒ 00:06:16.640 Uttam Kumaran: Yes.
59 00:06:16.640 ⇒ 00:06:21.920 Hassan: Nice guy. He’s very interesting, and he’s always looking to learn more is very curious about things. So
60 00:06:22.286 ⇒ 00:06:49.889 Hassan: I’ve my my background is also as a founder. So I was in the AI space and in AI finance startup didn’t work out and funding and stuff. And then, yeah, I just came into liquid donate and post that. And I, Craig, was like leading AI croc. So I knew a bunch of people in the AI space, and he’s like, I want to learn more about AI and how it’s working, and what I can do to innovate and talk to other smart people so like great. I have a few people I can connect you with.
61 00:06:50.263 ⇒ 00:07:00.370 Hassan: Yeah. And then we just stayed in touch post that like we just chatted, I think once a month, or something like that. And then he told me about the founder series. And yeah, it’s been
62 00:07:00.490 ⇒ 00:07:01.920 Hassan: been an instance from there.
63 00:07:03.030 ⇒ 00:07:03.760 Uttam Kumaran: That’s awesome.
64 00:07:04.600 ⇒ 00:07:32.921 Uttam Kumaran: Yeah, I got connected to Craig through another friend of mine and Ecom, yeah, I just like been talking to Craig, for maybe like 4 or 5 months. Now, just brain Forge is a data and AI agency. So we at work with a bunch of clients implementing analytics and sort of AI agents and different solutions. So just, we just talk every so often, and I attend some of his like founder series, events
65 00:07:33.660 ⇒ 00:07:53.250 Uttam Kumaran: And then, yeah, we’ve we’ve been helping him like randomly, here and there on like sort of just automations that he needs. I mean nothing so formal, but nothing so serious from our end, like just while when we have a little bit of time. But it looks like he’s leaning more into trying to automate more stuff for foundry and his sort of whatever he’s deciding on building.
66 00:07:53.270 ⇒ 00:08:15.250 Uttam Kumaran: So yeah, I mean, we’ve just been helping him like automate random stuff, and that’s what we do for clients. And he’s been helping. He’s been helping Brainforge like helping to get us some clients and intros and things like that. So we repaid the favor. But we’re busy now. So I was sort of like, hey, if you want us to dedicate time, let me know if you get. If you’re you’re more serious. And I actually want to make sure that we can allocate time.
67 00:08:15.280 ⇒ 00:08:21.507 Uttam Kumaran: you know, for your stuff. So then he was like, Yeah, I’m working with us on on this. Maybe all can talk and then decide on what the
68 00:08:21.930 ⇒ 00:08:25.199 Uttam Kumaran: sort of the initial roadmap is and stuff like that. So, yeah.
69 00:08:25.670 ⇒ 00:08:26.650 Hassan: And amber.
70 00:08:26.650 ⇒ 00:08:44.779 Uttam Kumaran: Amber. Who’s on this call as well? Amber is our project manager on our AI projects right now? So basically, anything that we do. We’re trying to trying to have no side quests anymore. So I’m like anything that needs to actually get done. I wanted to run through our typical process where we have at least
71 00:08:44.780 ⇒ 00:09:00.609 Uttam Kumaran: a Pm. And an engineer running stuff. And we, you know, we we use we have tickets and internal stuff as well. So for anything that’s like actually need to get done on time. I wanted to run through our typical process. So Amber’s here to sort of pick that up. You know anything we need to do.
72 00:09:01.360 ⇒ 00:09:06.380 Hassan: No, that makes a lot of sense. And then I’d love to kind of learn just a quick unlike what brain Forge does.
73 00:09:07.260 ⇒ 00:09:14.229 Uttam Kumaran: Yeah. So brain forge, started. So this company, I started in 2023 Brain Forge primarily started as
74 00:09:14.595 ⇒ 00:09:37.750 Uttam Kumaran: an agency that helps clients and Ecom and B, 2 B Sas stand up data infrastructure. So snowflake Etl tools, Dbt on actual modeling side, and then reporting and Bi. And then in that path, you know, I was using a lot of AI to sort of automate the business itself, and found that this was something that we actually had clients that were interested in
75 00:09:37.750 ⇒ 00:09:49.430 Uttam Kumaran: also purchasing from us, which was implementing automations, implementing things like clay building agents for them. And so we also do that as well. That’s probably an earlier service of ours. But
76 00:09:49.450 ⇒ 00:09:55.139 Uttam Kumaran: we use a lot of AI stuff internally, and you know we’re trying to get better at it on the sales side as well.
77 00:09:55.501 ⇒ 00:10:10.079 Uttam Kumaran: So yeah, I’m happy to go in, you know, anywhere deeper. But yeah, we’re we’re about anywhere on any given day, like 15 to 20 people sort of globally distributed. But we also have folks here in La. I’m in Austin, and we have some people in New York.
78 00:10:10.890 ⇒ 00:10:18.449 Hassan: Nice. That’s awesome. It’s actually help set up the back end plus like any AI agents that are needed for these e-commerce platforms.
79 00:10:18.450 ⇒ 00:10:19.950 Uttam Kumaran: That is correct. Yeah.
80 00:10:20.300 ⇒ 00:10:28.999 Hassan: Very cool. I’ve I’ve tapped in a little bit into the e-commerce space like I’ve worked with a few retailers here and there. This is just side projects. I do with my a friend of mine from Meta
81 00:10:29.611 ⇒ 00:10:42.260 Hassan: one of my grad school buddies. We. We just build like different things people want and just do things that we can like. Number one, keep our minds sharp, and then number 2 is like, we’ll learn from different industries on what’s going on.
82 00:10:42.932 ⇒ 00:10:51.950 Hassan: Like one of the things we started building for. This one company was just AI models and making them wear their clothes and generate like videos with them.
83 00:10:52.290 ⇒ 00:11:01.114 Hassan: Just to showcase like, Hey, you know, you don’t need to actually hire any actual AI models. But yeah, it’s a fun. Space retail is a fun space to be in
84 00:11:01.730 ⇒ 00:11:03.770 Hassan: lot to learn a lot of different problems.
85 00:11:03.770 ⇒ 00:11:08.249 Uttam Kumaran: There’s a lot of problems, and it’s very low margin. Yeah, which makes it
86 00:11:08.250 ⇒ 00:11:14.919 Uttam Kumaran: a tough customer. So we typically work with like folks that are making like 1020,000,000 and up because otherwise
87 00:11:15.040 ⇒ 00:11:19.430 Uttam Kumaran: their focus is not on data. It’s on their core, you know, product. So.
88 00:11:20.242 ⇒ 00:11:25.340 Uttam Kumaran: But like once they get that big, they have a lot of data problems. So that’s where we kind of come in and solve.
89 00:11:26.040 ⇒ 00:11:27.070 Hassan: 100%.
90 00:11:27.470 ⇒ 00:11:46.569 Hassan: That makes a lot of sense very cool. Well, I mean, I’m sure I can pick your brain a lot more, and then, can always have more chats, but for now we can quickly get on to discussing the platform that Craig was talking about. So he did. I don’t know if you also, he did recently just send an email like maybe an hour ago.
91 00:11:49.430 ⇒ 00:11:54.580 Hassan: and from what I saw he highlighted just essentially what he needs.
92 00:11:54.760 ⇒ 00:11:56.950 Hassan: Did you all get a chance to read the email.
93 00:11:57.790 ⇒ 00:12:11.090 Uttam Kumaran: I did, but I would love for us to read it together, because I don’t have. I have a much. I have a little bit of context on sort of the whole project, but would love to for to see that, but also like for you to translate kind of like
94 00:12:11.674 ⇒ 00:12:17.500 Uttam Kumaran: what it is we did build. We did build some stuff for his like Google sheet automation, which you may have seen.
95 00:12:17.800 ⇒ 00:12:24.090 Uttam Kumaran: But yeah, maybe we can walk through it. And again, I think this is Amber’s sort of 1st time taking a look at this stuff, so maybe we can all walk through it.
96 00:12:24.470 ⇒ 00:12:42.592 Hassan: Okay. Yeah. I mean, I can. I can read it out, and I will give it my best shot. I’ve probably worked on this like maybe a day or 2 together on this. So I think we’re all in a similar boat, but essentially amber for your context. Craig has this founder series. And it basically collects these.
97 00:12:44.000 ⇒ 00:12:45.140 Hassan: oh, great, yeah.
98 00:12:45.420 ⇒ 00:12:59.520 Hassan: that’s the that’s the email, correct. So Craig has this founder series. And in this founder series he’s got, you know, some, I think, a brands that range from 500 K in revenue to 10 million. I’m not sure about the range of the revenue of the brands.
99 00:12:59.810 ⇒ 00:13:21.779 Hassan: but and he brings in guest speakers who are who are at high values like maybe a hundred, 200 million 300 million brands, and the the that’s the value of revenue that they generate. So those are the guest speakers. And that’s how these younger companies come and attract in. Now what Craig’s is Craig is trying to do is he’s trying to essentially
100 00:13:21.940 ⇒ 00:13:26.200 Hassan: make it into a platform that he can essentially make revenue off of.
101 00:13:26.630 ⇒ 00:13:28.430 Hassan: And that includes
102 00:13:29.640 ⇒ 00:13:40.840 Hassan: yeah, to make revenue off of, he that includes a little bit of automation that he needs to extract data from the from his essentially viewers that he can then use to sell to sponsors.
103 00:13:41.316 ⇒ 00:13:59.589 Hassan: So then he’s got a few sponsorship deals that are coming in. But they request. Data of like, you know, what’s the what’s the type of brands that are doing that are in there? What are their market types? What are their revenues. Things of those sorts. So they so the sponsors then essentially come in at the right at the right value.
104 00:14:00.290 ⇒ 00:14:10.630 Hassan: That’s where he’s got like a Google sheet that’s been building it’s got all the people who are registered into. It’s a very small knit group of about 200 folks.
105 00:14:12.240 ⇒ 00:14:20.579 Hassan: And this is where he’s gonna like, put in put in the tasks below, is where we want to extract data from viewer to sell to sponsors.
106 00:14:21.986 ⇒ 00:14:29.569 Hassan: And then what we have right now is essentially a Google Google sheet with everyone’s just basic information.
107 00:14:29.710 ⇒ 00:14:34.019 Hassan: We need to include a link to their website. And then what
108 00:14:34.210 ⇒ 00:14:43.549 Hassan: they? I think he already has that link. But what he doesn’t have is the brand, size and scale like what is it at face value, and I think he shared a couple of tools.
109 00:14:43.750 ⇒ 00:14:49.729 Hassan: But that’s essentially what we need to what we need to extract out. So that would be some sort of
110 00:14:49.870 ⇒ 00:14:55.950 Hassan: either connection or extraction. Depending on the tool that he shared or a tool that we can find through
111 00:14:56.965 ⇒ 00:14:59.194 Hassan: so that’s the major one
112 00:15:00.300 ⇒ 00:15:01.669 Uttam Kumaran: That makes sense. Yeah.
113 00:15:02.706 ⇒ 00:15:16.149 Hassan: And then the second thing that I think, he put in. So I already got the brand values like the brands like there’s this company called Brand Fetch, which was allowed us to get their their logos and other things. So I’ve already added that in
114 00:15:17.035 ⇒ 00:15:27.390 Hassan: next, he said, case, sort of someone said, like, how I can get someone’s email address relatively quickly. When I put their name in Google Sheet, yeah. So he’s just looking into an Api connection.
115 00:15:28.148 ⇒ 00:15:37.970 Hassan: And I think the next thing he’s gonna do is essentially get a type form fill it out. And that’ll be pushed to Google Sheet, which isn’t too hard, right? Like that’s probably pretty simple to do.
116 00:15:38.250 ⇒ 00:15:44.239 Hassan: I think that form already has an integration pre-built out. If anything, we can probably use something like make AI
117 00:15:44.520 ⇒ 00:15:50.860 Hassan: to do those kind of workflows. But really just the meter thing that I see is just a revenue collection.
118 00:15:51.150 ⇒ 00:16:03.240 Uttam Kumaran: Yeah, I mean, we have a couple of places. We can get that from, like, I mean, you can get that from Apollo, from Linkedin, and like we can give a couple of those that seems pretty straightforward. I mean that I don’t think that would take us like
119 00:16:03.560 ⇒ 00:16:07.280 Uttam Kumaran: much time to do it all. But I guess where’s the handoff like, should we?
120 00:16:07.510 ⇒ 00:16:08.450 Uttam Kumaran: Should we just
121 00:16:08.790 ⇒ 00:16:14.740 Uttam Kumaran: build it and sort of like, hand it off to you or like? And also, if we can get a sense of like, what’s next
122 00:16:14.890 ⇒ 00:16:18.539 Uttam Kumaran: that way, we can start to plan out like, what else needs to happen.
123 00:16:18.670 ⇒ 00:16:21.660 Uttam Kumaran: Further than that all the sort of context we have so far.
124 00:16:21.930 ⇒ 00:16:36.840 Hassan: Okay, I see what you mean. Alright. So I do. I still have to connect with Craig once where I can understand exactly like what he’s looking to do. I know this data he’s using for sponsors. So your information that he needs to give to sponsors. It doesn’t look like he can present.
125 00:16:36.840 ⇒ 00:16:44.008 Uttam Kumaran: We can get a bunch more like info, like revenue and traffic. I feel like will be pretty easy for us to get
126 00:16:44.320 ⇒ 00:16:53.443 Hassan: Yes, revenue and traffic would be great. He’s also looking to do an eventual like thing that he was talking to me about was, this is down the road was an application
127 00:16:54.168 ⇒ 00:17:14.249 Hassan: where essentially these founders can connect with each other, or have, like an ask or be able to meet with each other. So this is again a club that’s pushed in like a very small, like tight knit club that he’s giving access to. There’s a company he shared with me. That does something similar. It’s like the top millionaires a billionaires club.
128 00:17:14.641 ⇒ 00:17:19.960 Hassan: That’s got like, it’s really hard to get into it. You obviously have to be a billionaire or someone really well known.
129 00:17:20.069 ⇒ 00:17:29.109 Hassan: And he’s trying to do something similar. But for the smaller earnings region like the 500 k. To like 10 million, or the 10 to 50 million range.
130 00:17:30.640 ⇒ 00:17:35.399 Uttam Kumaran: Oh, okay, so we basically try to help him identify who those people are. Something similar.
131 00:17:36.250 ⇒ 00:17:41.162 Hassan: Yeah, like, he already has some list right now of those people. And then it’s
132 00:17:42.000 ⇒ 00:17:49.180 Hassan: It’s us like essentially giving them a platform where those people can log in, sign up, and then just communicate with each other.
133 00:17:50.010 ⇒ 00:17:51.609 Uttam Kumaran: Oh, okay. Okay. I see.
134 00:17:52.870 ⇒ 00:18:00.063 Hassan: Yeah. And there’s a lot of those, I think those no code tools that already have this kind of setup. Like, I was thinking, something like softer.
135 00:18:00.760 ⇒ 00:18:05.090 Hassan: or one of those that already have these applications built out.
136 00:18:06.060 ⇒ 00:18:12.970 Uttam Kumaran: Yeah, I mean, if you if you can, if we can have a little bit of scope on like, hey, what automations need to get built. I’m fine with that, like we’re not. We’re not.
137 00:18:13.270 ⇒ 00:18:32.250 Uttam Kumaran: I would say. We’re not too much into the world of like building apps like full apps per se. But anything on the automation or lead, like basically lead enrichment or data side. You know, I feel pretty pretty confident about like we could do anything there? For this initial thing?
138 00:18:32.646 ⇒ 00:18:34.630 Hassan: Exactly. Need sorry. Go ahead.
139 00:18:34.630 ⇒ 00:18:51.580 Uttam Kumaran: For this initial like, okay, look up the revenue based on the form like, yeah, I think we. I mean, it’ll take us probably like an hour to to get something like that done. I just wanna get if there’s like a future sense of like, okay, what is the next like? Month or 2 of like we’re coming down the pipe and like, what would we handle? If it’s literally just this.
140 00:18:51.790 ⇒ 00:19:07.920 Uttam Kumaran: then I may just say, like we’ll just do it for free, because it’s it’s like, not that big. But if it’s gonna be like 5, 10 more things, I can’t be involved because I have to do a hundred things. So I wanna make sure that we have a team assigned, and that they’re sort of working with you. If you’re driving it forward.
141 00:19:08.520 ⇒ 00:19:21.490 Hassan: Yeah, no, I completely agree. So I think the best next steps are that one. We can kind of get back to Craig like saying that, hey? Just the demand for having the revenue and some data on these brands that exist.
142 00:19:21.620 ⇒ 00:19:32.059 Hassan: That’s not too bad. But we do definitely need a call, because I myself need clarity on exactly what the next like month or 2 need to look like again. I’ve had maybe one or 2 days worth of.
143 00:19:32.230 ⇒ 00:19:38.499 Hassan: I’ve had emails, no actual call on this. And then maybe one day worth of just actual doing work
144 00:19:38.800 ⇒ 00:19:42.349 Hassan: to FET some information for the sponsors. But that’s about it.
145 00:19:42.760 ⇒ 00:19:54.790 Uttam Kumaran: Okay, cool. Yeah. I mean, I feel like we can probably get stuff answered directly on our email. So yeah, I mean, I feel pretty if you want to send that note, and I can reply, if there’s anything, no context that.
146 00:19:55.040 ⇒ 00:19:55.290 Hassan: Look.
147 00:19:55.290 ⇒ 00:20:08.899 Uttam Kumaran: And then we can just go from there. I feel pretty good. Yeah, like again, if I wanna make sure that if it’s just like small automations here and there. Then we’ll just we’ll just take it but if it’s something larger I wanna make sure that our guys can dedicate time to it, you know.
148 00:20:09.510 ⇒ 00:20:11.280 Hassan: Yeah, yeah, sounds like a good plan.
149 00:20:11.405 ⇒ 00:20:11.780 Hassan: How are you?
150 00:20:11.780 ⇒ 00:20:18.039 Hassan: Okay? Cool. I’ll send out a follow up email. And then, I’ll try to get on a call with Craig, so that we can take it from there.
151 00:20:18.560 ⇒ 00:20:24.379 Uttam Kumaran: Okay. Awesome. And yeah, if you’re if you’re ever in, Austin, let me know, would love to to meet up. And if I can help.
152 00:20:24.380 ⇒ 00:20:25.000 Hassan: I am.
153 00:20:25.000 ⇒ 00:20:27.999 Uttam Kumaran: With anything else, please. Yeah, please let me know.
154 00:20:28.000 ⇒ 00:20:29.889 Hassan: If you’re in Maryland, I’m in Maryland.
155 00:20:29.890 ⇒ 00:20:30.770 Uttam Kumaran: Where in Maryland.
156 00:20:31.750 ⇒ 00:20:35.410 Hassan: I’m in. I’m near Columbia, like the Baltimore area.
157 00:20:35.410 ⇒ 00:20:49.100 Uttam Kumaran: Nice. I actually I was coming there actually, for my friend of mine used to live well, they used to have a boat in the harbor there, and so we used to. We used to go there every summer. They just sold it. But I go to I go to Maryland
158 00:20:49.629 ⇒ 00:20:56.280 Uttam Kumaran: here and there. My friend, he lives in he lives in Moncton, in like the woods.
159 00:20:56.740 ⇒ 00:21:03.029 Hassan: Oh, fancy I moved from California about like 4 months ago, 5 months ago.
160 00:21:03.030 ⇒ 00:21:04.330 Uttam Kumaran: Where in California.
161 00:21:04.820 ⇒ 00:21:08.320 Hassan: I was in San Jose, not San Jose. I was in Sunnyvale very.
162 00:21:08.320 ⇒ 00:21:11.090 Uttam Kumaran: Nice. Okay, I grew up in East Bay. So.
163 00:21:11.090 ⇒ 00:21:13.489 Hassan: Oh, okay, nice. I I miss it dearly.
164 00:21:13.490 ⇒ 00:21:16.155 Uttam Kumaran: You’re a long way from home.
165 00:21:16.600 ⇒ 00:21:19.859 Hassan: I do miss it dearly, but you know my siblings are close by my
166 00:21:20.500 ⇒ 00:21:25.349 Hassan: close by, and then my work is all remote, so I just had absolutely no reason.
167 00:21:25.980 ⇒ 00:21:29.509 Hassan: Yeah, obviously, hey, I found every single way to stay. But.
168 00:21:29.510 ⇒ 00:21:34.716 Uttam Kumaran: Maryland’s not horrible, you know. It’s definitely, I’m sure it’s cold.
169 00:21:35.560 ⇒ 00:21:37.039 Hassan: Mean compared to California.
170 00:21:37.040 ⇒ 00:21:48.665 Uttam Kumaran: Yeah, yeah, I mean the Bay area. Yeah, the Bay area. And and the East Bay, there’s like, no beating that. Yeah, I’m in Austin. It’s it’s close, but it’s
171 00:21:49.460 ⇒ 00:21:53.650 Uttam Kumaran: it’s still. Nothing beats like California weather, and like the lifestyle there. So
172 00:21:53.840 ⇒ 00:21:57.499 Uttam Kumaran: Amber’s in Amber’s in La, and we have a bunch of people from la, too. So.
173 00:21:57.500 ⇒ 00:21:58.070 Amber Lin: Yeah.
174 00:21:58.070 ⇒ 00:21:58.670 Hassan: Yeah.
175 00:21:58.670 ⇒ 00:22:06.340 Amber Lin: We have 4 or 5 people that lives really close. Some guy lives. One of our teammates lives a few blocks away from me. It was crazy.
176 00:22:08.630 ⇒ 00:22:11.060 Hassan: Well, that’s nice.
177 00:22:11.430 ⇒ 00:22:23.279 Hassan: but cool. Yeah. So well, I’ll I’ll send an email out and then we’ll stay in touch from there and then anything else. In the meantime that comes up we’ll either stay in touch over there or yeah, happy to always get on another call when needed.
178 00:22:23.280 ⇒ 00:22:25.099 Uttam Kumaran: Awesome. Perfect. Okay. Thanks.
179 00:22:25.100 ⇒ 00:22:26.950 Hassan: Alright! Take care you, too! Thanks.
180 00:22:26.950 ⇒ 00:22:27.520 Uttam Kumaran: Bye.
181 00:22:27.890 ⇒ 00:22:28.919 Hassan: Awesome bye, bye.