Meeting Title: Daily Sales Sync Date: 2024-12-12 Meeting participants: Nicolas Sucari, Uttam Kumaran, Robert Tseng, Miguel De Veyra, Casie Aviles
WEBVTT
1 00:02:33.750 ⇒ 00:02:36.950 Uttam Kumaran: Hey? Guys, sorry was on another call.
2 00:02:39.690 ⇒ 00:02:42.050 Nicolas Sucari: Hey, Tom, hey, Robert, you guys.
3 00:02:42.517 ⇒ 00:02:43.920 Robert Tseng: Hey? Martin? Yeah.
4 00:02:45.100 ⇒ 00:02:45.720 Uttam Kumaran: Hey?
5 00:02:46.160 ⇒ 00:02:49.459 Uttam Kumaran: Had a I had just 2 like really
6 00:02:49.960 ⇒ 00:02:53.290 Uttam Kumaran: sort of great calls this morning. We had one interview
7 00:02:53.340 ⇒ 00:02:56.290 Uttam Kumaran: for an AI person that went pretty well.
8 00:02:56.320 ⇒ 00:02:59.099 Uttam Kumaran: and then I just spoke with this guy.
9 00:02:59.400 ⇒ 00:03:05.270 Uttam Kumaran: His name is Stan he runs this company called Storm King.
10 00:03:07.620 ⇒ 00:03:12.009 Uttam Kumaran: Let me just find it storm King consulting?
11 00:03:12.689 ⇒ 00:03:18.679 Uttam Kumaran: Basically, it’s like a consulting. It’s like a partner network.
12 00:03:20.165 ⇒ 00:03:21.190 Uttam Kumaran: For
13 00:03:21.610 ⇒ 00:03:30.986 Uttam Kumaran: like connect for like large e-commerce brands. Basically they have basically, it’s kind of like they he runs
14 00:03:31.540 ⇒ 00:03:39.119 Uttam Kumaran: he runs a slack group and like a bunch of networking for really large like, think like, 25 million.
15 00:03:39.280 ⇒ 00:04:00.891 Uttam Kumaran: mostly 100 million plus e-commerce brands that are having that are just in his network. So these are like CEO Cmos, probably head of growth. And I got intro to him by a friend, and we just talked about like sort of what we’ve done in E-com, and and sort of our capabilities. And one of the things he mentioned is
16 00:04:01.260 ⇒ 00:04:03.940 Uttam Kumaran: He was like dude. One of the he said that
17 00:04:04.300 ⇒ 00:04:20.940 Uttam Kumaran: a lot of the Cmos that he talks to are basically getting frustrated with like dacity. And some of these bi tools, basically because it can actually get them a lot of the numbers that they need without additional work. And then, basically, I was like, yeah, do you need like modeling and data engineering and stuff like that?
18 00:04:21.110 ⇒ 00:04:27.210 Uttam Kumaran: And he was like, you’re totally right. And we kind of had a great conversation about like sort of the market there, and
19 00:04:27.583 ⇒ 00:04:37.850 Uttam Kumaran: one, a lot of the work that we’ve done for pool parts stuff I used to do for athletic greens. I’m sure the stuff we did we did for Javi. These are all things that
20 00:04:38.100 ⇒ 00:04:48.429 Uttam Kumaran: everybody at in the revenue range of e-commerce needs so basic. But that I think the problem that we’re having now is we need to punch up way higher, because
21 00:04:48.700 ⇒ 00:05:09.799 Uttam Kumaran: at the lower end. They’re just struggling to keep the lights on a lot of the time. And so for us, it’s like, how do we get towards companies that are 50 to 100 north of 50 million at least in revenue, and actually have, like this sort of problem that they’ve identified. The other thing that he mentioned is like 2 key triggers. One
22 00:05:10.070 ⇒ 00:05:25.369 Uttam Kumaran: find you said, find people that have just gotten acquired by PE firms because the data scrutiny that they’re gonna have is gonna be way higher. The second thing is find people that recently just got a capital capital injection from like Vc or otherwise.
23 00:05:25.810 ⇒ 00:05:28.709 Uttam Kumaran: because they’re gonna have a much stricter reporting requirements.
24 00:05:29.098 ⇒ 00:05:37.189 Uttam Kumaran: But he’s like dude, he said. He talked to like he’s talking to like 100 million 200 million dollar firms. I just can’t even get a sense of like how much they sold the other day.
25 00:05:37.220 ⇒ 00:05:40.990 Uttam Kumaran: And so the problems that we’re seeing at our level are the same problems at the top.
26 00:05:41.100 ⇒ 00:05:45.390 Uttam Kumaran: I basically told him that look for us. The biggest thing is like we’re small. And
27 00:05:45.460 ⇒ 00:05:53.390 Uttam Kumaran: he said, the range for them is like. Look, they can either go to like audacity, which just sells them tool, or they can go to like a deloitte, which is like
28 00:05:53.400 ⇒ 00:06:01.010 Uttam Kumaran: a huge annual contract. And I was like, Yeah, we’re we’re in the middle somewhere, I think was totally stuff we could service, and
29 00:06:01.150 ⇒ 00:06:19.635 Uttam Kumaran: the kind of like his pitch and sort of what his consulting firm called Storm King consulting does is they basically have a network of all these big e-commerce cmos. And he was pretty transparent. He’s like dude. I’ll just send you the list of everyone that’s in there. They host like virtual roundtables. They host like
30 00:06:19.970 ⇒ 00:06:26.070 Uttam Kumaran: events. It’s almost like a I don’t know. A friend of mine used to run a company. This was almost like an it
31 00:06:26.250 ⇒ 00:06:31.730 Uttam Kumaran: leader. Roundtable sort of like vendor network. And primarily what he does is like
32 00:06:31.770 ⇒ 00:06:34.849 Uttam Kumaran: sales and acquisition related consulting.
33 00:06:34.970 ⇒ 00:06:38.814 Uttam Kumaran: So I’m getting like his more details about his pitch from him. But
34 00:06:39.450 ⇒ 00:06:41.300 Uttam Kumaran: could be something to help us out.
35 00:06:43.950 ⇒ 00:06:51.760 Robert Tseng: Dude. I don’t know any firm that’s buying up Ecom firms right or Ecom companies right now, or and none of them are getting funded.
36 00:06:52.020 ⇒ 00:06:53.652 Uttam Kumaran: True, that’s very true.
37 00:06:54.590 ⇒ 00:06:59.819 Robert Tseng: This was like pre pandemic. Everyone was getting a ton of funding. This is exactly what
38 00:07:00.130 ⇒ 00:07:05.699 Robert Tseng: and and Dude Javi is is in that range. They they bring in 10 million a month.
39 00:07:06.800 ⇒ 00:07:10.629 Uttam Kumaran: Dude. Why are they being? Why are they penny pinching on like a grand.
40 00:07:10.830 ⇒ 00:07:18.990 Robert Tseng: Well, yeah, I think that this was interesting. From what you’re saying is, I mean dacity. I’ve heard of it. I know companies use it? What! What are what are they paying for? Dacity
41 00:07:19.120 ⇒ 00:07:20.040 Robert Tseng: and.
42 00:07:20.040 ⇒ 00:07:22.710 Uttam Kumaran: Daphne’s like a bi school. So.
43 00:07:23.000 ⇒ 00:07:27.812 Robert Tseng: But they also have pro serve right on with their with their tool, at least that I remember
44 00:07:28.080 ⇒ 00:07:29.779 Uttam Kumaran: Yeah. I gotta ask. Clint. Clint knows.
45 00:07:30.365 ⇒ 00:07:33.290 Robert Tseng: Was using destiny before- before.
46 00:07:33.290 ⇒ 00:07:41.110 Uttam Kumaran: He said he said he was like, Daphne is the number one tool that I’ve heard from all a lot of our people that they’re looking to switch off of.
47 00:07:43.260 ⇒ 00:07:52.490 Uttam Kumaran: he said. Above and beyond all tools. He’s like people are looking to switch off, which is not good for them, I said, I said, Look, that’s a software tool. They’re gonna sell that they can do everything for you.
48 00:07:52.650 ⇒ 00:07:58.650 Uttam Kumaran: But running a successful software company and a pro serve organization is difficult. So.
49 00:07:59.230 ⇒ 00:08:12.919 Robert Tseng: Yeah, I mean, they’re still well, they were well positioned in the shopify environment. So like any fast growing brand in shopify like knows audacity. So they probably tried it. And they realize at a certain point, it’s not working for them anymore.
50 00:08:14.600 ⇒ 00:08:15.210 Robert Tseng: Yeah.
51 00:08:16.719 ⇒ 00:08:39.770 Robert Tseng: but I mean, I’m I’m curious. Well, yeah, I mean as we’re I mean, I don’t want to derail. But as the with the Javi extension, I mean, yeah, like it’d be, I’d be curious what people are paying for audacity, what these, what these Cmos like, what their budget considerations are like, how do we get? How do we benchmark this? So we can try to like. I mean, I I mean, I think we have all the leverage with Joby right now, because they’re screwed without us.
52 00:08:39.820 ⇒ 00:08:46.410 Robert Tseng: you you we? I think we have a good case study coming out of Joby. What? What I showed Ahmad and Jared on Friday.
53 00:08:47.209 ⇒ 00:08:55.550 Robert Tseng: Was that the data quality improvements that we’ve driven for them by moving to the warehouse. It’s a magnitude of 40%,
54 00:08:55.600 ⇒ 00:09:07.460 Robert Tseng: you know, 40% plus. So that means their amplitude. Instance was, you know, was over reporting by 40% before. So for us to be able to like, reconcile that huge win on the data quality side.
55 00:09:07.460 ⇒ 00:09:26.899 Robert Tseng: And then I’m pushing out a couple of these like high highlight, like sound bite, worthy kind of like takeaways as as when we’re when we submit that deck early next week, but I mean for all’s worth. I think we did a really good job with Javi, and I think it’s a great case study that we can use for similarly sized brands.
56 00:09:30.030 ⇒ 00:09:31.519 Uttam Kumaran: Okay, okay, makes sense.
57 00:09:31.830 ⇒ 00:09:32.440 Robert Tseng: Yeah.
58 00:09:37.850 ⇒ 00:09:56.199 Robert Tseng: yeah, I mean, at this point, we’ve shown Aman that like, this is what happens when you control your data. And there’s no way he can go back. He, now that he knows that he was over reporting by 40%. There’s a fire under his ass and like. There’s no way that that the team will let him they need. They need us to maintain.
59 00:09:57.840 ⇒ 00:09:59.030 Uttam Kumaran: Okay. Okay.
60 00:09:59.030 ⇒ 00:10:06.489 Robert Tseng: Yeah, but cool. No, I mean, that’s a great brain trust to be a part of like, yeah.
61 00:10:06.490 ⇒ 00:10:06.870 Uttam Kumaran: See how much.
62 00:10:06.870 ⇒ 00:10:08.998 Robert Tseng: Talking to more Cmos.
63 00:10:09.530 ⇒ 00:10:12.980 Uttam Kumaran: No, I know. And that’s why I was also like, Look, we’re trying to punch above.
64 00:10:13.330 ⇒ 00:10:17.559 Uttam Kumaran: wait and get into. But they have. They have problems that are so basic.
65 00:10:17.760 ⇒ 00:10:28.040 Uttam Kumaran: And that’s what I told him. I said, look, we’re avoiding like b 2 BI mean, like, I just think the b 2 b Saas folks may be better for like staff on. But to come in and do like a larger project.
66 00:10:28.130 ⇒ 00:10:33.359 Uttam Kumaran: they’re not gonna bite because I feel like they usually have stuff taken care of, and startups don’t have any money.
67 00:10:33.470 ⇒ 00:10:40.170 Uttam Kumaran: So I was like, Look, we’re looking at manufacturing. We’re looking at finance. And then, like he, he’s his whole expertise is in E com. So
68 00:10:41.240 ⇒ 00:10:41.870 Uttam Kumaran: yeah.
69 00:10:43.270 ⇒ 00:10:43.940 Robert Tseng: Got it.
70 00:10:45.720 ⇒ 00:10:48.179 Uttam Kumaran: Cool for shot leads.
71 00:10:48.600 ⇒ 00:10:49.520 Uttam Kumaran: Yeah, let’s do it.
72 00:10:50.790 ⇒ 00:10:51.510 Uttam Kumaran: Okay.
73 00:10:53.338 ⇒ 00:10:54.950 Robert Tseng: I can go over. I’m doing.
74 00:10:55.180 ⇒ 00:11:04.360 Robert Tseng: I I moved. I moved owner to today because I was just like overwhelmed yesterday and so I’m I’m speaking with owner later today at at
75 00:11:04.640 ⇒ 00:11:08.400 Robert Tseng: like, right after the job you call from like 3, 3 to 4, pretty much.
76 00:11:08.420 ⇒ 00:11:10.639 Uttam Kumaran: Have that time blocked off.
77 00:11:10.730 ⇒ 00:11:15.610 Robert Tseng: So right now, I’m finishing up. I’m gonna record like a
78 00:11:18.090 ⇒ 00:11:35.579 Robert Tseng: like a readout of like what? How I’m presenting like the the real demo to them, so that can be a reference. And then I’m also like spinning it into our own, like internal like product analyst, whatever case study. So I’ll I’ll send those out soon. I’m like, almost done with it.
79 00:11:36.780 ⇒ 00:11:50.269 Robert Tseng: yeah. So hopefully, that’s like both. That’s both the sales demo and also like A and and for our internal recruiting that should work for our our case, study that we can send out once we start like
80 00:11:50.490 ⇒ 00:11:52.750 Robert Tseng: with with those recruiting campaigns.
81 00:11:53.450 ⇒ 00:11:54.230 Uttam Kumaran: Okay.
82 00:11:54.750 ⇒ 00:11:55.350 Robert Tseng: Yeah.
83 00:11:55.933 ⇒ 00:12:06.769 Robert Tseng: There’s another guy that reached out to me today. Uk, based like, kind of health tech startup. They’re big bigquery. Dbt, I don’t know if there’s like
84 00:12:06.810 ⇒ 00:12:10.440 Robert Tseng: engineering work right now. He just wants someone to come in and like
85 00:12:10.720 ⇒ 00:12:16.230 Robert Tseng: at like, see what’s wrong. I think it’s just data quality thing. He probably just like
86 00:12:16.430 ⇒ 00:12:19.769 Robert Tseng: half ass stitched the stuff together over the past year. And
87 00:12:20.163 ⇒ 00:12:26.329 Robert Tseng: I guess he’s desperate. So he’s reaching out so. That’s probably something. I’ll I’ll flip and and follow up on
88 00:12:27.024 ⇒ 00:12:31.561 Robert Tseng: I spoke to the the solar pump people yesterday.
89 00:12:32.600 ⇒ 00:12:38.279 Robert Tseng: yeah, I mean, I I think you were asking like, why not set a proposal right away? They said. They’re not moving urgently, so he said.
90 00:12:38.280 ⇒ 00:12:38.870 Uttam Kumaran: Early next week.
91 00:12:38.870 ⇒ 00:12:45.609 Robert Tseng: Is fine. I feel like they’re not going to be ready to start until January, but the call went well.
92 00:12:46.048 ⇒ 00:12:50.229 Robert Tseng: I think the short term is very clear cut like it’s a, I think, pretty.
93 00:12:50.260 ⇒ 00:13:00.099 Robert Tseng: pretty, straightforward kind of like like same similar like audit and tracking kind of like engagement that will ladder up into the stuff.
94 00:13:00.744 ⇒ 00:13:10.129 Robert Tseng: But yeah, I also kind of, you know, shared more about like the pull parts work that we do. And yeah, he’s like, yeah, he knows the pull parts guys. You know, the
95 00:13:10.140 ⇒ 00:13:23.870 Robert Tseng: the pump industry is quite small. So everyone knows each other. So he had a lot of questions about that, and thought that that case study that we had was really helpful for him. So I think, yeah, they’re they’re super interested, and
96 00:13:24.240 ⇒ 00:13:25.839 Robert Tseng: I think that that’ll be good.
97 00:13:26.270 ⇒ 00:13:35.129 Robert Tseng: I also share the proposal for ergo, so I mean, yeah, I just put 10 K. 10 K. For the 1st month, and then we’ll see.
98 00:13:35.920 ⇒ 00:13:40.020 Robert Tseng: Yeah, I think waiting waiting to hear back. Because I sent that out last night.
99 00:13:43.150 ⇒ 00:13:45.180 Robert Tseng: Yeah. So I think those are
100 00:13:45.190 ⇒ 00:13:59.489 Robert Tseng: really the things that are top of mind for me today. Yeah, the the. There’s some lead messages. I I said I was gonna reach out to the not guy Aditya. I haven’t sent it. I’m gonna send it today. So yeah, there’s a few messages I want to shoot out today.
101 00:13:59.810 ⇒ 00:14:00.460 Uttam Kumaran: Okay.
102 00:14:00.900 ⇒ 00:14:01.260 Robert Tseng: Yeah.
103 00:14:01.260 ⇒ 00:14:06.710 Uttam Kumaran: Okay, cool on my side. Yeah, we have the ABC home demo later today. So we’re like, kind of
104 00:14:07.010 ⇒ 00:14:12.770 Uttam Kumaran: like, there’s a couple of things that we’re improving on the demo before 3 that I’m kind of like just pushing the AI team on.
105 00:14:12.870 ⇒ 00:14:13.750 Uttam Kumaran: Okay.
106 00:14:14.850 ⇒ 00:14:18.440 Uttam Kumaran: So let’s see where we get to. There. We have a couple.
107 00:14:18.440 ⇒ 00:14:20.360 Robert Tseng: That’s not the thing with Christina. Right?
108 00:14:20.360 ⇒ 00:14:23.340 Uttam Kumaran: No, we do have a Christina call at 12, too.
109 00:14:23.850 ⇒ 00:14:25.390 Robert Tseng: I don’t know if we’ll pull that
110 00:14:25.440 ⇒ 00:14:31.529 Robert Tseng: cause that overlap with the the Javi thing. And I think, yeah, I should probably just be on the joby one. Okay.
111 00:14:31.910 ⇒ 00:14:33.319 Uttam Kumaran: Alright, cool, no worries.
112 00:14:33.510 ⇒ 00:14:34.070 Robert Tseng: Okay.
113 00:14:34.726 ⇒ 00:14:41.619 Uttam Kumaran: So that calls today that one. I’m just gonna basically it’s just gonna be like discovery. I’m just gonna try to figure out like everything that’s going on.
114 00:14:41.640 ⇒ 00:14:45.039 Uttam Kumaran: What they need. Cool opportunity.
115 00:14:45.220 ⇒ 00:14:50.280 Uttam Kumaran: Nice job have we done? We haven’t done anything with any other like events, companies, have we?
116 00:14:50.990 ⇒ 00:14:52.429 Uttam Kumaran: I don’t think I don’t think I’ve done.
117 00:14:52.430 ⇒ 00:14:55.370 Robert Tseng: Events like an event services, company.
118 00:14:55.370 ⇒ 00:14:56.360 Uttam Kumaran: Yeah.
119 00:14:57.070 ⇒ 00:14:59.020 Robert Tseng: No, I, yeah.
120 00:15:00.100 ⇒ 00:15:06.390 Uttam Kumaran: Okay, I’ll have to figure out like sort of what the deal is there. All I know is the Cs. But I don’t know if they have another arm of their business. So.
121 00:15:06.800 ⇒ 00:15:07.980 Robert Tseng: Yeah.
122 00:15:08.410 ⇒ 00:15:09.510 Uttam Kumaran: Let’s see.
123 00:15:10.075 ⇒ 00:15:14.530 Uttam Kumaran: I had a really good conversation with Sirius yesterday.
124 00:15:14.870 ⇒ 00:15:15.280 Robert Tseng: Yeah.
125 00:15:15.280 ⇒ 00:15:17.580 Uttam Kumaran: Like that. One’s a little bit of like a
126 00:15:18.220 ⇒ 00:15:24.499 Uttam Kumaran: shot in the dark like we don’t really own. Basically, they have a very big in into like
127 00:15:25.762 ⇒ 00:15:40.050 Uttam Kumaran: so it’s my friend Connor and and his mom. His mom runs this firm they do consulting, for, like the largest real estate folks real estate like by square foot owners, which is all the corporates in New York.
128 00:15:40.960 ⇒ 00:15:46.050 Uttam Kumaran: mainly on like analyzing their portfolio, analyzing their how they manage each of their sites.
129 00:15:47.500 ⇒ 00:15:58.718 Uttam Kumaran: I just demoed them all of our AI capabilities, which is actually helpful because I’m I’ve kind of passing on an AI. I’m asking. I passed on to, and a request for an AI capabilities. Deck
130 00:15:59.030 ⇒ 00:15:59.360 Robert Tseng: Yeah.
131 00:15:59.360 ⇒ 00:16:13.849 Uttam Kumaran: So that I don’t have to run. I don’t have to scramble like I did yesterday, but the pictures getting better, so it was worth just like going through that. Basically, they want to see if whether they can white label us as like potentially like an AI service that they can tag on to their business.
132 00:16:14.660 ⇒ 00:16:17.529 Uttam Kumaran: Think about it, and let us know.
133 00:16:18.090 ⇒ 00:16:35.469 Uttam Kumaran: So let’s see, they they’re not really on the transaction side, meaning like they’re not really on the buying and selling of real estate. They’re more on the management side, and like, actually like managing the building health stuff like that. So let’s see like they wanted to follow up again sometime in January. So
134 00:16:35.893 ⇒ 00:16:42.980 Uttam Kumaran: she like, they’re passionate about making money. So I’m just gonna throw what we have at them. I it’s also a friend of mine. So
135 00:16:43.360 ⇒ 00:16:52.159 Uttam Kumaran: let’s see what they say. Yeah, I think that’s really all the sales stuff
136 00:16:54.890 ⇒ 00:17:04.030 Uttam Kumaran: today. So I I sent out the clever proposal. I don’t know if you noticed he was like, Hey, we need us people does the price change. And I was like, no, so I do think that’s a good thing.
137 00:17:04.670 ⇒ 00:17:07.489 Uttam Kumaran: That is probably our price is in a good range.
138 00:17:07.829 ⇒ 00:17:09.649 Robert Tseng: Okay, yeah, no. I.
139 00:17:10.119 ⇒ 00:17:10.890 Uttam Kumaran: Yeah.
140 00:17:10.890 ⇒ 00:17:15.719 Robert Tseng: Yeah, cool. So let’s see, let’s see what he says. Yeah.
141 00:17:16.720 ⇒ 00:17:24.009 Uttam Kumaran: Yeah, he basically was like, look for in terms of our data security, we need everyone. Us. I was like, does that change the price I was like, no, that’s does not change the price.
142 00:17:24.380 ⇒ 00:17:35.140 Robert Tseng: Clint knows him, too. So I’m I’m gonna see Clint later today. And I’m gonna you know, just I know that I know they’re like, I know they’re buddies, I guess. So I’m gonna tell him to put in a good word for us.
143 00:17:35.470 ⇒ 00:17:36.850 Uttam Kumaran: Oh, cool, nice.
144 00:17:37.600 ⇒ 00:17:40.750 Uttam Kumaran: Yeah. Buy Clint. Buy Clint a beer or something, please on me.
145 00:17:43.417 ⇒ 00:17:48.650 Uttam Kumaran: Cool. Yeah. Ask Clint about the dasty stuff, too. He probably he knows that really really well.
146 00:17:48.850 ⇒ 00:17:49.940 Robert Tseng: Yeah, okay.
147 00:17:49.940 ⇒ 00:17:54.640 Uttam Kumaran: You know, you know. While they were trying to push that into Daphne, I think at some point.
148 00:17:54.900 ⇒ 00:17:56.420 Robert Tseng: Oh, really. Okay.
149 00:17:56.420 ⇒ 00:17:59.120 Uttam Kumaran: They wanted to make it like a module in dacity.
150 00:18:00.080 ⇒ 00:18:06.680 Robert Tseng: Oh, okay, interesting. Yeah, they’re the wild guys are hosting some happy hour today. So.
151 00:18:06.680 ⇒ 00:18:08.750 Uttam Kumaran: Oh, yeah, that’s right. That’s right. That’s right. Okay.
152 00:18:09.070 ⇒ 00:18:10.000 Robert Tseng: Okay.
153 00:18:11.800 ⇒ 00:18:18.870 Uttam Kumaran: cool. I think that’s a lot of what I have. The a lot of I’ve I’ve other 2 other interviews today.
154 00:18:19.515 ⇒ 00:18:25.410 Uttam Kumaran: One with a potential data guy, one with A AI person.
155 00:18:25.908 ⇒ 00:18:34.249 Uttam Kumaran: And then I have another AI interview tomorrow morning. I guess the only other thing I want to confirm is if we can all hop on the phone.
156 00:18:34.540 ⇒ 00:18:40.170 Uttam Kumaran: I mean today, or even after, like, I know, you’re running to Happy Hour. But sometime today to just
157 00:18:40.260 ⇒ 00:18:44.110 Uttam Kumaran: basically sign off on the Pm stuff.
158 00:18:45.420 ⇒ 00:18:46.350 Robert Tseng: Yeah.
159 00:18:47.850 ⇒ 00:18:49.889 Uttam Kumaran: I know you have your!
160 00:18:49.890 ⇒ 00:18:51.140 Uttam Kumaran: I think I dropped.
161 00:18:51.240 ⇒ 00:18:59.911 Robert Tseng: Like 12 to 2. I think I have a window. I’m trying to work towards like getting. I have like a Google sheet that I was just gonna like read over kind of report a loom for. But
162 00:19:00.140 ⇒ 00:19:00.640 Uttam Kumaran: Okay.
163 00:19:00.640 ⇒ 00:19:04.083 Robert Tseng: Yeah, kind of just I was like thinking through some sort of
164 00:19:04.370 ⇒ 00:19:19.890 Uttam Kumaran: I mean, we could also. We could also do tomorrow afternoon. But we may need to see like an hour, an hour and a half block, because we just have a bunch of stuff to cover, because I tomorrow’s our next week is our last week before Christmas. So if we want to test out some of the
165 00:19:20.210 ⇒ 00:19:23.219 Uttam Kumaran: sort of Pm. Stuff next week is a good week.
166 00:19:24.027 ⇒ 00:19:27.850 Uttam Kumaran: But I mean, look, it’s it’s what we can handle. So.
167 00:19:28.080 ⇒ 00:19:28.920 Robert Tseng: Yeah.
168 00:19:29.080 ⇒ 00:19:37.679 Robert Tseng: I mean, I’m fine with doing it. Tomorrow. I’m still gonna just like I’m gonna record all this stuff, Async, like, I’m I’m ready to to share share it. I just need to.
169 00:19:37.800 ⇒ 00:19:39.700 Robert Tseng: Yeah, I’m gonna record the
170 00:19:39.920 ⇒ 00:19:43.289 Robert Tseng: a couple of videos now and then. I’m gonna send them both out. But
171 00:19:43.640 ⇒ 00:19:48.160 Robert Tseng: yeah, I think we can. If you guys want some, take a day to kind of review, and then we can.
172 00:19:49.160 ⇒ 00:19:52.290 Robert Tseng: You are you kind of get it all in one. Go tomorrow. I’m I’m cool with that.
173 00:19:52.550 ⇒ 00:19:59.819 Uttam Kumaran: Okay, okay, cool. Then let me grab time. Tomorrow, maybe after the the Friday call or something, and then we could just all stuff out.
174 00:20:00.130 ⇒ 00:20:00.505 Robert Tseng: Yeah.
175 00:20:03.730 ⇒ 00:20:07.100 Uttam Kumaran: Okay. Cool. Anything else to discuss.
176 00:20:13.040 ⇒ 00:20:17.260 Miguel de Veyra: Yeah, sorry I was late. Were you guys able to discuss the ABC demo later on?
177 00:20:17.260 ⇒ 00:20:20.480 Uttam Kumaran: No, but maybe me and you can stay on. You want to chat about that.
178 00:20:20.480 ⇒ 00:20:21.930 Miguel de Veyra: Yeah, yeah, definitely.
179 00:20:21.930 ⇒ 00:20:22.780 Uttam Kumaran: Okay. Okay.
180 00:20:23.210 ⇒ 00:20:27.670 Uttam Kumaran: Alright. I think that’s it. Everyone. I guess. Miguel, yeah, stay on. We can talk about that.
181 00:20:27.930 ⇒ 00:20:30.540 Robert Tseng: Cool. Thank you. Guys. See, you guys.
182 00:20:30.540 ⇒ 00:20:31.270 Uttam Kumaran: Thanks. Guys.
183 00:20:35.880 ⇒ 00:20:36.980 Uttam Kumaran: Alright.
184 00:20:37.250 ⇒ 00:20:38.220 Miguel de Veyra: Still sick.
185 00:20:38.800 ⇒ 00:20:39.950 Uttam Kumaran: Still sick.
186 00:20:40.654 ⇒ 00:20:43.010 Miguel de Veyra: Yeah, dude. What do you wanna do here?
187 00:20:44.308 ⇒ 00:20:55.289 Miguel de Veyra: Cause there’s we can’t the time as mentioned, we can’t really adjust, because, you know it’s a rag issue, but I think it should be fine. To be honest, I I don’t need why it needs to be fast. I don’t see why it needs to be fast.
188 00:20:55.800 ⇒ 00:21:00.959 Uttam Kumaran: I mean, dude. It’s like you don’t. You don’t go to chat, gpt, and ask it. And it takes 2 min like you’re gonna you’re not. Gonna
189 00:21:01.120 ⇒ 00:21:02.460 Uttam Kumaran: you’re not gonna use it.
190 00:21:06.940 ⇒ 00:21:09.200 Miguel de Veyra: I mean, it’s running on all their data.
191 00:21:11.610 ⇒ 00:21:17.450 Uttam Kumaran: I know, but like it’s tough, demo to do. If it takes 5 min.
192 00:21:18.190 ⇒ 00:21:23.119 Miguel de Veyra: No, I don’t think it should it. It wouldn’t take 5 min, probably like 30 a minute at Max.
193 00:21:23.850 ⇒ 00:21:26.200 Uttam Kumaran: There’s nothing we could do to make it faster.
194 00:21:27.966 ⇒ 00:21:30.739 Miguel de Veyra: Well, there’s a demo way to do it.
195 00:21:30.790 ⇒ 00:21:34.905 Uttam Kumaran: Like we can just put, basically because Scott gave us like a couple of questions.
196 00:21:35.440 ⇒ 00:21:41.390 Uttam Kumaran: not that way. But like, I don’t know dude, I mean, I there’s really no faster. I mean.
197 00:21:41.920 ⇒ 00:21:45.029 Uttam Kumaran: maybe we need to do more R&D, but like, I feel like there’s
198 00:21:45.120 ⇒ 00:21:48.279 Uttam Kumaran: there are methods to make rag like way, faster.
199 00:21:52.210 ⇒ 00:21:56.959 Miguel de Veyra: Cause we’re all using the best model, and that’s all that’s already. N. 8 n.
200 00:22:00.450 ⇒ 00:22:00.980 Miguel de Veyra: so.
201 00:22:00.980 ⇒ 00:22:06.619 Uttam Kumaran: I mean, but like you’ve never worked on any rag that’s like like more like 5, 10 seconds.
202 00:22:07.490 ⇒ 00:22:12.439 Miguel de Veyra: No, no, unless the data it. It really depends on how small the data is, right. But
203 00:22:13.520 ⇒ 00:22:24.819 Miguel de Veyra: there’s like 30 40 pages that we’re basically scraping. And then to add in that, like the other stuff, too. So it’s pretty big. Their data size. If it’s like one or 2 page rag, then, you know.
204 00:22:25.830 ⇒ 00:22:37.580 Miguel de Veyra: cause the the way the way that’s why it’s fast in Chat Gpt is because when you upload the file that it’s it’s it gets added to the context, not the knowledge. So it’s it gets added to the prompt.
205 00:22:38.170 ⇒ 00:22:41.590 Miguel de Veyra: We can do that. But it’s gonna be like, super super expensive.
206 00:22:44.070 ⇒ 00:22:46.899 Uttam Kumaran: I mean, I guess, like I don’t know what the
207 00:22:48.930 ⇒ 00:23:00.181 Uttam Kumaran: but then dude, but relevance is doing rag in in a really fast amount of time right? So I don’t know what’s going on like I get what you’re saying. But I’m not. I’m not, Con. I’m not convinced.
208 00:23:02.538 ⇒ 00:23:09.230 Miguel de Veyra: No, no, we’re not. All of the demo we’re gonna use is the one that’s a bit faster, because relevance rag is really slow.
209 00:23:10.760 ⇒ 00:23:15.359 Miguel de Veyra: So the one we’re using for the demo the one Scott tried out, he said. It takes a minute
210 00:23:15.610 ⇒ 00:23:17.730 Miguel de Veyra: is already an 8 NA. 10.
211 00:23:18.980 ⇒ 00:23:22.190 Uttam Kumaran: Do we know what part of the rag is taking? A long time.
212 00:23:25.630 ⇒ 00:23:26.350 Miguel de Veyra: Basically.
213 00:23:26.350 ⇒ 00:23:31.350 Uttam Kumaran: This is where I wanted. This is where I wanted trace loop, because how do we know what part is
214 00:23:31.850 ⇒ 00:23:33.030 Uttam Kumaran: slowing down.
215 00:23:33.230 ⇒ 00:23:37.540 Miguel de Veyra: Actually, we can see it. Can we drag Casey into this call?
216 00:23:37.920 ⇒ 00:23:38.750 Uttam Kumaran: Sure.
217 00:23:39.390 ⇒ 00:23:40.629 Miguel de Veyra: Alright! Let me just
218 00:23:47.650 ⇒ 00:23:51.999 Miguel de Veyra: So I think he has the entire Nan setup already.
219 00:23:52.480 ⇒ 00:23:54.100 Miguel de Veyra: like it’s open on his end.
220 00:24:17.770 ⇒ 00:24:21.069 Miguel de Veyra: Wait, let me just call him if the case is here.
221 00:24:24.210 ⇒ 00:24:25.230 Miguel de Veyra: Hey, Casey.
222 00:24:30.150 ⇒ 00:24:31.080 Casie Aviles: Hey! Hey!
223 00:24:31.940 ⇒ 00:24:49.479 Miguel de Veyra: Hey, yeah, can you like? Cause we’re we’re like discussing on where you know it’s taking like a bit can you pull up the N. 8 and ABC. 8, basically agent we made, and then just cause I I know in the Runtime you can see like the time it took there.
224 00:24:50.840 ⇒ 00:24:52.010 Casie Aviles: Okay. Sure.
225 00:24:56.310 ⇒ 00:25:04.080 Uttam Kumaran: I mean, if we can’t solve it today, it’s fine. But like, do we? Where I can’t? We can’t have rag that takes 2 min like it’s there’s nothing.
226 00:25:04.080 ⇒ 00:25:04.770 Miguel de Veyra: And.
227 00:25:04.770 ⇒ 00:25:09.309 Uttam Kumaran: There’s no way like. So I mean, it’s a technology problem. We’ll we’ll have to figure it out.
228 00:25:09.310 ⇒ 00:25:11.320 Miguel de Veyra: Yeah, go to executions.
229 00:25:16.821 ⇒ 00:25:29.480 Miguel de Veyra: Well, what? Yeah. Another thing we can do, because it could be like the relevance Ui issue with them, because sometimes it also happens to my end, where you actually have to keep reloading.
230 00:25:30.640 ⇒ 00:25:34.739 Uttam Kumaran: Yeah. But dude, we’re gonna lose. We’re gonna lose. If we the demo doesn’t work.
231 00:25:35.020 ⇒ 00:25:36.590 Miguel de Veyra: Yeah, there, you go.
232 00:25:36.590 ⇒ 00:25:40.800 Uttam Kumaran: So like these aren’t all we can’t. We? We can’t have these. We can’t have these issues.
233 00:25:41.260 ⇒ 00:25:41.860 Miguel de Veyra: You?
234 00:25:43.650 ⇒ 00:25:46.659 Miguel de Veyra: Yeah, there you go. So this one took 24 seconds.
235 00:25:47.270 ⇒ 00:25:49.990 Miguel de Veyra: What was the query here for the 24 second one.
236 00:25:53.500 ⇒ 00:25:54.360 Casie Aviles: No.
237 00:25:54.590 ⇒ 00:25:56.760 Miguel de Veyra: I don’t think you can see it with, can we?
238 00:25:59.360 ⇒ 00:26:00.800 Casie Aviles: Yeah, this one. How this flag.
239 00:26:02.130 ⇒ 00:26:05.739 Miguel de Veyra: Okay. So this one took, you know, 24 seconds. Basically.
240 00:26:05.740 ⇒ 00:26:09.590 Uttam Kumaran: So what is, what piece of it takes the longest, that’s what that’s what I want to know.
241 00:26:11.400 ⇒ 00:26:12.510 Uttam Kumaran: You know what I mean.
242 00:26:12.880 ⇒ 00:26:13.600 Miguel de Veyra: Yeah.
243 00:26:14.640 ⇒ 00:26:16.920 Uttam Kumaran: If we don’t know it’s fine, if we don’t know, it’s okay. But.
244 00:26:19.090 ⇒ 00:26:23.179 Casie Aviles: I mean, I think it’s going to be the here the retrieval.
245 00:26:23.536 ⇒ 00:26:24.960 Miguel de Veyra: Yeah, that’s that part.
246 00:26:25.280 ⇒ 00:26:31.700 Casie Aviles: Also the the ui part, which is relevance, because, you know, it’s sending out
247 00:26:31.910 ⇒ 00:26:34.790 Casie Aviles: a request here via the webhook.
248 00:26:35.840 ⇒ 00:26:37.910 Casie Aviles: But hmm.
249 00:26:38.710 ⇒ 00:26:41.550 Uttam Kumaran: So that we so we need to improve like.
250 00:26:42.020 ⇒ 00:26:49.430 Uttam Kumaran: So then, this is something that do you think will be? I mean for me, I’m like, okay, I need some more time to basically do some research and find.
251 00:26:50.100 ⇒ 00:27:00.439 Uttam Kumaran: because I know retrieval, you can do a bunch of different things. So you have different ways to do indexing. You have different ways of embedding. We can try a faster database like.
252 00:27:02.910 ⇒ 00:27:05.990 Miguel de Veyra: Yeah, yeah, we can do some R&D on that definitely.
253 00:27:07.060 ⇒ 00:27:07.810 Miguel de Veyra: But I think.
254 00:27:07.810 ⇒ 00:27:09.350 Uttam Kumaran: That’s tough.
255 00:27:09.790 ⇒ 00:27:29.840 Miguel de Veyra: Yeah, that’s not possible today, definitely, I think another thing we can do like for future Demos also is, maybe it’s better because the relevancy wise. I don’t know what’s happening to them, but their Api is kind of good like. If you notice, if you try it out in Hpi, it doesn’t take that long, and Hpi, you know, has a pretty large database right.
256 00:27:30.290 ⇒ 00:27:34.679 Uttam Kumaran: Yeah, so like, that’s what I’m saying. What do you? What do we mean that it takes like 2 min.
257 00:27:35.827 ⇒ 00:27:46.899 Miguel de Veyra: It doesn’t reflect, even if the it doesn’t really take 2 min. I think that’s an exaggeration. But it doesn’t reload the page. Basically it doesn’t show the answer, even if there’s already an answer.
258 00:27:48.310 ⇒ 00:27:49.800 Uttam Kumaran: Oh, okay. Yeah.
259 00:27:49.800 ⇒ 00:27:51.579 Miguel de Veyra: So you have to reload it.
260 00:27:51.950 ⇒ 00:27:58.810 Miguel de Veyra: That’s why I keep reloading it. But if we create, that’s why I was gonna suggest, if we create like, basically like a Ui of
261 00:27:59.250 ⇒ 00:28:06.849 Miguel de Veyra: we, something like Hpi, right where, basically, if it’s like a demo, we just swap the agent ids, and that that works.
262 00:28:08.200 ⇒ 00:28:12.479 Uttam Kumaran: Yeah, we, I mean, we should just create like a brain forward. Demo. Ui.
263 00:28:12.900 ⇒ 00:28:16.269 Miguel de Veyra: Yeah, and then just swap the agent ids, and that should fix everything
264 00:28:16.990 ⇒ 00:28:27.989 Miguel de Veyra: I can work on that so like in the future. Because I think honestly, I think, since there’s like a lot of animations and relevance that’s probably adding, like, I would say, 1020 seconds.
265 00:28:28.420 ⇒ 00:28:30.640 Miguel de Veyra: and then the bugs probably, you know.
266 00:28:31.400 ⇒ 00:28:35.349 Miguel de Veyra: cause I don’t think Scott would know that it’s bugging definitely.
267 00:28:35.540 ⇒ 00:28:38.570 Miguel de Veyra: So he’s gonna say, you know, 5 min.
268 00:28:41.390 ⇒ 00:28:42.390 Uttam Kumaran: Yeah.
269 00:28:42.770 ⇒ 00:28:44.650 Miguel de Veyra: Yeah, there you go. It’s fast here.
270 00:28:51.230 ⇒ 00:28:53.720 Miguel de Veyra: Oh, maybe we can just use this for now. No.
271 00:28:56.240 ⇒ 00:28:57.340 Uttam Kumaran: Here’s what.
272 00:28:57.934 ⇒ 00:29:01.089 Miguel de Veyra: Look at the screen. It’s it’s pretty fast.
273 00:29:02.448 ⇒ 00:29:04.599 Miguel de Veyra: Wait, Casey, did you choose this or no?
274 00:29:05.991 ⇒ 00:29:07.099 Casie Aviles: No! This is using Rob.
275 00:29:09.590 ⇒ 00:29:10.489 Miguel de Veyra: Okay, there, you listen.
276 00:29:10.490 ⇒ 00:29:11.320 Uttam Kumaran: Pretty quick.
277 00:29:11.570 ⇒ 00:29:12.779 Miguel de Veyra: Let’s just use this. No.
278 00:29:15.060 ⇒ 00:29:15.525 Casie Aviles: Yeah.
279 00:29:17.470 ⇒ 00:29:20.839 Casie Aviles: All I’ve been asking these questions. It’s been answering.
280 00:29:23.370 ⇒ 00:29:25.699 Uttam Kumaran: Is this a public link? Or how do we get to this.
281 00:29:27.480 ⇒ 00:29:28.590 Casie Aviles: Yeah, this one, this one.
282 00:29:28.590 ⇒ 00:29:32.430 Uttam Kumaran: Can you try to? Can you? Can you? Can you open this in incognito? See if it works.
283 00:29:33.240 ⇒ 00:29:34.030 Casie Aviles: Yeah, sure.
284 00:29:38.420 ⇒ 00:29:40.509 Casie Aviles: Okay. And that’s the same.
285 00:29:40.510 ⇒ 00:29:42.040 Miguel de Veyra: Try? Something. Try, something. Yeah.
286 00:29:45.180 ⇒ 00:29:46.359 Casie Aviles: Maybe this one.
287 00:29:48.080 ⇒ 00:29:54.129 Miguel de Veyra: We might have to migrate out of of relevance, though they’re becoming too buggy.
288 00:29:54.730 ⇒ 00:29:56.500 Uttam Kumaran: You’re the one you’re the one that’s
289 00:29:56.690 ⇒ 00:30:00.139 Uttam Kumaran: for a I don’t mind for us internally, but dude like.
290 00:30:00.590 ⇒ 00:30:03.580 Miguel de Veyra: Yeah, yeah, never. We’re never gonna use relevance long. Like.
291 00:30:03.620 ⇒ 00:30:09.989 Uttam Kumaran: Relevance is for people that are like taking baby steps into this world like we’re gonna go. We’re gonna go another mile deeper.
292 00:30:10.300 ⇒ 00:30:10.970 Miguel de Veyra: Yeah.
293 00:30:10.970 ⇒ 00:30:14.330 Uttam Kumaran: It’s all gonna be custom for us, like it’s nice
294 00:30:14.970 ⇒ 00:30:18.340 Uttam Kumaran: for Demos, maybe, but like and for us internally. But.
295 00:30:18.740 ⇒ 00:30:24.679 Miguel de Veyra: Yeah, for production level. No, not, I think, for Demos. Yeah, definitely, but for production. No.
296 00:30:25.430 ⇒ 00:30:26.200 Uttam Kumaran: Yeah.
297 00:30:29.980 ⇒ 00:30:36.829 Miguel de Veyra: Let’s spend some time, probably next week, Casey doing R. And D on this. I don’t think there’s a lot of Demos we need to work on next week, anyways.
298 00:30:37.620 ⇒ 00:30:38.619 Casie Aviles: Yeah, sure. Sure.
299 00:30:38.990 ⇒ 00:30:41.970 Uttam Kumaran: Can you put this link in in notion, Casey.
300 00:30:43.180 ⇒ 00:30:45.150 Miguel de Veyra: This is a lot faster route.
301 00:30:46.000 ⇒ 00:30:47.359 Miguel de Veyra: Let’s just do this.
302 00:30:49.110 ⇒ 00:30:49.760 Casie Aviles: One.
303 00:30:53.780 ⇒ 00:30:59.009 Miguel de Veyra: Cause. I know, I think, one of the cause when I spoke with the relevance guys before
304 00:30:59.310 ⇒ 00:31:06.100 Miguel de Veyra: it could be the one adding, time is because they also have, because we have our core instructions right? And then our settings and everything.
305 00:31:06.290 ⇒ 00:31:14.780 Miguel de Veyra: They also have basically their own set of instructions on basically how to guide the agent so that could be like adding to the time.
306 00:31:18.040 ⇒ 00:31:24.610 Casie Aviles: Yeah. But this also, this one doesn’t have the booking yet. I mean, it’s going it can ask for like the details, like.
307 00:31:25.454 ⇒ 00:31:28.030 Miguel de Veyra: Yeah, yeah. We don’t have that feature for this yet. The tool.
308 00:31:28.030 ⇒ 00:31:30.670 Casie Aviles: Yeah, the 2, because it’s in relevance
309 00:31:30.800 ⇒ 00:31:34.930 Casie Aviles: when we implemented it. So yeah, it’s gonna ask something like this. But.
310 00:31:36.200 ⇒ 00:31:38.140 Uttam Kumaran: No dude. This is way quicker. Bro.
311 00:31:38.450 ⇒ 00:31:39.940 Miguel de Veyra: Yeah, yeah, let’s just do this.
312 00:31:41.300 ⇒ 00:31:44.080 Uttam Kumaran: See. See, Miguel, it can be quite it can be fast.
313 00:31:49.960 ⇒ 00:31:52.640 Uttam Kumaran: Yo. This works. This works great.
314 00:31:52.930 ⇒ 00:32:01.709 Miguel de Veyra: Yeah, yeah, let’s just do this good thing. We were debating on this with them last night. I was like, Yeah, let’s just make it on an end just in case worst case, scenario.
315 00:32:01.710 ⇒ 00:32:08.729 Uttam Kumaran: No, I mean, I want like we’re gonna move it. The only reason we’re not using all na, then, is just because it’s like a learning curve.
316 00:32:08.740 ⇒ 00:32:22.357 Uttam Kumaran: But honestly, what will happen is like, we will. We will come up with our own best sort of rag system, and then we can scale that you know. So let me send this. So let me send this to Scott.
317 00:32:48.160 ⇒ 00:32:53.239 Casie Aviles: Yeah, interesting, since these these ones are green. So there’s
318 00:32:53.870 ⇒ 00:32:56.689 Casie Aviles: so we know that it’s using route. And yeah.
319 00:32:56.690 ⇒ 00:32:57.970 Uttam Kumaran: Oh, okay. Okay.
320 00:32:57.970 ⇒ 00:32:58.390 Casie Aviles: How’s good?
321 00:32:58.390 ⇒ 00:32:59.999 Miguel de Veyra: Yeah, yeah. The content.
322 00:33:02.480 ⇒ 00:33:04.579 Miguel de Veyra: Can I have multiple tools here? Right.
323 00:33:05.966 ⇒ 00:33:09.080 Casie Aviles: Yeah, I think so. But I haven’t really attached.
324 00:33:09.080 ⇒ 00:33:11.740 Miguel de Veyra: Yeah, we yeah, we haven’t really looked into that.
325 00:33:13.060 ⇒ 00:33:15.160 Casie Aviles: Just this retrieval tool.
326 00:33:15.930 ⇒ 00:33:16.710 Miguel de Veyra: Yeah.
327 00:33:18.570 ⇒ 00:33:21.189 Casie Aviles: And yeah, I’m not sure how well it will work
328 00:33:21.330 ⇒ 00:33:26.920 Casie Aviles: for now. But the the cool thing is, you could also like drop Pdf files, and like, you know, other files.
329 00:33:26.920 ⇒ 00:33:27.690 Miguel de Veyra: Work out.
330 00:33:34.860 ⇒ 00:33:38.469 Miguel de Veyra: Wait! Where’s the auto? Where’s the automation for that.
331 00:33:39.490 ⇒ 00:33:41.600 Casie Aviles: This one. I put it here.
332 00:33:43.726 ⇒ 00:33:48.270 Miguel de Veyra: Okay, okay. I was so used to it, having, like the boxes.
333 00:33:48.270 ⇒ 00:33:48.900 Casie Aviles: Yeah.
334 00:33:53.660 ⇒ 00:33:54.160 Miguel de Veyra: Okay.
335 00:33:54.160 ⇒ 00:33:55.239 Casie Aviles: But yeah, yeah,
336 00:33:56.770 ⇒ 00:33:58.310 Uttam Kumaran: Okay, cool. Let’s see what he says.
337 00:33:59.280 ⇒ 00:33:59.620 Miguel de Veyra: Yeah,
338 00:34:03.250 ⇒ 00:34:06.590 Miguel de Veyra: cause tools, basically, are just, Api calls, right?
339 00:34:08.560 ⇒ 00:34:09.610 Casie Aviles: Yeah, yeah.
340 00:34:13.360 ⇒ 00:34:15.529 Miguel de Veyra: Yeah. Spend some time here next week.
341 00:34:34.699 ⇒ 00:34:40.469 Uttam Kumaran: Okay, guys, let’s just see what he says. I’m gonna I gotta start my day. Okay.
342 00:34:40.469 ⇒ 00:34:40.819 Miguel de Veyra: Okay.
343 00:34:40.820 ⇒ 00:34:44.689 Uttam Kumaran: Meeting our meetings in a while. So what else are you guys working on today?
344 00:34:47.292 ⇒ 00:34:52.349 Miguel de Veyra: This was like the last of it. We have like a call with Sophie later, like in about an hour
345 00:34:52.710 ⇒ 00:34:54.090 Miguel de Veyra: from Browser base.
346 00:34:54.090 ⇒ 00:34:55.500 Uttam Kumaran: Oh, nice. Okay. Great.
347 00:34:55.500 ⇒ 00:35:00.679 Miguel de Veyra: Yeah, cause I think that’s an I’m not sure if Case is working on any internal agents right now.
348 00:35:01.870 ⇒ 00:35:06.099 Casie Aviles: Yeah, like, the lead research agent is turned off right now because of the.
349 00:35:06.100 ⇒ 00:35:06.780 Miguel de Veyra: Oh, yeah. Yeah.
350 00:35:06.780 ⇒ 00:35:07.589 Casie Aviles: I haven’t been to Craig.
351 00:35:07.590 ⇒ 00:35:11.519 Miguel de Veyra: Oh, yeah, they had applied to me. By the way, about that. Just don’t use cloud ever again.
352 00:35:14.070 ⇒ 00:35:19.899 Miguel de Veyra: because it cost 4,000 tokens basically credits we don’t have like an Api in relevance for that.
353 00:35:20.830 ⇒ 00:35:21.400 Uttam Kumaran: So.
354 00:35:21.710 ⇒ 00:35:22.320 Miguel de Veyra: Yeah, just.
355 00:35:22.320 ⇒ 00:35:24.669 Uttam Kumaran: Are they gonna give us? Are they gonna give us credits back.
356 00:35:25.791 ⇒ 00:35:27.799 Miguel de Veyra: I’m negotiating with them so.
357 00:35:27.800 ⇒ 00:35:30.520 Uttam Kumaran: Tell them what the fuck tell him to give it back like. It’s terrible.
358 00:35:30.520 ⇒ 00:35:31.889 Miguel de Veyra: Yeah, I told him
359 00:35:32.110 ⇒ 00:35:37.689 Miguel de Veyra: there should be like at least a warning or something. Right? 2 runs a 4,000, I think. Yeah, yeah. But.
360 00:35:37.690 ⇒ 00:35:42.369 Uttam Kumaran: Yeah. Ask them for 10,000 credits. I mean, it’s 20 bucks. But like, ask them for to ask for it back like.
361 00:35:44.549 ⇒ 00:35:45.719 Miguel de Veyra: But yeah, okay.
362 00:35:46.440 ⇒ 00:35:51.190 Miguel de Veyra: And then I guess after that with them we’ll spend some time on
363 00:35:51.460 ⇒ 00:35:58.229 Miguel de Veyra: the vitaco stuff. I know not not via the cocoa, the browser based stuff
364 00:35:58.330 ⇒ 00:36:00.050 Miguel de Veyra: based on how the meeting goes.
365 00:36:00.440 ⇒ 00:36:02.670 Uttam Kumaran: Yeah, how did how did the
366 00:36:03.310 ⇒ 00:36:07.350 Uttam Kumaran: what? What’s next with Vitaco? Oh, just to like fix it, or what.
367 00:36:07.966 ⇒ 00:36:19.509 Miguel de Veyra: It’s running right now. So Eddie and I spoke over on email, and we’re just basically we just we just wanna monitor this week how it performs, but I’m checking it every day, and it’s doing well so far.
368 00:36:21.595 ⇒ 00:36:22.180 Uttam Kumaran: Okay.
369 00:36:22.180 ⇒ 00:36:31.569 Miguel de Veyra: Like? Because before he was asking me, why is there like 900, basically only 20 in stock. So I I added some changes, applied. Some, you know.
370 00:36:31.700 ⇒ 00:36:36.800 Miguel de Veyra: improvements on the code, basically just in house to our security stuff
371 00:36:37.700 ⇒ 00:36:44.270 Miguel de Veyra: to dial for the anti-bot detections. And yeah, it’s it kind of works. So it is working. But yeah.
372 00:36:44.600 ⇒ 00:36:48.040 Miguel de Veyra: I’m gonna I have a bit of questions for Sophie. Later.
373 00:36:49.670 ⇒ 00:36:50.260 Uttam Kumaran: Okay.
374 00:36:53.470 ⇒ 00:37:00.280 Uttam Kumaran: Alright, I’m gonna also organize like the AI board today. And then, yeah, some of these research things we can start next week.
375 00:37:00.970 ⇒ 00:37:02.509 Miguel de Veyra: Yeah, yeah, R&D stuff.
376 00:37:02.510 ⇒ 00:37:16.279 Uttam Kumaran: Yeah, I know we have. We have a couple of internal automations like the lead research, the Zoom agent. So we can just make sure we know what’s coming up for all those and start the project plan. And then, basically, Miguel, we’ll run like a we’ll. We’ll run a project
377 00:37:16.350 ⇒ 00:37:18.600 Uttam Kumaran: plan meeting next week.
378 00:37:18.840 ⇒ 00:37:21.910 Uttam Kumaran: Sort of with everything. Yeah, so we can
379 00:37:22.160 ⇒ 00:37:23.979 Uttam Kumaran: make sure we’re all on the same track.
380 00:37:24.950 ⇒ 00:37:26.119 Miguel de Veyra: Okay. Okay. Sure.
381 00:37:28.000 ⇒ 00:37:28.680 Uttam Kumaran: Okay.
382 00:37:29.010 ⇒ 00:37:31.240 Uttam Kumaran: Alright guys, I’ll talk to you guys, probably in a bit.
383 00:37:31.690 ⇒ 00:37:32.720 Miguel de Veyra: Okay, thanks.
384 00:37:32.720 ⇒ 00:37:33.529 Miguel de Veyra: Okay. Thank you.
385 00:37:33.530 ⇒ 00:37:34.430 Miguel de Veyra: Have a good day. Bye. Bye.
386 00:37:34.430 ⇒ 00:37:34.910 Uttam Kumaran: Bye.