Meeting Title: AI Meeting Date: 2024-12-05 Meeting participants: Mariane Cequina, Luke Daque, Nicolas Sucari, Uttam Kumaran, Ryan Brosas, Miguel De Veyra, Casie Aviles
WEBVTT
1 00:00:02.570 ⇒ 00:00:04.329 Miguel de Veyra: Okay, yeah, there you go.
2 00:00:06.410 ⇒ 00:00:15.550 Miguel de Veyra: So yeah, so I think for this 1st session with them, I think, what I, what I want to do is basically just ask everyone you know on
3 00:00:15.680 ⇒ 00:00:17.759 Miguel de Veyra: what’s something they could.
4 00:00:18.235 ⇒ 00:00:31.060 Miguel de Veyra: They think that any agent could help. I could give you like some examples of what we use, what I use it for. Maybe that will, you know, bring up some bells here. You don’t necessarily have to come up with something right now.
5 00:00:31.270 ⇒ 00:00:35.659 Miguel de Veyra: It could be, you know, just be shared on the Brainforge team or the AI Channel.
6 00:00:37.038 ⇒ 00:00:43.370 Miguel de Veyra: But yeah, let me share my screen course relevance.
7 00:00:54.440 ⇒ 00:00:55.939 Miguel de Veyra: Yeah. So here you go.
8 00:01:00.410 ⇒ 00:01:02.040 Miguel de Veyra: Can everyone see my screen?
9 00:01:04.300 ⇒ 00:01:04.970 Nicolas Sucari: Yes.
10 00:01:04.970 ⇒ 00:01:09.800 Miguel de Veyra: Okay, yeah. So here’s like one of the
11 00:01:10.250 ⇒ 00:01:21.160 Miguel de Veyra: well, this is this meeting Summarizer. But this is one of the agents I recently built, because this was on my personal chat Gpt, so I just built it here. It’s basically like a coding agent for us.
12 00:01:22.010 ⇒ 00:01:28.379 Miguel de Veyra: because, for example, one of the things we were working on currently is with Vitaco. I’m handing it over to Casey.
13 00:01:28.510 ⇒ 00:01:37.780 Miguel de Veyra: So we created basically this agent. The problem is pretty short. You know, you’re a senior level developer with expertise in this areas.
14 00:01:38.270 ⇒ 00:01:43.309 Miguel de Veyra: And then basically, what I asked is to what I asked it to do is basically to Qa, it.
15 00:01:44.120 ⇒ 00:01:52.449 Miguel de Veyra: Wait. Let me just oh, yeah, basically here, go to review it, basically. And then, you know, came up with some stuff that we could improve.
16 00:01:53.800 ⇒ 00:02:11.770 Miguel de Veyra: should I separate to multiple files, basically just talking to it, and then the file structure? And then eventually I asked it to. You know, I’m handing it over to one of our developers. Could you please create for him to follow? This would have been otherwise. You know I have to create on my own. But then, as you can see, everything is already in here, and I’ve handed it over to Casey.
17 00:02:13.366 ⇒ 00:02:15.490 Miguel de Veyra: But yeah, this is one of the
18 00:02:15.690 ⇒ 00:02:17.419 Miguel de Veyra: things that I use it, for
19 00:02:17.730 ⇒ 00:02:21.899 Miguel de Veyra: on my personal chat I’m not sure if it’s logged in here, let me check.
20 00:02:23.070 ⇒ 00:02:24.359 Miguel de Veyra: Oh, yeah, it is.
21 00:02:24.400 ⇒ 00:02:27.879 Miguel de Veyra: Wait! I do also have wait. Let me see.
22 00:02:29.390 ⇒ 00:02:35.540 Miguel de Veyra: Yeah. So I have, like this voice agent, prompt maker and the improver and maker. So it’s basically.
23 00:02:35.580 ⇒ 00:02:42.740 Miguel de Veyra: I just put all the details of a website here, and then it automatically creates that. But again, I have to move this into relevance. So everyone can see
24 00:02:44.700 ⇒ 00:02:46.560 Uttam Kumaran: Can you zoom in a little bit, Miguel?
25 00:02:47.595 ⇒ 00:02:48.030 Miguel de Veyra: Sorry!
26 00:02:50.270 ⇒ 00:02:52.580 Uttam Kumaran: Or just make maybe make your window smaller. I don’t know.
27 00:02:54.340 ⇒ 00:02:55.489 Miguel de Veyra: Oh! Is this better?
28 00:02:56.420 ⇒ 00:02:57.449 Uttam Kumaran: Yeah, this is good.
29 00:02:58.090 ⇒ 00:02:59.999 Miguel de Veyra: So improver and maker.
30 00:03:01.490 ⇒ 00:03:06.660 Miguel de Veyra: So basically, that’s, you know, that’s what I use to like. If I want to create like an agent.
31 00:03:06.760 ⇒ 00:03:10.259 Miguel de Veyra: to have like a baseline of protocols and instructions.
32 00:03:12.650 ⇒ 00:03:17.340 Miguel de Veyra: But yeah, I think that’s pretty much it on this one.
33 00:03:20.510 ⇒ 00:03:24.240 Miguel de Veyra: Casey is you’re the guy from the AI side. Is there like any agent you’re using
34 00:03:24.500 ⇒ 00:03:25.860 Miguel de Veyra: or prompts you’re using.
35 00:03:26.970 ⇒ 00:03:31.170 Casie Aviles: Yeah. I mean, currently, right now, I I guess I can share a little bit about
36 00:03:31.676 ⇒ 00:03:35.040 Casie Aviles: the internal agents that we have at the company. So
37 00:03:35.700 ⇒ 00:03:38.359 Casie Aviles: yeah, some a few ones that I’ve set up
38 00:03:38.500 ⇒ 00:03:41.420 Casie Aviles: was for the lead researcher agent, like
39 00:03:42.180 ⇒ 00:03:45.790 Casie Aviles: we have one right there over there, as you can see in the screen. Yeah.
40 00:03:46.390 ⇒ 00:03:49.939 Casie Aviles: So basically, I guess the problem that
41 00:03:50.600 ⇒ 00:03:57.450 Casie Aviles: this agent is trying to solve is with when it comes to researching leads like, it’s
42 00:03:57.978 ⇒ 00:04:02.889 Casie Aviles: it’s a manual process, right? Like we have to. You know, do a bunch of Google searches and
43 00:04:03.230 ⇒ 00:04:10.369 Casie Aviles: some scrolling and some yeah, and basically, what this agent does is to streamline that process. So
44 00:04:10.690 ⇒ 00:04:14.140 Casie Aviles: we’ve equipped it with a bunch of tools to do that
45 00:04:14.830 ⇒ 00:04:17.740 Casie Aviles: like Google search and then read.
46 00:04:17.890 ⇒ 00:04:25.639 Casie Aviles: And then also another thing that we could also use is perplexity. Right? So we’ve also started using perplexity in some of our agents to do that.
47 00:04:27.170 ⇒ 00:04:34.617 Casie Aviles: So yeah, basically, the idea is to yeah, make the research process easier. Get the necessary data. And you know,
48 00:04:35.140 ⇒ 00:04:41.890 Casie Aviles: get that initial repetitive tasks out of the way and to focus on just the output.
49 00:04:44.880 ⇒ 00:04:49.179 Miguel de Veyra: So here’s 1 of the things that in case you worked on like the past few days.
50 00:04:49.280 ⇒ 00:04:52.810 Miguel de Veyra: So it’s for a client potential client we have.
51 00:04:54.237 ⇒ 00:05:01.530 Miguel de Veyra: It’s basically like a research agent like the big things here. I from what we can at least see from their documents.
52 00:05:01.710 ⇒ 00:05:08.660 Miguel de Veyra: is, where’s the investment, the stats, basically, how many employees they have, how many clients they have.
53 00:05:08.780 ⇒ 00:05:12.940 Miguel de Veyra: And then, you know, some Linkedin data. And then
54 00:05:15.560 ⇒ 00:05:18.210 Miguel de Veyra: where’s the way I can’t see it funding.
55 00:05:18.853 ⇒ 00:05:20.269 Miguel de Veyra: Here we go funding round.
56 00:05:21.010 ⇒ 00:05:26.950 Miguel de Veyra: So basically, it just researches. If they have, you know, funding or not from here, it doesn’t look like they have it. Do they do have.
57 00:05:27.640 ⇒ 00:05:30.739 Miguel de Veyra: This is like the previous company automi that
58 00:05:30.810 ⇒ 00:05:43.310 Miguel de Veyra: me and Casey worked with. They do have like a funding, because, you know, when venture capitalists look for people are for companies to invest and they want to know if there’s fund basically, this is already involved.
59 00:05:43.580 ⇒ 00:05:46.889 Miguel de Veyra: So you know, it’s here. And yeah.
60 00:05:47.020 ⇒ 00:05:48.830 Miguel de Veyra: that’s what we get from it.
61 00:05:50.180 ⇒ 00:05:52.830 Miguel de Veyra: I know, Ryan, you’re working on some stuff
62 00:05:53.000 ⇒ 00:05:57.630 Miguel de Veyra: from the AI side of things. You’re using some agents. I’m not sure if it’s just
63 00:05:58.190 ⇒ 00:06:01.670 Miguel de Veyra: promptings on Gpt or Quad.
64 00:06:01.670 ⇒ 00:06:12.849 Luke Daque: Yeah, mostly, it’s just doing some prompts especially currently for the synthetic data creation. We did this with utham last, like, I think last week or the other week, I believe.
65 00:06:12.880 ⇒ 00:06:19.570 Luke Daque: where we did try to. Yeah, it’s basically just prompts to have a AI
66 00:06:19.600 ⇒ 00:06:21.700 Luke Daque: like generate a code for us,
67 00:06:23.480 ⇒ 00:06:33.829 Luke Daque: to generate synthetic data. Basically, we, we tried to do it like direct, like, making AI generate the synthetic data for us. But it
68 00:06:34.030 ⇒ 00:06:38.110 Luke Daque: didn’t do that. Well, so we
69 00:06:38.460 ⇒ 00:06:44.620 Luke Daque: we changed the process basically like to ask AI to generate a code that would
70 00:06:44.920 ⇒ 00:06:51.110 Luke Daque: essentially generate synthetic data as well. So yeah, that’s like one of the ways I’m using AI
71 00:06:51.650 ⇒ 00:06:56.059 Luke Daque: other ways I’ve been always using ever since we started
72 00:06:56.150 ⇒ 00:07:00.510 Luke Daque: in in brain. Forge was like having AI also like, Do
73 00:07:02.860 ⇒ 00:07:05.249 Luke Daque: like help improve code. Or like.
74 00:07:05.370 ⇒ 00:07:08.420 Luke Daque: when I’m creating a data model, for example, for
75 00:07:08.660 ⇒ 00:07:14.700 Luke Daque: a specific data model, I, I ask like AI to help us out in
76 00:07:14.710 ⇒ 00:07:18.500 Luke Daque: like making the code more effective, efficient. And stuff like that.
77 00:07:19.690 ⇒ 00:07:20.380 Luke Daque: Yeah,
78 00:07:21.360 ⇒ 00:07:29.978 Luke Daque: And even when I was using copilot in in Vs code before. Well, currently, I’m using cursor for us like my code.
79 00:07:30.680 ⇒ 00:07:31.979 Luke Daque: what do you call that?
80 00:07:32.570 ⇒ 00:07:37.309 Luke Daque: It’s it’s like it. Cursor is like Bs code, but it has AI with it.
81 00:07:37.310 ⇒ 00:07:37.720 Miguel de Veyra: Yep.
82 00:07:37.720 ⇒ 00:07:44.360 Luke Daque: So like, it can like basically complete the code even without me typing in it.
83 00:07:44.530 ⇒ 00:07:46.804 Luke Daque: So it makes a lot.
84 00:07:47.490 ⇒ 00:07:50.160 Luke Daque: it makes the process a lot faster. Basically
85 00:07:50.642 ⇒ 00:07:56.380 Luke Daque: in like creating data models, or even like dashboards, real dashboards and stuff like that. So yeah, it’s pretty
86 00:07:56.900 ⇒ 00:07:58.060 Luke Daque: pretty great.
87 00:08:00.600 ⇒ 00:08:04.645 Luke Daque: What I’ve also done. I tried doing in the past.
88 00:08:06.370 ⇒ 00:08:09.330 Luke Daque: was like, create. Try to have
89 00:08:09.960 ⇒ 00:08:15.940 Luke Daque: AI do code review in our Github repositories, although I haven’t like
90 00:08:16.050 ⇒ 00:08:25.969 Luke Daque: continued working on that one. But I did try that out before, and I did incorporate it in github actions. I was using open AI for that one.
91 00:08:26.030 ⇒ 00:08:34.959 Luke Daque: And like that’s I believe that’s why I asked you, Miguel, before, like, maybe we can do use notion for that instead of like.
92 00:08:34.960 ⇒ 00:08:35.570 Miguel de Veyra: But.
93 00:08:35.980 ⇒ 00:08:37.409 Luke Daque: Hard, coding it in a.
94 00:08:37.419 ⇒ 00:08:38.279 Miguel de Veyra: That they know.
95 00:08:38.280 ⇒ 00:08:40.491 Luke Daque: I haven’t. Unfortunately, I haven’t like
96 00:08:41.049 ⇒ 00:08:45.499 Luke Daque: Yeah, continued working on that one, but that would have been a a cool one to do.
97 00:08:45.760 ⇒ 00:08:48.919 Miguel de Veyra: Yeah, I remember we talked about it like 2 or 3 weeks ago. Now.
98 00:08:48.920 ⇒ 00:08:50.969 Luke Daque: Yeah, something like that. Yeah.
99 00:08:52.100 ⇒ 00:08:55.930 Luke Daque: So yeah, it’s been, that’s essentially like how I’ve been using AI.
100 00:08:55.930 ⇒ 00:09:00.450 Miguel de Veyra: The synthetic data you’ve mentioned. Is there? Is it more like dummy data? Sorry, not familiar.
101 00:09:00.450 ⇒ 00:09:04.470 Luke Daque: Yes, yes, it’s it’s dummy data, basically. So like,
102 00:09:05.280 ⇒ 00:09:09.989 Luke Daque: wait, I I can probably share my screen. Let me just pull pull that one up
103 00:09:10.590 ⇒ 00:09:14.570 Luke Daque: because it’s in a different. It’s in our brain forge repository.
104 00:09:21.850 ⇒ 00:09:23.539 Luke Daque: Just give me a second.
105 00:09:35.200 ⇒ 00:09:37.349 Luke Daque: So yeah, let me share my screen.
106 00:09:43.580 ⇒ 00:09:44.750 Luke Daque: Can you see my screen.
107 00:09:44.750 ⇒ 00:09:45.080 Miguel de Veyra: Yep.
108 00:09:46.000 ⇒ 00:09:47.690 Luke Daque: So this is cursor.
109 00:09:51.320 ⇒ 00:09:54.729 Luke Daque: Wait! Let me see if I can find the prompt that I did.
110 00:10:00.910 ⇒ 00:10:03.719 Miguel de Veyra: AI’s main job is basically to create data. Now.
111 00:10:04.270 ⇒ 00:10:08.430 Luke Daque: Yeah. So I’ll have. It might take a while for me to
112 00:10:09.200 ⇒ 00:10:20.989 Luke Daque: look for the the prompt but essentially what it did was, give us a a python script.
113 00:10:22.238 ⇒ 00:10:31.189 Luke Daque: Is it? Yeah. So we asked AI to generate a python script that would give us a synthetic data. I I can’t zoom in here, but this is like.
114 00:10:31.190 ⇒ 00:10:31.800 Miguel de Veyra: Yeah, yeah.
115 00:10:31.800 ⇒ 00:10:34.020 Luke Daque: Dummy data, basically. And it has
116 00:10:34.618 ⇒ 00:10:37.579 Luke Daque: all the fields that we need for this specific
117 00:10:38.439 ⇒ 00:10:42.629 Luke Daque: data. Right? So we, we asked it to generate
118 00:10:42.910 ⇒ 00:10:46.999 Luke Daque: data. That would be that would make sense for a manufacturing company.
119 00:10:47.330 ⇒ 00:10:50.239 Luke Daque: So we can create a real dashboard out of it.
120 00:10:50.360 ⇒ 00:10:56.020 Luke Daque: And basically, yeah, so this is just a hundred lines of data, I believe.
121 00:10:57.570 ⇒ 00:11:00.720 Luke Daque: For now, just we, we can, we can
122 00:11:01.090 ⇒ 00:11:04.670 Luke Daque: edit this as as we like. Like.
123 00:11:05.080 ⇒ 00:11:12.270 Luke Daque: So yeah, you can see that. Yeah, it has like randomness, it assigns random values to like each of the fields. And
124 00:11:13.470 ⇒ 00:11:17.850 Luke Daque: and yeah, it. It did that. So we didn’t have to like, think about
125 00:11:18.050 ⇒ 00:11:21.220 Luke Daque: how we want the randomness to be.
126 00:11:21.360 ⇒ 00:11:25.820 Luke Daque: And yeah, I can show you real quick, like how the dashboard looks like.
127 00:11:27.010 ⇒ 00:11:32.509 Nicolas Sucari: And the goal here is to create different data sets regarding different markets so that we can share
128 00:11:32.520 ⇒ 00:11:55.820 Nicolas Sucari: with possible leads when we are trying to get a new company, a new client and we are we we haven’t worked with a company in, or a client in that kind of area. The idea is to have, like these example data sets that we are creating so that we can share how the dashboards will look when they share with us like their data. And we start working.
129 00:11:56.740 ⇒ 00:12:03.520 Luke Daque: Yeah, so this is the example of like, when we already made this. So basically this data.
130 00:12:03.550 ⇒ 00:12:06.739 Luke Daque: the output of this script is a Csv file.
131 00:12:07.310 ⇒ 00:12:10.920 Luke Daque: So yeah, it’s something like this. It’s just a Csv file.
132 00:12:11.110 ⇒ 00:12:14.309 Luke Daque: And then we loaded this Csv file into
133 00:12:14.700 ⇒ 00:12:20.862 Luke Daque: a dashboard. And it looks something like this. It’s pretty much like very random. So
134 00:12:22.070 ⇒ 00:12:33.221 Luke Daque: it’s not very it. It’s not like very linear or something like that. So it makes sense like total calls made, for example, would go up and down per day, depending on the day
135 00:12:33.620 ⇒ 00:12:38.219 Luke Daque: yeah, stuff like that. So yeah, it’s pretty sick. Actually.
136 00:12:38.220 ⇒ 00:12:41.589 Miguel de Veyra: How how many, how many? Sorry? Yeah, it’s same.
137 00:12:41.620 ⇒ 00:12:47.319 Miguel de Veyra: How many data do you guys usually like, basically, for example, how many data are you guys looking to make
138 00:12:48.070 ⇒ 00:12:48.840 Miguel de Veyra: a thousand.
139 00:12:48.840 ⇒ 00:12:52.379 Nicolas Sucari: Like, how many data sets different data sets, or how many rows of.
140 00:12:52.380 ⇒ 00:12:53.740 Miguel de Veyra: Records basically yeah.
141 00:12:53.740 ⇒ 00:12:54.770 Luke Daque: Records.
142 00:12:54.860 ⇒ 00:12:58.510 Luke Daque: Yeah, currently, this is just a hundred records. We can up
143 00:12:59.122 ⇒ 00:13:00.579 Luke Daque: like, make this like a thousand.
144 00:13:00.580 ⇒ 00:13:04.289 Uttam Kumaran: It depends on like the it depends on the data set. I think, like.
145 00:13:04.290 ⇒ 00:13:04.960 Nicolas Sucari: Sad to hear.
146 00:13:04.960 ⇒ 00:13:12.970 Uttam Kumaran: Sales data. Then we probably want to do like, we’re trying to create data that looks like the companies we’re going after. So
147 00:13:13.340 ⇒ 00:13:21.589 Uttam Kumaran: you know hundreds of sales, thousands of sales per day. It kind of depends. The data set. The biggest thing is that it looks real. Right. So
148 00:13:21.710 ⇒ 00:13:24.230 Uttam Kumaran: if it’s a has seasonality
149 00:13:24.410 ⇒ 00:13:38.089 Uttam Kumaran: data goes down on weekends like there’s some realistic nature to it. And then, ideally, again, for me, the biggest thing was, we just have the the data generate new data every month.
150 00:13:38.230 ⇒ 00:13:48.340 Uttam Kumaran: right or or or basically what we do is actually, this is, I think we talked about this right. We just generate future data. And then that way, when the day hits, there’s always fresh data.
151 00:13:48.380 ⇒ 00:13:50.470 Uttam Kumaran: But the biggest thing here is
152 00:13:50.970 ⇒ 00:14:02.379 Uttam Kumaran: companies don’t. We can’t. When we do a demo of like what we can do and data, we can’t show other people’s data. So it’s kind of hard to give a sense of the types of things we can accomplish.
153 00:14:02.720 ⇒ 00:14:14.569 Uttam Kumaran: This is like a great way for us to show, like a fake company of like, hey? Your company’s data. Imagine it here and imagine you can do these things with it. So that’s basically the pitch.
154 00:14:15.100 ⇒ 00:14:15.630 Luke Daque: Yeah.
155 00:14:17.360 ⇒ 00:14:17.950 Miguel de Veyra: Okay.
156 00:14:19.130 ⇒ 00:14:27.090 Miguel de Veyra: okay, yeah, I can think of something. Let’s hop on a different call, maybe not this one. So we don’t want to take everyone’s time, but I think I can think of something.
157 00:14:28.140 ⇒ 00:14:29.600 Luke Daque: I’ll share it to you.
158 00:14:30.450 ⇒ 00:14:33.929 Miguel de Veyra: Okay, yeah. So a synthetic data agent. Let’s put that.
159 00:14:34.570 ⇒ 00:14:35.950 Miguel de Veyra: Okay. Yeah.
160 00:14:38.300 ⇒ 00:14:39.220 Miguel de Veyra: Wait. Sorry.
161 00:14:39.350 ⇒ 00:14:40.130 Miguel de Veyra: Ryan.
162 00:14:40.820 ⇒ 00:14:44.880 Miguel de Veyra: Okay, so the other thanks for sharing Luke.
163 00:14:45.180 ⇒ 00:14:45.599 Luke Daque: Thanks.
164 00:14:55.160 ⇒ 00:14:56.010 Miguel de Veyra: All right.
165 00:14:56.940 ⇒ 00:14:57.590 Ryan Brosas: Sorry.
166 00:14:58.250 ⇒ 00:15:00.019 Miguel de Veyra: Is there like anything you’re, you know.
167 00:15:00.190 ⇒ 00:15:03.410 Miguel de Veyra: using an AI agent for? So I know you’re in the content side right.
168 00:15:04.195 ⇒ 00:15:04.700 Ryan Brosas: Yeah.
169 00:15:05.930 ⇒ 00:15:17.670 Ryan Brosas: Yeah. For analyzing. The structure of template of specific individual, like Justin Welsh or like
170 00:15:18.558 ⇒ 00:15:24.779 Ryan Brosas: alex Hermosi. I used like a a like a a prompt
171 00:15:24.830 ⇒ 00:15:30.830 Ryan Brosas: that could, you know, destruct the structure. And I can follow that structure so we can make
172 00:15:31.090 ⇒ 00:15:38.139 Ryan Brosas: like follow their their how the content works and how we can, you know, use an app.
173 00:15:38.290 ⇒ 00:15:40.349 Ryan Brosas: apply it to our content as well.
174 00:15:41.073 ⇒ 00:15:47.579 Ryan Brosas: I use, and also, I think, for the research. I use like
175 00:15:48.090 ⇒ 00:15:51.539 Ryan Brosas: perplexity for our blog posts.
176 00:15:52.970 ⇒ 00:15:59.639 Ryan Brosas: to have, like a, you know, a detailed and have, like an analytic side of stuff in our blog posts.
177 00:15:59.880 ⇒ 00:16:08.970 Ryan Brosas: I think that’s all in my in my side, on on AI stuff. But you know I’m looking forward to like build you know. A content
178 00:16:10.113 ⇒ 00:16:15.839 Ryan Brosas: second brain content. I’m still building it. It’s quite a while right so.
179 00:16:15.840 ⇒ 00:16:16.370 Miguel de Veyra: Once.
180 00:16:17.127 ⇒ 00:16:23.210 Ryan Brosas: Yeah, I’m in relevance. But I’m looking forward to you know the new stuff in in in Claude.
181 00:16:24.069 ⇒ 00:16:33.309 Ryan Brosas: I’m looking like building like a a content that could use by Nicholas and Tom. So we can, you know, push contents on Linkedin.
182 00:16:33.712 ⇒ 00:16:44.940 Ryan Brosas: I will like feed it with information on how we you know how, how other specific individuals that is, you know, have having, like a good structure on content.
183 00:16:45.542 ⇒ 00:16:51.989 Ryan Brosas: like creating it more data on that stuff. And also, you know, as as also the
184 00:16:54.960 ⇒ 00:17:00.930 Ryan Brosas: yeah, I think that’s that’s 1 of what I want to do on on AI also.
185 00:17:01.373 ⇒ 00:17:04.890 Ryan Brosas: I think that’s all for for my side in the content side.
186 00:17:05.800 ⇒ 00:17:11.949 Miguel de Veyra: Okay, yeah, thanks for sharing. Let’s hop on a call. I think. Uttan, what could happen is, I hop on a call individually with them. Now.
187 00:17:11.950 ⇒ 00:17:17.979 Uttam Kumaran: Yeah, I actually told Ryan today to book a call next week. But I think each of these areas kind of become like.
188 00:17:18.520 ⇒ 00:17:31.480 Uttam Kumaran: how do we? OP, how do we use AI in different departments? Right? Like we have operations. We have engineering. We even have the AI department. We have like a bunch of stuff. So really, it’s just like.
189 00:17:32.090 ⇒ 00:17:37.629 Uttam Kumaran: partly, it’s like brain. Forge becomes a client for itself, right? So
190 00:17:37.950 ⇒ 00:17:44.359 Uttam Kumaran: that’s the thing that’s really lovely. I think these meetings help for everybody to kind of still share. But then, yeah, I think
191 00:17:44.440 ⇒ 00:17:50.100 Uttam Kumaran: I think you should just go chat with everybody, learn everybody’s use cases, because probably 10 min with you.
192 00:17:50.220 ⇒ 00:17:53.609 Uttam Kumaran: what take people on a completely different path?
193 00:17:54.057 ⇒ 00:18:10.140 Uttam Kumaran: And that’s really what I think a lot of people need. And also some people like for for me I use this stuff every day for like 2 years, but for some people this is new every day. So giving making the barrier to entry really low, giving access to all of our tools.
194 00:18:10.571 ⇒ 00:18:17.078 Ryan Brosas: We may not know what the problems are, but we definitely have the tools to solve. Probably every problem right in front of us.
195 00:18:17.460 ⇒ 00:18:23.829 Uttam Kumaran: And then, yeah, like, it’s just how do we? How do we get and make sure everybody’s using AI and their daily workflows?
196 00:18:23.980 ⇒ 00:18:32.794 Uttam Kumaran: That’s it cause even for me. It’s like I like. I wouldn’t have been able to do this business without the AI stuff like it’s way. It’s just like way too hard.
197 00:18:33.070 ⇒ 00:18:35.140 Miguel de Veyra: I’m not writing all those prompts.
198 00:18:35.340 ⇒ 00:18:38.780 Uttam Kumaran: Yeah. So I think it’s really great.
199 00:18:41.300 ⇒ 00:18:43.940 Nicolas Sucari: Yeah, I have some ideas, Miguel. So.
200 00:18:44.000 ⇒ 00:18:48.660 Nicolas Sucari: and I, I know how the AI can help me on my daily basis
201 00:18:49.064 ⇒ 00:19:09.620 Nicolas Sucari: to send updates to the clients knowing what was done like during the week. So maybe we can work towards something like that like reading all of the the latest conversation in slack for the past week, and creating like a status report, or something like that, or reading like a status of tasks in a notion board.
202 00:19:09.620 ⇒ 00:19:20.969 Nicolas Sucari: and seeing, like the the changes from one week to the other, one and then create like a slack message and send directly to the client something like that should would be like great.
203 00:19:21.550 ⇒ 00:19:28.370 Miguel de Veyra: Yeah, I was actually thinking of something similar earlier, because I was updating Eddie on stuff. So I think it’s like
204 00:19:28.550 ⇒ 00:19:40.820 Miguel de Veyra: I was thinking, maybe it would be like a good idea to just put the updates from either me or Casey into the slack channel, and then maybe just send it to a relevance agent, you know, hey, format this in a way that it’s, you know.
205 00:19:41.410 ⇒ 00:19:42.680 Miguel de Veyra: for the client.
206 00:19:42.890 ⇒ 00:19:49.140 Miguel de Veyra: and then we could review it first, st of course, before we send it to the client. Cause I know we
207 00:19:49.750 ⇒ 00:19:52.720 Miguel de Veyra: there’s some clients, especially on the data side, that are
208 00:19:53.390 ⇒ 00:19:59.529 Miguel de Veyra: high communication. Is that the term? Yeah. Yeah. So I was like, yeah, that’s probably one of the agents we want to build.
209 00:20:00.860 ⇒ 00:20:03.149 Nicolas Sucari: Yeah, I mean, I I like.
210 00:20:03.480 ⇒ 00:20:14.850 Nicolas Sucari: I I don’t mind on reviewing what the the agent can can create, but it’s like the previous step. What I’m trying to to build is like, how do we get all of
211 00:20:14.880 ⇒ 00:20:28.898 Nicolas Sucari: what what happens during the entire week into a status like? For now I I am kind of reviewing all of these like messages. I’m going to the notion boards. I’m seeing the pull requests. I’m seeing everything that was done, and then writing myself
212 00:20:29.489 ⇒ 00:20:50.439 Nicolas Sucari: like like a message, and sending it that to chat, gpt to format it, and then copy it and paste it to the client. But if there is something that can automatically go check all of these slack messages during the week, and the the tasks that were that were worked on and then create automatically the slack message. Then we just like check the message and see if that’s accurate or not.
213 00:20:51.030 ⇒ 00:20:59.659 Uttam Kumaran: Yeah, I think, Miguel. Also, it’s like it’s honestly kind of a mix of the stuff we’re doing on the content side. Because Ryan now has content brain that knows
214 00:20:59.730 ⇒ 00:21:04.090 Uttam Kumaran: how I wanted our verbiage to be like
215 00:21:04.410 ⇒ 00:21:28.570 Uttam Kumaran: when he generates stuff. Now, compared to 3 months ago, it actually sounds like pretty much like what I how I would write it, or how I would want it to write, and that took a journey right? So there’s probably prompts involved. Very similarly, I think for Nico, it’s like he has a structure by which he sends those updates. The inputs, though, need to come from slack email. And maybe even Github all get basically
216 00:21:28.700 ⇒ 00:21:34.952 Uttam Kumaran: process. And then client, it says, like, what do we do for this client this week? And then there’s an Update
217 00:21:35.320 ⇒ 00:21:38.329 Miguel de Veyra: Yeah, like that sort of thing
218 00:21:38.820 ⇒ 00:21:45.069 Miguel de Veyra: Nico, do you think it would be like? Let’s start, let’s start with something a bit simpler. Just so we could get our, you know.
219 00:21:45.130 ⇒ 00:21:53.029 Miguel de Veyra: put off the ground. But do you guys provide, do? Do we have like daily updates, for example, for a client like, hey, here’s what we did today.
220 00:21:53.100 ⇒ 00:22:01.539 Miguel de Veyra: Right? Cause I think what we could do if it’s like just daily, right may maybe someone working on data side. Here’s like the daily updates. Put it all. And then.
221 00:22:01.800 ⇒ 00:22:10.690 Miguel de Veyra: you know, we can just collect that at the end of the week. Send it all to an agent, process it. And I think that streamlines, basically everything you’re
222 00:22:11.210 ⇒ 00:22:12.109 Miguel de Veyra: working on.
223 00:22:12.110 ⇒ 00:22:34.710 Nicolas Sucari: Yeah, I mean, we don’t have like anything standardized like day in a daily basis. But we can. We can work on something. Yeah, totally. I mean, if if that’s the way, in order to start trying one of these agents and see how how that works. Yeah, let’s do it. I mean we can. We can. I think we can start using standardly again, maybe maybe just for me, so that I can get the habit of.
224 00:22:34.710 ⇒ 00:22:35.380 Uttam Kumaran: Oh, okay.
225 00:22:35.380 ⇒ 00:22:41.869 Nicolas Sucari: Everyday something, you know, and for each client. So I can write something for each client and see if that.
226 00:22:42.292 ⇒ 00:22:54.530 Uttam Kumaran: What’s even better is instead of stand up late, like, I’m sure these guys can create says, what what happened with these clients? You respond all that gets sent
227 00:22:54.740 ⇒ 00:23:08.729 Uttam Kumaran: to Csv. Or something. And then at the end of the week it’s all come. So that’s the biggest thing also is having it work with you like you can’t have it be a completely different workflow. Otherwise you’re never gonna adopt it right? So you’re almost building it for yourself.
228 00:23:08.930 ⇒ 00:23:23.570 Uttam Kumaran: So build something like that’s small. But also it’s like accomplishes the goal. And then, yeah, like, I mean again, I think we’re gonna try to gun for at least every 2 days. Updates, I think we’ll probably start there like next week.
229 00:23:23.800 ⇒ 00:23:29.120 Uttam Kumaran: So we I wanna try to find some way. For like, for example.
230 00:23:29.160 ⇒ 00:23:33.139 Uttam Kumaran: if you’re working on 4 clients, it should automatically say, Hey.
231 00:23:33.170 ⇒ 00:23:36.080 Uttam Kumaran: based on slack. Here’s probably what happened yesterday.
232 00:23:36.270 ⇒ 00:23:37.650 Nicolas Sucari: Exactly. Yeah.
233 00:23:37.650 ⇒ 00:23:41.979 Uttam Kumaran: That’s right, and maybe at least it is 50%, or at least it like
234 00:23:42.090 ⇒ 00:23:45.840 Uttam Kumaran: gives you the reminder to do the rest right. Stuff like that.
235 00:23:45.840 ⇒ 00:23:46.490 Miguel de Veyra: Yeah.
236 00:23:46.490 ⇒ 00:23:49.989 Nicolas Sucari: Yeah, yeah, yeah, that would be awesome.
237 00:23:50.440 ⇒ 00:23:56.100 Nicolas Sucari: But yeah, we can start working on something like that, Miguel. We can get in touch later and and see how we can do
238 00:23:56.230 ⇒ 00:23:57.940 Nicolas Sucari: Kickoff kind of did things.
239 00:24:00.320 ⇒ 00:24:05.070 Miguel de Veyra: Okay. Yeah. Okay. So last, Maria, Hello.
240 00:24:05.790 ⇒ 00:24:06.650 Mariane Cequina: Hello. Yeah.
241 00:24:07.780 ⇒ 00:24:10.700 Miguel de Veyra: So I think, yeah, yeah, is there like
242 00:24:10.740 ⇒ 00:24:15.850 Miguel de Veyra: any specific thing in operations that because I know you’re working with a lot of documentation.
243 00:24:16.650 ⇒ 00:24:20.840 Miguel de Veyra: right? So is there like a specific, you know, scenario, that
244 00:24:21.000 ⇒ 00:24:25.820 Miguel de Veyra: or daily thing, you can do that. Basically, you could, you could use some help using an agent.
245 00:24:27.180 ⇒ 00:24:35.509 Mariane Cequina: In Asian or like in in. How can I integrate like the task in notion? I can, because Nico last said, that you know you can just
246 00:24:35.860 ⇒ 00:24:47.869 Mariane Cequina: notify. I can actually, I can automate the if there are like assigned tasks in a specific person in notion, it can be sent in the slack as notification. Is that something that you’d want me to do.
247 00:24:49.130 ⇒ 00:24:49.660 Nicolas Sucari: Yeah.
248 00:24:49.660 ⇒ 00:24:55.220 Nicolas Sucari: I think we can do that. But I don’t think we need an a agent to do that. Maybe maybe there.
249 00:24:55.700 ⇒ 00:24:58.680 Nicolas Sucari: between notion and slack, and we can directly do that.
250 00:24:58.790 ⇒ 00:25:02.889 Nicolas Sucari: But yeah, I mean, we can try it. And we can. Yeah, look into that.
251 00:25:03.455 ⇒ 00:25:05.634 Nicolas Sucari: The the only thing is
252 00:25:06.630 ⇒ 00:25:17.370 Nicolas Sucari: As for now, as we are using like a different client page for for each client, it’s a different databases for each kind of roadmap
253 00:25:17.380 ⇒ 00:25:36.060 Nicolas Sucari: and and backlog of tasks. Right? So if, for example, here Luke is working with 2 different clients, we should like integrate 2 different databases into that note into that slack message so that he gets hit by that kind of agent, and remind him about all of the tasks from the different projects, right.
254 00:25:36.070 ⇒ 00:25:39.879 Nicolas Sucari: because we don’t have, like all in the same in the same database, in notion.
255 00:25:40.470 ⇒ 00:25:41.160 Mariane Cequina: Okay.
256 00:25:42.010 ⇒ 00:25:44.500 Nicolas Sucari: Okay. But yeah, I mean, need something that we can try.
257 00:25:44.930 ⇒ 00:26:01.389 Mariane Cequina: Yeah, maybe I’ll just present, first, st because I’m trying to strategize a lot, especially in the leads, because I talked with Utam about, you know, as as you mentioned before, like, there are a lot of clients. And you you guys also wanted to have, like a different views from external, even the internal. So I’m trying
258 00:26:01.730 ⇒ 00:26:07.510 Mariane Cequina: this person, then create like a workflow and present it to you guys, I think that’s ideology.
259 00:26:08.120 ⇒ 00:26:19.220 Nicolas Sucari: I I think that’s better. I mean, that’s why I I was not like pushing into doing that automations on what we have right now, I was just waiting until you can get your hands on
260 00:26:19.838 ⇒ 00:26:36.709 Nicolas Sucari: working on on these new client pages and and task management workflow. Once that is ready. I think we can think of what is necessary in order to automate and to start like sending that those kind of messages as reminders or as tasks reminder for everyone.
261 00:26:37.380 ⇒ 00:26:49.359 Mariane Cequina: Yeah, definitely cause cause. Honestly, I also wanted to understand more about the process as well. So that’s why I have to make a scratch first, st and then before we like, automate us and improve.
262 00:26:49.510 ⇒ 00:26:50.310 Mariane Cequina: Yeah.
263 00:26:50.310 ⇒ 00:26:52.640 Nicolas Sucari: Okay, perfect. Yeah.
264 00:26:52.940 ⇒ 00:26:53.590 Mariane Cequina: Okay.
265 00:26:54.350 ⇒ 00:27:01.750 Miguel de Veyra: Oh, I’m fine, Maureen. Sorry I’m not really specifically like super familiar. Do you also like, for example, Utah.
266 00:27:01.820 ⇒ 00:27:06.639 Miguel de Veyra: if there’s like a lead that comes in is Marian, the one working on documenting everything.
267 00:27:06.990 ⇒ 00:27:07.470 Mariane Cequina: No, no.
268 00:27:08.150 ⇒ 00:27:08.969 Miguel de Veyra: Oh, okay.
269 00:27:09.550 ⇒ 00:27:16.000 Uttam Kumaran: Well, we, I think we’re gonna we’re gonna basically right now, we’ve just basically finished the content departments
270 00:27:16.170 ⇒ 00:27:22.750 Uttam Kumaran: notion. We’re gonna we’re moving on one by one by one. So ideally, what will happen
271 00:27:22.780 ⇒ 00:27:48.900 Uttam Kumaran: like in my ideal world? Leads are gonna come from any which way, like I get an email, someone hits me up or something. They come to the site. All of it will get funneled, basically into slack and then also into notion, somehow. Right? So. But we’re figuring out the process right now. It’s mainly like, can we just figure out the manual thing that it gets tracked in notion, because right now, notion is gonna be basically, the company operates on notion slack and zoom
272 00:27:48.900 ⇒ 00:28:00.370 Uttam Kumaran: right? So we’re kind of restricting any other place where information can get dispersed. The other thing is all of that needs to end up back into an AI agent somehow. Right? So if you were to ask
273 00:28:00.400 ⇒ 00:28:10.990 Uttam Kumaran: the brain Forge AI agent. What leads came up this week that are new? It should answer that question right? And that’s the stuff that we are working on with Casey just like throwing all of notion into stuff
274 00:28:11.160 ⇒ 00:28:19.619 Uttam Kumaran: like that’s the stuff longer term we’ll figure out. But as long as it’s in notion categorized somewhere, that’s the ideal thing.
275 00:28:21.260 ⇒ 00:28:33.100 Uttam Kumaran: How it gets into notion, notion, added web hooks and stuff, too. So I think there’ll be some better ways of getting it into notion. But right now, it’s maybe one or 2 per week.
276 00:28:33.240 ⇒ 00:28:40.580 Uttam Kumaran: But again, like as we grow, these things get harder and harder. So yeah.
277 00:28:42.310 ⇒ 00:28:49.439 Nicolas Sucari: Yeah, the issue with notion is, if you’re not like on top of it and trying to update everything, it’s kind of
278 00:28:49.470 ⇒ 00:29:09.060 Nicolas Sucari: as soon as you finish creating a documentation database. And you don’t look at it anymore, like it becomes old. So that’s like our challenge is to be actively updating everything and keeping it, keeping it up to date. Until we figure out like, what’s the best way of doing this without doing it manually.
279 00:29:11.580 ⇒ 00:29:12.320 Nicolas Sucari: Yep.
280 00:29:16.250 ⇒ 00:29:29.889 Miguel de Veyra: Okay, yeah. Cause I was thinking earlier, if it helped me in documenting basically that stuff for the handover, if we’re doing it for a bunch of clients, I think you know long term it could really help out.
281 00:29:31.370 ⇒ 00:29:53.789 Nicolas Sucari: Yeah, I mean, what Marianne is doing is creating, like the the process on how we are gonna structure, all of the different areas in notion. But then, like creating the documentation for a client, that’s something we can work on, because that’s that that is gonna be needed for every client in at some point where we should like start
282 00:29:54.560 ⇒ 00:30:03.469 Nicolas Sucari: sending them the documentation in in a certain way. And that’s where you guys can step in and try to help us automate. How is that created right.
283 00:30:04.750 ⇒ 00:30:05.840 Miguel de Veyra: Yep, definitely.
284 00:30:07.940 ⇒ 00:30:13.009 Miguel de Veyra: Okay. Yeah, I’m I mean, we’re a bit over like 3 min, but I think that’s
285 00:30:13.740 ⇒ 00:30:18.480 Miguel de Veyra: pretty much it forever. For everyone, unless.
286 00:30:18.480 ⇒ 00:30:20.869 Nicolas Sucari: Guys. It’s a great, great 1st meeting of.
287 00:30:20.870 ⇒ 00:30:21.709 Miguel de Veyra: Yeah, I know.
288 00:30:23.160 ⇒ 00:30:27.729 Uttam Kumaran: So what’s next? What’s next, Miguel like? What’s the plan? After this.
289 00:30:27.730 ⇒ 00:30:32.400 Miguel de Veyra: I’ll hop on a call, probably next week or tomorrow
290 00:30:32.450 ⇒ 00:30:38.840 Miguel de Veyra: I’ll start hopping because I took down notes, so I’ll start hopping, hopping into calls with everyone.
291 00:30:39.030 ⇒ 00:30:43.100 Miguel de Veyra: And then basically just plan something out with them individually.
292 00:30:43.100 ⇒ 00:30:44.010 Nicolas Sucari: Excellent.
293 00:30:44.010 ⇒ 00:30:44.630 Mariane Cequina: Okay.
294 00:30:44.630 ⇒ 00:30:47.639 Miguel de Veyra: And then maybe I’ll create like a new channel in slack
295 00:30:48.100 ⇒ 00:30:51.890 Miguel de Veyra: for that specific case, because I don’t wanna fill up the Aid chat right.
296 00:30:51.890 ⇒ 00:30:54.500 Uttam Kumaran: Yeah, we have. There’s there’s another AI channel.
297 00:30:57.340 ⇒ 00:30:59.370 Uttam Kumaran: There’s a lot of channels, though. I’m gonna.
298 00:30:59.370 ⇒ 00:31:05.219 Nicolas Sucari: Yeah, yeah, there is one that’s automations. AI, I think that’s the one that you don’t wanna like.
299 00:31:05.890 ⇒ 00:31:09.920 Uttam Kumaran: There is automations. AI, that’s the one. Yeah, let’s just use that one.
300 00:31:10.930 ⇒ 00:31:13.950 Miguel de Veyra: Oh, should we do it in automations? AI. Then.
301 00:31:15.190 ⇒ 00:31:15.750 Nicolas Sucari: Yeah.
302 00:31:16.160 ⇒ 00:31:16.853 Miguel de Veyra: Okay. Okay.
303 00:31:17.200 ⇒ 00:31:23.980 Uttam Kumaran: Because I actually want everybody to see it. Like, frankly, I’m also thinking, maybe we just at some point, May, I’m thinking we even
304 00:31:24.560 ⇒ 00:31:28.409 Uttam Kumaran: one of the ideas I have is like to just push everything to Brainforge team.
305 00:31:28.450 ⇒ 00:31:29.990 Mariane Cequina: But I do think that.
306 00:31:30.210 ⇒ 00:31:38.830 Uttam Kumaran: Right now, I think we have a good enough of channels. What I’ll probably do is I’m gonna look at the data actually, and just figure out which channels aren’t being used. And then.
307 00:31:38.870 ⇒ 00:31:40.910 Uttam Kumaran: yeah, probably deleting them. But.
308 00:31:41.180 ⇒ 00:31:41.710 Miguel de Veyra: You’re not.
309 00:31:41.710 ⇒ 00:31:42.330 Nicolas Sucari: Yeah.
310 00:31:42.890 ⇒ 00:31:43.736 Miguel de Veyra: Okay, yeah.
311 00:31:44.380 ⇒ 00:31:45.820 Miguel de Veyra: I think we had a good session.
312 00:31:46.650 ⇒ 00:31:47.180 Uttam Kumaran: Okay.
313 00:31:47.650 ⇒ 00:31:48.670 Nicolas Sucari: Thanks. Miguel.
314 00:31:48.670 ⇒ 00:31:52.420 Miguel de Veyra: Okay, thank you. Okay, thank you. Guys, thanks.
315 00:31:52.420 ⇒ 00:31:54.159 Mariane Cequina: Thank you. Thank you.
316 00:31:54.160 ⇒ 00:31:54.730 Mariane Cequina: Day.
317 00:31:54.730 ⇒ 00:31:55.330 Luke Daque: Bye, bye.