Meeting Title: Brainforge Internship Weekly Check-in Date: 2025-07-10 Meeting participants: Demilade Agboola, Vishnu
WEBVTT
1 00:00:51.060 ⇒ 00:00:52.080 Vishnu: Hello!
2 00:00:52.290 ⇒ 00:00:53.430 Demilade Agboola: Hi! Vishna!
3 00:00:53.940 ⇒ 00:00:57.999 Vishnu: Hey? Hi! Hi! Damelit! Am I pronouncing your name right?
4 00:00:58.000 ⇒ 00:00:59.740 Demilade Agboola: It’s Danilade.
5 00:00:59.890 ⇒ 00:01:02.479 Vishnu: Demi. Ladi. Okay. Hi demi. Ladi.
6 00:01:04.060 ⇒ 00:01:05.720 Demilade Agboola: I’m doing very well. How are you?
7 00:01:06.400 ⇒ 00:01:08.850 Vishnu: I’m doing good. Thanks for asking.
8 00:01:08.850 ⇒ 00:01:17.299 Demilade Agboola: Yeah. I see this is like the end of your second week, almost close to the end of your second week. How’s it been so far.
9 00:01:18.818 ⇒ 00:01:21.090 Vishnu: It’s been good, I would say
10 00:01:21.756 ⇒ 00:01:31.380 Vishnu: again, I was for most of the bit exploring notion exploring. Slack the Brainforge Demo website.
11 00:01:31.590 ⇒ 00:01:34.030 Vishnu: the past code bases and stuff.
12 00:01:34.300 ⇒ 00:01:34.770 Demilade Agboola: Okay.
13 00:01:35.232 ⇒ 00:01:36.618 Vishnu: Apart from that
14 00:01:37.780 ⇒ 00:01:46.590 Vishnu: Avesh had given given me a bunch of tasks. So the 1st task was to like build a clockify to Snowflake. Etl pipeline.
15 00:01:47.200 ⇒ 00:01:49.340 Vishnu: Oh, okay.
16 00:01:49.833 ⇒ 00:01:59.719 Vishnu: yeah. That was one thing I I did like. So I tested out the pipeline. I did build it out. I had issues with the Snowflake authorization.
17 00:02:00.690 ⇒ 00:02:04.630 Vishnu: So I think there was 2 factor on, on turned on. So
18 00:02:05.234 ⇒ 00:02:19.079 Vishnu: while it was getting data from the clockify Api. It was not being stored into Snowflake, and also I hadn’t used Snowflake before, so I was. I’m just still getting used to the ui the
19 00:02:20.120 ⇒ 00:02:21.040 Vishnu: of
20 00:02:21.170 ⇒ 00:02:35.789 Vishnu: stack in general. So like, I was trying to make a a database. It was so I didn’t have the right permissions, and then so I did update a wish on the same today. And he said.
21 00:02:36.000 ⇒ 00:02:44.039 Vishnu: Try a different approach. I did try that. That didn’t work, too, and I did tell him so. He he just sent me some other approach to try now.
22 00:02:44.250 ⇒ 00:02:50.238 Vishnu: Oh, yeah, I’m gonna try that out. And apart from that, he also wanted me to.
23 00:02:50.990 ⇒ 00:02:57.369 Vishnu: Another task that he gave me was like a bunch of Api endpoints that he wants them to show up as
24 00:02:57.900 ⇒ 00:03:01.529 Vishnu: workbooks or worksheets in Google sheets.
25 00:03:01.980 ⇒ 00:03:02.680 Demilade Agboola: Okay.
26 00:03:02.680 ⇒ 00:03:08.170 Demilade Agboola: So yeah, these are the 2 things that I’m trying to like, get complete to complete by the end of today.
27 00:03:08.530 ⇒ 00:03:31.190 Vishnu: And apart from that, there was like building a productivity dashboard connecting real data to Snowflake. I did try that again. I had issues with snowflake connection. And then there was one other task that he wanted me to create a Dbt project for productivity. And like just that. So that was just a repo. And like just sample data, he said, asked me to put in that.
28 00:03:31.670 ⇒ 00:03:33.736 Vishnu: But as of today, like
29 00:03:34.420 ⇒ 00:03:41.339 Vishnu: I’m trying to like finish the clockify Etl pipeline and the
30 00:03:41.450 ⇒ 00:03:48.999 Vishnu: endpoints Api endpoints from operating. I’m trying to get it into excel Google sheets.
31 00:03:50.837 ⇒ 00:03:52.980 Demilade Agboola: That’s what I’m up to like.
32 00:03:55.091 ⇒ 00:04:02.339 Demilade Agboola: So far. Have you found it any of the tasks to be too hard or too stressful? Or is it just a function of?
33 00:04:02.890 ⇒ 00:04:06.120 Demilade Agboola: Or do you feel like? Let me, here’s the thing.
34 00:04:06.390 ⇒ 00:04:11.430 Demilade Agboola: Do you think they’re too easy, too hard, too stressful? Do you think they’re just the right amount of difficulty.
35 00:04:13.275 ⇒ 00:04:18.810 Vishnu: I think it is the right amount of difficulty. I wouldn’t say it’s too hard or too
36 00:04:19.029 ⇒ 00:04:39.001 Vishnu: easy. Because, like, yeah, like, I, I’m just thinking, like, if I yeah, I know, I think I know what I have to do. So I get started doing it, implementing it. I might run into issues. And then I try to like, Okay, Google, search, ask chat, Gpt, and see if this approach is right or wrong.
37 00:04:39.600 ⇒ 00:04:45.939 Vishnu: And then yeah, so like the Etl pipeline. It took me like 4 to 5 h.
38 00:04:46.220 ⇒ 00:04:47.100 Vishnu: Oh.
39 00:04:48.620 ⇒ 00:04:49.280 Demilade Agboola: Okay.
40 00:04:49.280 ⇒ 00:04:52.179 Vishnu: The 1st day, and then after that
41 00:04:52.683 ⇒ 00:04:55.595 Vishnu: again. So I’m also like trying to like
42 00:04:56.800 ⇒ 00:05:06.159 Vishnu: work another job in between this like with my professor. So it’s sort of for me difficult to balance, because this is like real work.
43 00:05:06.470 ⇒ 00:05:13.960 Vishnu: But I feel like I’m learning like I I feel this is the right amount of work like, I wouldn’t complain about it, because
44 00:05:14.290 ⇒ 00:05:18.260 Vishnu: clearly, like, this is what data engineers do. Right?
45 00:05:18.780 ⇒ 00:05:19.970 Demilade Agboola: Yeah, yeah.
46 00:05:20.240 ⇒ 00:05:35.889 Demilade Agboola: I was gonna ask, do you have any like desired outcomes? Cause you know I was not. I was never, never really part of like the interviewing process or anything. So I just want to know if you have any goals for your internship, and if there are any things you want to learn in particular.
47 00:05:36.180 ⇒ 00:05:37.280 Demilade Agboola: specifically.
48 00:05:37.280 ⇒ 00:05:45.919 Vishnu: Yes. So when I when I gave the interview, the idea was that I’m gonna join full time. And I I really wanted to work on AI machine learning part.
49 00:05:46.120 ⇒ 00:06:08.590 Vishnu: But then, for now I I feel like, yeah, might be I should just spend some more time getting familiarized with like snowflake, dbt, and learning this because the natural progression after this would be to like so yeah, my, I would say, I am comfortable with, like natural language processing deep learning and machine learning
50 00:06:10.240 ⇒ 00:06:14.929 Vishnu: stuff projects along those lines, or at least projects that I’ve built. I’m comfortable with.
51 00:06:16.360 ⇒ 00:06:17.390 Vishnu: So
52 00:06:17.640 ⇒ 00:06:31.569 Vishnu: I feel yes, like the task that oasis giving me now is like new to me, but not completely new. But it’s unlike anything that I’ve done before. So I’m getting familiarized with this. And
53 00:06:32.240 ⇒ 00:06:35.940 Vishnu: later on, I would say, like, once I’m get like
54 00:06:36.560 ⇒ 00:06:41.930 Vishnu: a couple of weeks into it. Probably, if you have AI projects that would be good if you could assign me like.
55 00:06:43.610 ⇒ 00:06:50.360 Demilade Agboola: Okay, just just a general how things are.
56 00:06:51.168 ⇒ 00:06:53.979 Demilade Agboola: So we have the data team.
57 00:06:54.130 ⇒ 00:06:55.779 Demilade Agboola: We have the AI team.
58 00:06:57.234 ⇒ 00:07:02.650 Demilade Agboola: So the data team were responsible for. So are responsible for
59 00:07:02.810 ⇒ 00:07:07.510 Demilade Agboola: implementing like data engineering solutions and just ensuring that we’re producing
60 00:07:08.040 ⇒ 00:07:11.449 Demilade Agboola: the best data for whatever clients we have.
61 00:07:12.705 ⇒ 00:07:13.360 Demilade Agboola: Okay?
62 00:07:14.380 ⇒ 00:07:25.919 Demilade Agboola: And then so that’s made up of. I wish myself Kyle, Luke and Annie.
63 00:07:26.880 ⇒ 00:07:27.660 Vishnu: Okay.
64 00:07:28.400 ⇒ 00:07:35.260 Demilade Agboola: So myself. A wish. Engineers, Kyle, look also engineers.
65 00:07:35.850 ⇒ 00:07:38.670 Demilade Agboola: Then Annie is a data analyst.
66 00:07:39.200 ⇒ 00:07:39.720 Vishnu: Okay.
67 00:07:39.720 ⇒ 00:07:45.960 Demilade Agboola: And then we have the AI team. So the AI team they’re more responsible for, like the
68 00:07:46.070 ⇒ 00:07:50.079 Demilade Agboola: building, the models and the deploying them.
69 00:07:50.560 ⇒ 00:07:56.749 Demilade Agboola: So I don’t know if you’ve seen things like our zoom summarizer. I don’t know if you’ve seen any zoom summarize meetings.
70 00:07:56.750 ⇒ 00:08:02.659 Vishnu: Yes, avish was telling about it. And yeah, I’ve I’ve seen the messages in the slack channel, too.
71 00:08:02.660 ⇒ 00:08:06.313 Demilade Agboola: Yeah. So we have zoom summarizers. We have
72 00:08:07.200 ⇒ 00:08:15.739 Demilade Agboola: models that we can ask about clients that we’re working on, and they will give us the responses based off messages in slack.
73 00:08:15.930 ⇒ 00:08:16.460 Vishnu: Okay.
74 00:08:16.570 ⇒ 00:08:18.720 Demilade Agboola: Rooms and things like that.
75 00:08:21.120 ⇒ 00:08:32.750 Vishnu: I have a question. So are these models like, built from scratch by Brainforge team, or they’re built from existing models like we’re fine tuning it.
76 00:08:33.079 ⇒ 00:08:34.209 Demilade Agboola: My guess is.
77 00:08:34.830 ⇒ 00:08:35.450 Vishnu: Notification.
78 00:08:35.450 ⇒ 00:08:41.900 Demilade Agboola: Yeah. My guess is we’re fine tuning it, but like I haven’t built, I haven’t like done an in-depth building session with them.
79 00:08:42.299 ⇒ 00:08:46.429 Demilade Agboola: But given the turnaround time, I guess, is we’re fine tuning it.
80 00:08:46.840 ⇒ 00:08:49.860 Vishnu: Got it. Okay, okay, okay.
81 00:08:49.860 ⇒ 00:08:59.360 Demilade Agboola: So yeah. So the AI team is Cassie Miguel, and give me one second.
82 00:09:02.470 ⇒ 00:09:07.469 Demilade Agboola: I believe it’s Cassie Miguel. Is there any other person? Sometimes I forget.
83 00:09:11.370 ⇒ 00:09:12.600 Demilade Agboola: Give me one second.
84 00:09:27.410 ⇒ 00:09:33.070 Demilade Agboola: so I’m trying to see the the if I can find the list of everybody. So if that’ll be better.
85 00:09:35.030 ⇒ 00:09:45.709 Demilade Agboola: a better way to see it. Yes, Mustafa, not bad.
86 00:09:46.900 ⇒ 00:09:47.510 Vishnu: Okay. Okay.
87 00:09:47.510 ⇒ 00:09:49.790 Demilade Agboola: Mustafa, Cassie Miguel.
88 00:09:50.360 ⇒ 00:09:55.510 Demilade Agboola: Those are the people. Actually, in the AI team.
89 00:09:59.080 ⇒ 00:10:03.099 Demilade Agboola: And so they are the ones responsible for building out a lot of the
90 00:10:03.490 ⇒ 00:10:07.030 Demilade Agboola: building and deploying a lot of the models that we see.
91 00:10:08.670 ⇒ 00:10:10.570 Demilade Agboola: Got it. Okay? Okay.
92 00:10:12.540 ⇒ 00:10:14.468 Vishnu: Yeah for me, like
93 00:10:15.770 ⇒ 00:10:27.823 Vishnu: like, I would love to work with them. But as of now, I would like to like the tasks that are given to me and things that are in hand. Yeah, I’m trying to implement those. And if I run into issues
94 00:10:28.695 ⇒ 00:10:34.019 Vishnu: can I like reach out to you all like, I still haven’t run into major issues like the
95 00:10:34.810 ⇒ 00:10:44.650 Vishnu: but yeah, if I can reach out to you all and like if you can walk me through like, and I get to learn from how how y’all problem solved. That would be great like.
96 00:10:45.910 ⇒ 00:10:52.799 Demilade Agboola: Okay, sounds good. I just wanna say that. I think overall.
97 00:10:53.950 ⇒ 00:11:12.740 Demilade Agboola: I know that being able to use like build out the AI is really like, like, I guess that it’s the thing is the the new book. Everyone wants to do it. Deep learning all that stuff. I’m trying to, Mcps. All that. Every everyone is like. That’s the craze right now.
98 00:11:12.740 ⇒ 00:11:13.300 Vishnu: Yes.
99 00:11:14.290 ⇒ 00:11:21.040 Demilade Agboola: Ultimately I will say this. I feel like one of the things when I was learning like data. One of the things my mentor told me, is
100 00:11:21.150 ⇒ 00:11:28.479 Demilade Agboola: the fundamentals will never go out of vogue. And so I feel like it’s important to learn the fundamentals, because at the end of the day.
101 00:11:28.700 ⇒ 00:11:36.719 Demilade Agboola: Even some of these things that they do. We still have to help them load the data into the into like Snowflake. Right? So it’s.
102 00:11:36.720 ⇒ 00:11:37.050 Vishnu: Yes.
103 00:11:37.050 ⇒ 00:11:42.670 Demilade Agboola: It’s kind of like integrated and just being able to understand how to be able to do
104 00:11:42.860 ⇒ 00:11:59.870 Demilade Agboola: things across the data. Space is very important, you might not. You don’t have to like. Learn it to the point that you’re the best in the world at it, sure. But being able to understand. You know how the ingestion takes place, what you need to do to ingest all those kind of things. They will be very important lessons.
105 00:12:01.580 ⇒ 00:12:02.830 Vishnu: Okay. Okay.
106 00:12:02.830 ⇒ 00:12:04.170 Demilade Agboola: Yeah, so
107 00:12:08.890 ⇒ 00:12:24.090 Vishnu: Yeah, I completely agree with that. Yes, like the foundational aspects of these domains are not going anywhere, and anything that is built is built on top of it. So having a grasp on that is is like, super important. Yeah, yeah, completely agree.
108 00:12:24.300 ⇒ 00:12:30.420 Demilade Agboola: Yeah. So I would always just say, like, you know, try to ensure that you
109 00:12:30.830 ⇒ 00:12:38.819 Demilade Agboola: you’d like, learn as much as possible. Because it’s very possible to get. Oh, I want to do the AI stuff, and then
110 00:12:38.820 ⇒ 00:13:01.630 Demilade Agboola: it makes things that you’re doing right now. Maybe not as interesting or feel as useful. Quote unquote, because it’s like, I’m not saying, that’s what you feel or that’s but I just want you to know that like if it gets hard, or if it feels very frustrating, it’s still ultimately data. And it’s still a process about learning and ingesting and moving data from 1 point to another point.
111 00:13:01.850 ⇒ 00:13:05.970 Demilade Agboola: which is always something that will happen before you. You can start training your data.
112 00:13:07.740 ⇒ 00:13:08.320 Vishnu: Okay.
113 00:13:08.320 ⇒ 00:13:09.250 Demilade Agboola: Yeah, so.
114 00:13:09.250 ⇒ 00:13:10.369 Vishnu: Yes. Yeah.
115 00:13:11.507 ⇒ 00:13:15.579 Demilade Agboola: But yeah, ultimately, I am glad you are finding things.
116 00:13:16.915 ⇒ 00:13:18.940 Demilade Agboola: Useful so far.
117 00:13:19.130 ⇒ 00:13:21.510 Demilade Agboola: Yes, I think next week
118 00:13:21.990 ⇒ 00:13:30.080 Demilade Agboola: what we can do is, I can. I can show you like a sheet. And just next week you can. Before next week’s meeting. You can put things there
119 00:13:31.020 ⇒ 00:13:34.669 Demilade Agboola: feedback, basically anything you want to like mention
120 00:13:35.411 ⇒ 00:13:39.979 Demilade Agboola: anything that is, you know, pressing, you could be able.
121 00:13:39.980 ⇒ 00:13:40.410 Vishnu: Okay.
122 00:13:40.410 ⇒ 00:13:44.100 Demilade Agboola: It could be about the company, it could be about whatever like whatever is on your mind.
123 00:13:44.510 ⇒ 00:13:44.870 Vishnu: Okay.
124 00:13:44.870 ⇒ 00:13:49.669 Demilade Agboola: Okay, let me just try and get an idea of like how to make this productive for you.
125 00:13:51.540 ⇒ 00:13:55.290 Vishnu: Yes, okay. Yeah. Alright. I’ll I’ll give you my feedback. And yeah.
126 00:13:55.290 ⇒ 00:14:10.979 Demilade Agboola: Okay, alright. And then hopefully, another thing that we can learn as things go on is just like figuring out how to like log things, figuring out how to like solve things faster. And that is very important in just general, like
127 00:14:11.630 ⇒ 00:14:14.390 Demilade Agboola: this piece of work is like
128 00:14:14.670 ⇒ 00:14:27.700 Demilade Agboola: being able to communicate like your deadlines being able to communicate when you’re falling behind being, it’s be able to communicate any issues you know, cause we work with clients and clients tend to have expectations of us.
129 00:14:28.430 ⇒ 00:14:34.600 Demilade Agboola: and being able to communicate, helps us ease those expectations, or helps us manage those expectations.
130 00:14:35.550 ⇒ 00:14:35.940 Vishnu: Okay.
131 00:14:35.940 ⇒ 00:14:43.669 Demilade Agboola: You know, just all those kind of things. So I want you to think of this as learning the technical skills, but also the soft skills as well.
132 00:14:45.100 ⇒ 00:14:47.969 Vishnu: Okay. Yes, yes, yeah. I’m good. Yeah.
133 00:14:48.420 ⇒ 00:15:01.630 Vishnu: So when I’m falling back on something or like, something just comes up up hopefully, the yeah, that won’t happen like, but if that happens I have to let you know right.
134 00:15:02.609 ⇒ 00:15:11.099 Demilade Agboola: It depends on who assigned the project to you. But definitely, whoever like is expecting the project from you. So if it’s a if it’s a waste you have to let a wish know.
135 00:15:12.520 ⇒ 00:15:13.290 Vishnu: Okay.
136 00:15:13.290 ⇒ 00:15:14.140 Demilade Agboola: Yeah.
137 00:15:14.820 ⇒ 00:15:20.070 Demilade Agboola: So I’m I’m largely here for any assistance you might need in terms of like
138 00:15:22.070 ⇒ 00:15:30.100 Demilade Agboola: to ensure that this product, this phase, this mentoring like this. Internship phase is useful for you.
139 00:15:30.300 ⇒ 00:15:36.903 Demilade Agboola: Right if they feel like, hey, I have too much time on my hands. If you feel like
140 00:15:38.600 ⇒ 00:15:47.190 Demilade Agboola: I’m doing things that don’t feel. I can’t see the utility to my career like things like that, like, if we can have those sort of conversations and.
141 00:15:47.880 ⇒ 00:15:53.319 Demilade Agboola: I’m just here to help you ensure that, like every week that passes by, we’re maximizing to the fullest.
142 00:15:54.450 ⇒ 00:15:58.589 Vishnu: Got it. Yes, okay, yeah. I appreciate that. I’ll I’ll let you know.
143 00:15:58.590 ⇒ 00:15:59.480 Demilade Agboola: Alright, then.
144 00:15:59.760 ⇒ 00:16:00.400 Vishnu: Yeah.
145 00:16:00.780 ⇒ 00:16:04.550 Demilade Agboola: Okay, then. If you have nothing else, I think we can hop off.
146 00:16:05.980 ⇒ 00:16:15.050 Vishnu: Oh, yeah, nothing else from my side. Yeah, thanks. Thanks for the introduction. Happy to meet you. Yeah.
147 00:16:15.480 ⇒ 00:16:17.100 Demilade Agboola: All right, then take care of it.
148 00:16:17.100 ⇒ 00:16:19.919 Vishnu: Bye, bye, you, too, thank you. Bye.