Meeting Title: Brainforge Team Member Introduction Date: 2025-07-31 Meeting participants: Vashdev Heerani, Annie Yu
WEBVTT
1 00:01:14.820 ⇒ 00:01:16.280 Annie Yu: Hello! Vashta!
2 00:01:22.700 ⇒ 00:01:23.260 Vashdev Heerani: Cool.
3 00:01:26.530 ⇒ 00:01:27.579 Annie Yu: How are you?
4 00:01:27.990 ⇒ 00:01:29.510 Vashdev Heerani: I’m good. How are you?
5 00:01:30.180 ⇒ 00:01:31.379 Annie Yu: Not too bad.
6 00:01:33.530 ⇒ 00:01:38.789 Annie Yu: So I guess this meeting is just for us to make intro.
7 00:01:38.970 ⇒ 00:01:39.820 Vashdev Heerani: Yes.
8 00:01:40.440 ⇒ 00:01:41.240 Annie Yu: Okay?
9 00:01:41.840 ⇒ 00:01:43.607 Annie Yu: And you’re based in
10 00:01:44.500 ⇒ 00:01:45.390 Vashdev Heerani: Pakistan.
11 00:01:45.550 ⇒ 00:01:47.999 Annie Yu: Pakistan, and you’re near. Yeah.
12 00:01:48.000 ⇒ 00:01:49.350 Vashdev Heerani: Yes, yes.
13 00:01:51.720 ⇒ 00:01:58.859 Annie Yu: And how’d you decided to to join you? Join Brain Forge, I guess. Always poached you.
14 00:01:59.080 ⇒ 00:01:59.830 Vashdev Heerani: Yes.
15 00:02:02.655 ⇒ 00:02:13.819 Annie Yu: Yeah, okay, so yeah, I guess. Let me, I and you’ve met with pretty much everyone.
16 00:02:13.820 ⇒ 00:02:15.280 Vashdev Heerani: Yes, kind of.
17 00:02:15.720 ⇒ 00:02:21.820 Annie Yu: Okay? And what what you talk about, like each other’s background. And the role here.
18 00:02:22.000 ⇒ 00:02:24.999 Vashdev Heerani: Mostly background. And the work here. Yes.
19 00:02:26.230 ⇒ 00:02:31.560 Annie Yu: Yeah. So I I think I’m the only
20 00:02:31.910 ⇒ 00:02:40.330 Annie Yu: not sure if I’m the only, but I think probably the analyst here. I guess Robert also does that
21 00:02:40.970 ⇒ 00:02:46.470 Annie Yu: so prior to Brainforge, I joined. I joined back in around.
22 00:02:47.360 ⇒ 00:02:49.529 Annie Yu: Think, April.
23 00:02:50.360 ⇒ 00:02:59.710 Annie Yu: I think I I did like a 2 week trial kind of thing toward the end of March, and then officially left my previous job and then left.
24 00:02:59.940 ⇒ 00:03:04.459 Annie Yu: Oh, and then join here as a full timer in April.
25 00:03:05.180 ⇒ 00:03:07.260 Annie Yu: It’s kinda crazy. How it’s it’s
26 00:03:07.560 ⇒ 00:03:21.755 Annie Yu: wait. It’s only 3 months. Okay, I feel like it’s been longer. But but yeah, and prior to that I I’ve always been in like more like consumer insights. So like consumer analytics, side of things.
27 00:03:22.150 ⇒ 00:03:22.890 Vashdev Heerani: Hmm.
28 00:03:23.810 ⇒ 00:03:31.580 Annie Yu: Let me recall. So in my previous role I have been in like voice of customer, where we
29 00:03:31.920 ⇒ 00:03:37.430 Annie Yu: set up like quatrics, and then
30 00:03:38.320 ⇒ 00:03:43.450 Annie Yu: analyze, like what people are saying and then influence, how the team
31 00:03:44.620 ⇒ 00:03:48.960 Annie Yu: optimize their product and as well as
32 00:03:49.450 ⇒ 00:03:56.250 Annie Yu: the website. And then before that, I was so that was like a brief time
33 00:03:56.660 ⇒ 00:04:07.520 Annie Yu: with Microsoft, and then, before that I was, I was. I would say I was in Nike. I think Nike was where I got inspired like to
34 00:04:07.920 ⇒ 00:04:17.685 Annie Yu: deepen in into like consumer insights. World. So I started there as a summer intern with it. Kind of interesting project
35 00:04:18.500 ⇒ 00:04:25.199 Annie Yu: analyzing what colors and then skew productivity, so help the team to to essentially
36 00:04:26.320 ⇒ 00:04:36.439 Annie Yu: it’s still their decision, but like help them to identify what’s productive versus what’s not and what’s potentially ready to divest
37 00:04:36.965 ⇒ 00:04:43.730 Annie Yu: and then also help with like store allocation, that kind of thing. But I would say like things.
38 00:04:45.238 ⇒ 00:04:46.870 Annie Yu: Thanks back at Niq
39 00:04:47.490 ⇒ 00:05:05.310 Annie Yu: were very slow compared to here. So I remember moving moving into this world. I initially was like, Okay, things move so fast now and then. People here here is not as silo, because I think I was in a bigger company. People has their like a very specific role.
40 00:05:06.580 ⇒ 00:05:15.937 Annie Yu: So I think there’s good and bad, but it’s it’s pretty cool. I think I’m very challenged here, because I’m doing like different things that I didn’t really had to do like
41 00:05:16.770 ⇒ 00:05:41.290 Annie Yu: like I didn’t. I’ve never created my Pr before coming here. So that’s something that I I’ve been working. I’m still not like Super, super, great, or like super, fast, as like a wish. But I think I’ve learned a lot here, and then I’m also getting my I had masters in supply chain analytics, but it was more so like like
42 00:05:42.110 ⇒ 00:05:59.819 Annie Yu: part of supply chain management and part of analytics programming. But now I’m getting my second master’s. I’m just doing one course per semester in data science, because I think that’s eventually where I want to move into.
43 00:06:00.380 ⇒ 00:06:01.350 Annie Yu: I’m
44 00:06:01.780 ⇒ 00:06:15.169 Annie Yu: I was never really great as like stats or probability. So that’s where I’m like trying to sharpen my skills at. And and then obviously, some applied Ml, which is I kind of like to do on the side.
45 00:06:15.660 ⇒ 00:06:18.389 Annie Yu: Yeah. So that’s a bit about me.
46 00:06:19.700 ⇒ 00:06:25.229 Vashdev Heerani: Very nice. So I will go with the with the similar thing first.st
47 00:06:25.490 ⇒ 00:06:26.370 Vashdev Heerani: So
48 00:06:26.997 ⇒ 00:06:44.939 Vashdev Heerani: last project. Well, my, my last project was around. I work with them around 6 year, and that was very enterprise application, and I had to focus on on one project here. This things are are going very fast.
49 00:06:45.550 ⇒ 00:06:46.080 Annie Yu: Yeah.
50 00:06:46.430 ⇒ 00:06:49.715 Vashdev Heerani: Also, I’m I’m doing a master in data, science.
51 00:06:50.080 ⇒ 00:06:51.180 Annie Yu: Oh, for real!
52 00:06:51.180 ⇒ 00:06:51.670 Vashdev Heerani: Yes.
53 00:06:51.670 ⇒ 00:06:57.870 Annie Yu: Are you doing like? Are you like hardcore doing it full time, or you are more like me just.
54 00:06:59.110 ⇒ 00:07:06.030 Vashdev Heerani: I am doing a full time like I I have covered 6 course of my master, so it’s.
55 00:07:06.030 ⇒ 00:07:06.680 Annie Yu: Wow!
56 00:07:06.680 ⇒ 00:07:10.869 Vashdev Heerani: 8 course of master and I. I covered 6 course.
57 00:07:12.150 ⇒ 00:07:14.529 Annie Yu: That’s I’ve almost done.
58 00:07:15.181 ⇒ 00:07:17.670 Vashdev Heerani: Yeah, almost done. Almost done. It’s kind of
59 00:07:18.470 ⇒ 00:07:28.739 Vashdev Heerani: so. I was initially thinking that I I had one client, and I had the time, so I can complete the master very easily.
60 00:07:30.180 ⇒ 00:07:30.920 Vashdev Heerani: Yeah.
61 00:07:31.280 ⇒ 00:07:32.350 Annie Yu: So, yeah, like.
62 00:07:34.486 ⇒ 00:07:42.773 Vashdev Heerani: I it. It’s it’s kind of I’m toward nlp. Kind of stuff llm kind of stuff. So
63 00:07:43.792 ⇒ 00:07:50.850 Vashdev Heerani: I’m very interested in kind of data and kind of stuff. So that’s why it’s very interesting for me.
64 00:07:51.445 ⇒ 00:07:56.344 Vashdev Heerani: Yeah, I I did top last semester in the last semester.
65 00:07:57.180 ⇒ 00:08:02.629 Annie Yu: Yeah, yeah, that’s fun. Maybe I’ll ask you some questions as I go.
66 00:08:03.090 ⇒ 00:08:03.780 Vashdev Heerani: Thank you.
67 00:08:03.780 ⇒ 00:08:19.259 Annie Yu: I, yeah, I’m still deciding how I like it, though, because I think at least the data scientists around me. They are all doing like very long term projects so like one project, maybe a quarter, and I don’t know if I like the idea of that like.
68 00:08:20.140 ⇒ 00:08:21.090 Annie Yu: I think I
69 00:08:21.230 ⇒ 00:08:35.599 Annie Yu: so, but I think it’s beneficial for me to learn anyway. So if I want to stay as an analyst, maybe we can apply some of that somewhere. But yeah, we’ll we’ll see.
70 00:08:36.409 ⇒ 00:08:39.829 Vashdev Heerani: So I I did graduation back in 2,017
71 00:08:40.700 ⇒ 00:08:46.719 Vashdev Heerani: when I started developing a web application back in in.net
72 00:08:47.474 ⇒ 00:08:54.929 Vashdev Heerani: in 2,018. I work as a as a data scientist where I used to write some.
73 00:08:55.210 ⇒ 00:09:02.479 Vashdev Heerani: I’ve got some to find that customer chance feedback from the customers kind of stuff.
74 00:09:02.920 ⇒ 00:09:12.360 Vashdev Heerani: Then in back in 2,019, I started working as a hadoop developer initially, engineering project pipelines.
75 00:09:12.470 ⇒ 00:09:24.960 Vashdev Heerani: So around 6 months, I worked on hadoop. Then I started working as a spark developer where I used to to handle a spark data pipeline using the airflow and the snowflake
76 00:09:25.625 ⇒ 00:09:31.300 Vashdev Heerani: then I started working on data breaks back in 2,022,
77 00:09:31.430 ⇒ 00:09:48.150 Vashdev Heerani: and then 2,023. I started working on the informatica system where I used to manage a legacy database system where we had a script from a script was written in nineties, and I had to manage those scripts.
78 00:09:48.150 ⇒ 00:09:49.070 Vashdev Heerani: Wow!
79 00:09:50.730 ⇒ 00:10:00.239 Vashdev Heerani: And last 6 months. Yeah, last 6 months I started working as a data analyst as well. I worked on power bi
80 00:10:00.280 ⇒ 00:10:29.069 Vashdev Heerani: reporting kind of stuff. So I I moved we. We had a in the previous company. We had a Logi we had a Logi system and that is also a tool to for reporting. So we moved all our system from logic to power. Bi. So I I was responsible to move the the reports from Logi to power. Bi the Logi was written the Xml. So I moved to the power Bi.
81 00:10:29.880 ⇒ 00:10:30.240 Annie Yu: Yeah.
82 00:10:30.240 ⇒ 00:10:33.140 Vashdev Heerani: That’s that’s that’s it. From my side.
83 00:10:33.520 ⇒ 00:10:38.430 Annie Yu: So it sounds like you’ve done pretty much everything so.
84 00:10:38.430 ⇒ 00:10:39.100 Vashdev Heerani: Kind of.
85 00:10:39.160 ⇒ 00:10:45.519 Annie Yu: I did like being an I guess data engineer is what you like the most compared to all the other.
86 00:10:45.520 ⇒ 00:10:49.030 Vashdev Heerani: Yes, because I I did around 6 year
87 00:10:49.570 ⇒ 00:11:08.220 Vashdev Heerani: in the data engineering field. So I tried everything from web development to data, science to data engineering. And lastly, I worked on data. And one more thing that I I had very big team in the data engineering side where I used to work around
88 00:11:08.500 ⇒ 00:11:09.860 Vashdev Heerani: 6 year with them.
89 00:11:10.740 ⇒ 00:11:14.082 Vashdev Heerani: So it was very long period. Then we
90 00:11:15.200 ⇒ 00:11:37.890 Vashdev Heerani: yeah, I was very comfortable, and we we moved our system like initially. We worked on hadoop. Then we worked on spark. Then we worked on database. Then we work on informatica system. So we moved our system step by steps. So whenever there’s upper management change. And they ask to change the technology and the tools. So we we moved our systems.
91 00:11:38.689 ⇒ 00:11:49.900 Vashdev Heerani: Yeah, that was very large company that manage around 75% in person, presence in the Us. And Canada.
92 00:11:50.080 ⇒ 00:11:53.479 Vashdev Heerani: So we we wrote a system for them.
93 00:11:54.010 ⇒ 00:12:00.630 Annie Yu: Yeah, yeah, that’s amazing. I think I’m the opposite.
94 00:12:01.660 ⇒ 00:12:26.870 Annie Yu: I’m on the opposite side. I know. I I probably never want to be a data engineer. I think I don’t really like enjoy, like I think I think, being an analyst, of course, we have to write query, but I I think I rather do that on the from the downstream rather than setting up things from the upstream. So, yeah.
95 00:12:26.870 ⇒ 00:12:35.439 Vashdev Heerani: Data. Engineering is very interesting. Field like, if you start this, then you will love to work on these kind of problem.
96 00:12:36.120 ⇒ 00:12:36.990 Annie Yu: Yeah.
97 00:12:38.150 ⇒ 00:12:44.859 Vashdev Heerani: I love. I love creating a dashboard in the power Bi, but it’s not
98 00:12:44.970 ⇒ 00:12:48.910 Vashdev Heerani: enjoying as much as I do in the engineering side.
99 00:12:50.450 ⇒ 00:12:56.029 Annie Yu: Yeah, have you used real? Yet? The I think they use it. A, lot, here.
100 00:12:57.020 ⇒ 00:12:57.550 Vashdev Heerani: Sorry!
101 00:12:57.860 ⇒ 00:12:59.710 Annie Yu: Real real data.
102 00:13:01.360 ⇒ 00:13:01.680 Annie Yu: Yep.
103 00:13:01.680 ⇒ 00:13:02.100 Vashdev Heerani: I.
104 00:13:02.100 ⇒ 00:13:02.430 Annie Yu: Oh!
105 00:13:02.430 ⇒ 00:13:06.439 Vashdev Heerani: Yeah, i i i’m learning here all the time.
106 00:13:07.493 ⇒ 00:13:15.966 Annie Yu: I’ve been local about. I’ve I’ve been vocal about it. I don’t like it. But so I just, I just want to hear your opinion. But
107 00:13:16.440 ⇒ 00:13:25.089 Annie Yu: yeah, I I’m not as comfortable with like Yaml. I never really had to touch that
108 00:13:25.890 ⇒ 00:13:32.649 Annie Yu: before before the role here. But that’s something I’ve also like learned here.
109 00:13:33.510 ⇒ 00:13:42.220 Vashdev Heerani: So so initially I was I was scared after the Xml files. So I
110 00:13:48.710 ⇒ 00:13:54.541 Vashdev Heerani: affirmative so, and I had to create a power bi report for that.
111 00:13:55.100 ⇒ 00:14:19.979 Vashdev Heerani: I had no other information like a filter on the data the the data columns, the data sources. So what I did. I I got the help from the Llms like and Gemini. So that’s why I learned things from. I learned things from like I had to
112 00:14:20.230 ⇒ 00:14:23.279 Vashdev Heerani: make my hand dirty with the Xml as well.
113 00:14:25.610 ⇒ 00:14:29.627 Annie Yu: And which client projects. Are you on?
114 00:14:30.130 ⇒ 00:14:39.079 Vashdev Heerani: So right now, right now I was assigned 2 clients like Aden and as well as ABC. Platform.
115 00:14:39.390 ⇒ 00:14:40.120 Annie Yu: Yes.
116 00:14:41.280 ⇒ 00:14:44.430 Annie Yu: And then the data, the data platform, the internal one.
117 00:14:44.430 ⇒ 00:14:46.939 Vashdev Heerani: Yeah. Yeah. The internal one. Data platform.
118 00:14:47.360 ⇒ 00:14:50.249 Annie Yu: Yeah, so sounds like we’ll, we’ll be working a lot.
119 00:14:50.560 ⇒ 00:15:16.799 Vashdev Heerani: Yeah, so it’s it’s kind of for now things are going very fast for me, like I had to switch my contacts from one project to another project, and I don’t have any idea like what’s going on with one project. And then I have. I got the request from other project other project that I would work on this past the previous work.
120 00:15:16.830 ⇒ 00:15:33.850 Vashdev Heerani: So it’s kind of it’s it’s kind of a new thing for me to to switch the context very quickly. Because in previous company, I I just had one context and one focus on one project that I had worked.
121 00:15:34.190 ⇒ 00:15:41.590 Vashdev Heerani: So it’s kind of I had to learn a lot of things from here to work on a different project at the same time.
122 00:15:42.280 ⇒ 00:15:56.665 Annie Yu: Yeah, I think the context switching has been like a big challenge for for a lot of people, I think, especially for me. I I can only speak for myself, but I I get distracted very easily, and I hate I hate it.
123 00:15:57.300 ⇒ 00:16:07.674 Annie Yu: When I get hacked when I’m working on something else. So I sometimes mute my slack just because I want to focus on something. And I think that’s something that
124 00:16:08.865 ⇒ 00:16:27.334 Annie Yu: actually encouraged some. The people to block like 4 h, maybe. And then you can just focus, do your focus work so hopefully that that works for you. But I guess you also have time difference with most of the some of the teammates. So that’s probably some good advantage, too.
125 00:16:27.690 ⇒ 00:16:29.010 Vashdev Heerani: Yes, yes.
126 00:16:31.100 ⇒ 00:16:31.750 Annie Yu: Yeah.
127 00:16:35.510 ⇒ 00:16:42.439 Annie Yu: And you said, you also work with data bricks. So I also work with data bricks. But as an
128 00:16:44.590 ⇒ 00:17:00.949 Annie Yu: also as like an analyst, so I only do my final join between some tables there and then push it back to Snowflake. I remember that’s why I did. But what other? I never really know the like, what other things
129 00:17:01.150 ⇒ 00:17:04.680 Annie Yu: are happening around. Data breaks so.
130 00:17:04.680 ⇒ 00:17:05.270 Vashdev Heerani: So.
131 00:17:05.819 ⇒ 00:17:06.339 Annie Yu: Yeah.
132 00:17:06.339 ⇒ 00:17:08.569 Vashdev Heerani: We? We can like
133 00:17:08.799 ⇒ 00:17:15.809 Vashdev Heerani: like, if we use warehouse and the snowflake. So instead of using warehouse in the snowflake, we can.
134 00:17:16.129 ⇒ 00:17:23.999 Vashdev Heerani: we can do all the all the work kind of stuff in in the database as well. So this is kind of distributed computation.
135 00:17:24.119 ⇒ 00:17:38.919 Vashdev Heerani: So all the data breaks. They can handle this kind of distributed computation. And then we, we get the parallelism of the task that that’s why we get a lot of tasks very quickly.
136 00:17:40.710 ⇒ 00:17:44.210 Annie Yu: So what’s the equivalent? I think I’m
137 00:17:44.370 ⇒ 00:17:52.230 Annie Yu: yeah, that’s something. I also am still learning about all the tools like, who’s what’s what’s equivalent? And what’s what’s
138 00:17:52.830 ⇒ 00:17:55.080 Annie Yu: upstream or downstream?
139 00:17:55.360 ⇒ 00:18:08.239 Vashdev Heerani: Okay? So so if if we if we can create an other like a similar kind of stuff on the open source, we can use a spark and buy spark.
140 00:18:09.660 ⇒ 00:18:15.589 Vashdev Heerani: So Spark, SQL. Is is doing the same job as as
141 00:18:16.145 ⇒ 00:18:37.564 Vashdev Heerani: as database. But it’s kind of data breaks. We have to manage very few things, but in this file we have to manage memory. We have to manage cores of the system. We have have to manage everything that that we we can give to the task we can assign to the task like we can. We can.
142 00:18:38.510 ⇒ 00:18:49.689 Vashdev Heerani: we can manage how we can distribute it? Or how? How can we distribute our task? In some in in chunks, how can we create how much
143 00:18:50.470 ⇒ 00:18:54.119 Vashdev Heerani: worker we can create, we can assign to the master nodes.
144 00:18:54.420 ⇒ 00:18:59.799 Vashdev Heerani: So we can do kind of everything with ourself with this far.
145 00:19:00.000 ⇒ 00:19:15.670 Vashdev Heerani: That’s why, sometime spark we we have age to use the spark because it’s open source, and we have to just pay for that, like a issue to instant services like where we host spark.
146 00:19:15.830 ⇒ 00:19:23.945 Vashdev Heerani: So this is kind of similar things that we can do so with. With this far we we have a lot of power to
147 00:19:24.600 ⇒ 00:19:34.179 Vashdev Heerani: to manage, or all the system ourselves. We can do this in database, but not as much as we do in this part.
148 00:19:35.130 ⇒ 00:19:53.169 Vashdev Heerani: And as far as little bit less cost here than than the database. Because when we use a a huge data processing jobs, we we need if we do this in the database that will be more more expensive than this part.
149 00:19:53.990 ⇒ 00:19:58.595 Annie Yu: Yeah, okay, that’s interesting. Yeah. I remember
150 00:19:59.380 ⇒ 00:20:14.685 Annie Yu: in my past life. I I was running a query, and it took so long I eventually got a like a warning email from from Nike is saying, you you’ve spent this much money, and this query is taking this long.
151 00:20:15.050 ⇒ 00:20:42.879 Vashdev Heerani: So so sometimes, sometime, we scared to use like paid services computation like sometime we use we scared to use database computation power. Sometime we scared to use the snowflake computation power, because they they manage everything. And whenever we, because these these services are as you pay pay as you go. So whenever we we
152 00:20:43.558 ⇒ 00:20:52.320 Vashdev Heerani: we try to compute on the snowflake, it will give us a huge bill at the end of the month.
153 00:20:52.450 ⇒ 00:20:58.089 Vashdev Heerani: So sometimes this happens so so managing our
154 00:20:58.500 ⇒ 00:21:01.610 Vashdev Heerani: reducing the cost we can use in as far
155 00:21:01.860 ⇒ 00:21:10.400 Vashdev Heerani: so Spark has. Like we, we need a dedicated resources for to manage spark things.
156 00:21:11.140 ⇒ 00:21:13.090 Annie Yu: Yeah, that makes sense.
157 00:21:13.090 ⇒ 00:21:18.120 Vashdev Heerani: You know, and for Emr machine, if we are
158 00:21:18.640 ⇒ 00:21:44.300 Vashdev Heerani: if we use Emr machine, so Emr is is also a good good to start if we can use Ec 2 instance as well as Emr. So Emr has everything that that is required for data engineering. So we don’t need to worry about the things like the services that that is required for data engineering like a Spa like any other processing tools.
159 00:21:44.310 ⇒ 00:22:04.330 Vashdev Heerani: But Ec, 2 instance we can create, we can create any anything like we can install any software over the Ec 2 instance, that is the less cost than the Emr machines. So this is kind of whenever we need to reduce the cost, we have to find the let to
160 00:22:04.400 ⇒ 00:22:23.420 Vashdev Heerani: options that how can we reduce the cost like we we were using? We were using snowflake. Then it it was around 55 to 6 60,000 per month cost. So that is very huge for the company to bear
161 00:22:23.838 ⇒ 00:22:40.999 Vashdev Heerani: so they asked us to to find something else to so that we can. We can we can decouple the storage as well as the processing power. So what we did, we actually we actually moved to redshift
162 00:22:41.290 ⇒ 00:22:52.310 Vashdev Heerani: and and the other like like data on the S 3 bucket. And then we we used spark as a processing power.
163 00:22:52.620 ⇒ 00:22:58.280 Vashdev Heerani: So we pull the data from S. 3 bucket and do the processing on the spark
164 00:22:58.410 ⇒ 00:23:02.510 Vashdev Heerani: and then submit the result to the to the redshift.
165 00:23:02.740 ⇒ 00:23:07.579 Vashdev Heerani: So this is kind of cost saving system that we have to develop.
166 00:23:10.130 ⇒ 00:23:22.360 Annie Yu: yeah, I, yeah, I’m, I’m gonna be honest, I don’t follow all the software, you just mentioned. But I, I will, I will. I’ll try to pick up my knowledge around that.
167 00:23:23.020 ⇒ 00:23:23.730 Annie Yu: Yeah.
168 00:23:26.910 ⇒ 00:23:42.750 Vashdev Heerani: So that is kind of the stuff that I I learned in 6 years, and I switched my tool from one to another to another to another. Like, I, I did 6 to 7. I moved from 6 to 7 tools
169 00:23:43.185 ⇒ 00:23:46.110 Vashdev Heerani: my data from 6 to 7 tools. So I
170 00:23:46.210 ⇒ 00:24:02.760 Vashdev Heerani: yeah, I learned a lot of things to do do this kind of stuff. I also did the devops kind of stuff as well where I used to manage gitlab and used to write Ci CD. Pipeline for the gitlab as well.
171 00:24:03.230 ⇒ 00:24:03.710 Annie Yu: Yeah,
172 00:24:04.170 ⇒ 00:24:09.370 Vashdev Heerani: Kind of dockerized system as well. I did the dockerized containerization as well.
173 00:24:10.070 ⇒ 00:24:16.588 Annie Yu: Yeah, so you’re like an actual full stack, I guess.
174 00:24:18.270 ⇒ 00:24:32.470 Vashdev Heerani: So it’s kind of. But then when you have yeah, when you work on lot of tools, then you cannot work on, do you? You are not expert to the one specific tool. So
175 00:24:32.580 ⇒ 00:24:43.510 Vashdev Heerani: I did a lot of things on the, on the lot of different tools. But it’s not kind of that. I I am expert on one particular tool.
176 00:24:44.420 ⇒ 00:24:53.719 Annie Yu: But you always will have, I guess, a preference right? You. You want to learn something more. And you you know that you probably don’t like this tool that much.
177 00:24:54.030 ⇒ 00:25:08.260 Vashdev Heerani: Yes, kind of. I always wanted to learn something new like last year I I was dedicatedly working on the data engineering side, and I wanted to
178 00:25:08.510 ⇒ 00:25:14.479 Vashdev Heerani: to learn to explore the Llm kind of system. So I I
179 00:25:14.730 ⇒ 00:25:25.996 Vashdev Heerani: like, I, I went to the Youtube. I’ve tried different videos. But it’s it’s kind of. When you don’t have a target you will skip after 5 or 10 min the
180 00:25:27.030 ⇒ 00:25:38.830 Vashdev Heerani: and then you will forget the topic. The very next day. So what I did, I started my master in the data science and the Lla kind of stuff.
181 00:25:39.070 ⇒ 00:25:40.779 Annie Yu: Oh, that’s it!
182 00:25:41.350 ⇒ 00:25:42.370 Vashdev Heerani: Yeah.
183 00:25:42.960 ⇒ 00:25:45.330 Annie Yu: Yeah, that’s fun.
184 00:25:46.108 ⇒ 00:25:59.021 Annie Yu: Yeah, I’m I’m doing like I said, I just, I’m just doing one course per semester. So I think. And I’ve I’ve only finished 2 courses now, so I still got a long time to go.
185 00:25:59.850 ⇒ 00:26:05.161 Annie Yu: But maybe I’ll ask you some questions if I’ve ever ever get stuck.
186 00:26:05.570 ⇒ 00:26:06.100 Vashdev Heerani: Sure.
187 00:26:09.240 ⇒ 00:26:14.550 Annie Yu: Do you? Do you have like family like? What? What’s your life outside of work?
188 00:26:16.070 ⇒ 00:26:23.940 Vashdev Heerani: So. Yes, I do have. Like I I did. I get married in back in 2,000
189 00:26:24.210 ⇒ 00:26:28.020 Vashdev Heerani: 23, I guess. And bye.
190 00:26:28.190 ⇒ 00:26:28.630 Annie Yu: Here.
191 00:26:30.620 ⇒ 00:26:36.450 Vashdev Heerani: So I I had the daughter in 2024, and then
192 00:26:37.992 ⇒ 00:26:43.020 Vashdev Heerani: I have a son in march 2025.
193 00:26:43.690 ⇒ 00:26:46.300 Annie Yu: No every year.
194 00:26:48.060 ⇒ 00:26:50.820 Vashdev Heerani: Sorry I stopped. I dropped here.
195 00:26:51.150 ⇒ 00:26:59.510 Annie Yu: Yeah, that’s amazing. No, that’s amazing. And your your kids can be friends with wish kids as well.
196 00:26:59.510 ⇒ 00:27:02.239 Vashdev Heerani: Right now. We haven’t met yet.
197 00:27:02.650 ⇒ 00:27:03.863 Annie Yu: Oh, heaven!
198 00:27:05.090 ⇒ 00:27:05.990 Annie Yu: Eventually!
199 00:27:06.890 ⇒ 00:27:08.030 Vashdev Heerani: I haven’t been too late.
200 00:27:12.773 ⇒ 00:27:27.720 Annie Yu: Yeah, I think I don’t have other things to talk about now, but it’s really great meeting you and I am. I’m positive I will need a lot of your help going forward so really happy that you’re here.
201 00:27:28.130 ⇒ 00:27:29.609 Vashdev Heerani: I’m very excited, too.
202 00:27:29.840 ⇒ 00:27:30.360 Vashdev Heerani: Thank you.
203 00:27:31.200 ⇒ 00:27:33.810 Annie Yu: Yeah. Well, have a good night. Vashtev.
204 00:27:34.020 ⇒ 00:27:37.109 Vashdev Heerani: Have a good good day. Bye.