Meeting Title: Brainforge New Hire Intro Chat Date: 2026-05-04 Meeting participants: Amber Lin, Davis Dunham
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1 00:00:47.190 ⇒ 00:00:48.420 Amber Lin: Hey there!
2 00:00:48.880 ⇒ 00:00:50.190 Davis Dunham: Hey, Amber, how you doing?
3 00:00:50.190 ⇒ 00:00:53.129 Amber Lin: I’m doing well! Where are you right now? You’re outside?
4 00:00:53.130 ⇒ 00:00:57.740 Davis Dunham: I am outside, I decided to get some sunlight for the, that is very nice.
5 00:00:57.740 ⇒ 00:01:01.699 Amber Lin: You can also go on a walk if you want, if you want to just call on the phone.
6 00:01:01.880 ⇒ 00:01:07.250 Davis Dunham: Oh, heard. A-okay, I have, the laptop right here, I’m feeling comfy.
7 00:01:07.450 ⇒ 00:01:12.269 Amber Lin: Cool! Wait, where are you based?
8 00:01:12.600 ⇒ 00:01:13.710 Davis Dunham: Austin.
9 00:01:14.130 ⇒ 00:01:15.400 Amber Lin: Oh…
10 00:01:15.400 ⇒ 00:01:18.560 Davis Dunham: I met Utom in person, which is how I got the job.
11 00:01:18.560 ⇒ 00:01:21.070 Amber Lin: They’re cool. They’re cool.
12 00:01:21.070 ⇒ 00:01:22.849 Davis Dunham: Hmm. Where are you based?
13 00:01:23.160 ⇒ 00:01:24.280 Amber Lin: I’m in LA.
14 00:01:24.670 ⇒ 00:01:25.240 Davis Dunham: heard.
15 00:01:25.240 ⇒ 00:01:26.280 Amber Lin: 2 hours.
16 00:01:27.260 ⇒ 00:01:30.569 Davis Dunham: Understood, yeah. Yeah, 2-hour difference, that’s crazy.
17 00:01:30.750 ⇒ 00:01:31.490 Amber Lin: Yeah.
18 00:01:32.050 ⇒ 00:01:34.309 Davis Dunham: Well, hey, it’s very nice to meet you.
19 00:01:34.310 ⇒ 00:01:47.880 Amber Lin: It is, and thank you for sharing last Friday. I found it really cool, so I wanted to… wanted to ask you how you did it, and I think Uten probably would want you to do, like, a company-wide share if you want to do that.
20 00:01:48.130 ⇒ 00:01:48.840 Amber Lin: But…
21 00:01:48.840 ⇒ 00:01:54.619 Davis Dunham: Yeah, no, absolutely. I, it was… I was a little nervous, as you could tell.
22 00:01:55.060 ⇒ 00:02:06.410 Davis Dunham: I, But it was, it was fun. I’m glad. Kayla said that I was, that it was, it was understandable, which is the whole… my only real goal, was for it to be understandable for everyone.
23 00:02:06.410 ⇒ 00:02:06.870 Amber Lin: Yeah.
24 00:02:06.870 ⇒ 00:02:14.580 Davis Dunham: I, yeah, it’s funny, actually, I come from the service industry, so I was working in fine dining before this, and…
25 00:02:14.580 ⇒ 00:02:27.249 Amber Lin: Okay. Yeah, I know. What did you do before? Did you… sorry, I know you’re going on that drive, I was so curious, like, were you always, like, doing AI stuff, even in fine dining?
26 00:02:27.470 ⇒ 00:02:33.830 Davis Dunham: It’s been… no, no, not even. I actually was, a, like, server and,
27 00:02:34.660 ⇒ 00:02:43.650 Davis Dunham: yeah, I was serving tables. So, I… I… but on the side, as, like, my personal hobby, was doing AI stuff, building apps.
28 00:02:43.980 ⇒ 00:03:00.649 Davis Dunham: And, it actually all started for me during COVID, which is how, like, I met Utom in the first place. I met him at my friend’s birthday, and, told him about, this project I worked on way back when, and it was this stupid idea, honestly, but,
29 00:03:00.690 ⇒ 00:03:13.599 Davis Dunham: I, during the COVID era, you know, it was, full debauchery mode, and I was trying to fix my sleep schedule, so I made a, like, YOLO-powered, auto-aiming, like, water gun alarm clock.
30 00:03:15.360 ⇒ 00:03:19.820 Davis Dunham: And this was before any of, like, the chatbots were really out yet. I mean, I think that was.
31 00:03:19.820 ⇒ 00:03:20.260 Amber Lin: What?
32 00:03:20.260 ⇒ 00:03:22.959 Davis Dunham: GPT, like, 3 era, so…
33 00:03:23.190 ⇒ 00:03:27.890 Davis Dunham: I just, like, figured out how to do it, and coded it all from scratch, and
34 00:03:28.800 ⇒ 00:03:47.780 Davis Dunham: you know, my little ESP32 as the, like, microcontroller for the server motors and stuff like that, and I just… I loved it. I was, like, I always knew, I’ve always been, interested, and… and tech’s always been a hobby for me. But that’s when it became real, and ever since then, I’ve been working on some projects, and I’ve told them about my…
35 00:03:47.900 ⇒ 00:03:51.399 Davis Dunham: I was trying to make, essentially, like, a Jarvis at home.
36 00:03:52.110 ⇒ 00:03:52.680 Amber Lin: Okay.
37 00:03:52.680 ⇒ 00:03:53.070 Davis Dunham: That was an…
38 00:03:53.070 ⇒ 00:04:00.039 Amber Lin: So, you do software and hardware, kind of. You build it for the actual physical product.
39 00:04:00.300 ⇒ 00:04:13.859 Davis Dunham: Yes, and it was kind of on, like, a hobbyist scale prior. But then, at some point recently, I got together with a buddy of mine, and we were building a website called Jungle Gem.
40 00:04:14.180 ⇒ 00:04:22.540 Davis Dunham: And the whole idea behind this website is, like, it’d be like the iTunes equivalent of, like, yoga, workout videos, that kind of thing, just for movement.
41 00:04:22.730 ⇒ 00:04:32.070 Davis Dunham: And the whole idea is when you pay for the video, and then that’s it, and you just have the video, and there’s some more thought that needs to go into that to make it, like, truly viable.
42 00:04:32.210 ⇒ 00:04:38.839 Davis Dunham: But built out the whole website, and, like, have a bunch of stuff working. I’ve been working with, like, Cloudflare, some other, like, Supabase.
43 00:04:39.070 ⇒ 00:04:45.550 Davis Dunham: For, like, backend stuff. And, told Utem about this, and he was like, that’s… this is awesome. And, in all honesty.
44 00:04:45.690 ⇒ 00:04:50.799 Davis Dunham: my goal with Jarvis was, like, a much smaller-scale version of what I think, kind of.
45 00:04:51.490 ⇒ 00:04:53.960 Davis Dunham: One of the overall goals of the company is
46 00:04:54.370 ⇒ 00:05:03.509 Davis Dunham: forges, which is, like, having a centralized location that has access to all of these tools and abilities that can do a lot of this
47 00:05:04.970 ⇒ 00:05:11.930 Davis Dunham: let me… yeah, for you, you know? It can cut out a lot of the inefficiency in doing this, because, like, one of the biggest issues is, like.
48 00:05:12.520 ⇒ 00:05:18.479 Davis Dunham: I guess what I see in Brainforge right now is there’s already so many really good tools that are built out.
49 00:05:18.990 ⇒ 00:05:20.150 Davis Dunham: that work.
50 00:05:20.280 ⇒ 00:05:28.010 Davis Dunham: And the issue is, with people coming on, you know, people that weren’t the ones that were building them, coming on and doing consulting work and helping.
51 00:05:29.390 ⇒ 00:05:38.650 Davis Dunham: it’s hard to find all these tools, and as human beings, it’s hard to maintain, like, a, you know, information in that way. Like, you have your flow, and that flow works.
52 00:05:40.280 ⇒ 00:05:53.659 Davis Dunham: But it’s, you know, once that flow works, at that point you start getting, like, kind of set into that as well. So if you don’t have a system that’s going to recommend things that are available that will help that workflow, then people just won’t find them in the first place.
53 00:05:53.660 ⇒ 00:05:55.239 Amber Lin: You’ve just built, right?
54 00:05:56.430 ⇒ 00:06:01.049 Amber Lin: Is it available right now? Is there a way I could play around with it or test it?
55 00:06:01.050 ⇒ 00:06:02.200 Davis Dunham: the orchestrators?
56 00:06:02.390 ⇒ 00:06:03.210 Amber Lin: Yeah.
57 00:06:03.210 ⇒ 00:06:07.579 Davis Dunham: Yes, so it is, it’s currently, it’s currently live on, Brainforge platform.
58 00:06:08.100 ⇒ 00:06:12.769 Amber Lin: Okay, I think I just need to pull the version of the repo.
59 00:06:15.430 ⇒ 00:06:18.009 Davis Dunham: And it’s… I think it’s… I believe it’s live on main at this point.
60 00:06:18.190 ⇒ 00:06:26.750 Amber Lin: Okay, awesome. I’ll go… I’ll go look at it. How did you approach building that? Was there, like, something you followed? What… what is, like, the…
61 00:06:27.090 ⇒ 00:06:34.959 Amber Lin: Industry best practice, or what do people say about building orchestrators and, like, organizing skills and all that?
62 00:06:35.210 ⇒ 00:06:50.510 Davis Dunham: So, I was reading… Utam sent me, like, an X thread, that was, talking about, like, how people organize skills, and I was looking into it, because I actually… I’m fairly familiar with OpenCode, Claude code at this point, but I wasn’t super familiar with
63 00:06:51.770 ⇒ 00:06:59.279 Davis Dunham: skill usage prior to him putting me on this project, which was honestly kind of scary, because this one is, with skills, it’s so…
64 00:06:59.510 ⇒ 00:07:04.320 Davis Dunham: soft. You know, it’s not like you’re hitting, like, a hard error
65 00:07:04.700 ⇒ 00:07:07.259 Davis Dunham: In, like, a website’s not deploying because there’s.
66 00:07:07.260 ⇒ 00:07:07.610 Amber Lin: Yeah.
67 00:07:07.610 ⇒ 00:07:16.520 Davis Dunham: problem. It’s like, there is this wording that is leading to some kind of inefficiency, and right now with our setup, it’s a little hard to figure out
68 00:07:17.100 ⇒ 00:07:21.369 Davis Dunham: where exactly that happens, other than for… for, like, from user feedback, so…
69 00:07:21.830 ⇒ 00:07:22.980 Davis Dunham: I want to set up…
70 00:07:23.450 ⇒ 00:07:36.379 Davis Dunham: either, like, a feedback form or a Slack channel, maybe, where it’s just, like, specifically skill-based feedback, like, hey, this thing happened, and asked me this extra question, I think we can skip that, you know, where we can add stuff, but…
71 00:07:36.700 ⇒ 00:07:40.640 Davis Dunham: I’m getting a little far out of bounds with the question. The whole idea was just that
72 00:07:43.580 ⇒ 00:07:58.610 Davis Dunham: I don’t want to eliminate skills, because all of these skills do a, like, specific, important thing within the company that, like, people prior to me already defined. They want this workflow to exist the way that it does, but the issue is discoverability.
73 00:07:58.800 ⇒ 00:08:08.839 Davis Dunham: And, you know, I mean, you can have… I mean, at this point, we’re getting to large context links with these models, and they can read all the stuff, but why spend the extra tokens
74 00:08:09.650 ⇒ 00:08:15.189 Davis Dunham: When we can summarize or link all of these skills into one
75 00:08:15.330 ⇒ 00:08:18.170 Davis Dunham: Larger thing, and add modes to that skill.
76 00:08:18.500 ⇒ 00:08:26.849 Amber Lin: So, the orchestrator you built, is it an agent, or is it a skill? Like, what do you use? It’s a skill. Oh, okay.
77 00:08:26.990 ⇒ 00:08:29.750 Davis Dunham: So the orchestrators are all just, essentially…
78 00:08:29.750 ⇒ 00:08:33.530 Amber Lin: skill directory file and figure… figures out how to…
79 00:08:33.530 ⇒ 00:08:36.519 Davis Dunham: So, there are two levels. There’s an index.
80 00:08:37.360 ⇒ 00:08:46.269 Davis Dunham: In the index, I have a git action running, so every time that main updates and the skills have changed, it reruns the index, and then it also…
81 00:08:46.410 ⇒ 00:08:58.329 Davis Dunham: Auto-classifies new skills that are added as, potentially good candidates for a new orchestrator, and then if enough of those skills amass, it will just create a new orchestrator.
82 00:08:58.830 ⇒ 00:09:08.209 Davis Dunham: And so, it isn’t one singular orchestrator, it is multiple orchestrators. So before, we had a bunch of different linear skills, or, like, legal skills.
83 00:09:08.220 ⇒ 00:09:25.099 Davis Dunham: And so before, if I… going from memory, like, we had, like, legal dash NDA, legal- you know, contract, legal-humanize. And… Humanize one might have been a standalone one, but either way,
84 00:09:25.470 ⇒ 00:09:36.689 Davis Dunham: The idea with the orchestrator is that we already have all of these skills that are labeled as legal, just make a legal-orcheter, and add all of those skills and as modes within that orchestrator.
85 00:09:36.890 ⇒ 00:09:41.100 Davis Dunham: So, because the end goal is for the…
86 00:09:41.740 ⇒ 00:09:49.380 Davis Dunham: Whether we’re using cursor or open code, because those are the two ones that I’m aware of that the company is mainly using.
87 00:09:50.340 ⇒ 00:09:56.610 Davis Dunham: Making it easier… making that box bigger, so that the agent knows
88 00:09:57.050 ⇒ 00:10:03.419 Davis Dunham: Or can more easily assume, hey, we’re working on legal stuff right now, let me look through the legal orchestrator specifically.
89 00:10:03.660 ⇒ 00:10:10.609 Davis Dunham: and see if I can find a mode in which this is clearly using, and if it can’t tell, it’ll ask you what mode do you want to use.
90 00:10:12.310 ⇒ 00:10:18.319 Amber Lin: Very cool. Okay, I just, I just pulled from Maine, so I’m gonna see… what it’s like.
91 00:10:18.430 ⇒ 00:10:27.550 Amber Lin: Well, I think this weekend, I was reading over, like, AI harnesses and what that includes, because mostly before, I’ve been working at
92 00:10:27.980 ⇒ 00:10:47.769 Amber Lin: like, cursor level, it wasn’t… it wasn’t really like, oh, we have an agent, we have orchestration, evals, testing, having those loops, so, like, hearing, like, orchestration immediately to me, I was like, I didn’t know what you were referring to, but…
93 00:10:47.770 ⇒ 00:10:48.240 Davis Dunham: Yes.
94 00:10:48.240 ⇒ 00:10:50.540 Amber Lin: Thank you for explaining, that was very helpful.
95 00:10:50.670 ⇒ 00:11:04.689 Davis Dunham: Yes, it’s… I will say that the concept is actually fairly simple when it comes down to, like, what I… the changes that I made. It really is just like a… I just reorganized the skills. I didn’t do anything super fancy. I’m trying to keep it simple for now, just for…
96 00:11:04.690 ⇒ 00:11:06.409 Amber Lin: What do you want to do next?
97 00:11:06.840 ⇒ 00:11:12.439 Davis Dunham: Next, is… well, Utam has me on a, like, slide generation project.
98 00:11:12.590 ⇒ 00:11:15.150 Amber Lin: Oh, I’m very interested.
99 00:11:15.150 ⇒ 00:11:32.080 Davis Dunham: The deck generator, which I’m not super familiar with, the way that, like, the bots interface with PowerPoint or generating PowerPoints, like, style files. So I had to do a little bit of studying up on that, just to make sure that I can test it correctly.
100 00:11:32.200 ⇒ 00:11:38.660 Davis Dunham: But with the skill thing, I have actually a number of ideas for…
101 00:11:39.380 ⇒ 00:11:55.929 Davis Dunham: optimizing skill usage, and tool usage in general, because, you know, I guess at this point, my understanding is, is that you can kind of separate these things into 3 different tiers, or maybe not tiers, but 3 different, like, subcategories, and you have the tools, which are…
102 00:11:56.890 ⇒ 00:12:06.750 Davis Dunham: maybe external connections, but maybe not. And you have skills which aren’t external connections, they’re just kind of instructions. And then MCPs, which are, you know, just external connections.
103 00:12:07.130 ⇒ 00:12:13.280 Davis Dunham: And… With these… I… I want…
104 00:12:14.050 ⇒ 00:12:17.239 Davis Dunham: The end goal is to create a custom harness.
105 00:12:18.490 ⇒ 00:12:26.859 Amber Lin: Okay. Don’t we already have something like that? I know Utam was already working on our internal platform, which is…
106 00:12:27.060 ⇒ 00:12:28.280 Amber Lin: kind of…
107 00:12:28.900 ⇒ 00:12:39.710 Amber Lin: our own cursor. So, have you seen that, that they are developing? So, if you’re interested in our, like, custom harness, I bet UTM already has something.
108 00:12:40.730 ⇒ 00:12:49.639 Davis Dunham: Wait, give me just one second. I’ll want my… My Bluetooth just broke.
109 00:12:50.000 ⇒ 00:12:50.780 Amber Lin: So I’m using.
110 00:12:50.780 ⇒ 00:12:55.630 Davis Dunham: I’m using these… this is the first time I’ve done these, these headphones with the…
111 00:12:57.260 ⇒ 00:13:02.310 Davis Dunham: Okay, it’s not an option anymore, that’s okay. I’ll just go… I’ll just go no headphones.
112 00:13:02.310 ⇒ 00:13:03.300 Amber Lin: Okay.
113 00:13:03.300 ⇒ 00:13:04.640 Davis Dunham: Not worth figuring out right now.
114 00:13:04.640 ⇒ 00:13:06.730 Amber Lin: Casual call, no worries.
115 00:13:07.360 ⇒ 00:13:09.249 Davis Dunham: Say that one more time, so sorry.
116 00:13:09.250 ⇒ 00:13:28.589 Amber Lin: Yeah, I said we have… we have been developing our own internal platform, which is kind of like our own cursor. That’s kind of warming around that and our internal AI team’s doing, so we should have a custom harness already. So if you’re interested, maybe ask UTAM to see what they have developed.
117 00:13:28.590 ⇒ 00:13:32.200 Amber Lin: I mean, I learned the term from him, so he definitely has.
118 00:13:32.250 ⇒ 00:13:34.530 Amber Lin: I think that would be a good starting point.
119 00:13:35.250 ⇒ 00:13:40.400 Davis Dunham: Yeah, absolutely. Cause in the end, the whole goal is to…
120 00:13:40.460 ⇒ 00:13:59.589 Davis Dunham: be able to get metrics from it as well, in terms of skill usage and how well it works. Because when we’re using these external harnesses so far, we’re kind of just going on… all we can get is feedback from the team, which is fine, but not as efficient for,
121 00:13:59.680 ⇒ 00:14:04.010 Davis Dunham: I clear, you know, In the end, did this work? Did this not work?
122 00:14:04.470 ⇒ 00:14:05.240 Amber Lin: That’s true.
123 00:14:05.680 ⇒ 00:14:13.620 Amber Lin: Hong! Sorry, I asked you a lot about work. This was not… I, like, I was very interested, I mostly wanted to, like, get to know you.
124 00:14:13.620 ⇒ 00:14:14.519 Davis Dunham: Yeah, no, absolutely.
125 00:14:14.520 ⇒ 00:14:20.169 Amber Lin: So, you’re… you’re in Austin… have you always been in Austin, or did you move there?
126 00:14:20.460 ⇒ 00:14:23.140 Davis Dunham: I moved to Austin, from the Houston area.
127 00:14:23.960 ⇒ 00:14:26.170 Amber Lin: Oh, so you grew up in Texas?
128 00:14:26.170 ⇒ 00:14:27.030 Davis Dunham: Yes.
129 00:14:27.280 ⇒ 00:14:28.540 Amber Lin: Cooler, cool.
130 00:14:28.540 ⇒ 00:14:30.239 Davis Dunham: What about you, Amber? Are you from California?
131 00:14:30.240 ⇒ 00:14:41.070 Amber Lin: No, no, I’m from China, but I moved to Cali, like, 3 years ago. I was… I was abroad in other countries before, so…
132 00:14:41.070 ⇒ 00:14:42.290 Davis Dunham: That’s lovely.
133 00:14:42.440 ⇒ 00:14:43.010 Amber Lin: Hey, Bill.
134 00:14:43.010 ⇒ 00:14:45.840 Davis Dunham: So what is your… what’s your, technical background?
135 00:14:46.300 ⇒ 00:14:53.450 Amber Lin: Oh… Let’s see… how would you… how do you define technical? There’s a.
136 00:14:53.450 ⇒ 00:14:53.920 Davis Dunham: Well, I guess…
137 00:14:53.920 ⇒ 00:14:55.830 Amber Lin: I think people say technical.
138 00:14:56.210 ⇒ 00:14:59.499 Davis Dunham: That’s… that’s very fair, very fair. I guess what,
139 00:14:59.650 ⇒ 00:15:05.099 Davis Dunham: what, like, what would you… what would you classify your area of expertise to be? I know that, Utam mentioned…
140 00:15:05.310 ⇒ 00:15:09.139 Davis Dunham: that you worked at, EY before this?
141 00:15:09.560 ⇒ 00:15:14.659 Amber Lin: Yeah, I mainly did… I did consulting, and then I did data.
142 00:15:14.660 ⇒ 00:15:15.099 Davis Dunham: I knew it.
143 00:15:15.100 ⇒ 00:15:19.520 Amber Lin: consulting and kind of did a little bit of data, but that was more spreadsheets.
144 00:15:19.530 ⇒ 00:15:29.959 Amber Lin: So, I started in consulting, did my whole internships and stuff, all in consulting, worked at EY, and then I started working at Brainforge, and
145 00:15:29.960 ⇒ 00:15:40.169 Amber Lin: That was a little bit more than a year ago, and back then, we were so much smaller, like, our biggest deal back then was 20K.
146 00:15:40.950 ⇒ 00:15:59.250 Amber Lin: And it is so different right now. So back then, we had no project management, and Utama Robert was doing everything. They were working on projects, managing projects, and doing sales. So I was like, hey, I don’t know project management, but you guys need one, so I was doing some project management for them.
147 00:15:59.810 ⇒ 00:16:14.100 Amber Lin: So we kind of explored different… different ways to manage projects and kind of arrived at what we have now. And then I was like, hey, I’m kind of sick of project management. I don’t… I don’t think the mode that we are doing it right now needs
148 00:16:14.580 ⇒ 00:16:18.800 Amber Lin: be to project manage, because we have the CSOs, or whoever’s
149 00:16:19.030 ⇒ 00:16:33.570 Amber Lin: knows the project well enough to also do work in project manage, so I started on the data strategy side. So, we also didn’t have anyone on the strategy side other than Robert back then, so…
150 00:16:33.690 ⇒ 00:16:52.510 Amber Lin: when I say strategies, mostly some data analysis and some, oh, hey, company, this client, this is your opportunity size and this thing, or hey, this is, your campaign outcomes and what this means. Did that for a bit, and then slowly and slowly got more into the
151 00:16:52.560 ⇒ 00:16:56.549 Amber Lin: technical side of the… of data, so I’m…
152 00:16:56.970 ⇒ 00:17:01.809 Amber Lin: Starting to do a data analysis, like dashboarding, and the actual
153 00:17:01.980 ⇒ 00:17:11.690 Amber Lin: like, went into Snowflake, and then went deeper. I’m doing some dbt certifications right now, so I’m trying to learn a bit more on the AE side.
154 00:17:11.970 ⇒ 00:17:12.300 Davis Dunham: Yeah.
155 00:17:12.300 ⇒ 00:17:19.640 Amber Lin: I would say that’s as far as my technical depths go. That sounds more impressive.
156 00:17:19.640 ⇒ 00:17:20.450 Davis Dunham: to me.
157 00:17:20.450 ⇒ 00:17:23.710 Amber Lin: I appreciate it. It doesn’t beat your story.
158 00:17:23.900 ⇒ 00:17:32.630 Amber Lin: I admire people who, like, learn from scratch. I’m trying to do that with AI on the side, it’s just…
159 00:17:33.210 ⇒ 00:17:47.109 Amber Lin: like, I’m doing our… I’m kind of helping with our learning and development, and then trying to learn my own AI stuff, so that’s why I was very interested in how you did the orchestration. But we are doing some.
160 00:17:47.110 ⇒ 00:17:47.700 Davis Dunham: Yeah.
161 00:17:47.700 ⇒ 00:17:52.750 Amber Lin: stuff in the company, so there’s all, like, there’s a lot of flexibility in where we want to go.
162 00:17:54.520 ⇒ 00:17:56.760 Davis Dunham: Yeah, yeah, I’m… I am…
163 00:17:56.970 ⇒ 00:18:15.330 Davis Dunham: so excited to… to be a part of Brain Forks, and to, like, I… it’s cool because this project I was working on prior, which is the… like, I was trying to just make Jarvis at home, and the whole goal was just to, like, you know, it was more of a… I didn’t… I didn’t really know the use case for it, but I knew that it was.
164 00:18:15.330 ⇒ 00:18:15.790 Amber Lin: Cool.
165 00:18:15.790 ⇒ 00:18:16.639 Davis Dunham: be something where I could, like.
166 00:18:17.740 ⇒ 00:18:22.059 Davis Dunham: I put on my little, my little Google Glass that I bought, the little, you know, the OG Google Glass.
167 00:18:22.380 ⇒ 00:18:32.819 Davis Dunham: talk to it, and be like, hey, like, you know, I want to go eat somewhere tonight, and it suggests it, but it’s also like, hey, by the way, like, you’re broke, and rent’s coming up, so you should not go out to eat tonight, you know?
168 00:18:33.290 ⇒ 00:18:33.820 Davis Dunham: Something like that.
169 00:18:35.000 ⇒ 00:18:35.590 Davis Dunham: That’s good.
170 00:18:35.590 ⇒ 00:18:36.650 Amber Lin: Cool, yeah.
171 00:18:36.880 ⇒ 00:18:39.589 Amber Lin: Wait, are you full-on with us right now?
172 00:18:40.130 ⇒ 00:18:42.369 Davis Dunham: I, am signing the contract today.
173 00:18:42.540 ⇒ 00:18:44.969 Amber Lin: Oh, okay! Exciting!
174 00:18:45.260 ⇒ 00:18:47.299 Davis Dunham: Yeah, as a junior engineer.
175 00:18:47.940 ⇒ 00:18:55.200 Amber Lin: Wow. So, wait, you… Is this your first, like, job in AI?
176 00:18:55.600 ⇒ 00:18:56.469 Davis Dunham: It is.
177 00:18:56.470 ⇒ 00:18:58.300 Amber Lin: Wow, that’s so exciting!
178 00:18:58.300 ⇒ 00:19:02.299 Davis Dunham: Yeah, yeah, I am… I am very, very excited. I’ve been,
179 00:19:02.630 ⇒ 00:19:10.210 Davis Dunham: doing a lot of, like, hobby stuff, and kind of teaching myself a lot of stuff over the course of the past, I’d say, like, 4 or 5 years.
180 00:19:10.210 ⇒ 00:19:10.840 Amber Lin: Huh?
181 00:19:10.840 ⇒ 00:19:14.419 Davis Dunham: It’s something that I’ve always found an interest in, and I was looking for…
182 00:19:14.730 ⇒ 00:19:19.560 Davis Dunham: a good way in to actually being able to do this on a day-to-day basis, and…
183 00:19:19.560 ⇒ 00:19:20.520 Amber Lin: Oh.
184 00:19:20.850 ⇒ 00:19:29.810 Davis Dunham: I talked to Utama a couple months back, and he was like, why don’t you do this? He was like, keep working on stuff, and… and learning, and we’ll talk about it again. And so we talked about it again, and…
185 00:19:29.810 ⇒ 00:19:30.259 Amber Lin: I guess.
186 00:19:30.260 ⇒ 00:19:31.930 Davis Dunham: he’s happy with what I’ve done so far.
187 00:19:33.210 ⇒ 00:19:38.220 Amber Lin: That’s awesome! I’ll, I’ll, I’ll have to say, I know…
188 00:19:38.340 ⇒ 00:19:42.219 Amber Lin: Kayla… I meet with Kayla pretty regularly, and I know we were…
189 00:19:42.220 ⇒ 00:19:58.470 Amber Lin: trying to hire for an engineer for a long time, and there were a lot of people who made it to the final round who has been working in engineering for a long time, and they… and the time and Robert probably was like, no, I don’t want them, so I was very… I was very curious into who they chose, so…
190 00:19:58.470 ⇒ 00:20:00.000 Amber Lin: Says a lot about you.
191 00:20:00.260 ⇒ 00:20:07.259 Davis Dunham: I… I appreciate… I’m really happy that I impressed at least enough to… to get here, you know? I, I,
192 00:20:07.530 ⇒ 00:20:26.409 Davis Dunham: Yeah, I’m really excited. I’m excited to do some good work for the company, too. And for the record, I am, by, you know, no means an expert, at this point, but I’d love to meet with you again, and we can talk about some AI stuff, and some stuff that, like, you can do on the side just to get, like, comfortable with it, because it really is…
193 00:20:26.520 ⇒ 00:20:29.009 Davis Dunham: Very accessible, there’s just so much.
194 00:20:29.470 ⇒ 00:20:39.580 Amber Lin: That is… that is very true, and I… the project I’m trying to work on right now is… well, I just brainstormed yesterday, so I wouldn’t call working on.
195 00:20:40.390 ⇒ 00:20:53.790 Amber Lin: is a course-like structure to kind of walk me through, AI harnesses, front-end, back-end, and what it looks like. And Utam, I think one of.
196 00:20:53.790 ⇒ 00:20:54.310 Davis Dunham: Yeah.
197 00:20:54.310 ⇒ 00:20:57.649 Amber Lin: Their friends build, like, an open code.
198 00:20:57.790 ⇒ 00:21:12.419 Amber Lin: school, and… let me send you the link. I think once you enroll, your conversations in OpenCode automatically changes some stuff in the website and the UI, so that was pretty interesting, so maybe…
199 00:21:12.490 ⇒ 00:21:21.489 Amber Lin: as, like, a… as my learning project, I might do, like, a… build a website that connects to GitHub and checks if you did something, and then…
200 00:21:21.760 ⇒ 00:21:30.619 Amber Lin: just a course that runs through AI, but just an idea. I might talk to Uta Moore Brawl about, like, learning courses and all that.
201 00:21:32.090 ⇒ 00:21:39.840 Davis Dunham: Yeah, absolutely. I, I’d also, I would love, a, like, crash course on some of the more, like.
202 00:21:40.260 ⇒ 00:21:48.440 Davis Dunham: the commercial side of, like, some of the data analytics and stuff that you’re working on, and the project management, because at this point, I’d say my biggest…
203 00:21:48.770 ⇒ 00:22:06.009 Davis Dunham: biggest gap in my knowledge… well, actually, I wouldn’t call it the biggest gap, but a gap that I’ve identified in my knowledge is, of some of the terminology, honestly, and I guess just the idea of, like, best practices. I want to get comfortable
204 00:22:06.920 ⇒ 00:22:09.429 Davis Dunham: Being able to speak the lingo.
205 00:22:10.200 ⇒ 00:22:13.940 Davis Dunham: And also understanding, like, expectations.
206 00:22:13.940 ⇒ 00:22:22.410 Amber Lin: Something… well, a skill I think you probably came across as very helpful for me is, I think, the best practice researcher?
207 00:22:23.410 ⇒ 00:22:25.530 Amber Lin: In our… in our…
208 00:22:26.320 ⇒ 00:22:38.789 Amber Lin: Rainforest Platform repo. I think that could be really helpful. I use that to research, kind of like, oh, what does AI harness mean? But I think… but I bet it will be good in terms of, like, hey, what is the best practice in
209 00:22:38.990 ⇒ 00:22:44.710 Amber Lin: strategy analytics, or talk about this and that, and then if you… You think?
210 00:22:45.070 ⇒ 00:22:58.199 Amber Lin: maybe try asking Kirscher to take our past logs, or see if we have playbooks about… about those things. I think we have pretty good documentation. And then…
211 00:22:58.320 ⇒ 00:23:02.540 Amber Lin: Robert is… Robert or Jasmine is the…
212 00:23:03.140 ⇒ 00:23:18.539 Amber Lin: main person on this side, so Jasmine is the service lead, so if you have any technical questions about, oh, oh, how are things done right now, she’ll be good to answer. And then Robert gets a bit busy, but he used to be the one leading the service.
213 00:23:19.560 ⇒ 00:23:27.480 Davis Dunham: Heard, heard. I will, I’ll definitely reach out to Jasmine then as well, and also, I’ll do a little research on my… on my own first, so I know what questions to ask.
214 00:23:27.480 ⇒ 00:23:44.210 Amber Lin: Yeah, and everybody… I think everybody in sales and marketing was forced to do those materials, so, like, maybe we internally have some documents explaining this to our clients, like, hey, this is what we do, and this is what’s needed, so that could also be helpful.
215 00:23:44.750 ⇒ 00:23:46.390 Davis Dunham: Yeah, absolutely.
216 00:23:46.780 ⇒ 00:23:47.770 Davis Dunham: Trust me.
217 00:23:48.080 ⇒ 00:23:49.390 Davis Dunham: Okay, cool.
218 00:23:49.890 ⇒ 00:23:59.589 Amber Lin: Yeah, we’re at time, so I really appreciate you spending time to talk to me. It’s been a great conversation getting to know you. I look forward to talking soon.
219 00:23:59.820 ⇒ 00:24:02.930 Davis Dunham: Yeah, absolutely, Amber. Great to meet you. I’ll, talk to you at some point soon.
220 00:24:03.080 ⇒ 00:24:03.760 Amber Lin: Alright.
221 00:24:03.950 ⇒ 00:24:04.840 Amber Lin: Bye.
222 00:24:04.840 ⇒ 00:24:05.360 Davis Dunham: Bye.