2026-04-03_friday_brainforge_demos_retro_a36eb4f7

Meeting Title: Friday Brainforge Demos & Retro Date: 2026-04-03 Meeting participants: Brylle Girang, Garrett Gibson, Ashwini Sharma, Samuel Roberts, Kaela Gallagher, Rico Rejoso, Pranav, Ruixi Wen, Jorrel Sto. Tomas, Advait Nandakumar Menon, Uttam Kumaran, Greg Stoutenburg, Amber Lin, Demilade Agboola, Hannah Wang, Robert Tseng, Awaish Kumar, Holly Condos

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WEBVTT

1 00:04:57.160 ⇒ 00:04:58.510 Brylle Girang: Hey, Gareth!

2 00:05:02.150 ⇒ 00:05:04.300 Garrett Gibson: Hey, is it, real?

3 00:05:05.140 ⇒ 00:05:06.080 Brylle Girang: It’s Bryle.

4 00:05:06.080 ⇒ 00:05:08.590 Garrett Gibson: Oh, brow. Hey, how’s it going, man?

5 00:05:08.590 ⇒ 00:05:10.450 Brylle Girang: It’s good, it’s good.

6 00:05:10.450 ⇒ 00:05:11.130 Garrett Gibson: Nice to…

7 00:05:12.300 ⇒ 00:05:14.659 Brylle Girang: This is your day… day zero, right?

8 00:05:14.860 ⇒ 00:05:24.350 Garrett Gibson: Yeah, exactly, yeah. Monday, it’s officially day one, so… it’s good to see a preview of the demo meetings, yeah, things like that.

9 00:05:24.830 ⇒ 00:05:28.119 Brylle Girang: Glad that you can join us today. It’s going to be exciting.

10 00:05:28.520 ⇒ 00:05:33.010 Garrett Gibson: Yeah, absolutely. How long, have you been with the company?

11 00:05:33.530 ⇒ 00:05:38.000 Brylle Girang: Oh, I’m fairly, I’m fairly new. I joined, late February, and I have.

12 00:05:38.000 ⇒ 00:05:38.870 Garrett Gibson: Okay, cool.

13 00:05:38.870 ⇒ 00:05:47.130 Brylle Girang: jumping around roles. Right now, I’m leading learning and development, so later I’m going to be diving more into that.

14 00:05:47.770 ⇒ 00:05:54.989 Garrett Gibson: Okay, perfect. That’s good to know. Yeah, I know we have a meeting, on Mondays, so… yeah, looking forward to, to talking more.

15 00:05:55.210 ⇒ 00:06:00.189 Brylle Girang: Yeah, yeah, I just want to make sure that, you know, you can kick…

16 00:06:00.620 ⇒ 00:06:02.850 Brylle Girang: Things on your day one.

17 00:06:03.170 ⇒ 00:06:10.579 Garrett Gibson: Oh, yeah, actually that one, yeah, just because I had a lot of meetings coming, maybe I’ll move it, like, to Tuesday?

18 00:06:10.780 ⇒ 00:06:13.779 Garrett Gibson: If that’s okay? Like, just maybe check your calendar.

19 00:06:14.160 ⇒ 00:06:15.110 Brylle Girang: Okay, sure.

20 00:06:15.340 ⇒ 00:06:15.910 Garrett Gibson: Yeah.

21 00:06:16.910 ⇒ 00:06:21.990 Garrett Gibson: yeah, just, I don’t have so many back-to-backs.

22 00:06:22.800 ⇒ 00:06:25.539 Brylle Girang: It is, it is what it is, so…

23 00:06:25.540 ⇒ 00:06:30.080 Garrett Gibson: Yeah, day one is kind of… that’s, like, how it is.

24 00:06:31.410 ⇒ 00:06:33.920 Brylle Girang: Well, your week one will be, will be like that, bud.

25 00:06:34.320 ⇒ 00:06:44.709 Brylle Girang: Yeah, you are fairly lucky, because we just recently cut down on our meetings. Maybe 3 weeks ago, we had

26 00:06:44.910 ⇒ 00:06:47.990 Brylle Girang: Maybe, 20 meetings in a week, and we…

27 00:06:47.990 ⇒ 00:06:48.390 Garrett Gibson: No.

28 00:06:48.390 ⇒ 00:06:54.679 Brylle Girang: We have started to cut that down, so you joined in a fairly better place now.

29 00:06:54.680 ⇒ 00:07:03.010 Garrett Gibson: That’s good, yeah. Well, I mean, I’m not opposed to meetings, but I think it’s… there’s a fine line, you know, kind of.

30 00:07:03.010 ⇒ 00:07:05.289 Brylle Girang: Exactly, exactly.

31 00:07:05.290 ⇒ 00:07:06.970 Garrett Gibson: Exactly.

32 00:07:07.390 ⇒ 00:07:11.840 Garrett Gibson: I like strategic, you know, kind of tactical meetings.

33 00:07:14.810 ⇒ 00:07:16.850 Brylle Girang: They’re… they should be joining soon.

34 00:07:17.120 ⇒ 00:07:17.780 Garrett Gibson: Okay.

35 00:07:18.030 ⇒ 00:07:18.960 Garrett Gibson: That’s good.

36 00:07:18.960 ⇒ 00:07:20.269 Brylle Girang: Where are you based?

37 00:07:20.440 ⇒ 00:07:24.209 Garrett Gibson: I’m actually… I’m in the LA area, I’m in, Redondo Beach, how about you?

38 00:07:24.820 ⇒ 00:07:26.620 Brylle Girang: Oh, I’m from the Philippines.

39 00:07:27.020 ⇒ 00:07:29.739 Garrett Gibson: Oh, okay, cool. You’re over there. Are you in,

40 00:07:30.210 ⇒ 00:07:32.550 Garrett Gibson: What is it, Manila? Is that the main…

41 00:07:32.790 ⇒ 00:07:39.460 Brylle Girang: Yeah, Manila is the main city, I’m, like… Two provinces down.

42 00:07:39.460 ⇒ 00:07:40.210 Garrett Gibson: Okay, cool.

43 00:07:40.410 ⇒ 00:07:43.390 Brylle Girang: But still, you know, fairly accessible.

44 00:07:43.770 ⇒ 00:07:47.669 Garrett Gibson: Yeah, yeah. What time is it over there right now? It’s, 10 here right now.

45 00:07:47.950 ⇒ 00:07:49.339 Brylle Girang: Yeah, it’s 1AM.

46 00:07:49.520 ⇒ 00:07:51.980 Garrett Gibson: Okay, it’s late for you.

47 00:07:51.980 ⇒ 00:07:53.760 Brylle Girang: We are night owls here.

48 00:07:53.760 ⇒ 00:07:56.569 Garrett Gibson: That’s cool.

49 00:07:58.320 ⇒ 00:07:59.000 Brylle Girang: I agree, sweetie.

50 00:07:59.000 ⇒ 00:08:01.130 Garrett Gibson: to work at a different time.

51 00:08:02.160 ⇒ 00:08:02.980 Ashwini Sharma: Hello.

52 00:08:03.740 ⇒ 00:08:04.930 Ashwini Sharma: Hi, Esri. How’s it going?

53 00:08:05.180 ⇒ 00:08:06.430 Garrett Gibson: Nice to meet you.

54 00:08:07.980 ⇒ 00:08:09.070 Ashwini Sharma: We get it.

55 00:08:11.470 ⇒ 00:08:16.340 Garrett Gibson: I’m, joining the team on Monday, I’m just, checking out the demos.

56 00:08:45.260 ⇒ 00:08:47.379 Garrett Gibson: Hey, Caleb! How you doing?

57 00:08:47.380 ⇒ 00:08:48.790 Kaela Gallagher: How’s it going?

58 00:08:49.120 ⇒ 00:08:52.939 Garrett Gibson: Good. Day zero.

59 00:08:53.250 ⇒ 00:08:54.330 Kaela Gallagher: Here we go.

60 00:08:56.900 ⇒ 00:08:58.320 Kaela Gallagher: Hey, Sam, maybe…

61 00:08:59.220 ⇒ 00:09:00.050 Brylle Girang: Hello.

62 00:09:00.050 ⇒ 00:09:01.200 Kaela Gallagher: Hey, Sweeney.

63 00:09:01.200 ⇒ 00:09:02.170 Ashwini Sharma: Hello.

64 00:09:03.760 ⇒ 00:09:05.300 Kaela Gallagher: Happy Friday, guys!

65 00:09:06.480 ⇒ 00:09:07.460 Garrett Gibson: See, Freddy.

66 00:09:14.640 ⇒ 00:09:18.570 Kaela Gallagher: We’ll wait a few minutes for everybody to… Hop on.

67 00:09:19.370 ⇒ 00:09:21.660 Kaela Gallagher: Ashwini, do you want me to…

68 00:09:22.710 ⇒ 00:09:25.939 Kaela Gallagher: Share the slides from my screen, would that be easiest?

69 00:09:27.070 ⇒ 00:09:35.060 Ashwini Sharma: Sure, anything… oh, no, let me share for a few moments, and then if somebody else wants to do that, feel free, or I can continue till the end, not an issue.

70 00:09:35.230 ⇒ 00:09:40.609 Brylle Girang: Yeah, maybe, maybe Ashwini shares first, then I share, then you can take over, Kayla.

71 00:09:41.960 ⇒ 00:09:43.649 Brylle Girang: Would that, would that work? Because.

72 00:09:43.650 ⇒ 00:09:44.800 Kaela Gallagher: Presentation.

73 00:09:45.020 ⇒ 00:09:48.430 Brylle Girang: Yeah, I’m going to share something that’s not going… that’s not in the slide deck.

74 00:09:49.010 ⇒ 00:09:51.909 Kaela Gallagher: Okay, Bea, I can take over after you then, if…

75 00:09:51.910 ⇒ 00:09:52.230 Brylle Girang: Okay.

76 00:09:52.230 ⇒ 00:09:54.639 Kaela Gallagher: If you want to start, and then maybe I finish.

77 00:09:55.380 ⇒ 00:09:55.990 Brylle Girang: Okay.

78 00:09:56.510 ⇒ 00:09:57.600 Kaela Gallagher: Okay.

79 00:10:02.610 ⇒ 00:10:04.539 Kaela Gallagher: Hey, Pranav. Hey, Miranda.

80 00:10:05.760 ⇒ 00:10:07.339 Ruixi Wen: Hey, Carla.

81 00:10:07.740 ⇒ 00:10:08.580 Pranav: Hey, guys.

82 00:10:09.750 ⇒ 00:10:11.280 Brylle Girang: Happy Friday, guys!

83 00:10:12.540 ⇒ 00:10:13.450 Garrett Gibson: Ready?

84 00:10:26.030 ⇒ 00:10:27.000 Kaela Gallagher: Hey, girl.

85 00:10:47.030 ⇒ 00:10:48.229 Kaela Gallagher: Hi, I’d be…

86 00:10:50.600 ⇒ 00:10:53.009 Advait Nandakumar Menon: Hey, Kayla, how’s it going? Hey, guys.

87 00:10:53.010 ⇒ 00:10:54.770 Kaela Gallagher: Good. How are you?

88 00:10:56.290 ⇒ 00:10:57.380 Advait Nandakumar Menon: I’m doing fine.

89 00:10:58.490 ⇒ 00:10:59.080 Kaela Gallagher: Good.

90 00:11:31.030 ⇒ 00:11:31.899 Uttam Kumaran: Hey, everyone.

91 00:11:33.630 ⇒ 00:11:34.590 Kaela Gallagher: Hey, Tom.

92 00:11:35.340 ⇒ 00:11:37.390 Uttam Kumaran: Okay, sorry, I’m just in the car, but…

93 00:11:37.710 ⇒ 00:11:39.469 Uttam Kumaran: Hey, good to see you, Garrett.

94 00:11:39.470 ⇒ 00:11:43.640 Garrett Gibson: Welcome. Good to see you as well. D-Zero.

95 00:11:46.060 ⇒ 00:11:48.019 Uttam Kumaran: Day zero, yeah, day negative one, maybe.

96 00:11:48.020 ⇒ 00:11:48.630 Garrett Gibson: Yeah.

97 00:11:55.710 ⇒ 00:12:03.830 Uttam Kumaran: Cool. I think, I think we’re ready to go. I mean, I think, really today, I wanted to do,

98 00:12:04.240 ⇒ 00:12:14.049 Uttam Kumaran: a couple things. One is I think the focus is really gonna be on a few people on this call to kind of, like, take lead today. I think… I’ll share a few updates just on, like.

99 00:12:14.700 ⇒ 00:12:23.550 Uttam Kumaran: Q2 and, just share a little bit of some perspective on… on, like, some service changes.

100 00:12:23.770 ⇒ 00:12:26.929 Uttam Kumaran: But yeah, I mean, I’m pumped. I think we have some awesome…

101 00:12:27.180 ⇒ 00:12:32.379 Uttam Kumaran: Some folks are sharing some awesome things today, so whoever’s leading, you can… you guys can, go ahead.

102 00:12:41.000 ⇒ 00:12:42.940 Ashwini Sharma: Yo, is my screen visible?

103 00:12:45.290 ⇒ 00:12:46.030 Uttam Kumaran: Yes.

104 00:12:48.720 ⇒ 00:12:53.289 Ashwini Sharma: How do I put it in the presentation mode? Is there a button somewhere? This one?

105 00:12:53.290 ⇒ 00:12:54.000 Uttam Kumaran: If you…

106 00:12:54.000 ⇒ 00:12:55.120 Ashwini Sharma: Yeah.

107 00:12:55.210 ⇒ 00:12:59.609 Uttam Kumaran: It should be that, but you can also click on the bottom button next to slideshow.

108 00:13:00.400 ⇒ 00:13:01.060 Ashwini Sharma: Oh, this one.

109 00:13:01.060 ⇒ 00:13:01.460 Uttam Kumaran: That’s right.

110 00:13:01.460 ⇒ 00:13:02.369 Ashwini Sharma: Yeah, yeah.

111 00:13:03.340 ⇒ 00:13:04.300 Uttam Kumaran: Yeah, yeah, okay, good.

112 00:13:06.620 ⇒ 00:13:13.410 Ashwini Sharma: Alright, so this is our agenda, and start with the icebreaker.

113 00:13:15.050 ⇒ 00:13:19.470 Ashwini Sharma: Alright, so those of you who don’t know me, I am Ashwini, I’m from India.

114 00:13:19.800 ⇒ 00:13:23.609 Ashwini Sharma: I live in the northern… northeastern part of India.

115 00:13:23.690 ⇒ 00:13:40.399 Ashwini Sharma: Somewhere over here, right? And, I can reach 3 countries in a matter of a few hours driving my car. On the west, it’s Nepal, on the northeast, it’s Bhutan, and then Bangladesh is just a 45 minutes’ drive away.

116 00:13:40.520 ⇒ 00:13:45.610 Ashwini Sharma: And if I have to drive, I can go back to China also, up to China, so that’s about 8 hours away.

117 00:13:46.350 ⇒ 00:13:55.660 Ashwini Sharma: I live directly below the Himalayas, this is the Himalayan Belt, right, this one. And then as you descend from it, the first plains is where I live.

118 00:13:55.900 ⇒ 00:13:59.599 Ashwini Sharma: On a clear day, I can see this thing from my hometown.

119 00:14:00.710 ⇒ 00:14:01.410 Ashwini Sharma: And…

120 00:14:01.410 ⇒ 00:14:04.159 Uttam Kumaran: Wow, that’s incredible.

121 00:14:04.540 ⇒ 00:14:05.490 Samuel Roberts: engraved here.

122 00:14:05.770 ⇒ 00:14:06.160 Ashwini Sharma: Yeah.

123 00:14:06.160 ⇒ 00:14:11.090 Uttam Kumaran: Wait, Sweeney, where… can you show the map again? Where… where is it? Where… where are you at?

124 00:14:11.480 ⇒ 00:14:11.890 Uttam Kumaran: One more time?

125 00:14:12.110 ⇒ 00:14:15.640 Ashwini Sharma: over here, this place called Siligori.

126 00:14:15.980 ⇒ 00:14:16.700 Ashwini Sharma: This is…

127 00:14:16.700 ⇒ 00:14:17.339 Uttam Kumaran: Oh, Lord.

128 00:14:18.050 ⇒ 00:14:22.779 Ashwini Sharma: This entire region, right? This is the Himalayan Belt. You can see all the snow over here.

129 00:14:24.450 ⇒ 00:14:27.409 Uttam Kumaran: Whoa… We have some great Nepalese food.

130 00:14:27.730 ⇒ 00:14:29.569 Ashwini Sharma: Oh, yeah, yeah, yeah, food is good.

131 00:14:32.770 ⇒ 00:14:33.660 Ashwini Sharma: Yeah.

132 00:14:33.900 ⇒ 00:14:48.970 Ashwini Sharma: Yeah, that’s, about me. As I said, okay, would I, would I give up, rather, coffee, tea, or phone? I think it is always going to be coffee or tea, because, and I’ve done that previously for a couple of months.

133 00:14:49.440 ⇒ 00:14:53.719 Ashwini Sharma: I have not given up my phone for one month. I’ve done it for max 10 days, I think.

134 00:14:56.370 ⇒ 00:15:03.889 Uttam Kumaran: I don’t even know how I would… how I… without coffee, how could I even be awake to use my phone? I don’t know.

135 00:15:05.230 ⇒ 00:15:06.889 Uttam Kumaran: Which one? Yeah.

136 00:15:10.930 ⇒ 00:15:11.540 Uttam Kumaran: Anyone else?

137 00:15:11.910 ⇒ 00:15:14.289 Uttam Kumaran: strong feelings one way or another.

138 00:15:14.680 ⇒ 00:15:24.880 Samuel Roberts: I would rather give up my phone and keep the coffee, but I think it’s… I would… if, like, a real scenario would probably be, like, the coffee could go, and I need my phone.

139 00:15:25.070 ⇒ 00:15:25.890 Samuel Roberts: Yeah.

140 00:15:26.630 ⇒ 00:15:32.950 Samuel Roberts: But if I had to… if I had… in an ideal world where I could just be like, no more phone, give me the coffee, definitely.

141 00:15:36.040 ⇒ 00:15:38.200 Kaela Gallagher: I would… I would definitely…

142 00:15:39.080 ⇒ 00:15:39.560 Pranav: Go ahead.

143 00:15:39.560 ⇒ 00:15:40.180 Kaela Gallagher: Okay.

144 00:15:41.110 ⇒ 00:15:50.269 Kaela Gallagher: I was gonna say, I would definitely have to give up the coffee or the tea, because my family doesn’t live anywhere near me, and I need to be able to call them.

145 00:15:53.650 ⇒ 00:15:54.080 Uttam Kumaran: Fair.

146 00:15:54.220 ⇒ 00:15:55.980 Pranav: Yeah, I was gonna say the same thing.

147 00:15:56.200 ⇒ 00:16:00.980 Pranav: But I was gonna say, what if I use caffeine mints instead? Is that cheating?

148 00:16:01.680 ⇒ 00:16:04.959 Uttam Kumaran: I think that’s… I think that’s a good loophole.

149 00:16:04.960 ⇒ 00:16:07.079 Pranav: Edge case.

150 00:16:07.250 ⇒ 00:16:09.560 Demilade Agboola: Oh, just using energy drinks, you know?

151 00:16:09.840 ⇒ 00:16:10.420 Uttam Kumaran: Jesus.

152 00:16:11.890 ⇒ 00:16:19.950 Greg Stoutenburg: Yeah, you can tell who… you can tell who are not coffee drinkers at this moment. Like, oh yeah, just use an energy drink, what are you talking about? It’s not even similar.

153 00:16:20.670 ⇒ 00:16:23.489 Uttam Kumaran: Yeah, it’s a different… it’s just… yeah.

154 00:16:23.870 ⇒ 00:16:24.709 Greg Stoutenburg: Not the same.

155 00:16:24.710 ⇒ 00:16:32.289 Uttam Kumaran: Exactly, yeah, I’m not like, oh yeah, I don’t have coffee today, let me get, like, a Red Bull. Not one for one.

156 00:16:37.310 ⇒ 00:16:43.949 Greg Stoutenburg: I don’t even want my phone, so… yeah. It’s just ex… this isn’t the cultural expectation. I wouldn’t have one at all.

157 00:16:44.980 ⇒ 00:16:48.230 Uttam Kumaran: Yeah, I’m the same way. I feel like I would get a landline.

158 00:16:49.930 ⇒ 00:16:54.110 Greg Stoutenburg: Yeah, correction, landline, that’s what I should have said, yeah. I can go back to 1995, no problem.

159 00:16:54.430 ⇒ 00:16:56.149 Uttam Kumaran: We can be pen pals, Greg.

160 00:16:58.100 ⇒ 00:16:59.250 Uttam Kumaran: Alright, nice question.

161 00:16:59.250 ⇒ 00:17:02.800 Greg Stoutenburg: And I’ll write you a letter. How will I be in touch? I’ll write you a letter. I’ll include a photograph.

162 00:17:03.040 ⇒ 00:17:09.289 Uttam Kumaran: I’ll say… I’ll send… write a letter that says, please, please, can you check out this ticket for me when you get a moment?

163 00:17:10.380 ⇒ 00:17:11.230 Demilade Agboola: I think, I think.

164 00:17:11.230 ⇒ 00:17:14.090 Uttam Kumaran: And I’ll write out the… I’ll write out the URL of the ticket.

165 00:17:15.530 ⇒ 00:17:15.900 Demilade Agboola: I think.

166 00:17:15.900 ⇒ 00:17:17.420 Samuel Roberts: Send it out, send it in.

167 00:17:17.420 ⇒ 00:17:22.500 Demilade Agboola: Get Utam and Greg off Slack, and they can just be writing letters to themselves.

168 00:17:24.069 ⇒ 00:17:33.699 Uttam Kumaran: Yeah, I know, yeah. Mostly, as you guys can tell, if I’m on Slack, I’m talking to myself. Most of the time, anyways.

169 00:17:35.479 ⇒ 00:17:36.919 Uttam Kumaran: That’s hilarious.

170 00:17:42.890 ⇒ 00:17:44.870 Ashwini Sharma: Alright, I think we can move on.

171 00:17:48.950 ⇒ 00:17:52.690 Brylle Girang: Great, okay, I’m just going to hijack screen sharing.

172 00:17:52.690 ⇒ 00:17:54.030 Ashwini Sharma: Sure, sure, sure.

173 00:17:58.470 ⇒ 00:18:08.940 Brylle Girang: Yeah, so I’m going to be building on Casey’s previous lab share, where we briefly dive upon the history of AI, and how we evolved from

174 00:18:09.630 ⇒ 00:18:21.929 Brylle Girang: from wherever we were 10 years ago to wherever we are now. But now, I’m just going to focus on one specific thing here, and that’s going to be skills. And for this first section, I just wanted to

175 00:18:22.050 ⇒ 00:18:25.440 Brylle Girang: I just wanted to try and reminisce about

176 00:18:25.760 ⇒ 00:18:43.400 Brylle Girang: the history of skills. So, we started, when it comes to generative AI and talking with AI, we started with manual prompting, like, retyping context every session, we… there were prompt engineers coming up, there were prompt tools coming up, there were prompt frameworks.

177 00:18:43.400 ⇒ 00:18:51.229 Brylle Girang: coming up everywhere, so we were stuck on that phase for, like, months or so. And then Cursor, I think

178 00:18:51.540 ⇒ 00:19:01.580 Brylle Girang: by 2025, started launching cursor rules. So what this does is… If a user says something.

179 00:19:01.770 ⇒ 00:19:05.600 Brylle Girang: cursor, or the agent, does it. And that was the first step.

180 00:19:06.100 ⇒ 00:19:15.230 Brylle Girang: Anthropic came along, and then they introduced skills, which is… which are basically step-by-step procedures, or SOPs, for our agents.

181 00:19:15.350 ⇒ 00:19:33.129 Brylle Girang: And this history lesson is kind of reflecting where we are right now in Brainforge. We were manually prompting most of the time, and now we’re trying to launch, or we’re trying to really push aggressively towards skills.

182 00:19:33.530 ⇒ 00:19:47.329 Brylle Girang: And in the future, and hopefully this future is going to be the next two weeks, we want to get the automations. So, skills that run on their own. And the main reason why automations are the next step is because without skills.

183 00:19:47.330 ⇒ 00:19:59.259 Brylle Girang: our automations would be, like, 100 lines long, because we… we need to… we need… we need to specifically tell AI what they need to do every… every 8 a.m. in the morning.

184 00:19:59.790 ⇒ 00:20:15.009 Brylle Girang: So automations are the next frontier after skills, and this month, I want to emphasize that we will be… really be doubling down on skills, so we can proceed into automations and hopefully save more time for everyone.

185 00:20:17.220 ⇒ 00:20:27.789 Brylle Girang: Yeah, so why are skills important? So again, without skills, we prompt every time. We try to write 100 lines.

186 00:20:27.830 ⇒ 00:20:41.510 Brylle Girang: of a single prompt, just to get things right. It takes longer, because the agent also needs to rediscover the entire platform, try to search wherever something is within our vault.

187 00:20:41.510 ⇒ 00:20:53.109 Brylle Girang: And there are lots of back and forths, etc. The negative things that I’m seeing are laid down here. And with a skill, as much as possible, if you say it once, the agent gets it right.

188 00:20:53.340 ⇒ 00:21:12.920 Brylle Girang: And one of the most important things here that I also want to emphasize is that with skills, we are compounding, like, the company knowledge. It’s not just going to be your own prompt, it’s going to be for everyone, for the whole team. So skills live in a repo, everyone can access it, everyone’s going to be happy.

189 00:21:13.300 ⇒ 00:21:17.769 Brylle Girang: So… and, I just wanted to also…

190 00:21:19.060 ⇒ 00:21:32.009 Brylle Girang: I’m not going to say that this is a deep dive, but this is just a quick explanation on why it’s faster if you use skills instead of, you know, instruction docs or just normal markdown files.

191 00:21:32.040 ⇒ 00:21:39.250 Brylle Girang: So the way that skills and commands and rules are structured is that there is a front matter.

192 00:21:39.690 ⇒ 00:21:55.489 Brylle Girang: So the front matter is this fart, is this… is this part, not fart, sorry, but it’s this part in the skills markdown file, and this is the first thing that that agent tries to look for when it’s searching our codebase.

193 00:21:55.530 ⇒ 00:21:59.079 Brylle Girang: So instead of the agent trying to read all of this.

194 00:21:59.090 ⇒ 00:22:16.020 Brylle Girang: If we have skills, if we have commands in our repo, it only reads this. And reminiscing on Casey’s previous lab share, tokens are, like, the main energy that agents are using, and with less things to look at.

195 00:22:16.060 ⇒ 00:22:23.810 Brylle Girang: It uses less tokens, it is faster, more efficient, saves us Couple of dollars every run.

196 00:22:24.490 ⇒ 00:22:38.259 Brylle Girang: So again, there’s front matter, and then there’s the main body. And the main body is only red if the agent determines that the skill or the command is applicable to whatever you want to do, based on the front matter.

197 00:22:39.210 ⇒ 00:22:46.510 Brylle Girang: Yeah, before I proceed, any thoughts there, any ideas that you want to share regarding these skills?

198 00:22:50.400 ⇒ 00:23:09.419 Greg Stoutenburg: Is it analogous, you’d say, to, just having, like, you know, if you could program a whole block of text in F1, so that when you go into a text field, hit F1, that whole text field appears, would you say that a skill is analogous to that, given that you don’t have to write the whole prompt, it’s just kind of having the text at the ready?

199 00:23:10.230 ⇒ 00:23:13.589 Brylle Girang: You mean, like, a macro, a keyboard macro? Is that it?

200 00:23:13.610 ⇒ 00:23:16.099 Greg Stoutenburg: I mean, is it sort of analogous to that, in a way?

201 00:23:16.730 ⇒ 00:23:17.560 Brylle Girang: Because, you know…

202 00:23:17.560 ⇒ 00:23:18.370 Greg Stoutenburg: from.

203 00:23:20.790 ⇒ 00:23:34.099 Brylle Girang: I would say yes, yes. It basically does the same thing. You use one skill, you press one button, it does everything that is… that you are instructing it to do. So, step by step, like an SOP.

204 00:23:35.570 ⇒ 00:23:37.560 Brylle Girang: So yeah, it is analogous to that.

205 00:23:40.900 ⇒ 00:23:42.730 Brylle Girang: Okay,

206 00:23:43.140 ⇒ 00:23:51.280 Brylle Girang: So, I wanted to dive into what the skills are in the platform right now, and I’m going to focus on these four, because I believe that these are…

207 00:23:51.320 ⇒ 00:24:05.919 Brylle Girang: this can be used across all roles, across all departments, across all service lines within Brainforge, and these are really, really amazing. So if you haven’t tried this yet, please, please do try it. The first skill is Document Council.

208 00:24:06.060 ⇒ 00:24:15.090 Brylle Girang: So, we, we have, we have personas in the skill. So, this basically says that, hey, cursor, hey, agent, I want to review something.

209 00:24:15.680 ⇒ 00:24:32.360 Brylle Girang: act like Otam, act like Greg, act like Ashwini, act like Demi, and then provide me feedback based on this. And the amazing part is that since our vote, our forge, is built amazingly within our transcripts, or around our transcripts, around our company knowledge.

210 00:24:32.360 ⇒ 00:24:40.670 Brylle Girang: it can accurately try to take the persona of something like, you know, Utam. So, I am personally using this.

211 00:24:40.670 ⇒ 00:24:52.939 Brylle Girang: if I want to see what your feedback are without actually asking you, and this is… I think this is automatically running when we also submit PRs or ask for pull requests, but

212 00:24:53.370 ⇒ 00:25:00.250 Brylle Girang: Try to use this if you want to get feedback from people who are sleeping, so that’s… that’s going to be really useful.

213 00:25:00.460 ⇒ 00:25:02.699 Brylle Girang: The second skill is grill me.

214 00:25:03.130 ⇒ 00:25:21.360 Brylle Girang: So, what this does is it acts more, how do you call this? It acts similarly to a thesis panelist. So, what it does is it asks you questions regarding what you want to do, or your own questions.

215 00:25:21.420 ⇒ 00:25:34.980 Brylle Girang: And it tries to basically grill me. So I don’t think I need to dive… dive deep into that. It grills you until your plan, until your question is 100% solid.

216 00:25:35.250 ⇒ 00:25:40.580 Brylle Girang: Yeah, basically have 5 people review your docs or thoughts.

217 00:25:40.670 ⇒ 00:25:52.660 Brylle Girang: The third one is Visual Explainer, and the main reason why I hijacked the screen sharing is because I want to show how this skill works, and what you’re seeing right now was built using Visual Explainer.

218 00:25:52.670 ⇒ 00:26:02.159 Brylle Girang: So it can create slides, it can create diagrams for you, so this will be really useful if you want to, have some visuals

219 00:26:02.160 ⇒ 00:26:21.209 Brylle Girang: for whatever you want to show, whatever you want to present, or maybe whatever you want to show to a client. So this is… this whole slide, this whole deck is created using Visual Explainer, and I just mentioned that, hey, use Brainforge official branding. That’s why this is screen. And I also use Impeccable, something that we have

220 00:26:21.210 ⇒ 00:26:23.180 Brylle Girang: We have also…

221 00:26:23.600 ⇒ 00:26:32.319 Brylle Girang: added just recently to our repo. Just make sure that this is a quiet style and not the AI slop that is full of creation.

222 00:26:32.750 ⇒ 00:26:52.009 Brylle Girang: And then the fourth skill is Deck Review, so this will be useful if you have presentations, and you just want some feedback from the agent or from the AI. This specifically looks into our Brainforge deck guidelines, so this is a set of guidelines within a repo.

223 00:26:52.060 ⇒ 00:26:57.790 Brylle Girang: Where it tells us what a deck should contain, the formatting should be, etc.

224 00:26:58.480 ⇒ 00:27:05.339 Brylle Girang: These are just 4 out of 100 plus skills that we have right now in the repo. And…

225 00:27:05.650 ⇒ 00:27:13.570 Brylle Girang: please, please, use this. This saves lots of time. These are really amazing when it comes to actually

226 00:27:13.720 ⇒ 00:27:17.990 Brylle Girang: Presenting or delivering something at 10 times the level.

227 00:27:18.550 ⇒ 00:27:28.220 Brylle Girang: Any thoughts here? Are there skills that you’re looking for? Do you have any ideas on what skills you might want to use in the future?

228 00:27:30.860 ⇒ 00:27:37.099 Pranav: I have a quick question on you creating this deck. So, did you just use Visual Explainer

229 00:27:37.300 ⇒ 00:27:40.430 Pranav: Once to create it, or was it, like, per slide?

230 00:27:41.120 ⇒ 00:27:59.179 Brylle Girang: Oh, I used the skill once, just to make sure that I get the foundations right, and then I just iterated and just told it to, you know, add slides, edit this, etc. The output will be an HTML file, but you can also ask Cursor to, like, edit stuff out.

231 00:27:59.210 ⇒ 00:28:08.350 Brylle Girang: For you. So yes, I used the visual explainer once, and then I did not… I didn’t try to prompt it again, once it has the initial slide deck.

232 00:28:09.160 ⇒ 00:28:11.810 Pranav: Okay, good to know, yeah, so this is HTML. Okay, interesting.

233 00:28:11.810 ⇒ 00:28:12.320 Brylle Girang: Yep.

234 00:28:12.320 ⇒ 00:28:16.680 Uttam Kumaran: Are there any other skills, B, that you’re, like, using really heavily these days?

235 00:28:18.470 ⇒ 00:28:29.780 Brylle Girang: maybe 3 weeks ago, I have been heavily using EP Audit, just to make sure that our linear tickets are really up-to-date. Aside from these four, I am using…

236 00:28:29.780 ⇒ 00:28:43.969 Brylle Girang: It’s not in the repo yet, but personally, I have been using Cloud Education Skills for… as part of my learning and development plans. So, Cloud Education Skills is a cloud repo where it collects everything

237 00:28:44.000 ⇒ 00:28:54.620 Brylle Girang: That can help you build efficient learning plans, or… it uses several learning frameworks, such as the I Do, we do, you do frameworks, etc.

238 00:28:54.790 ⇒ 00:29:06.660 Brylle Girang: But there are lots of skills already available in the web, and if you search… I’m pretty sure that if you search for a problem and then just add skill, there’s something that’s going to show up.

239 00:29:06.840 ⇒ 00:29:15.260 Brylle Girang: But yeah, to answer that, aside from these four, the main skills that I’m using are educational-based.

240 00:29:21.810 ⇒ 00:29:35.070 Brylle Girang: Oh, there’s also a Prepare Stand Up Deck skill, but that’s not 100% yet, so if you need to, like, have an initial draft of your deck that you need to share to the client, use Prepare Stand Up Deck.

241 00:29:35.240 ⇒ 00:29:38.070 Brylle Girang: I’m also adding it here.

242 00:29:40.010 ⇒ 00:29:41.840 Brylle Girang: This will give you…

243 00:29:42.170 ⇒ 00:29:56.949 Brylle Girang: an initial draft of some talking points or topics that you want to focus on. It searches the transcripts, it searches linear, and then tries to collate all of those into digestible updates for our clients.

244 00:29:57.770 ⇒ 00:30:04.589 Greg Stoutenburg: Will that use some of the skill set of Visual Explainer as well, to, like, actually create a whole deck, or is it more like…

245 00:30:04.720 ⇒ 00:30:06.660 Greg Stoutenburg: Text that you have to copy over.

246 00:30:07.100 ⇒ 00:30:10.829 Brylle Girang: It’s more of a text that you need to copy over to Google Slides.

247 00:30:13.200 ⇒ 00:30:25.269 Brylle Girang: But, you know, skills are… skills can be… can be used sequentially, so you can use, hey, hey cursor, prepare a stand-up deck, then use visual explainer to create the deck. That’s going to work.

248 00:30:31.230 ⇒ 00:30:33.089 Brylle Girang: Alright,

249 00:30:34.000 ⇒ 00:30:46.470 Brylle Girang: So, how do we create skills? How do I actually create this stuff? So, the first step is you need to spot the pattern, so make sure that you have a problem that you want to solve.

250 00:30:46.490 ⇒ 00:31:00.740 Brylle Girang: The second step, this is what I’m personally doing. I’m always using plan mode in cursor to help me create the skills. So this makes sure that I can thoroughly review whatever cursor is going to make or create for me.

251 00:31:00.900 ⇒ 00:31:05.780 Brylle Girang: And it also makes sure that cursor actually thinks through about how to create the skill.

252 00:31:05.930 ⇒ 00:31:10.450 Brylle Girang: And then the third one, once the plan is good, I ask Cursor to build it.

253 00:31:10.560 ⇒ 00:31:25.059 Brylle Girang: So, it’s a three-step process, and it can save us, you know, 10 hours per week if you do it correctly. So, save this. I’m also going to share this. This is going to be part of the modules that we’re going to be talking about more later.

254 00:31:25.170 ⇒ 00:31:28.819 Brylle Girang: But this is how to create the skill. Any questions here?

255 00:31:33.150 ⇒ 00:31:33.770 Greg Stoutenburg: Correct.

256 00:31:34.970 ⇒ 00:31:38.849 Greg Stoutenburg: I just used Visual Explainer on the next slide. It’s awesome.

257 00:31:38.850 ⇒ 00:31:40.600 Brylle Girang: Amazing.

258 00:31:40.600 ⇒ 00:31:41.640 Greg Stoutenburg: Okay.

259 00:31:42.170 ⇒ 00:31:48.429 Brylle Girang: So, okay, that was a bit of a spoiler, but since there are no questions…

260 00:31:48.840 ⇒ 00:31:53.609 Brylle Girang: We have… we have a mini activity here, so…

261 00:31:54.640 ⇒ 00:31:59.799 Brylle Girang: For the remaining time of this lab share, we have this mini activity where

262 00:32:00.010 ⇒ 00:32:18.460 Brylle Girang: I want everyone here to try and build a skill within 5 minutes. So the price here is 50 USD, and here is how we will choose the actual winner. So, if you finish a skill within a 5-minute window, by finish, we mean create a skill.

263 00:32:18.770 ⇒ 00:32:37.330 Brylle Girang: if you can pitch how you create the skill, what it does, etc, we will be launching a Zoom poll, where everyone in this call will be voting whose skill was the most useful, and whoever the winner is gets the 50 USD gift card from Kayla and Otam.

264 00:32:37.350 ⇒ 00:32:41.390 Brylle Girang: So, is everyone ready? I’m going to start the timer now.

265 00:32:42.620 ⇒ 00:32:47.900 Uttam Kumaran: Does everyone… does everyone know how to make a scale? Is everyone, like… Good with the setup here?

266 00:32:52.200 ⇒ 00:32:54.929 Brylle Girang: I think Sam is already drafting his skill plan.

267 00:33:00.390 ⇒ 00:33:00.970 Samuel Roberts: We’ll see.

268 00:33:00.970 ⇒ 00:33:02.359 Uttam Kumaran: Okay, cool. It’s all yours, V.

269 00:33:02.600 ⇒ 00:33:05.670 Brylle Girang: Okay, okay, 5 minutes, everyone, you know.

270 00:33:06.070 ⇒ 00:33:16.439 Brylle Girang: Let’s go! Create a skill within 5 minutes, pitch it, we choose the winner, whoever wins gets $50. Let’s go! This is 5 minutes of us in awkward silence.

271 00:33:17.800 ⇒ 00:33:21.119 Uttam Kumaran: Okay, you could talk about something.

272 00:33:21.700 ⇒ 00:33:22.310 Kaela Gallagher: I feel like we should.

273 00:33:22.310 ⇒ 00:33:22.810 Uttam Kumaran: Yeah, I don’t.

274 00:33:22.810 ⇒ 00:33:24.680 Kaela Gallagher: It’s a Jeopardy theme song.

275 00:33:25.570 ⇒ 00:33:30.089 Brylle Girang: Yeah, I was actually thinking of adding some sort of music here.

276 00:33:30.090 ⇒ 00:33:34.069 Uttam Kumaran: Yeah, B needs to do a cappella, Jeopardy! theme song.

277 00:33:34.750 ⇒ 00:33:36.389 Uttam Kumaran: For 5 minutes.

278 00:33:36.390 ⇒ 00:33:37.210 Brylle Girang: No.

279 00:33:39.100 ⇒ 00:33:43.000 Brylle Girang: $15 for 5 minutes, pretty good value.

280 00:33:45.600 ⇒ 00:34:04.829 Brylle Girang: So, I saw something here where it mentioned that if you’re just looking at your agent code, you’re already behind, because apparently lots of people are running 10 agents at once, and I think one of those people is Otam.

281 00:34:05.550 ⇒ 00:34:08.050 Brylle Girang: You’re running parallel agents, right?

282 00:34:08.949 ⇒ 00:34:16.040 Uttam Kumaran: Yeah, I haven’t… yeah, it’s… I… mine is a little bit too much. I’m not gonna… I’m not gonna participate in this game.

283 00:34:16.219 ⇒ 00:34:20.749 Uttam Kumaran: This is just my day-to-day. It’s not a game for me.

284 00:34:21.130 ⇒ 00:34:21.710 Uttam Kumaran: It’s…

285 00:34:21.710 ⇒ 00:34:22.439 Brylle Girang: It’s crazy.

286 00:34:22.440 ⇒ 00:34:31.980 Uttam Kumaran: No, I’m pumped. I think we have some amazing skills. B, also, you’d be a good, like, you’d be a good livestreamer, I feel like. You know, I feel like you’re good at just…

287 00:34:32.540 ⇒ 00:34:37.100 Uttam Kumaran: Like, you have energy, I wanna… I just people getting people to chat.

288 00:34:37.900 ⇒ 00:34:40.420 Brylle Girang: I’m not ready to shift careers yet.

289 00:34:40.420 ⇒ 00:34:41.350 Uttam Kumaran: Okay, okay.

290 00:34:43.980 ⇒ 00:34:45.799 Brylle Girang: Preparing as a limit?

291 00:34:46.210 ⇒ 00:34:52.750 Uttam Kumaran: Yeah, maybe we could… I can talk about a couple skills that I’m working on, even outside of this, so…

292 00:34:53.060 ⇒ 00:34:57.240 Uttam Kumaran: I’m, I want to expand our,

293 00:34:57.410 ⇒ 00:35:01.539 Uttam Kumaran: sort of slide deck builder skill, so it actually will create Google Slides.

294 00:35:01.830 ⇒ 00:35:05.060 Uttam Kumaran: That way we can…

295 00:35:05.210 ⇒ 00:35:10.669 Uttam Kumaran: have it almost end-to-end edit and create Google Slides versus, like, having to create the text.

296 00:35:10.810 ⇒ 00:35:19.469 Uttam Kumaran: That’s something I’m working on. I’m also gonna work on some skills and things for Kayla for recruiting, so that she can quickly

297 00:35:19.640 ⇒ 00:35:24.289 Uttam Kumaran: You know, get back to candidates, provide feedback, so that’s a couple things.

298 00:35:24.420 ⇒ 00:35:27.410 Uttam Kumaran: Yeah, I was doing a bunch of design work this week, so…

299 00:35:27.510 ⇒ 00:35:29.490 Uttam Kumaran: That’s why I was using the impeccable.

300 00:35:29.770 ⇒ 00:35:32.990 Uttam Kumaran: stuff be, so… Yeah, it was fun.

301 00:35:35.980 ⇒ 00:35:39.000 Brylle Girang: I actually tried Impeccable, it is amazing.

302 00:35:39.660 ⇒ 00:35:40.170 Brylle Girang: Master.

303 00:35:40.170 ⇒ 00:35:40.880 Uttam Kumaran: really good.

304 00:35:41.040 ⇒ 00:35:43.939 Brylle Girang: Its main… its main goal is to make sure that

305 00:35:44.360 ⇒ 00:35:47.660 Brylle Girang: whatever the front end is, is not AI slob.

306 00:35:50.420 ⇒ 00:35:53.029 Brylle Girang: Wow! Greg is already done?

307 00:35:53.660 ⇒ 00:35:56.109 Brylle Girang: And it’s committed and shipped, wow.

308 00:35:57.050 ⇒ 00:36:00.220 Greg Stoutenburg: It might stink, I don’t know, but you didn’t say anything about quality.

309 00:36:00.590 ⇒ 00:36:04.339 Brylle Girang: Let’s let the audience decide on that.

310 00:36:04.340 ⇒ 00:36:07.639 Uttam Kumaran: What do you mean you didn’t say anything about quality? You’re gonna get $0 if it sucks.

311 00:36:07.640 ⇒ 00:36:08.190 Greg Stoutenburg: So…

312 00:36:11.250 ⇒ 00:36:14.030 Greg Stoutenburg: We’re gonna improve it iteratively.

313 00:36:17.230 ⇒ 00:36:22.949 Uttam Kumaran: You literally take a screenshot of the criteria, you’re like, make me a skill that makes… wins me $50.

314 00:36:22.950 ⇒ 00:36:24.499 Greg Stoutenburg: I should’ve just done that, jeez.

315 00:36:25.900 ⇒ 00:36:27.259 Uttam Kumaran: A little bit too meta.

316 00:36:27.670 ⇒ 00:36:30.309 Samuel Roberts: That’s a little bit what I was doing.

317 00:36:30.310 ⇒ 00:36:30.840 Greg Stoutenburg: That’s cool.

318 00:36:30.840 ⇒ 00:36:31.300 Uttam Kumaran: Of course.

319 00:36:31.300 ⇒ 00:36:34.860 Greg Stoutenburg: Well, I didn’t know that this competition was coming, so I actually just, like…

320 00:36:35.030 ⇒ 00:36:40.540 Greg Stoutenburg: I just took what I just said right before you announced it, I was like, oh, I should… well, that would be the skill, so…

321 00:36:41.430 ⇒ 00:36:42.520 Greg Stoutenburg: Just make it clear.

322 00:36:42.520 ⇒ 00:36:46.669 Uttam Kumaran: AI is not that good at ideating, you know? Take some creative brain.

323 00:36:47.490 ⇒ 00:36:48.180 Greg Stoutenburg: Yeah.

324 00:36:49.070 ⇒ 00:36:54.709 Uttam Kumaran: And ultimately, B is the judge, so… I don’t know, you gotta think about what B’s bias… what his biases are.

325 00:36:55.070 ⇒ 00:37:00.279 Brylle Girang: It’s not me, so everyone here will vote, so everyone here’s the judge.

326 00:37:00.490 ⇒ 00:37:02.539 Uttam Kumaran: Oh, fair, fair, okay, okay.

327 00:37:03.160 ⇒ 00:37:07.609 Brylle Girang: It’s not going to be the Coliseum where I just, you know, thumbs up or thumbs down.

328 00:37:08.600 ⇒ 00:37:10.669 Uttam Kumaran: Dude, it’s so awesome that you made this…

329 00:37:10.900 ⇒ 00:37:14.589 Uttam Kumaran: like, HTML, and with a live cloth, like, this is awesome.

330 00:37:15.390 ⇒ 00:37:22.620 Brylle Girang: It is… the main reason why I went away from slides is… the primary reason is I don’t want Greg to start building the skill.

331 00:37:22.830 ⇒ 00:37:33.389 Brylle Girang: Before we… before we met. And the second reason is because it has these interactive elements, right? There’s… there’s a timer, we can embed videos here, so…

332 00:37:34.070 ⇒ 00:37:35.200 Brylle Girang: Basically, anything that…

333 00:37:35.200 ⇒ 00:37:41.220 Uttam Kumaran: I mean, there’s a shot, we move away from Google Slides, you know, I think it’s just…

334 00:37:41.690 ⇒ 00:37:46.529 Uttam Kumaran: So I’m gonna Google stuff is easy to share, so I’m still kind of committed to it, but yeah.

335 00:37:52.980 ⇒ 00:37:59.970 Brylle Girang: Should we have Greg pitch? Yeah, definitely, but I… in 15 seconds. Is ever… is anyone else done?

336 00:38:03.920 ⇒ 00:38:06.360 Pranav: I think I’m done. I pushed something.

337 00:38:07.070 ⇒ 00:38:09.100 Brylle Girang: Okay, so Greg, Greg, you’re…

338 00:38:09.100 ⇒ 00:38:09.930 Demilade Agboola: jump in.

339 00:38:10.370 ⇒ 00:38:17.179 Brylle Girang: Perfect, perfect. So, actually, if you can just pitch first, and then push second, that would be amazing.

340 00:38:18.540 ⇒ 00:38:28.800 Brylle Girang: Alright, we’re done! Time’s up! So, let’s have Greg pitch first, and then Pranav, and then Bami, or anyone else, Don, but let’s go through that.

341 00:38:29.490 ⇒ 00:38:34.800 Brylle Girang: route. Okay, Greg, you can, you can hijack screen sharing if you want to show how you built it.

342 00:38:36.240 ⇒ 00:38:41.059 Greg Stoutenburg: Okay, cool. So, let me get this.

343 00:38:41.940 ⇒ 00:38:45.630 Greg Stoutenburg: So, this is a deck that was just created using the new skill.

344 00:38:47.170 ⇒ 00:39:01.949 Greg Stoutenburg: I haven’t seen it yet, but I bet it’s amazing. I bet it’s worth approximately $50. So, a blocker that we’ve all experienced is that we use AI to create a slide deck, but then the output is a whole bunch of text that has to be wrangled into the format of your deck.

345 00:39:01.950 ⇒ 00:39:24.660 Greg Stoutenburg: And this turns into a, you know, this wastes a lot of time, it’s very time-consuming. You might be rushing to get something done before the client meeting, and then you get this output that’s mostly a lot of text, slowing you down. So, what I did is I created a cursor skill that would combine two different things together. One is creating high-quality content that would go into a deck, and the other is the visual elements of the deck itself, so that in one

346 00:39:24.800 ⇒ 00:39:33.729 Greg Stoutenburg: in one simple go, you can one-shot a deck from, from a prompt. So, this is… here, the prompt that I used is…

347 00:39:35.730 ⇒ 00:39:39.789 Greg Stoutenburg: That’s where I combined the existing skills.

348 00:39:40.410 ⇒ 00:39:48.190 Greg Stoutenburg: is to run this skill, use Client Stand Up Visual Deck to prepare a deck based on the most recent default scope of work that you find in the vault.

349 00:39:48.740 ⇒ 00:39:53.109 Greg Stoutenburg: And then it did this, and here we go. So,

350 00:39:53.780 ⇒ 00:39:59.229 Greg Stoutenburg: Let’s take a look. Okay, so it didn’t find a statement of work. That’s fine.

351 00:39:59.410 ⇒ 00:40:01.039 Greg Stoutenburg: Let’s take a look at our deck.

352 00:40:01.540 ⇒ 00:40:03.180 Greg Stoutenburg: It looks pretty.

353 00:40:03.350 ⇒ 00:40:05.230 Greg Stoutenburg: Alright, we’ve got an agenda.

354 00:40:08.310 ⇒ 00:40:09.730 Greg Stoutenburg: We’ve got a nice heading.

355 00:40:11.520 ⇒ 00:40:13.009 Greg Stoutenburg: We’ve got an update.

356 00:40:15.110 ⇒ 00:40:21.339 Greg Stoutenburg: Visuals look nice. Kind of simple. No, images or anything like that, but, you know, the backgrounds look good.

357 00:40:21.570 ⇒ 00:40:23.950 Greg Stoutenburg: Very easy to follow, very clean looking.

358 00:40:26.770 ⇒ 00:40:32.169 Greg Stoutenburg: Relevant… relevant information, It’s accurate, we’re up to date.

359 00:40:34.040 ⇒ 00:40:39.500 Greg Stoutenburg: This looks… Nearly pitchable as is?

360 00:40:39.750 ⇒ 00:40:44.430 Greg Stoutenburg: I would want more unexpected ROI for the client?

361 00:40:44.820 ⇒ 00:40:52.819 Greg Stoutenburg: But yeah, there you go. So I think we’ve… I think we’ve one-shotted a deck that probably requires minimal revision before we could take a live.

362 00:40:53.610 ⇒ 00:40:54.710 Greg Stoutenburg: There’s my pitch.

363 00:40:57.920 ⇒ 00:40:59.040 Brylle Girang: Amazing.

364 00:40:59.260 ⇒ 00:41:12.600 Brylle Girang: Amazing. I think we have potential here into making sure that the visual explainer deck is up to Brainforge design standards, so that our visual explainers are going to be uniform, but that is amazing.

365 00:41:14.340 ⇒ 00:41:19.289 Brylle Girang: So, took the tip into making sure that skills are used sequentially. So, Greg.

366 00:41:19.400 ⇒ 00:41:23.510 Brylle Girang: Use… prepare a stand-up deck and a visual explainer, combining the two one.

367 00:41:23.920 ⇒ 00:41:26.549 Greg Stoutenburg: I wanted to just, yeah, rip it in one go.

368 00:41:27.240 ⇒ 00:41:28.090 Greg Stoutenburg: Yeah.

369 00:41:28.370 ⇒ 00:41:34.400 Brylle Girang: Cool! Okay, Pranav, you want to… Percent what you built.

370 00:41:35.590 ⇒ 00:41:39.129 Pranav: Sure, we will see what it did in real time.

371 00:41:40.750 ⇒ 00:42:00.410 Pranav: Yeah, so, a little bit of context here, Robert kind of told me maybe a couple weeks ago about a client that, seemed like a really interesting client. We wanted to get a cool demo out to them. We already kind of got some demos out to them, but I was thinking about how we could make a better one for them.

372 00:42:00.560 ⇒ 00:42:01.660 Pranav: And…

373 00:42:02.280 ⇒ 00:42:13.999 Pranav: context on the client, district attorney office in New York, so I had to learn what all of that means. What does their workflows look like? What are the different positions that are happening there?

374 00:42:14.000 ⇒ 00:42:24.040 Pranav: And so, earlier this week, I remember I was on a call with Greg and Utam, and I was talking about how a skill where we can just do that research and figure out what are…

375 00:42:24.470 ⇒ 00:42:28.170 Pranav: what are the workflows that are happening within that company?

376 00:42:28.800 ⇒ 00:42:33.200 Pranav: That we feel like we can maybe help…

377 00:42:33.330 ⇒ 00:42:49.929 Pranav: we can help build them some type of demo to, like, showcase that, yeah, we can work on a project for you guys. And so, with this, I was like, okay, given a landing page, and giving additional context about what exactly I want to be building,

378 00:42:50.080 ⇒ 00:42:53.729 Pranav: Like, for which specific part of their, you know, company?

379 00:42:53.980 ⇒ 00:43:02.039 Pranav: Help me come up with an understanding of their workflows, and then also give me some demo ideas at the end.

380 00:43:04.600 ⇒ 00:43:14.170 Pranav: while I was building this, and as this is executing right now, I was thinking about, okay, this also needs to be tied into, like, email threads, Slack channels, where, like, additional context was given.

381 00:43:14.170 ⇒ 00:43:26.219 Pranav: Because, you know, Robert had, like, an unrecorded call with them, and he gave me kind of, like, a summary of it, which exists in Slack, which exists in email as well. So that should be used as context to even further refine, like, what this demo should look like.

382 00:43:26.330 ⇒ 00:43:28.229 Pranav: But, I think…

383 00:43:28.480 ⇒ 00:43:37.339 Pranav: if I can get another maybe 50 minutes with this, you know, I can probably get all of that integrated, but I do like this idea.

384 00:43:38.220 ⇒ 00:43:39.720 Pranav: I think it could be useful.

385 00:43:42.600 ⇒ 00:43:46.900 Pranav: still building out this discovery demo, so… I don’t know…

386 00:43:47.570 ⇒ 00:43:52.399 Pranav: Greg had something pretty to show. I don’t know if I have something pretty to show yet.

387 00:43:54.160 ⇒ 00:43:56.409 Greg Stoutenburg: Run it through Visual Explainer.

388 00:43:56.980 ⇒ 00:43:57.860 Pranav: Hmm…

389 00:43:58.360 ⇒ 00:44:03.510 Robert Tseng: When it’s finished loading, send it to me, I’m curious to read it. Maybe I’ll shoot it over to them.

390 00:44:03.510 ⇒ 00:44:06.920 Uttam Kumaran: Robert’s like, Robert’s like, I’m gonna… I actually need that right now.

391 00:44:09.250 ⇒ 00:44:11.209 Greg Stoutenburg: Pilot is the sales pitch in 20 minutes.

392 00:44:11.450 ⇒ 00:44:11.830 Robert Tseng: Oh, yeah.

393 00:44:12.130 ⇒ 00:44:12.730 Pranav: Yeah.

394 00:44:13.390 ⇒ 00:44:15.560 Pranav: Okay, the lead in one sentence.

395 00:44:15.910 ⇒ 00:44:16.500 Greg Stoutenburg: Cool.

396 00:44:23.780 ⇒ 00:44:24.909 Robert Tseng: That’s not far off.

397 00:44:26.200 ⇒ 00:44:27.000 Pranav: Yeah.

398 00:44:27.520 ⇒ 00:44:28.000 Greg Stoutenburg: Sweet.

399 00:44:28.000 ⇒ 00:44:32.360 Robert Tseng: ICP score out of 50 is an interesting answer, but…

400 00:44:33.730 ⇒ 00:44:34.999 Brylle Girang: What is ICP?

401 00:44:36.480 ⇒ 00:44:45.769 Robert Tseng: Ideal customer profile, it’s like a scoring framework to decide if, if an account is close to what we would consider to be a good account.

402 00:44:49.570 ⇒ 00:44:52.999 Greg Stoutenburg: Or for the Detroiters among us, it’s insane clown posse.

403 00:44:53.960 ⇒ 00:44:54.730 Uttam Kumaran: Yes.

404 00:44:55.510 ⇒ 00:44:57.040 Robert Tseng: Is that actually a thing?

405 00:44:57.510 ⇒ 00:44:59.000 Uttam Kumaran: Easily confused.

406 00:44:59.080 ⇒ 00:45:01.410 Greg Stoutenburg: Easily confused.

407 00:45:03.330 ⇒ 00:45:03.929 Samuel Roberts: It could be an idea.

408 00:45:03.930 ⇒ 00:45:04.870 Robert Tseng: No way!

409 00:45:05.290 ⇒ 00:45:07.120 Robert Tseng: I don’t want to click on that, I’m afraid of clowns.

410 00:45:08.170 ⇒ 00:45:19.760 Greg Stoutenburg: Oh, don’t… they’re scary clowns. And rappers, and they spray Faygo soda all over everyone, which is a sort of Detroit brand. Domino’s.

411 00:45:20.530 ⇒ 00:45:23.590 Brylle Girang: Cool! Demi, you want to take a shot?

412 00:45:29.570 ⇒ 00:45:32.110 Brylle Girang: You’re on mute if you’re speaking over there.

413 00:45:32.110 ⇒ 00:45:39.939 Demilade Agboola: Sorry, my bad, I was… I thought I was… I was speaking. So I think my… what I thought of, and what I’m trying to do.

414 00:45:40.130 ⇒ 00:45:47.340 Demilade Agboola: And… This is just basically… Come up with a… what’s it called?

415 00:45:47.680 ⇒ 00:45:50.130 Demilade Agboola: A daily to-do briefing.

416 00:45:50.330 ⇒ 00:45:55.939 Demilade Agboola: So, since we have, like, access to Slack, and Linear, and just, like.

417 00:45:56.610 ⇒ 00:45:58.700 Demilade Agboola: Notion and all the other stuff.

418 00:46:00.900 ⇒ 00:46:09.360 Demilade Agboola: The basic idea of this is every morning you can kind of just run it, and it’ll come up with, like, a to-do list for you for that day, based off of, like.

419 00:46:09.620 ⇒ 00:46:14.640 Demilade Agboola: transcripts from calls, Slack messages,

420 00:46:15.530 ⇒ 00:46:22.869 Demilade Agboola: linear, as well as, like, Notion. So the basic idea is, yeah, you can just get, like, a list of things in your…

421 00:46:23.430 ⇒ 00:46:24.350 Demilade Agboola: What’s it called?

422 00:46:26.130 ⇒ 00:46:37.930 Demilade Agboola: in your DM every morning, and then you can kind of just have a clear idea of what to do, which can be useful when you’re across multiple clients, and you lose track of things sometimes, so it’s just very…

423 00:46:39.730 ⇒ 00:46:44.420 Demilade Agboola: concise for you. You don’t have to go through multiple places to get all the information you need to start your day.

424 00:46:46.890 ⇒ 00:46:51.970 Brylle Girang: Amazing. Imagine if this was automated, and it just sends directly to you as a DM.

425 00:46:52.880 ⇒ 00:46:54.010 Demilade Agboola: We can do that.

426 00:46:54.010 ⇒ 00:46:56.559 Greg Stoutenburg: Yeah, that’s great That’s cool, Danny.

427 00:46:56.560 ⇒ 00:47:03.290 Demilade Agboola: I want to say run, but I’m not sure what channel ID this is, so I don’t want to, like, send it to some random place, but…

428 00:47:03.640 ⇒ 00:47:10.570 Uttam Kumaran: This is actually great, dude, I actually need this. I was gonna work on this this weekend. This would be so helpful.

429 00:47:11.030 ⇒ 00:47:18.999 Demilade Agboola: I also put calendar as well, but I skipped because I haven’t yet. Again, like I said, this is just me, like, one-shotting it, so I haven’t, like, put the calendar details and stuff.

430 00:47:19.060 ⇒ 00:47:35.379 Demilade Agboola: But yeah, you should look through your calendar, your emails, like, basically all the different things you have, and say, hey, these are the things you will need to get done today, and you can… and Slack as well, and, like, Lanier and all of that. So, you can kind of use that and say, hey, this is what you need to do.

431 00:47:35.750 ⇒ 00:47:37.689 Demilade Agboola: This is stuff from Slack.

432 00:47:38.440 ⇒ 00:47:43.130 Demilade Agboola: This is stuff from Notion, and from recent calls, this, this, this, this, this.

433 00:47:44.450 ⇒ 00:47:55.519 Demilade Agboola: So, yeah, but yeah, this will be the… what it should be like. I’m just not going to click send, because again, like I said, I’m not sure what channel ID this is, and I don’t want to spam a random channel with this.

434 00:47:58.440 ⇒ 00:47:58.790 Brylle Girang: Okay.

435 00:47:58.790 ⇒ 00:47:59.380 Demilade Agboola: Great idea.

436 00:48:00.170 ⇒ 00:48:18.809 Brylle Girang: Thank you, thank you, Demi. So yeah, well, since there were only 3 presenters, they will be the ones eligible for the 50 USD gift card. We’ll do the voting async instead, so we’ll just send a Slack message, and then we’ll ask you to vote using emojis. And then, yeah, we’ll just reach out to whoever the winner is.

437 00:48:20.700 ⇒ 00:48:23.780 Uttam Kumaran: Did anyone else have any skills that they did… that they worked on?

438 00:48:26.410 ⇒ 00:48:27.820 Greg Stoutenburg: Oh, my God!

439 00:48:29.750 ⇒ 00:48:37.200 Amber Lin: Some that are not complete enough, and not complete within the 5-minute time frame, so that’s cheating to all of us.

440 00:48:37.200 ⇒ 00:48:40.159 Uttam Kumaran: Wait, explain the idea.

441 00:48:40.390 ⇒ 00:48:42.050 Amber Lin: Well.

442 00:48:42.050 ⇒ 00:48:43.090 Uttam Kumaran: I just want to hear some of the ideas.

443 00:48:43.820 ⇒ 00:48:44.480 Uttam Kumaran: Yeah.

444 00:48:44.970 ⇒ 00:49:04.429 Amber Lin: I was working on this skill that, for Omni, that, well, first grabs the visualizations from existing dashboards, and then applies it to new ones. Essentially what I’ve done before, but in a skill, but…

445 00:49:05.210 ⇒ 00:49:09.929 Amber Lin: I did this before we started the contest, and

446 00:49:10.220 ⇒ 00:49:19.239 Amber Lin: I continue to do it after we finish the contest, so I would like to have people… have our three contestants,

447 00:49:19.350 ⇒ 00:49:21.690 Amber Lin: They actually did it within a time frame.

448 00:49:27.590 ⇒ 00:49:34.710 Brylle Girang: Thank you, Amber. Okay, that’s cool, yeah, but yeah, we’ll have to… to stick with the three contestants.

449 00:49:35.620 ⇒ 00:49:43.570 Brylle Girang: Okay, the reality show is over. I’m going to be passing it to Greg or Kayla, whoever’s going to lead.

450 00:49:46.050 ⇒ 00:49:51.569 Kaela Gallagher: I can share my screen, but then, Greg, if you want to do your section.

451 00:49:51.800 ⇒ 00:49:52.200 Greg Stoutenburg: True.

452 00:49:52.200 ⇒ 00:49:54.039 Kaela Gallagher: Over it like that, okay.

453 00:50:00.380 ⇒ 00:50:02.300 Greg Stoutenburg: We go to… oh, okay.

454 00:50:02.840 ⇒ 00:50:05.540 Greg Stoutenburg: Yeah, I’ll be concise.

455 00:50:06.430 ⇒ 00:50:25.060 Greg Stoutenburg: Yeah, okay, so… so I, I, I pushed a cursor skill, the, the deck review one was me a few days ago, and so I think it was for this occasion that B and your time were like, hey, you know, to talk a little bit about how you got here. So I think I’ve always,

456 00:50:25.410 ⇒ 00:50:28.389 Greg Stoutenburg: Oh, okay. It’s time to vote. Sweet.

457 00:50:31.330 ⇒ 00:50:50.419 Greg Stoutenburg: I think I’m someone who’s always tried to automate things, because, it’s… I mean, if you could see my desk… well, actually, why not? Take a look at my desk? Here, this is what my desk looks like. See all these sticky notes and stuff all over the place? This is the kind of life I live, and I’d like for it to be better. So I’ve always tried to automate things and make, you know, make things more organized where I can.

458 00:50:50.420 ⇒ 00:51:08.700 Greg Stoutenburg: And so that meant, like, when I was in 10th grade, I was using my TI-83 Plus to… like, I made a program with the, with the quadratic formula so that I wouldn’t have to do the math in class, so I was looking for those sorts of things kind of early on. But then when… when AI started becoming a thing.

459 00:51:08.700 ⇒ 00:51:15.370 Greg Stoutenburg: I use it mostly as a thought partner, or help me organize things, like come up with ideas, draft a brief document, things like that.

460 00:51:15.750 ⇒ 00:51:29.619 Greg Stoutenburg: And then, in a previous role, I started using it professionally to help me actually build things. So I was a growth PM at a manufacturing software company, and road mapping for an engineering team of 10.

461 00:51:29.620 ⇒ 00:51:39.879 Greg Stoutenburg: was, you know, a lot of work. Like, that’s a lot of people’s plates that need to be full, they need to be working on interesting things, and it needs to be well-structured and organized. So, I… I plugged in,

462 00:51:39.880 ⇒ 00:51:42.869 Greg Stoutenburg: I plugged in cursor… no, it wasn’t cursor, what was I using?

463 00:51:43.100 ⇒ 00:51:52.619 Greg Stoutenburg: plugged in, I think maybe just my own Claude account to GitHub, and I was like, alright, I’ve got this idea, I’m gonna take this roadmap, and I’m just gonna create epics for the next, like, the next 2 months.

464 00:51:53.060 ⇒ 00:51:59.699 Greg Stoutenburg: And, it was awesome. I mean, it was… it was incredible how much I was able to automate in the creation of issues.

465 00:51:59.700 ⇒ 00:52:18.129 Greg Stoutenburg: and tickets and epics, and it created, but I did a horrible job of it, and I spent the next couple of weeks deleting issues, and tailoring issues, because I had put so much garbage out there into the universe and had to fix it up. But still, you know, the vision was clear, like, I can use this.

466 00:52:18.130 ⇒ 00:52:25.819 Greg Stoutenburg: to do effective things and faster and at scale. So, then the next step, after I had created lots of issues and epics.

467 00:52:25.820 ⇒ 00:52:38.829 Greg Stoutenburg: that were organized but not good, was to try to improve their quality. So, I spent some time making sure that… I just sort of, like, you know, walked back a little bit from being too quick and ambitious.

468 00:52:38.830 ⇒ 00:52:49.570 Greg Stoutenburg: To, to just, yeah, trying to improve quality by putting in place systems that would review tickets for certain characteristics before they would be assigned to anyone.

469 00:52:49.570 ⇒ 00:52:54.449 Greg Stoutenburg: And that felt like a development, right? Kind of like putting some guardrails in place.

470 00:52:54.940 ⇒ 00:53:13.499 Greg Stoutenburg: And then, finally, in the last several months of that role, I was building AI features for developers, and that required learning about things that I just didn’t know anything about. So, prior to… prior to actually working on AI feature development, I was, you know, I was a sort of…

471 00:53:13.550 ⇒ 00:53:25.810 Greg Stoutenburg: I was a sort of rough user. I wasn’t doing much more than what someone would do with ChatGPT, only I managed to make it execute something. But I didn’t really understand how any of it worked, and…

472 00:53:25.810 ⇒ 00:53:38.800 Greg Stoutenburg: I had to change that as I was, you know, scoping out features to keep us at least on par with the competition and try to get ahead. Many of you have probably heard of, like, N8N, for example. The company that I was working with.

473 00:53:38.800 ⇒ 00:53:51.299 Greg Stoutenburg: was, they manage Node-RED, which is a low-code, no-code development tool, open source, been around a long time, used in a lot of manufacturing contexts. And…

474 00:53:51.580 ⇒ 00:54:04.339 Greg Stoutenburg: we were seeing competitors like N8N start to release all this stuff where you could just take text and turn it into cool features in workflows, and so I needed to stay on that. So that… that was a major learning journey for me.

475 00:54:04.810 ⇒ 00:54:28.280 Greg Stoutenburg: So I felt like I’d come a long way by then. And then I got hired at Brainforge, and within, you know, within your first 4 hours at Brainforge, you will have seen Utam say, ask Cursor how to do that. And, so we’re, you know, we’re moving along very quickly from the time that we land, and so I thought, like, alright, I can see that, like, I’m gonna be accelerating here.

476 00:54:28.280 ⇒ 00:54:39.900 Greg Stoutenburg: In terms of, AI knowledge and application. So, I have learned a ton, and sped up a ton, and I would sort of, like, put it at these three

477 00:54:39.900 ⇒ 00:54:41.740 Greg Stoutenburg: points.

478 00:54:41.770 ⇒ 00:54:50.180 Greg Stoutenburg: needing to save time creating something when I can adhere to known best practices. So, the first thing that I felt I created that was, like.

479 00:54:50.290 ⇒ 00:55:05.179 Greg Stoutenburg: good and useful using AI, where I felt like it really sped me up, was a complete event tracking plan. It wasn’t perfect, but a complete event tracking plan, where I was able to, feed in a bunch of stuff I knew about the client and about a product.

480 00:55:05.180 ⇒ 00:55:13.290 Greg Stoutenburg: along with, Amplitude just supplies best practices and a template tracking plan, and go, make me a tracking plan according to this.

481 00:55:13.290 ⇒ 00:55:31.770 Greg Stoutenburg: Now, the tracking plan is longer than that, but because there are, sort of, best practices that can be fed in already, you can hit enter on that, and have, you know, in my case, by plugging Claude into PowerPoint, literally a complete spreadsheet, which is amazing. And then you don’t have to go in and edit it, but still, saved hours.

482 00:55:32.470 ⇒ 00:55:52.210 Greg Stoutenburg: Next, need to save time by adhering to policy. So the cursor skill that Bea mentioned before about reviewing dashboards and decks came out of, jasmine put together a nice Notion doc on Brainforge standards for decks and dashboards. Now, to review all that and imagine that as a sort of checklist in your head as you’re looking through some work.

483 00:55:52.210 ⇒ 00:56:09.629 Greg Stoutenburg: you know, that’s gonna take some time, and so I thought, there’s gotta be an easier way to do this. I’m gonna just try to create a skill that just applies it. So you feed in a deck or a dashboard, and it’ll, like, give you a scorecard and call things out. And I thought that’s gonna be, you know, again, a way to take some known standard and apply it to a piece of work.

484 00:56:09.860 ⇒ 00:56:21.869 Greg Stoutenburg: And then finally, what’s, what’s helped me start to speed up as CSO is I need to get out of the way because my team’s really fast. So, Amber and Edvate will… can just, you know.

485 00:56:21.960 ⇒ 00:56:40.499 Greg Stoutenburg: plow through some dashboards and some topic building and things like that, and I’m like, alright, you know, I need to not be the one who’s slowing down the team because, because there are things, you know, falling through the cracks that I’m not following up on. So, thinking along the way… along the lines of what Demi showed a moment ago with something that will, like, review and check progress on things.

486 00:56:40.500 ⇒ 00:56:59.139 Greg Stoutenburg: I created a cursor automation, I’ve only put it for now in the default channel, but I’ll, extend it elsewhere. No, sorry, in the element channel, but I’ll extend it elsewhere, that looks at, cursor and linear… sorry, that looks at linear and Slack and, identifies tickets that haven’t been touched in a couple of days.

487 00:56:59.140 ⇒ 00:57:04.110 Greg Stoutenburg: And just provides, like, an overview, and says, you know, tags a person and says, hey, you know, take a look at this.

488 00:57:04.110 ⇒ 00:57:10.200 Greg Stoutenburg: Because we don’t want to let anything go stagnant. So, yeah, and I feel like, you know, on this path.

489 00:57:10.620 ⇒ 00:57:18.579 Greg Stoutenburg: I’ve gotten faster and more organized. It’s not perfect, you know, there’s still work to be done, of course, but, it’s helping me feel on top of things in a way that,

490 00:57:18.720 ⇒ 00:57:22.740 Greg Stoutenburg: In a way that’s really helpful, you know, and isn’t just a bunch of sticky notes on my desk.

491 00:57:22.950 ⇒ 00:57:30.919 Greg Stoutenburg: And then real quick, I guess just in the last, you know, minute here, just in the last couple weeks, some things that have been sort of inspirations that have helped me along.

492 00:57:30.930 ⇒ 00:57:47.279 Greg Stoutenburg: we were playing Uno with, with my partner’s kids, and she was, her son was using ChatGPT to play Uno, and it was slowing everything down horribly. Like, anytime he got a card, he was, like, typing into ChatGPT, like, here’s the card, here’s what the deck is now. And I thought, this is terrible.

493 00:57:47.360 ⇒ 00:58:05.240 Greg Stoutenburg: But instead of saying, don’t do it, there’s gotta be a better way to do this. So, I built an AI Uno player, with her daughter, and it’s still a horrible way to play Uno, like, bringing in an AI that you have to update, but it works, and it won. So… I don’t know, that’s some kind of validation.

494 00:58:05.240 ⇒ 00:58:16.960 Greg Stoutenburg: I got a half an hour with Jarrell earlier in the week, and he showed me some stuff that he’s doing with cowork that was just, like, mind-blowing. The way that the connectors were, like, doing things.

495 00:58:16.960 ⇒ 00:58:28.599 Greg Stoutenburg: rather than, you know, telling you how to do things, it would just… that’s kind of where the automation idea came from. Like, I… I don’t need to… I want to not need to execute a command.

496 00:58:28.600 ⇒ 00:58:46.349 Greg Stoutenburg: to get that overview from Linear and from Slack, it’ll just do it, and so that’s what the automation is doing. And then finally, just, like, really appreciating the clarity that came from having an organized linear initiative for, the Element Omni project, and Bea put that together, and just seeing how

497 00:58:46.460 ⇒ 00:58:59.640 Greg Stoutenburg: that organization really aligned the team, I went, man, there’s got to be a way to just make this happen, and not be the one to, you know, slow it down and have to do it manually. So, yeah, that’s… that’s the direction I’ve been going, and it’s been great.

498 00:59:00.230 ⇒ 00:59:01.090 Greg Stoutenburg: Thanks.

499 00:59:01.600 ⇒ 00:59:02.769 Greg Stoutenburg: For giving me the mic.

500 00:59:08.220 ⇒ 00:59:23.799 Brylle Girang: Okay, back to me. This will be quick, so the main reason why we had that whole fiasco earlier is that’s going to be the introduction for Brainforge learning and development team. Our main focus for this quarter is to get everyone certified, so we will be sharing

501 00:59:23.820 ⇒ 00:59:36.869 Brylle Girang: a whole roadmap for certifications involving Omni and Snowflake as part of our first round. There would be upskilling programs, there would be foundation modules that we will be launching starting next week.

502 00:59:36.870 ⇒ 00:59:44.009 Brylle Girang: But the main goal for learning and development is to ensure that everyone here in Brainforge gets to upskill

503 00:59:44.320 ⇒ 00:59:48.420 Brylle Girang: At a much faster rate, and make sure that new people

504 00:59:48.640 ⇒ 00:59:53.429 Brylle Girang: can be onboarded to Courser and all our standards at day one.

505 00:59:54.020 ⇒ 01:00:02.790 Brylle Girang: But yeah, that’s for learning and development. We will be sharing more information on this starting next week, but really stoked for this, really excited.

506 01:00:11.980 ⇒ 01:00:19.009 Rico Rejoso: Alright, hey team, quick operations update on how we route things to the operations team.

507 01:00:19.070 ⇒ 01:00:36.520 Rico Rejoso: So, we’ve consolidated how you submit ops requests, so there’s just one clear plan instead of juggling different Slack shortcuts or commands. The goal is just simple, it is to get you unblocked faster, and make sure that no request gets unnoticed, okay? So right now, we have three,

508 01:00:37.510 ⇒ 01:00:49.359 Rico Rejoso: options in Linear S, we have the general ops request, which is, for any ops-related, questions or inquiries that is… that is not expense or access-related. Expense requests…

509 01:00:51.030 ⇒ 01:01:08.019 Rico Rejoso: which is a, for any reimbursement for food, equipment, and others. Access request is for tools, permissions, and account access. So, we didn’t remove any categories as we have before, we just put in… we just put everything in one menu, which is in Linear S. So, how do we use it?

510 01:01:08.080 ⇒ 01:01:17.249 Rico Rejoso: just type in slash ask, choose operations, then pick the option that best matches your situation. So in the next slide, Kayla, if you can show it.

511 01:01:20.940 ⇒ 01:01:30.960 Rico Rejoso: Yeah, it just shows how you use Linear Ass. This is for those who haven’t tried yet, and for new team members, you just have to type in As in Slack.

512 01:01:31.430 ⇒ 01:01:38.999 Rico Rejoso: best would be on our Brainforge team channel, and you can access it. Same with the platform option. Choose Operations, and just pick out the

513 01:01:39.810 ⇒ 01:01:45.419 Rico Rejoso: The best option that, suits the situation that you’re in, or that you’re, using it for, okay?

514 01:01:45.650 ⇒ 01:01:49.629 Rico Rejoso: So, why we’re doing this, or, what…

515 01:01:49.740 ⇒ 01:02:09.189 Rico Rejoso: what you see here is that what used to come in through, separate shortcuts before, using different, Slack slash commands, are now in one linear path, or linear as path, so it’s the same with, requesting PTO, or out of office, or sick form, so everything is unified, so it’s tracked in the same way.

516 01:02:09.330 ⇒ 01:02:28.689 Rico Rejoso: Okay, so we’re trying to make getting help from operation as smooth as possible. If something about this flow feels more inconvenient and clear, or if you have a suggestion, let us know, we’re happy to consider anything that would help the team. And again, thank you for your cooperation in helping us keep… or help us keeping everything visible and easy to pretty much.

517 01:02:28.780 ⇒ 01:02:31.280 Rico Rejoso: That’s it from operations team.

518 01:02:35.240 ⇒ 01:03:00.219 Kaela Gallagher: Okay, awesome. Just a couple people updates real quickly. I know I sent a message to the team yesterday about our open roles and referrals, but wanted to just remind everybody we have a significant referral bonus to offer, so it could be anywhere from, like, $1,500to$3,300. So I wanted to call this out. If you know of any AI engineers, data, analytics.

519 01:03:00.220 ⇒ 01:03:09.170 Kaela Gallagher: engineers, or partnership manager, I’d love to connect with them, and, my calendar is in the Slack thread as well.

520 01:03:10.940 ⇒ 01:03:13.469 Kaela Gallagher: Okay, moving on…

521 01:03:14.590 ⇒ 01:03:29.810 Kaela Gallagher: We have had a few new team members join in the past couple of weeks, so I wanted to quickly intro them, and sorry to put you all on the spot, but if you could introduce yourselves, tell us your location, and one fun or interesting fact about yourself.

522 01:03:29.960 ⇒ 01:03:31.000 Kaela Gallagher: Would love.

523 01:03:31.000 ⇒ 01:03:35.210 Garrett Gibson: Kayla, I actually have to drop here in a second, to pick up my daughter. Do you mind if I go first?

524 01:03:35.210 ⇒ 01:03:36.339 Kaela Gallagher: Yeah, go first.

525 01:03:36.340 ⇒ 01:03:36.760 Garrett Gibson: Sure.

526 01:03:36.870 ⇒ 01:03:44.219 Garrett Gibson: Hey everyone, I’m Garrett Gibson, yeah, joining as the new, Senior Strategy Lead, on the team.

527 01:03:44.590 ⇒ 01:03:52.559 Garrett Gibson: Just coming from, Disney, so I was working as a contractor there, on a data lake initiative.

528 01:03:52.960 ⇒ 01:03:56.919 Garrett Gibson: For their ad sales organization.

529 01:03:57.120 ⇒ 01:04:02.589 Garrett Gibson: Specifically. And yeah, really excited to, be a part of the team.

530 01:04:03.050 ⇒ 01:04:03.810 Garrett Gibson: Yep.

531 01:04:03.810 ⇒ 01:04:05.709 Kaela Gallagher: Where are you located, and what…

532 01:04:05.710 ⇒ 01:04:08.880 Garrett Gibson: Oh, yeah, I’m in the LA area, in Redondo Beach.

533 01:04:10.040 ⇒ 01:04:10.700 Garrett Gibson: Yep.

534 01:04:11.550 ⇒ 01:04:13.260 Kaela Gallagher: Okay. Boom. Awesome.

535 01:04:15.100 ⇒ 01:04:17.909 Kaela Gallagher: Miranda Jarrell Advait?

536 01:04:21.630 ⇒ 01:04:22.510 Ruixi Wen: Oh, I can start.

537 01:04:22.510 ⇒ 01:04:23.740 Advait Nandakumar Menon: I can go next.

538 01:04:24.570 ⇒ 01:04:25.769 Advait Nandakumar Menon: Oh, okay, you can go.

539 01:04:25.980 ⇒ 01:04:39.300 Ruixi Wen: Okay. Hi, I’m Miranda, I’m drawing as an AI product manager, kicking off, like, our crew project, with Sutam and Sam right now. Hopefully can share more details in the following weeks.

540 01:04:39.300 ⇒ 01:04:51.949 Ruixi Wen: I’m currently based in San Francisco, and a fun fact about me. I have a sister who’s, like, younger than me by 14 years, so she’s, like, currently 9, but we’re really close, yeah.

541 01:04:53.080 ⇒ 01:04:55.510 Kaela Gallagher: Alright, awesome, welcome to the team.

542 01:04:56.320 ⇒ 01:04:57.640 Ruixi Wen: Thank you, yeah.

543 01:04:59.240 ⇒ 01:05:09.369 Ruixi Wen: Yeah, I have, I have 3 degrees. I mean, I mean, so does, Robert, Amber, and Caleb.

544 01:05:11.320 ⇒ 01:05:12.110 Kaela Gallagher: Cool.

545 01:05:12.770 ⇒ 01:05:16.119 Kaela Gallagher: Alright, Envy, you want to go next?

546 01:05:17.080 ⇒ 01:05:31.119 Advait Nandakumar Menon: Yeah, sure. Sorry, my video is off. I’m having network issues, and I’m trying to save bandwidth on the video. Well, hey everyone, I’m Advait, and I’m a data analyst and engineer with a background in consulting.

547 01:05:31.120 ⇒ 01:05:40.460 Advait Nandakumar Menon: So, I’m based in Cincinnati, Ohio right now. Originally, I’m from India, and I came to the States in 2023 to pursue my master’s.

548 01:05:40.460 ⇒ 01:05:51.489 Advait Nandakumar Menon: So, I’m really excited to be here and be a part of the team. It’s been close to 2 weeks now. So, yeah, it’s been fun working here, and I hope I can contribute even more in the upcoming weeks.

549 01:05:53.640 ⇒ 01:05:55.490 Kaela Gallagher: Awesome. Welcome!

550 01:05:56.290 ⇒ 01:05:58.610 Kaela Gallagher: Jarrell, do you want to go next?

551 01:05:59.010 ⇒ 01:05:59.780 Jorrel Sto. Tomas: Whoa.

552 01:06:00.020 ⇒ 01:06:09.010 Jorrel Sto. Tomas: Hi, everyone. My name is Jarrell. Most… I’m based here in LA, so some of you have already seen me. But

553 01:06:09.090 ⇒ 01:06:17.609 Jorrel Sto. Tomas: Yeah, my background, did data, I’m an entrepreneur, I have a wide network of LA folks I know.

554 01:06:17.650 ⇒ 01:06:26.589 Jorrel Sto. Tomas: I’ve known Robert for quite… quite a long time at this point. I feel like we’re getting old. But yeah, I’m… I’m sharing… I’m…

555 01:06:26.590 ⇒ 01:06:41.529 Jorrel Sto. Tomas: I’m doing GTM and sales, mostly really trying to, trying to focus on getting more deals closed. That’s, yeah, I’ve only been here for about 2 weeks, but, yeah, I’m really excited, like, working with all of you. They…

556 01:06:41.560 ⇒ 01:06:56.809 Jorrel Sto. Tomas: Brainforge has really something special. It… I’ve never seen this much agentic use before, even in the tech companies that I’m exposed to right now. So I’m just really excited to evangelize what everyone’s doing here. Internally, externally.

557 01:06:56.940 ⇒ 01:07:16.609 Jorrel Sto. Tomas: Yeah, so I’m trying to pump pedal to the metal, and Robert knows that. So, but yeah, so yeah, if you guys have any questions, I’ve been in the AI space for a long time, so that’s why I demoed some stuff with Greg. I use AI in, like, my entire life stack at this point.

558 01:07:16.660 ⇒ 01:07:28.889 Jorrel Sto. Tomas: So, if you have any questions, or want to see some stuff that I… or how I use it, same with Greg, like, feel free to reach out, I’m happy to demo… demo and evangelize.

559 01:07:28.890 ⇒ 01:07:37.650 Jorrel Sto. Tomas: Right now, I’m obsessed with the new 1-bit models, so I’m working on some stuff with the, the inference models on your phones, so…

560 01:07:37.660 ⇒ 01:07:42.909 Jorrel Sto. Tomas: Oh yeah, my key fact, yeah, I’m like, I know, like, everyone on Sautel, so if you’re ever on my side of LA,

561 01:07:42.930 ⇒ 01:07:50.859 Jorrel Sto. Tomas: Just let me know. I know most of the business owners, I eat free at most places here, so, yeah, anyways, that’s, that’s my fun fact.

562 01:07:51.010 ⇒ 01:07:52.120 Jorrel Sto. Tomas: Yeah. Thanks, Ken.

563 01:07:52.120 ⇒ 01:07:53.050 Kaela Gallagher: Thank you for that.

564 01:07:53.370 ⇒ 01:07:55.149 Jorrel Sto. Tomas: I was struggling to think… That was Robert.

565 01:07:55.170 ⇒ 01:08:01.869 Kaela Gallagher: Alright, cool. Well, welcome, we’re excited to have you all on the team.

566 01:08:02.040 ⇒ 01:08:08.540 Kaela Gallagher: Okay, cool. Well, drum roll please, we’re about to announce the team member of the month for April.

567 01:08:08.790 ⇒ 01:08:10.340 Kaela Gallagher: It is…

568 01:08:11.390 ⇒ 01:08:20.110 Kaela Gallagher: B! Congratulations! I snuck this slide in at the end, so I hope you didn’t see it until now, but

569 01:08:20.490 ⇒ 01:08:40.859 Kaela Gallagher: Congrats to be Team Member of the Month for April. Some things that your team members, your peers had to say about you is that you’re dependable when the team needs you, you’re super proactive, you always get really creative about problem solving and have a strong sense of ownership over your areas, and just a really positive presence to every kind

570 01:08:40.859 ⇒ 01:08:45.720 Kaela Gallagher: virtual room that you walked in… walk into. So, Congratulations!

571 01:08:46.170 ⇒ 01:08:48.180 Brylle Girang: Thank you! Thank you, guys.

572 01:08:49.830 ⇒ 01:08:50.910 Kaela Gallagher: Awesome.

573 01:08:52.899 ⇒ 01:08:53.790 Kaela Gallagher: Alright.

574 01:08:54.460 ⇒ 01:08:55.479 Kaela Gallagher: Moving on.

575 01:08:55.770 ⇒ 01:08:56.649 Kaela Gallagher: Robert?

576 01:08:56.950 ⇒ 01:09:12.220 Robert Tseng: I actually have the drop, so I don’t think I can give them my update, but actually, okay, I’ll just say it. All right, all right, okay, I’ll try to keep it in, like, 15 seconds. So, I mean, Q1 overall, I think we didn’t hit our aggressive target, but in Q2, we’re focusing more.

577 01:09:12.220 ⇒ 01:09:21.279 Robert Tseng: There’s some interesting, like, lead activity here. I would say there are some open opportunities for the team to kind of contribute to, so Refer Partnerships Manager.

578 01:09:21.279 ⇒ 01:09:34.250 Robert Tseng: If you have anybody that you’re… and you want to click into that job description, you can ask Kayla. And then we also want to enable people to kind of create content on LinkedIn. I know some of you have already been doing that, but that’s actually been really good for visibility.

579 01:09:34.310 ⇒ 01:09:40.549 Robert Tseng: And then we just need to get more certifications done so we can keep things moving, moving along with our partners.

580 01:09:40.920 ⇒ 01:09:43.640 Robert Tseng: Alright, that’s all for me. Thank you, everyone.

581 01:09:43.850 ⇒ 01:09:45.010 Kaela Gallagher: Awesome, thanks.

582 01:09:45.939 ⇒ 01:09:48.569 Kaela Gallagher: Alright, Utam, any final words from you?

583 01:09:48.819 ⇒ 01:09:57.509 Uttam Kumaran: Yeah, I just think that this is… this has been great. I’m so pumped to see everybody making skills and using AI, like, I… I think…

584 01:09:57.539 ⇒ 01:10:09.939 Uttam Kumaran: one thing you’re also going to see this quarter is things should get really neater on the delivery side, so it should be really clear where you’re allocated, what clients you’re working on, what expectations are. And so, yeah, I’m super excited.

585 01:10:10.029 ⇒ 01:10:25.109 Uttam Kumaran: going to this quarter. Probably in two weeks from now, I’ll present a little bit of what we’re gonna end up presenting at VixelCon, which is a conference that we’re attending later this month. So probably more to come there, but yeah, this was an awesome meeting, really, really motivating.

586 01:10:26.710 ⇒ 01:10:28.360 Kaela Gallagher: Alright, awesome!

587 01:10:29.260 ⇒ 01:10:38.149 Kaela Gallagher: Thanks, everybody! If you have any kudos or shoutouts to give, we’ll do that in a Slack thread, and we’ll let everyone go since we’re over time.

588 01:10:38.830 ⇒ 01:10:39.710 Kaela Gallagher: Alright.

589 01:10:40.270 ⇒ 01:10:41.479 Kaela Gallagher: Thank you, guys.

590 01:10:41.960 ⇒ 01:10:42.430 Uttam Kumaran: Thank you.

591 01:10:42.430 ⇒ 01:10:43.250 Kaela Gallagher: Have a great weekend.

592 01:10:43.250 ⇒ 01:10:44.150 Brylle Girang: everyone.

593 01:10:45.360 ⇒ 01:10:45.950 Kaela Gallagher: Okay.

594 01:10:46.160 ⇒ 01:10:46.810 Uttam Kumaran: I…