Meeting Title: Daily AI Team Sync Date: 2025-03-12 Meeting participants: Janna Wong, Amber Lin, Miguel De Veyra, Casie Aviles
WEBVTT
1 00:03:00.280 ⇒ 00:03:04.329 Amber Lin: Good morning, Miguel. Oh, good afternoon for you!
2 00:03:04.630 ⇒ 00:03:06.285 Miguel de Veyra: No, it’s good evening.
3 00:03:06.900 ⇒ 00:03:08.349 Amber Lin: And good evening.
4 00:03:09.210 ⇒ 00:03:10.380 Miguel de Veyra: Yeah, wow.
5 00:03:10.550 ⇒ 00:03:11.423 Amber Lin: The bed.
6 00:03:13.010 ⇒ 00:03:16.240 Miguel de Veyra: Oh, oh, shit! Yeah. 6. Am your time. No. 7.
7 00:03:16.570 ⇒ 00:03:18.060 Amber Lin: It’s 6 am. I don’t know.
8 00:03:20.280 ⇒ 00:03:23.399 Miguel de Veyra: Is this usually? What time you wake up or not? Really.
9 00:03:24.021 ⇒ 00:03:27.469 Amber Lin: Since I’ve been working at this job. Yes.
10 00:03:29.060 ⇒ 00:03:35.019 Amber Lin: which has been 4 days in these 4 days. I’ve started to wake up at 5, 30.
11 00:03:35.830 ⇒ 00:03:36.840 Miguel de Veyra: Yeah.
12 00:03:37.310 ⇒ 00:03:43.220 Amber Lin: Yeah, but I like it. It helps me actually wake up earlier. So it’s nice.
13 00:03:43.640 ⇒ 00:03:47.079 Miguel de Veyra: Yeah, it’s different. When you actually wake up early, you get a lot more done.
14 00:03:47.390 ⇒ 00:03:48.409 Amber Lin: I know.
15 00:03:49.270 ⇒ 00:03:56.880 Amber Lin: But you guys wake up a little bit later now, right? Cause it’s so, I believe it’s quite hard to
16 00:03:57.510 ⇒ 00:03:59.859 Amber Lin: stay in sync with everyone.
17 00:04:00.630 ⇒ 00:04:07.290 Miguel de Veyra: Yeah, usually like bef, especially before I stay up until like 5 6 Am. My time. But.
18 00:04:07.290 ⇒ 00:04:08.040 Amber Lin: Oh! Oh!
19 00:04:08.040 ⇒ 00:04:13.429 Miguel de Veyra: Yeah, I get like, I get sick always. So I was like, Yeah, no, I’ll just work on the day.
20 00:04:15.410 ⇒ 00:04:16.250 Amber Lin: Yeah. And.
21 00:04:16.250 ⇒ 00:04:19.099 Miguel de Veyra: Let me send this meeting to Casey.
22 00:04:19.579 ⇒ 00:04:20.949 Amber Lin: Okay. Great.
23 00:04:32.230 ⇒ 00:04:39.419 Miguel de Veyra: Yeah, I don’t think Patrick will be joining Janna. I’m not sure if you guys have spoken.
24 00:04:40.105 ⇒ 00:04:47.400 Amber Lin: I talked to her. I just text I was just texting with her, and here’s what she said.
25 00:04:51.740 ⇒ 00:05:12.099 Amber Lin: she just got yesterday. She was telling me. The axiom is working to scrape the base panels. Resolutions, partners. Oh, hello, Jenna! I was just Updating Miguel on your progress. But since you’re here I will let you do. I will let you do the talking.
26 00:05:13.115 ⇒ 00:05:27.780 Janna Wong: Oh, sounds great. Oh, I was able to make it work with axiom. And then, yeah, yesterday I had some issues with Zapier. Didn’t trigger. So what I did was I reconnected it with my account instead?
27 00:05:27.920 ⇒ 00:05:38.120 Janna Wong: And then, yeah, for some reason, it added in clay, so yeah, we’re able to like get the Linkedin profiles based on their name and their company.
28 00:05:38.120 ⇒ 00:05:38.920 Amber Lin: Wow!
29 00:05:38.920 ⇒ 00:05:41.970 Janna Wong: Yeah, I can share my screen if that helps.
30 00:05:41.970 ⇒ 00:05:43.610 Amber Lin: Yeah. Totally.
31 00:05:44.720 ⇒ 00:05:47.340 Janna Wong: So wait, let me click on.
32 00:05:47.580 ⇒ 00:05:49.010 Janna Wong: Wait. Can you see my screen.
33 00:05:49.460 ⇒ 00:05:50.989 Amber Lin: Yes, I can see your screen.
34 00:05:51.590 ⇒ 00:05:52.470 Janna Wong: Do you see clay?
35 00:05:52.470 ⇒ 00:05:53.280 Amber Lin: Let’s see.
36 00:05:53.280 ⇒ 00:05:54.050 Janna Wong: Yes.
37 00:05:54.050 ⇒ 00:05:54.450 Amber Lin: Oh, you know.
38 00:05:54.450 ⇒ 00:05:54.920 Amber Lin: Yes, yes.
39 00:05:55.190 ⇒ 00:05:56.830 Janna Wong: So, yeah.
40 00:05:57.430 ⇒ 00:06:09.840 Janna Wong: this is axiom. And then, basically, if you play this, this will go through this site. Since this site is like, doesn’t have any like logins and stuff. You just need to like.
41 00:06:10.243 ⇒ 00:06:18.750 Janna Wong: get all of the list and then iterate through each of one. And then when we run. It basically goes through this list and then put them in a.
42 00:06:19.570 ⇒ 00:06:20.390 Amber Lin: Yeah.
43 00:06:20.959 ⇒ 00:06:24.910 Janna Wong: Basically, it’s just a few. I just added like 20, because.
44 00:06:24.910 ⇒ 00:06:25.700 Amber Lin: Oh, it’s.
45 00:06:25.700 ⇒ 00:06:27.860 Janna Wong: A free version. So
46 00:06:28.540 ⇒ 00:06:44.650 Janna Wong: yeah, so I can basically increase the size here to 200 or basically, what limit you want and then add them here. Then it goes through this one and then add it here so that it automatically gets the linkedin profiles.
47 00:06:44.650 ⇒ 00:06:47.770 Amber Lin: Is that a scraping tool.
48 00:06:48.515 ⇒ 00:06:50.390 Janna Wong: Not really. It’s actually a.
49 00:06:50.390 ⇒ 00:06:50.840 Miguel de Veyra: All sorts.
50 00:06:50.840 ⇒ 00:06:52.520 Janna Wong: Automation Tool.
51 00:06:52.520 ⇒ 00:06:53.220 Miguel de Veyra: Yeah.
52 00:06:53.380 ⇒ 00:07:01.129 Amber Lin: Oh, and you write this free scraping tool scraping code to put that in the browser automation.
53 00:07:01.130 ⇒ 00:07:03.820 Janna Wong: Yeah, it automatically gets this.
54 00:07:04.580 ⇒ 00:07:06.919 Janna Wong: Yeah, I hope that helps, though.
55 00:07:07.080 ⇒ 00:07:08.210 Janna Wong: Yeah, it will.
56 00:07:08.210 ⇒ 00:07:09.670 Amber Lin: Oh, yeah, we.
57 00:07:09.670 ⇒ 00:07:11.669 Janna Wong: For the other side. Yeah. Sorry.
58 00:07:12.070 ⇒ 00:07:13.239 Amber Lin: Go ahead. Sorry.
59 00:07:13.440 ⇒ 00:07:24.649 Janna Wong: Sorry. Sorry for the other site. This one. I’m still having issues with this, because I’m not able to like select this text, because when I click on it, it goes through here. So yeah. Sorry. Sorry I cut you off. Am
60 00:07:25.010 ⇒ 00:07:25.530 Janna Wong: that.
61 00:07:25.530 ⇒ 00:07:27.059 Miguel de Veyra: But I inspect.
62 00:07:33.560 ⇒ 00:07:38.069 Janna Wong: Yeah. I also tried with an 8. And with this it still doesn’t get the.
63 00:07:38.070 ⇒ 00:07:44.769 Miguel de Veyra: Yeah, yeah, the problem there is this class is pro, most probably react to be honest.
64 00:07:44.980 ⇒ 00:07:45.920 Janna Wong: Yeah.
65 00:07:46.160 ⇒ 00:07:48.409 Miguel de Veyra: So you have to use rejects here.
66 00:07:50.480 ⇒ 00:07:51.110 Janna Wong: Check.
67 00:07:52.040 ⇒ 00:07:52.800 Janna Wong: I tried.
68 00:07:52.800 ⇒ 00:07:55.160 Janna Wong: So you’re both and so on.
69 00:07:56.200 ⇒ 00:07:58.908 Janna Wong: Still doesn’t let me get.
70 00:08:00.970 ⇒ 00:08:02.450 Janna Wong: I’ll try with rejects.
71 00:08:02.570 ⇒ 00:08:03.210 Janna Wong: Okay, okay.
72 00:08:03.210 ⇒ 00:08:07.879 Miguel de Veyra: Yeah. See? See? Sorry, Amber. I’m gonna speak in Filipino. But see.
73 00:08:07.880 ⇒ 00:08:08.410 Amber Lin: Oh!
74 00:08:09.840 ⇒ 00:08:10.980 Janna Wong: All good.
75 00:08:11.782 ⇒ 00:08:14.190 Miguel de Veyra: Think, a refix.
76 00:08:15.160 ⇒ 00:08:15.800 Janna Wong: All right.
77 00:08:16.260 ⇒ 00:08:17.930 Miguel de Veyra: Indate.
78 00:08:17.930 ⇒ 00:08:18.680 Janna Wong: Thereof.
79 00:08:19.180 ⇒ 00:08:23.089 Miguel de Veyra: See, Ralph, yeah, Ralph would be the good person to ask, cause he he has a lot of experience.
80 00:08:23.090 ⇒ 00:08:24.010 Miguel de Veyra: Oh, that’s in here.
81 00:08:24.740 ⇒ 00:08:29.239 Miguel de Veyra: Okay, yeah, he’s very good at scraping app sites. I I go to him for help
82 00:08:29.500 ⇒ 00:08:31.420 Miguel de Veyra: if I need to scrape. But yeah, sorry.
83 00:08:31.820 ⇒ 00:08:39.400 Miguel de Veyra: But basically, from what I see what you can do. Here is this class, I would assume class infinite scroll isn’t dynamic.
84 00:08:39.820 ⇒ 00:08:41.760 Janna Wong: Yeah, it’s dynamic.
85 00:08:41.880 ⇒ 00:08:42.650 Janna Wong: The below.
86 00:08:42.650 ⇒ 00:08:43.650 Miguel de Veyra: One time.
87 00:08:44.260 ⇒ 00:08:48.509 Miguel de Veyra: And then, yeah, so what you could do is rejects. Div, div.
88 00:08:48.630 ⇒ 00:08:51.850 Miguel de Veyra: so class div, and then just get the.
89 00:08:52.420 ⇒ 00:08:57.230 Miguel de Veyra: Honestly, I copy, paste it, yeah. And then just put it to Gpt, hey, what’s the best rate?
90 00:08:57.930 ⇒ 00:08:59.250 Miguel de Veyra: That’s what I would do.
91 00:08:59.745 ⇒ 00:09:00.240 Amber Lin: Okay.
92 00:09:00.240 ⇒ 00:09:02.310 Janna Wong: Okay, okay, I’ll try. Will do.
93 00:09:02.800 ⇒ 00:09:03.959 Janna Wong: So. Yeah, so far.
94 00:09:03.960 ⇒ 00:09:07.670 Miguel de Veyra: Yeah, technically, you can get the contents if you get the
95 00:09:08.020 ⇒ 00:09:11.179 Miguel de Veyra: just a unique identifier and then just loop on.
96 00:09:11.840 ⇒ 00:09:13.209 Janna Wong: Loop on every yeah.
97 00:09:13.210 ⇒ 00:09:13.820 Miguel de Veyra: Yeah.
98 00:09:13.820 ⇒ 00:09:14.680 Janna Wong: 0.
99 00:09:14.920 ⇒ 00:09:16.610 Miguel de Veyra: As long as you get the identifier.
100 00:09:17.597 ⇒ 00:09:19.270 Janna Wong: Yeah, let’s try.
101 00:09:20.460 ⇒ 00:09:29.330 Amber Lin: So I can tell Robert that we have good progress on this, or have you already told him? That will be great as well? If not, I call him.
102 00:09:29.330 ⇒ 00:09:30.989 Janna Wong: Yeah, I haven’t told him yet.
103 00:09:30.990 ⇒ 00:09:39.153 Amber Lin: Okay, I will tell Robert this is great news, and it also helps him know that. Oh, Jenna’s really capable. And she’s working on this
104 00:09:39.590 ⇒ 00:09:46.999 Amber Lin: on our progress. Yeah, I tried scraping something for my other gig this weekend, and it is.
105 00:09:48.310 ⇒ 00:09:50.260 Miguel de Veyra: Yes, oh, Paul. Jack!
106 00:09:50.260 ⇒ 00:09:51.380 Janna Wong: Zoom, that’s good.
107 00:09:51.790 ⇒ 00:09:52.690 Amber Lin: Okay, I will.
108 00:09:52.690 ⇒ 00:09:59.009 Miguel de Veyra: Or if you have, if you have money to spare, try Browser Base. They’re very good, very expensive.
109 00:09:59.010 ⇒ 00:10:01.589 Amber Lin: 5 browser.
110 00:10:01.870 ⇒ 00:10:03.050 Miguel de Veyra: Browser base.
111 00:10:03.960 ⇒ 00:10:05.120 Amber Lin: Oh!
112 00:10:06.000 ⇒ 00:10:10.980 Miguel de Veyra: We use that here, but cause we had the client before Vitaco, but.
113 00:10:11.140 ⇒ 00:10:11.640 Amber Lin: It’s just.
114 00:10:11.640 ⇒ 00:10:15.600 Miguel de Veyra: Super expensive like monthly, would be around 20,000, just for call.
115 00:10:17.680 ⇒ 00:10:18.479 Amber Lin: And if that’s.
116 00:10:18.480 ⇒ 00:10:22.730 Miguel de Veyra: One product, too. So it’s like, yeah, it’s not really feasible.
117 00:10:23.040 ⇒ 00:10:25.568 Amber Lin: Goodness. Okay, I will look into that.
118 00:10:26.690 ⇒ 00:10:30.400 Amber Lin: And I know there’s also like
119 00:10:30.640 ⇒ 00:10:37.519 Amber Lin: AI scraping tools. I don’t know. I will look in. I’ll look more into that. Okay.
120 00:10:37.913 ⇒ 00:10:46.690 Amber Lin: Miguel, what’s the progress on your end? I’m trying. Yesterday the meeting took so long. So I’m trying to make it quick. So you guys don’t have to stay there forever.
121 00:10:46.840 ⇒ 00:10:48.325 Miguel de Veyra: Hi, yep, yep,
122 00:10:48.970 ⇒ 00:10:54.779 Miguel de Veyra: So for my end. I’ve been primarily working on the update agent like that’s where I
123 00:10:55.010 ⇒ 00:10:58.230 Miguel de Veyra: I’ve been working on. It’s pretty much working.
124 00:10:58.460 ⇒ 00:11:00.710 Miguel de Veyra: Oh, wait! Let me just share my screen after.
125 00:11:00.710 ⇒ 00:11:02.310 Amber Lin: That that’s great.
126 00:11:03.100 ⇒ 00:11:07.849 Miguel de Veyra: Let me. And then, yeah, let me just. But I still need to clean it. Of course.
127 00:11:08.970 ⇒ 00:11:15.500 Miguel de Veyra: But yeah, so basically, there’s an agent that they’ll talk to
128 00:11:15.960 ⇒ 00:11:19.859 Miguel de Veyra: this agent. Sorry the naming is kinda messed up.
129 00:11:20.090 ⇒ 00:11:27.960 Miguel de Veyra: But finding Nemo is basically just, you know, get the record that they want to check to update. First.st
130 00:11:29.280 ⇒ 00:11:36.239 Miguel de Veyra: So the way that happens is we just pass, you know, the titles and stuff to this one.
131 00:11:36.800 ⇒ 00:11:40.940 Miguel de Veyra: And actually, I can just it’ll take a bit of time, but let me just demo it.
132 00:11:40.940 ⇒ 00:11:43.259 Amber Lin: Totally. Yeah. Let’s do that.
133 00:11:44.150 ⇒ 00:11:54.650 Miguel de Veyra: So here’s the database we have, and here’s well, it’s not really formatted right? It’s kind of hard to format it if it’s from AI. But let’s say, for example.
134 00:11:55.690 ⇒ 00:12:02.609 Miguel de Veyra: that’s fine. Boom. So this one term cell term cell, this is term cell here.
135 00:12:02.770 ⇒ 00:12:04.912 Miguel de Veyra: Okay, let’s just ask the bot
136 00:12:06.470 ⇒ 00:12:07.400 Amber Lin: Hey!
137 00:12:07.500 ⇒ 00:12:13.220 Miguel de Veyra: I need to update the thermal cell dots.
138 00:12:14.410 ⇒ 00:12:18.020 Miguel de Veyra: so if it doesn’t mess with me, it should find it.
139 00:12:22.660 ⇒ 00:12:25.189 Miguel de Veyra: It’s there, but it’s very slow. Of course.
140 00:12:25.890 ⇒ 00:12:26.710 Amber Lin: It’s okay.
141 00:12:28.610 ⇒ 00:12:30.360 Miguel de Veyra: Okay, there you go. There’s an answer.
142 00:12:32.200 ⇒ 00:12:33.170 Amber Lin: Oh!
143 00:12:33.880 ⇒ 00:12:38.170 Miguel de Veyra: And then wait. Why is it not? And there you go.
144 00:12:38.490 ⇒ 00:12:41.060 Miguel de Veyra: So now it got, you know. Here.
145 00:12:41.390 ⇒ 00:12:43.039 Amber Lin: Oh, my goodness!
146 00:12:43.040 ⇒ 00:12:44.750 Miguel de Veyra: And then what we can do is.
147 00:12:44.750 ⇒ 00:12:46.083 Amber Lin: So happy!
148 00:12:47.454 ⇒ 00:13:00.529 Miguel de Veyra: For service. We for we added a new service manager and photos. Look regarding.
149 00:13:03.680 ⇒ 00:13:07.910 Miguel de Veyra: let’s update the docs. Let’s update the docs piece.
150 00:13:08.420 ⇒ 00:13:11.240 Miguel de Veyra: Probably a bit more complicated than this, but for testing.
151 00:13:12.454 ⇒ 00:13:13.300 Amber Lin: Okay.
152 00:13:14.060 ⇒ 00:13:20.299 Miguel de Veyra: So the bot is automatically, gonna you know, stop biting each other.
153 00:13:23.190 ⇒ 00:13:25.019 Miguel de Veyra: Sorry my cats are fighting again.
154 00:13:25.808 ⇒ 00:13:26.881 Amber Lin: That’s so. Cute.
155 00:13:27.240 ⇒ 00:13:28.669 Miguel de Veyra: The permafighting.
156 00:13:28.670 ⇒ 00:13:29.740 Amber Lin: Get a cat!
157 00:13:30.930 ⇒ 00:13:34.480 Miguel de Veyra: Yeah, it’s taking a bit of time. But there you go. It should be here.
158 00:13:35.440 ⇒ 00:13:46.419 Amber Lin: I mean, that’s okay. We’re not gonna show the real time to the client. I’m gonna show them the screenshots. And they’re like, Oh, my God! Yes, so anything is cool.
159 00:13:47.330 ⇒ 00:13:53.269 Miguel de Veyra: Yeah. And then, yeah, this really takes a lot of time. Because, you know, it’s not like getting data. It’s actually processing the data.
160 00:13:54.710 ⇒ 00:14:00.080 Miguel de Veyra: So yeah, as you can see. Right? Service managers. Only horoslope brickal.
161 00:14:00.080 ⇒ 00:14:00.980 Amber Lin: Yeah.
162 00:14:00.980 ⇒ 00:14:07.740 Miguel de Veyra: And then here is not yet here. And then could you please confirm, yes, everything looks great.
163 00:14:08.170 ⇒ 00:14:10.250 Miguel de Veyra: everything looks good.
164 00:14:10.940 ⇒ 00:14:14.609 Miguel de Veyra: So now it’s gonna update. It’s gonna use this tool order 66.
165 00:14:15.810 ⇒ 00:14:20.379 Amber Lin: I see. Can I see the chat interface again, so I can screenshot.
166 00:14:20.380 ⇒ 00:14:21.900 Miguel de Veyra: Now wait! Wait! It’s still loading.
167 00:14:22.280 ⇒ 00:14:23.180 Amber Lin: Okay.
168 00:14:23.640 ⇒ 00:14:31.290 Miguel de Veyra: But yeah, it’s loading, loading. So the process is, it’s gonna delete actually everything here. And then just what’s.
169 00:14:31.290 ⇒ 00:14:35.010 Amber Lin: So yeah, that’s why it takes a long time. It takes a very long time.
170 00:14:35.040 ⇒ 00:14:36.770 Amber Lin: See? I see.
171 00:14:38.220 ⇒ 00:14:44.949 Miguel de Veyra: So the way that works is basically wait. This is the update. Yeah. So the way that works is we get the request
172 00:14:45.210 ⇒ 00:14:52.279 Miguel de Veyra: and then adding static to the context, we get all the records. Wait, let me check if it’s there.
173 00:14:53.170 ⇒ 00:14:55.050 Miguel de Veyra: and there you go. It’s still working.
174 00:14:55.260 ⇒ 00:14:56.060 Amber Lin: Great.
175 00:14:57.300 ⇒ 00:15:00.249 Miguel de Veyra: I mean, it’s put here, we get the specific record.
176 00:15:00.430 ⇒ 00:15:05.040 Miguel de Veyra: We cleaned the record basically on what the update was. And then we
177 00:15:05.250 ⇒ 00:15:08.460 Miguel de Veyra: basically updated, we get all the rows. Turn that into.
178 00:15:10.670 ⇒ 00:15:12.409 Miguel de Veyra: Why is it done? Not yet.
179 00:15:12.740 ⇒ 00:15:15.360 Miguel de Veyra: That’s taking a bit too long to be liking.
180 00:15:15.360 ⇒ 00:15:23.299 Amber Lin: I mean when it’s when it’s all code. So if the central Doc is centrally all lines of code.
181 00:15:24.500 ⇒ 00:15:33.399 Amber Lin: could we have control essentially like control F and replace on specific lines of code? Or do we have to delete everything.
182 00:15:34.942 ⇒ 00:15:39.400 Miguel de Veyra: No, no, it’s automatically gonna be done with deleting of everything and stuff.
183 00:15:39.730 ⇒ 00:15:40.450 Amber Lin: Oh!
184 00:15:40.450 ⇒ 00:15:48.799 Miguel de Veyra: There’s probably a bug as I changed something, but I don’t know it’s still running it. Oh, there you go! Succeeded so long. Time
185 00:15:50.900 ⇒ 00:15:55.950 Miguel de Veyra: took a minute. Oh, I should honestly, I should probably just put the response here.
186 00:15:57.550 ⇒ 00:16:01.850 Miguel de Veyra: But then, if we go here, there you go. CC auto, sloptical.
187 00:16:02.240 ⇒ 00:16:17.240 Amber Lin: Oh, yay, okay, I’m gonna put that in. Actually, can I see the chat? The last prompt that you gave them? Cause I I wanna, tell them like, Oh, we are updating it.
188 00:16:17.390 ⇒ 00:16:22.710 Amber Lin: We need to confirm, okay, all right.
189 00:16:22.710 ⇒ 00:16:23.779 Miguel de Veyra: I’ll share this talk to you.
190 00:16:23.780 ⇒ 00:16:25.840 Amber Lin: Awesome. Yes, please.
191 00:16:25.840 ⇒ 00:16:26.850 Miguel de Veyra: And then
192 00:16:29.200 ⇒ 00:16:33.950 Miguel de Veyra: and then, Jen, I’m just gonna share this to you to actually, I’m gonna share to them, too.
193 00:16:34.830 ⇒ 00:16:35.360 Janna Wong: Good.
194 00:16:36.350 ⇒ 00:16:39.799 Amber Lin: You want to just send it in our AI Channel chat.
195 00:16:40.010 ⇒ 00:16:40.959 Miguel de Veyra: Oh, yeah, yeah, but.
196 00:16:40.960 ⇒ 00:16:42.110 Amber Lin: Copy, the link.
197 00:16:45.020 ⇒ 00:16:49.850 Miguel de Veyra: What happened to Casey, maybe fell asleep.
198 00:16:49.850 ⇒ 00:17:05.130 Miguel de Veyra: Yeah, probably. But that’s fine. It’s almost done. And then the other thing I was working on is this is technically done. I just started working on this today. But it’s technically working already, because we we mentioned update right? So.
199 00:17:05.130 ⇒ 00:17:05.700 Amber Lin: Yeah.
200 00:17:05.700 ⇒ 00:17:10.470 Miguel de Veyra: Now, this is up adding to the vector, as you can see, you know, it’s pretty much working.
201 00:17:10.470 ⇒ 00:17:11.385 Amber Lin: Yeah.
202 00:17:12.540 ⇒ 00:17:17.395 Miguel de Veyra: I didn’t take the what do you call this? I didn’t take
203 00:17:18.119 ⇒ 00:17:26.660 Miguel de Veyra: the time execution time into consideration, because, unlike, you know, if they’re updating documents. They’re not really. Yeah. They can wait.
204 00:17:27.290 ⇒ 00:17:29.719 Amber Lin: Yeah, so update.
205 00:17:29.720 ⇒ 00:17:37.850 Miguel de Veyra: It’s proof of concept, anyways, that hey? It can be done. But I think you know, we can get the stuff working first, st and then we optimize it.
206 00:17:38.390 ⇒ 00:17:56.829 Amber Lin: Totally that makes total sense. Okay, the updating function of this system but is working. I mean, that’s all we plan for this week or next week we could go into the creating training documents which I don’t think I don’t know if it’ll be that hard, because.
207 00:17:56.830 ⇒ 00:17:58.390 Miguel de Veyra: I think this is. It’s the same one, though.
208 00:17:58.550 ⇒ 00:18:01.559 Amber Lin: Talking to. What do you mean?
209 00:18:02.010 ⇒ 00:18:06.800 Miguel de Veyra: Isn’t the creating documents. This training documents is, I think it’s the same as this one.
210 00:18:07.340 ⇒ 00:18:16.280 Amber Lin: Kind of. Yeah, I think we just. It’s only that we didn’t demo that. I think when they talk about training documents, it’s like you
211 00:18:16.540 ⇒ 00:18:24.250 Amber Lin: ask them to create one from from scratch based on the structure that they had before. Maybe they’ll say.
212 00:18:24.250 ⇒ 00:18:30.889 Miguel de Veyra: Of course, best control with they. I know I know I created something like that for them before.
213 00:18:30.890 ⇒ 00:18:31.870 Amber Lin: Hmm.
214 00:18:32.070 ⇒ 00:18:35.450 Miguel de Veyra: That was the reason we got them. We showed them that training.
215 00:18:37.920 ⇒ 00:18:41.779 Miguel de Veyra: I think. Yeah, it’s this one which utham passed already.
216 00:18:42.160 ⇒ 00:18:45.080 Miguel de Veyra: Yeah. But it was this one. We have that in a 10.
217 00:18:46.100 ⇒ 00:18:46.830 Amber Lin: Oh, great!
218 00:18:46.830 ⇒ 00:18:50.830 Miguel de Veyra: That was the I think that was the 1st thing they saw. But yeah, let’s just clarify.
219 00:18:51.490 ⇒ 00:18:52.250 Miguel de Veyra: They want.
220 00:18:53.490 ⇒ 00:19:01.430 Amber Lin: I see. Okay? I’m gonna I’m gonna write that there clarify
221 00:19:01.630 ⇒ 00:19:06.570 Amber Lin: phone functionality wanted. Okay, that is awesome.
222 00:19:07.476 ⇒ 00:19:08.490 Amber Lin: I think
223 00:19:09.640 ⇒ 00:19:24.980 Amber Lin: since that’s working. I don’t know. Oh, and the Loom video was really helpful. They’re already started. So today, what I’m gonna do is I have a meeting with Shannon and Grace later, and
224 00:19:25.400 ⇒ 00:19:36.960 Amber Lin: I want, I hope their agent is working because the setup is really simple. So I just wanna know what kind of questions you guys want me to ask them. That will be helpful.
225 00:19:39.300 ⇒ 00:19:47.489 Miguel de Veyra: Oh, I guess just a review of their 1st experience with the bot, like, you know.
226 00:19:48.080 ⇒ 00:20:03.600 Miguel de Veyra: Was it accurate? Not going to be accurate? Because I don’t think they know if it’s accurate or not. But I think more on the responses, you know. Did do they like it? How would they like it. How would they want it to be improved? Or is there a specific format that they think would be a bit more helpful.
227 00:20:04.300 ⇒ 00:20:05.270 Amber Lin: Oh!
228 00:20:05.270 ⇒ 00:20:07.839 Miguel de Veyra: But basically, I think they’re just general. The view.
229 00:20:08.020 ⇒ 00:20:15.740 Amber Lin: Okay? Yeah. And then, if they have great feed, good feedback on them, we’ll just take the good feedback and present it in the meeting.
230 00:20:15.740 ⇒ 00:20:17.130 Miguel de Veyra: Yeah, yeah, yeah.
231 00:20:19.130 ⇒ 00:20:25.249 Miguel de Veyra: And then the scraping. I probably won’t be able to work on it. I know Casey might not be able to work on it, either.
232 00:20:26.742 ⇒ 00:20:35.077 Amber Lin: I I know you guys told me that it’s not really scraping and just copy and pasting, and then do chat, gpt.
233 00:20:35.440 ⇒ 00:20:37.509 Miguel de Veyra: Were you able to ask which websites.
234 00:20:37.510 ⇒ 00:20:42.630 Amber Lin: Yes, I have. So I’ve clarified what locations and what services.
235 00:20:42.840 ⇒ 00:20:50.410 Amber Lin: and mainly just for ABC, because the other 2 is just already is essentially 2 services that we already have down.
236 00:20:50.600 ⇒ 00:20:54.740 Miguel de Veyra: Okay. Did you list anywhere where those links are? Sorry.
237 00:20:54.740 ⇒ 00:20:59.978 Amber Lin: Yes, is in the notion that I sent yesterday. I know it’s a little bit in the bottom.
238 00:21:00.660 ⇒ 00:21:01.960 Miguel de Veyra: Okay, wait time.
239 00:21:02.790 ⇒ 00:21:08.770 Miguel de Veyra: Sorry. Let me share screen. Just I think I can work on it a bit today, since the ad is going to be, but this is
240 00:21:09.470 ⇒ 00:21:10.940 Miguel de Veyra: oh, that’s cool.
241 00:21:11.420 ⇒ 00:21:14.829 Amber Lin: Scroll up a little bit. Yeah. Web size to scrape
242 00:21:19.930 ⇒ 00:21:28.319 Amber Lin: ABC. For pest services, I believe, just for pest services. These are the locations. I probably didn’t spell everything correctly.
243 00:21:28.870 ⇒ 00:21:33.509 Amber Lin: And then for the other 2, it’s super
244 00:21:33.930 ⇒ 00:21:39.079 Amber Lin: like each website is just one service. So I don’t think we really need to script anything.
245 00:21:40.350 ⇒ 00:21:43.929 Miguel de Veyra: Okay, sorry. So do we go to their ABC.
246 00:21:43.930 ⇒ 00:21:50.839 Amber Lin: Yeah, if you see is that the right website?
247 00:21:51.560 ⇒ 00:21:54.779 Amber Lin: I feel like this is not the right website. ABC, home.
248 00:21:54.780 ⇒ 00:21:57.849 Miguel de Veyra: Is it going either? Is it? It could be this one.
249 00:21:59.500 ⇒ 00:22:03.889 Amber Lin: Can you? Yeah, yeah, it’s the ABC Home Commercial. It’s this one.
250 00:22:04.240 ⇒ 00:22:07.280 Miguel de Veyra: And then I I’m assuming we go for pass.
251 00:22:08.590 ⇒ 00:22:11.200 Amber Lin: Yes, on the top.
252 00:22:11.730 ⇒ 00:22:13.060 Miguel de Veyra: There’s only 2 tests.
253 00:22:13.820 ⇒ 00:22:23.520 Amber Lin: Yeah, that’s their post. So we will go scroll up to home services. And oh, I guess we pick Austin as an example.
254 00:22:24.180 ⇒ 00:22:29.379 Amber Lin: and we go to past.
255 00:22:29.380 ⇒ 00:22:30.529 Miguel de Veyra: This one right.
256 00:22:30.790 ⇒ 00:22:43.749 Amber Lin: I believe, I think. Oh, here I think what they want us to do is to script everything, because there’s really not a lot here. They don’t go into that much detail.
257 00:22:44.460 ⇒ 00:22:46.270 Amber Lin: So they kind of just
258 00:22:47.440 ⇒ 00:22:54.570 Amber Lin: I believe they want us to scrape everything so we can do the oh, by the ways, because if they have pests, they probably have.
259 00:22:54.730 ⇒ 00:23:01.049 Amber Lin: like lawn problems or tree problems that’s causing the pest. So
260 00:23:01.830 ⇒ 00:23:04.980 Amber Lin: the website is still decently high level.
261 00:23:05.370 ⇒ 00:23:11.809 Amber Lin: If you tell me how to copy and paste, or if there’s a specific format you prefer, I could copy and paste.
262 00:23:11.810 ⇒ 00:23:14.169 Miguel de Veyra: Yeah, let me share.
263 00:23:14.560 ⇒ 00:23:19.020 Miguel de Veyra: Sorry. Let me stop sharing, because my messenger is open on my personal Gpt.
264 00:23:20.090 ⇒ 00:23:20.800 Amber Lin: Okay.
265 00:23:22.320 ⇒ 00:23:22.830 Miguel de Veyra: Yeah.
266 00:23:26.590 ⇒ 00:23:28.840 Miguel de Veyra: okay, let me share again.
267 00:23:31.590 ⇒ 00:23:32.640 Miguel de Veyra: Okay, here.
268 00:23:33.608 ⇒ 00:23:41.509 Miguel de Veyra: You know. So ideally, what we would do is we go here? I would honestly just do this.
269 00:23:43.090 ⇒ 00:23:44.150 Miguel de Veyra: Probably not that.
270 00:23:44.150 ⇒ 00:23:45.130 Amber Lin: Central, a.
271 00:23:48.580 ⇒ 00:23:55.780 Miguel de Veyra: Just do this, and then on the same
272 00:23:59.270 ⇒ 00:24:01.909 Miguel de Veyra: something. I. This is usually how I do it.
273 00:24:02.800 ⇒ 00:24:04.000 Amber Lin: Hmm, okay.
274 00:24:04.000 ⇒ 00:24:05.090 Miguel de Veyra: Then.
275 00:24:05.090 ⇒ 00:24:08.030 Amber Lin: Oh, what crazy!
276 00:24:08.710 ⇒ 00:24:10.060 Amber Lin: Wow!
277 00:24:10.540 ⇒ 00:24:11.909 Miguel de Veyra: Now you have it.
278 00:24:12.980 ⇒ 00:24:13.700 Amber Lin: Okay.
279 00:24:15.870 ⇒ 00:24:23.890 Miguel de Veyra: So this is how purpose. That’s why we avoid doing this. Because it’s very, very tedious, like, yeah, it’s very easy. But then you have to do it for.
280 00:24:23.890 ⇒ 00:24:35.000 Amber Lin: Yeah, let let me do it. Your you guys are skilled. Labor should go to making these models. I will. Put all of that into a Google Doc
281 00:24:35.270 ⇒ 00:24:39.039 Amber Lin: and then share with you guys.
282 00:24:39.040 ⇒ 00:24:41.020 Miguel de Veyra: Okay. Okay, yeah.
283 00:24:41.020 ⇒ 00:24:46.730 Miguel de Veyra: Honestly, I think the these docs are like, for example, past in the rodent. These are all in the central dock.
284 00:24:47.830 ⇒ 00:24:49.969 Miguel de Veyra: I would assume it should be right.
285 00:24:50.480 ⇒ 00:24:54.719 Amber Lin: I mean, I don’t know if they did any updates since then.
286 00:24:54.740 ⇒ 00:24:56.529 Miguel de Veyra: Oh, no, it’s not only.
287 00:24:56.530 ⇒ 00:25:05.589 Amber Lin: Yeah, I think we should just scrape all the I’ll I’ll do it. What cleaning do we need after we get it from Gpt? Because now it’s not even the answer right?
288 00:25:05.590 ⇒ 00:25:06.820 Miguel de Veyra: Almost here.
289 00:25:07.480 ⇒ 00:25:08.850 Amber Lin: Fire, ant.
290 00:25:09.220 ⇒ 00:25:10.030 Miguel de Veyra: Alright!
291 00:25:11.450 ⇒ 00:25:12.469 Miguel de Veyra: Yeah, I think it’s here.
292 00:25:12.470 ⇒ 00:25:19.850 Amber Lin: Yeah, but it doesn’t have. But it doesn’t. Does it have the locations for each one?
293 00:25:20.170 ⇒ 00:25:21.810 Amber Lin: Because that’s what they want.
294 00:25:23.982 ⇒ 00:25:25.380 Miguel de Veyra: Okay. So they want the location.
295 00:25:25.380 ⇒ 00:25:37.250 Amber Lin: Yeah. So they cause each location has different services. So we have to be very specific of, oh, under Austin, we have this under that we have these, but under that we don’t have those.
296 00:25:37.590 ⇒ 00:25:40.779 Miguel de Veyra: Isn’t that on the zip thing? Wait! Let me check.
297 00:25:41.520 ⇒ 00:25:42.260 Amber Lin: Maybe.
298 00:25:44.225 ⇒ 00:25:48.219 Miguel de Veyra: No, I think this is only yeah. I think this is only the people.
299 00:25:48.840 ⇒ 00:25:53.209 Miguel de Veyra: but ideally they should have put it here, you know, like what services they would have added.
300 00:25:53.530 ⇒ 00:26:00.230 Amber Lin: Yeah, I know ideally, but apparently not, or else they will not ask us to scrape things.
301 00:26:00.970 ⇒ 00:26:02.170 Miguel de Veyra: That’s their date.
302 00:26:04.370 ⇒ 00:26:05.550 Amber Lin: Yeah.
303 00:26:05.770 ⇒ 00:26:08.170 Miguel de Veyra: I mean, technically, it’s over here.
304 00:26:09.230 ⇒ 00:26:16.820 Miguel de Veyra: Wait. Technically, it’s here, because if it’s, you know, for for Georgetown, I would suppose G. 10 is.
305 00:26:16.820 ⇒ 00:26:17.380 Amber Lin: Yeah.
306 00:26:17.380 ⇒ 00:26:18.310 Miguel de Veyra: There’s no B-road.
307 00:26:18.310 ⇒ 00:26:21.869 Amber Lin: Yeah, if it has a person there, it should work right?
308 00:26:22.480 ⇒ 00:26:23.400 Miguel de Veyra: Yeah.
309 00:26:23.850 ⇒ 00:26:29.969 Amber Lin: Okay? Then I will go and verify if this is correct.
310 00:26:30.230 ⇒ 00:26:43.100 Amber Lin: And then based on this, could we, when when the bot asks a availability question of, oh, is this available in my area? Can we based on this, Doc? Tell them, yes or no?
311 00:26:43.870 ⇒ 00:26:47.279 Miguel de Veyra: Maybe we can try. Just the bot knows that document.
312 00:26:47.490 ⇒ 00:26:49.130 Amber Lin: Okay. Gotcha.
313 00:26:49.630 ⇒ 00:26:54.450 Miguel de Veyra: Nope, let’s touch something else, probably not duplicated.
314 00:26:57.310 ⇒ 00:26:58.730 Miguel de Veyra: Don’t do myself.
315 00:27:02.140 ⇒ 00:27:03.310 Miguel de Veyra: ABC.
316 00:27:04.190 ⇒ 00:27:09.870 Miguel de Veyra: Live, then, let’s say, is there.
317 00:27:10.720 ⇒ 00:27:14.675 Amber Lin: Let’s check if there’s nothing in.
318 00:27:15.780 ⇒ 00:27:17.700 Miguel de Veyra: Something that’s black.
319 00:27:17.700 ⇒ 00:27:22.860 Amber Lin: Wait is, these are these locations in Austin? Oh, okay, sure.
320 00:27:22.860 ⇒ 00:27:23.320 Miguel de Veyra: Austin.
321 00:27:23.320 ⇒ 00:27:27.369 Amber Lin: In. Okay. So we’ll say we’re in Austin.
322 00:27:28.550 ⇒ 00:27:32.700 Miguel de Veyra: Oh, we can try thermosel on this Zip code.
323 00:27:33.900 ⇒ 00:27:36.659 Miguel de Veyra: Do you offer thermosal services on Yada.
324 00:27:36.660 ⇒ 00:27:37.870 Amber Lin: Sure. Okay.
325 00:27:41.100 ⇒ 00:27:42.789 Amber Lin: And I should say, No.
326 00:27:54.900 ⇒ 00:27:56.339 Miguel de Veyra: See what he touches.
327 00:27:57.280 ⇒ 00:27:57.830 Miguel de Veyra: Cheese.
328 00:27:57.830 ⇒ 00:28:01.240 Miguel de Veyra: Okay, now they would know it definitely. Sheets, data.
329 00:28:02.170 ⇒ 00:28:02.520 Amber Lin: Okay.
330 00:28:02.520 ⇒ 00:28:04.270 Miguel de Veyra: That’s great!
331 00:28:04.530 ⇒ 00:28:08.930 Amber Lin: Yeah. Oh, did did they provide us with the sheet data?
332 00:28:08.930 ⇒ 00:28:10.700 Miguel de Veyra: Yes, yes, this was theirs.
333 00:28:10.900 ⇒ 00:28:11.840 Amber Lin: Okay.
334 00:28:13.520 ⇒ 00:28:15.940 Miguel de Veyra: Rory Bernhardt. Wait. Sorry.
335 00:28:17.680 ⇒ 00:28:20.830 Miguel de Veyra: Wait because there’s a lot of it could be canceled.
336 00:28:23.980 ⇒ 00:28:25.660 Miguel de Veyra: It could become free.
337 00:28:25.840 ⇒ 00:28:35.619 Miguel de Veyra: Yeah, it could be Campy. So it is correct. Chem, free is like a different service type. So there is. Rory is on, you know.
338 00:28:36.390 ⇒ 00:28:37.800 Amber Lin: It is correct.
339 00:28:39.490 ⇒ 00:28:41.529 Amber Lin: We can. I see the answer.
340 00:28:43.910 ⇒ 00:28:45.519 Miguel de Veyra: Yes, we do offer.
341 00:28:49.380 ⇒ 00:28:53.610 Amber Lin: Oh, wow! Okay, I need to put that down.
342 00:28:56.900 ⇒ 00:29:08.489 Amber Lin: great. And then they’re talking about all the web scraping because they want it to be up to date. But their website? It’s so it’s so non granular because it’s just, oh, Austin.
343 00:29:08.820 ⇒ 00:29:15.609 Amber Lin: can you try to just ask, do you offer thermosil stuff in Austin.
344 00:29:15.860 ⇒ 00:29:21.320 Amber Lin: Then I want to see if the bot asks clarifying questions of what do you mean? Where in Austin
345 00:29:22.260 ⇒ 00:29:26.250 Amber Lin: I just want to see how flexible it is, since this is so fast.
346 00:29:27.210 ⇒ 00:29:32.459 Miguel de Veyra: I know what it’s gonna do. It’s gonna check. And then it’s gonna get all the locations. This one will take time.
347 00:29:32.460 ⇒ 00:29:33.680 Miguel de Veyra: It didn’t.
348 00:29:33.850 ⇒ 00:29:37.009 Amber Lin: Oh! What! Oh!
349 00:29:37.010 ⇒ 00:29:39.260 Miguel de Veyra: No, it’s fine. It’s gonna throw an hour.
350 00:29:41.060 ⇒ 00:29:46.040 Miguel de Veyra: Why, though. Wait? Oh, because there’s no username. Yeah, that’s fine.
351 00:29:47.700 ⇒ 00:29:51.150 Miguel de Veyra: Yes, we do offer thermosal services here in Austin.
352 00:29:51.780 ⇒ 00:29:54.160 Amber Lin: Oh, that is great!
353 00:29:54.400 ⇒ 00:29:55.980 Miguel de Veyra: Yeah, it listed.
354 00:29:56.630 ⇒ 00:29:57.440 Miguel de Veyra: Okay.
355 00:29:57.680 ⇒ 00:30:07.619 Amber Lin: Yeah. But we asked about Austin, so we could ask the bot to ask clarifying questions based on that. I don’t know how hard it is to implement. But I think this is enough.
356 00:30:08.110 ⇒ 00:30:09.849 Miguel de Veyra: Yeah, I mean, I would
357 00:30:10.420 ⇒ 00:30:17.930 Miguel de Veyra: personally say, this is a better answer. Yeah, I mean, yes, you know, we do offer terminal services in Austin. Here are the technicians available.
358 00:30:18.190 ⇒ 00:30:18.520 Amber Lin: Oh!
359 00:30:18.520 ⇒ 00:30:20.159 Miguel de Veyra: For a different zip codes.
360 00:30:20.500 ⇒ 00:30:23.899 Amber Lin: Can we try? What do you not have in Austin?
361 00:30:24.340 ⇒ 00:30:26.509 Amber Lin: Will it be able to answer that.
362 00:30:29.360 ⇒ 00:30:36.259 Miguel de Veyra: Yeah, I think it should be, but I don’t think there’s anything that they don’t offer in Austin, since it’s the capital of Texas.
363 00:30:36.260 ⇒ 00:30:37.360 Amber Lin: Noticed oops.
364 00:30:43.100 ⇒ 00:30:50.089 Miguel de Veyra: Probably cause it’s not naval. I what’s happened? It doesn’t matter.
365 00:30:52.780 ⇒ 00:30:57.050 Miguel de Veyra: No, I’m sorry. But I don’t have access to the specific information. Oh, yeah, yeah.
366 00:30:58.150 ⇒ 00:30:59.830 Miguel de Veyra: because it didn’t know what to do.
367 00:31:00.990 ⇒ 00:31:01.940 Amber Lin: Hmm.
368 00:31:02.570 ⇒ 00:31:06.019 Miguel de Veyra: But I don’t think anyone would not answer would question this.
369 00:31:07.500 ⇒ 00:31:08.500 Amber Lin: That’s true.
370 00:31:10.120 ⇒ 00:31:10.949 Miguel de Veyra: I mean we can.
371 00:31:10.950 ⇒ 00:31:14.070 Miguel de Veyra: We can put. We can put, I guess, a prompt let me.
372 00:31:14.070 ⇒ 00:31:16.479 Amber Lin: I guess I think it would be fine.
373 00:31:16.610 ⇒ 00:31:17.170 Miguel de Veyra: Yeah.
374 00:31:18.170 ⇒ 00:31:21.280 Miguel de Veyra: But yeah, I think that should be fine for now. No, all right.
375 00:31:21.280 ⇒ 00:31:23.370 Miguel de Veyra: I was gonna ask, Hey, what’s that in San Antonio?
376 00:31:23.370 ⇒ 00:31:32.680 Amber Lin: Yeah, they’ll just keep asking, oh, is this available? Or is that available? So I think that’s good. Okay, I will take web scraping off the table because it’s
377 00:31:32.820 ⇒ 00:31:35.681 Amber Lin: not a good use of our time.
378 00:31:36.190 ⇒ 00:31:45.260 Amber Lin: the structure of the answers, have we changed anything about basically.
379 00:31:45.260 ⇒ 00:31:47.780 Miguel de Veyra: I just added, where is it?
380 00:31:48.010 ⇒ 00:31:56.629 Miguel de Veyra: The structure is? Wait, wait. The technical response additional notes to the session guideline. Yeah, basically, I just answered, you know.
381 00:31:56.790 ⇒ 00:32:02.940 Miguel de Veyra: Ask if it’s too general. Ask for specific details. Keep the responses, 2 to 3 sentences.
382 00:32:03.450 ⇒ 00:32:15.449 Miguel de Veyra: and then, for you know, for step by step, instructions. Use one word bullet stuff. And then, yeah, I mean, basically, I just tried to limit it, but not really limited too much, as you know.
383 00:32:15.450 ⇒ 00:32:25.850 Amber Lin: Okay, I think the answers today are very different. Let’s just try one last prompt about the same day. Rescheduling thing that we
384 00:32:26.790 ⇒ 00:32:30.320 Amber Lin: yeah, it’s the same example that Janice gave me. So.
385 00:32:39.440 ⇒ 00:32:42.320 Miguel de Veyra: How do you find the same?
386 00:32:43.060 ⇒ 00:32:44.319 Miguel de Veyra: I’m just going through them.
387 00:32:50.280 ⇒ 00:32:51.570 Miguel de Veyra: Oh, hey, Casey.
388 00:32:53.380 ⇒ 00:32:54.420 Amber Lin: Hey! She!
389 00:32:55.300 ⇒ 00:32:55.980 Janna Wong: Hi Casey.
390 00:32:55.980 ⇒ 00:32:59.269 Casie Aviles: Hey, guys? Sorry. I thought the meeting was 9, 30.
391 00:32:59.750 ⇒ 00:33:05.270 Amber Lin: Oh, all good. Don’t worry. I know it was changed a little bit. Oh, what happened.
392 00:33:06.480 ⇒ 00:33:07.160 Miguel de Veyra: There you go!
393 00:33:07.750 ⇒ 00:33:12.700 Amber Lin: Okay, I think it’s
394 00:33:12.880 ⇒ 00:33:20.339 Amber Lin: I think Janice would still say it’s a little bit long, but it has improved significantly from before.
395 00:33:21.168 ⇒ 00:33:22.639 Amber Lin: I think this is.
396 00:33:22.660 ⇒ 00:33:31.329 Miguel de Veyra: Terrible answer, though attempt to retain the customer using the top preset sheet. I I mean, I’m not sure if it’s accurate. If the customer insists on rescheduling.
397 00:33:31.970 ⇒ 00:33:35.939 Amber Lin: Yeah, let me go check with check with Janice.
398 00:33:36.710 ⇒ 00:33:38.519 Miguel de Veyra: Identify the specialist.
399 00:33:39.690 ⇒ 00:33:46.440 Miguel de Veyra: I mean what I can do here, by the way, is, summarize it unless the I don’t know.
400 00:33:47.820 ⇒ 00:33:53.770 Miguel de Veyra: Wait! Sorry. There’s someone calling me Hi!
401 00:33:55.590 ⇒ 00:33:57.539 Amber Lin: Hi, Casey, how’s progress?
402 00:33:57.940 ⇒ 00:34:00.589 Casie Aviles: Hey? Yeah, I managed to.
403 00:34:00.590 ⇒ 00:34:01.050 Miguel de Veyra: Thank you.
404 00:34:01.050 ⇒ 00:34:03.930 Miguel de Veyra: Like, implement. The thumbs up, thumbs down.
405 00:34:04.600 ⇒ 00:34:05.500 Amber Lin: Oh, wow!
406 00:34:06.880 ⇒ 00:34:09.229 Amber Lin: Can I see? I know he’s sharing screen.
407 00:34:09.239 ⇒ 00:34:10.189 Casie Aviles: Yeah, yeah, sure.
408 00:34:10.940 ⇒ 00:34:13.419 Amber Lin: And everyone to share screen. Yeah, there we go.
409 00:34:13.420 ⇒ 00:34:18.750 Casie Aviles: Okay, right? Okay. So let me just
410 00:34:19.270 ⇒ 00:34:22.649 Casie Aviles: yeah. Here, like, I managed to implement these. But.
411 00:34:23.690 ⇒ 00:34:24.730 Amber Lin: Wow!
412 00:34:28.139 ⇒ 00:34:35.099 Casie Aviles: I guess the next step like when you, when you click the buttons like you get this messages like.
413 00:34:35.100 ⇒ 00:34:35.630 Amber Lin: Let me!
414 00:34:35.639 ⇒ 00:34:37.809 Casie Aviles: Thanks for your feedback, and then.
415 00:34:38.120 ⇒ 00:34:39.659 Amber Lin: Oh, great!
416 00:34:40.300 ⇒ 00:34:42.320 Casie Aviles: Yeah, so.
417 00:34:42.320 ⇒ 00:34:44.220 Miguel de Veyra: They’re from Texas. Say, howdy!
418 00:34:44.570 ⇒ 00:34:45.750 Miguel de Veyra: Thanks for that!
419 00:34:48.489 ⇒ 00:34:57.609 Casie Aviles: Yeah. But yeah, I guess the next thing that I we could still do here is these aren’t actually logged yet, like it’s just sending the messages so.
420 00:34:58.002 ⇒ 00:34:58.787 Amber Lin: For nothing.
421 00:34:59.180 ⇒ 00:34:59.700 Miguel de Veyra: Okay.
422 00:34:59.700 ⇒ 00:35:04.100 Casie Aviles: Yeah, but yeah.
423 00:35:05.460 ⇒ 00:35:05.700 Miguel de Veyra: Is.
424 00:35:05.700 ⇒ 00:35:10.000 Miguel de Veyra: Is it like Casey? Sorry? Just a quick question. Is it like only 2 buttons? Max.
425 00:35:10.950 ⇒ 00:35:12.860 Casie Aviles: I think you can add more.
426 00:35:13.070 ⇒ 00:35:18.159 Miguel de Veyra: Because what what we can possibly do is 1, 2, 3, 4, 5 rate. The message.
427 00:35:18.965 ⇒ 00:35:19.770 Amber Lin: Hmm.
428 00:35:19.770 ⇒ 00:35:23.729 Miguel de Veyra: No, what do you think? But yeah, let’s get this working first, st because I think that’s like an easy change.
429 00:35:24.611 ⇒ 00:35:26.859 Amber Lin: Cause they can grade it. Now, right?
430 00:35:27.470 ⇒ 00:35:30.430 Miguel de Veyra: I wouldn’t say we go 10. I think it’s just too much.
431 00:35:31.550 ⇒ 00:35:32.270 Amber Lin: Oh!
432 00:35:32.550 ⇒ 00:35:36.329 Miguel de Veyra: 1, 2, 3, 4, 5 would be the way to go, or up and down.
433 00:35:36.330 ⇒ 00:35:37.339 Casie Aviles: Yeah, this is.
434 00:35:37.660 ⇒ 00:35:44.079 Amber Lin: Probably just up and down because the guys are in a call. So they probably don’t have brain capacity. So.
435 00:35:44.080 ⇒ 00:35:46.589 Miguel de Veyra: True, true, they don’t have the okay.
436 00:35:47.000 ⇒ 00:35:47.860 Amber Lin: Yeah.
437 00:35:47.860 ⇒ 00:35:48.680 Miguel de Veyra: Nice.
438 00:35:48.870 ⇒ 00:36:09.550 Amber Lin: See, that’s so awesome. Website improved. Yeah, I’ll ask Patrick what he has for the improving speed and accuracy. That’s not our job currently, and it feels a good feedback. Look good, the structure improved. Oh, what else do we need to do?
439 00:36:11.239 ⇒ 00:36:15.460 Amber Lin: Let’s see, what do we want to do today?
440 00:36:16.222 ⇒ 00:36:18.750 Miguel de Veyra: I mean, I’m gonna continue on the adding and updating.
441 00:36:18.750 ⇒ 00:36:20.179 Amber Lin: Okay, that’s great.
442 00:36:20.180 ⇒ 00:36:23.959 Miguel de Veyra: Fully done. It’s working. But I would say it’s like around 70, 80% to 80.
443 00:36:23.960 ⇒ 00:36:30.239 Amber Lin: Yeah, okay, great. And maybe, oh, create this training document for me. And they create a new one.
444 00:36:30.350 ⇒ 00:36:33.759 Amber Lin: If you got capacity for that, that’s just an idea.
445 00:36:34.223 ⇒ 00:36:35.149 Miguel de Veyra: Thing, document.
446 00:36:36.080 ⇒ 00:36:45.519 Amber Lin: So the document update agent, you know, we’re working on updates right now, there’s also this create function that’s totally up to you.
447 00:36:47.055 ⇒ 00:36:55.009 Miguel de Veyra: Wait. Sorry isn’t the now I now I think about it. Isn’t the create document agent, just same as the Add new document.
448 00:36:56.486 ⇒ 00:36:58.550 Amber Lin: Yes, it is.
449 00:36:58.730 ⇒ 00:37:00.459 Miguel de Veyra: Technically, right, okay, okay.
450 00:37:00.460 ⇒ 00:37:03.329 Amber Lin: Technically. Yes, yes, yes, you are correct.
451 00:37:03.910 ⇒ 00:37:07.069 Miguel de Veyra: Okay, yeah, yeah. Then, we are pretty much done that part, too.
452 00:37:07.300 ⇒ 00:37:10.979 Amber Lin: Oh, great! I think we just and I put them together.
453 00:37:11.120 ⇒ 00:37:12.960 Miguel de Veyra: Yeah, I’ll just clean them up.
454 00:37:13.330 ⇒ 00:37:18.540 Amber Lin: Yeah. Great Casey, what do you want to work on today?
455 00:37:19.720 ⇒ 00:37:21.479 Casie Aviles: Hmm, I think I could.
456 00:37:21.720 ⇒ 00:37:28.799 Casie Aviles: Yeah, I could have like the thumbs up thumbs down stored somewhere, because it’s not being stored anywhere right now?
457 00:37:29.321 ⇒ 00:37:32.758 Casie Aviles: Okay. And what else do we need to do?
458 00:37:33.140 ⇒ 00:37:37.040 Miguel de Veyra: Casey, the implementation of brain trust to Google cloud.
459 00:37:38.380 ⇒ 00:37:43.339 Casie Aviles: I mean, it’s we don’t really need to modify the code there anymore, because it’s a separate like
460 00:37:43.800 ⇒ 00:37:50.300 Casie Aviles: python script. So I mean, yeah, I could show you how it looks like, because I already saw the logs that Yvette did like.
461 00:37:50.590 ⇒ 00:37:57.009 Miguel de Veyra: It’s already working on. Gcp, I I mean, it’s brain trust. I mean, if it’s not that, if it’s then, you know no need.
462 00:37:58.650 ⇒ 00:38:02.919 Casie Aviles: Yeah. So just to give you guys a better idea of what how I did it.
463 00:38:03.550 ⇒ 00:38:10.796 Casie Aviles: So yeah, over here, like, this is the script that gets triggered the ABC. Home. Eval script here.
464 00:38:11.800 ⇒ 00:38:18.620 Casie Aviles: yeah. And you could see like who triggered it. And it’s event over here. So I can see whenever they’re testing.
465 00:38:19.250 ⇒ 00:38:21.280 Amber Lin: And it should oh, wow!
466 00:38:21.540 ⇒ 00:38:24.030 Casie Aviles: Yeah, it should show up here as well.
467 00:38:24.770 ⇒ 00:38:25.260 Amber Lin: That’s.
468 00:38:25.260 ⇒ 00:38:26.390 Casie Aviles: On brain trust.
469 00:38:27.390 ⇒ 00:38:28.590 Miguel de Veyra: Oh, nice!
470 00:38:29.300 ⇒ 00:38:34.329 Casie Aviles: Yeah, so this is the question, although the the only thing is that we don’t see
471 00:38:34.530 ⇒ 00:38:38.900 Casie Aviles: who triggered it here on brain trust. Because it’s it, says Miguel. But.
472 00:38:40.090 ⇒ 00:38:41.350 Miguel de Veyra: That’s my question.
473 00:38:41.350 ⇒ 00:38:42.379 Miguel de Veyra: Api, I mean.
474 00:38:42.380 ⇒ 00:38:43.549 Casie Aviles: Yeah, token. Yeah.
475 00:38:44.100 ⇒ 00:38:47.590 Casie Aviles: But yeah, here’s the question, what types of termite service.
476 00:38:47.590 ⇒ 00:38:49.629 Amber Lin: How do we do? Wow!
477 00:38:50.040 ⇒ 00:38:51.419 Miguel de Veyra: How? How do you connect it? Bro.
478 00:38:52.260 ⇒ 00:38:56.030 Casie Aviles: I use windmill. So it’s not on Google Cloud.
479 00:38:57.020 ⇒ 00:38:59.489 Casie Aviles: I wanted to keep it modular. So.
480 00:39:00.630 ⇒ 00:39:04.550 Miguel de Veyra: Is it on? And 2 like, how do? How does the
481 00:39:04.850 ⇒ 00:39:06.649 Miguel de Veyra: how do we pass the data to windmill.
482 00:39:07.330 ⇒ 00:39:10.629 Casie Aviles: Oh, it’s through a Http. Request.
483 00:39:10.770 ⇒ 00:39:12.720 Miguel de Veyra: I wanna edit and.
484 00:39:13.040 ⇒ 00:39:14.540 Casie Aviles: Yeah, I added one.
485 00:39:15.279 ⇒ 00:39:15.880 Miguel de Veyra: Smart.
486 00:39:17.380 ⇒ 00:39:19.130 Casie Aviles: Yeah, here, this one.
487 00:39:19.870 ⇒ 00:39:22.169 Miguel de Veyra: True. Why make it hard? Right?
488 00:39:23.760 ⇒ 00:39:27.160 Miguel de Veyra: I the one thing I would change here.
489 00:39:27.400 ⇒ 00:39:30.210 Miguel de Veyra: Wait! Sorry. Go go back to any. Then
490 00:39:33.130 ⇒ 00:39:37.600 Miguel de Veyra: this this is the response. Right? The edit fields, the last one.
491 00:39:37.920 ⇒ 00:39:44.330 Casie Aviles: You know. I’m not sure why what this node does, but I just kept it there and added it before.
492 00:39:44.330 ⇒ 00:39:57.789 Miguel de Veyra: Yeah, that’s basically it’s a workflow. Okay? Cause I, the respond to web book, we could probably because this is just in terms of improving the response. Times. We could probably move this response web book all the way back.
493 00:39:58.140 ⇒ 00:40:01.289 Miguel de Veyra: right all the way.
494 00:40:01.290 ⇒ 00:40:02.469 Casie Aviles: Oh, you missed one before.
495 00:40:02.470 ⇒ 00:40:03.530 Miguel de Veyra: Thank you. Yeah.
496 00:40:04.970 ⇒ 00:40:10.289 Casie Aviles: Right? Yeah, yeah, we could, because Patrick told me that the snowflake part is
497 00:40:10.800 ⇒ 00:40:14.740 Casie Aviles: kind of slow. So yeah, maybe if we move this here.
498 00:40:14.740 ⇒ 00:40:16.730 Miguel de Veyra: Yeah, then it wouldn’t matter. Yeah.
499 00:40:16.890 ⇒ 00:40:22.789 Miguel de Veyra: Now, the Snowflake part is just no, I think, yeah, I I mean, we can just move it. And that’s fixed right.
500 00:40:23.640 ⇒ 00:40:24.760 Casie Aviles: Yeah, yeah, sure.
501 00:40:25.790 ⇒ 00:40:28.290 Miguel de Veyra: We’ll probably do that to every bot. To be honest.
502 00:40:30.650 ⇒ 00:40:32.299 Miguel de Veyra: Okay. Yeah. Nice. Good. Job.
503 00:40:32.300 ⇒ 00:40:33.320 Amber Lin: That’s awesome.
504 00:40:33.320 ⇒ 00:40:33.920 Miguel de Veyra: We’re technically.
505 00:40:34.242 ⇒ 00:40:51.009 Amber Lin: I must run. Yes, we are technically done. I will see if I can think of anything we should still work on. But I think we’re pretty solid, and we’re pretty good. And honestly, if we don’t have to spend any that many hours and still get the pay awesome. So.
506 00:40:51.010 ⇒ 00:40:53.549 Miguel de Veyra: Probably should have mentioned that in the recorded call.
507 00:40:53.960 ⇒ 00:40:56.939 Amber Lin: For the so no, for the client.
508 00:40:57.313 ⇒ 00:40:58.060 Miguel de Veyra: Okay. Okay.
509 00:40:58.060 ⇒ 00:40:59.580 Amber Lin: Oh, for us.
510 00:41:00.046 ⇒ 00:41:08.360 Amber Lin: I get paid by the hour, so it’s like, if I do less, I get paid less, but the client pays a fixed fee, and that’s a benefit
511 00:41:08.730 ⇒ 00:41:19.159 Amber Lin: having a fixed fee. Okay, I think this is great. I’m gonna work on making it sound amazing which it already is, and well done.
512 00:41:19.350 ⇒ 00:41:22.060 Amber Lin: I think that’s all else.
513 00:41:22.060 ⇒ 00:41:24.140 Miguel de Veyra: What about the Scots recommendations.
514 00:41:24.740 ⇒ 00:41:26.380 Amber Lin: I don’t know what he’s doing.
515 00:41:27.400 ⇒ 00:41:30.350 Miguel de Veyra: I don’t know. Let’s think, do what’s important to be honest.
516 00:41:30.460 ⇒ 00:41:39.900 Amber Lin: Yeah, I think it, and sidetracked me to think too much about the web scraping, which shouldn’t be where we spend the time. So that’s my take.
517 00:41:40.520 ⇒ 00:41:46.759 Miguel de Veyra: Okay, yeah, no worries. I mean, until autumn closes another deal with them. I wouldn’t really add more features.
518 00:41:47.170 ⇒ 00:41:52.779 Amber Lin: Yeah, totally. That’s all. I will work with him on the proposal and stuff.
519 00:41:52.890 ⇒ 00:41:59.640 Amber Lin: Okay, guys, I must jump. I had a meeting 8 min ago. It’s so nice to you guys, and I will slack you guys.
520 00:41:59.640 ⇒ 00:42:00.580 Miguel de Veyra: Thanks. Everyone have a good.
521 00:42:00.580 ⇒ 00:42:01.040 Amber Lin: Alright!
522 00:42:01.040 ⇒ 00:42:01.460 Casie Aviles: Thank you.
523 00:42:01.460 ⇒ 00:42:02.050 Amber Lin: Bye.