Publish Your Analysis Everywhere with AI

0:00 share about all the different types of you know areas that we could just publish analysis with Cloud Code and hopefully we'll have enough time to go through demos of multiple styles of you know publishing analysis with Cloud Code. Yeah, and we basically take one analysis and we try to share with you a Google Doc, a Notion page and Google Slides deck. All of it coming from this analysis within minutes and hopefully no reformatting. We never know with live sessions how things look like, but you know, let's try and get going.

0:40 Okay. Have you seen this happen with you? You all see my screen, right? Do you see my screen? >> Yes, so second you finish the analysis the thing is that >> Okay, awesome. Yeah. So, I'd love to know you all like raise your hand if this happened to you. You finished the analysis, the thinking is done, you basically know what you want to present. The answer is clear, but you still have oh, the two hours of work. I don't know what I was thinking. I think it sometimes even takes up to a day or even more, right? To get the analysis into the right format, into the right dog, into getting the right visualizations and you know all of that.

1:24 Have this happened with you? You have the story, but it takes a ton of time to put that together. Oh, this is some fun messages, but yeah. Yeah, right? So, hoping um we'll share some techniques with you. This is something that Shawn High and I have incredibly benefited from Cloud Code on how we get the content into a deck format in like in a minutes. But you need to have all the scaffolding ready.

2:03 You need to have all the types of formatting that you'd like the deck to look like and you know all of that ready. Obviously, that takes time to get the system set up. But once you know how you want it, what you want it and you have some templates and frameworks on how you'd like the analysis to show up, it actually is way less amount of time to get this done. Okay. Cool. So, let's make this basically concrete, right? So, think about your last analysis, the actual thinking, the exploration, the insight and recommendation, you know, maybe all of that takes like 30 to 60 minutes for you to like, you know, understand and go through, right? But now The 30-60 minutes is By that what I meant was to like once you have everything ready, to know what you want, how you want the story to look like, right?

2:56 But how you basically get the packaging done, the Google Doc with proper formatting, the charts look presentable, does the titles of the chart look well, where do you want to place the chart, you know, all of that stuff, right? That's where you take sometimes even more than a simple analysis. So, every person like in your team pays the same amount of tax every time. This is something that I would say that's agnostic to your own data, right? Even if different types of content, the packaging actually takes really long than the amount of thinking that you need to do.

3:34 And the real problem is actually you know, it's not it's not the speed. It's I would probably call it like the sequence. The problem isn't that people are like slow at formatting. It's basically one manual step after the other and all of these sequential steps basically takes a ton of time. And then you basically are doing it by hand, right? Um you are you go through one after the other, ensure that does the slide one or like the first part of the doc, does it resonate well with the second part of the doc, are things coherent with each other, and you know, all of that. So, there's a lot of uh like back and forth that you're doing. And this is what, you know, chain like takes the time.

4:21 So, uh what are we trying to do here? Uh we basically have the idea of analyzing it once and repackaging it for the audience depending on the type of audience that you're going to tackle and um ensuring that you have less manual step-to-step work, but all the styles of formatting, the the templates of how you'd like the, you know, the doc to look like or the the analysis to look like, you have all of that pre-decided within frameworks already so that once you have a final analysis and once you know the story you'd like to tell, it's literally a minutes uh worth of time for Claude to, you know, push something into like an analysis format.

5:09 Okay. So, basically every format has a different style of job. This is the demo. In this demo, that's what we're trying to do, but obviously at your work for the type of work that you're doing, for the type of stakeholders you work with, this is going to change for you. But in the demo, this is what we are going to tackle, right? So, uh this is like almost like an audience format map um where basically we're going to have like a key insight. Each format is a different job.

5:38 The Google Doc is going to be the doc that you share with your VP where it's most like a decision record. All it has is executive summary, what are the findings, what is the recommendation. Like make it as simple as possible. VPs don't have enough time. All they need is give me what you're trying to tell me and why. Right? So that's what we're trying to share with the Google Doc today. It's basically the type of you know, analysis or output you'd like to share with a VP.

6:10 Now, what about the Notion Doc? Notion Doc in this demo, we're going to share it like it's like a living wiki wiki page. You have your analysis, you've done tons of work, right? You have multiple layers within the analysis, you have different charts you'd like to show. You in fact might even want to show some of the code that was run with like you know, you want to share your source doc or something with like a Databricks notebook or something like that. Or maybe you want to copy-paste the stuff. I tried to do one variety here today. But you could choose the type of variety you would like to share with your team, right? So Notion is going to be way more detailed and contains all different things that you would like to share with your teammate so that they could you know, help review the analysis and ensure that you have something really solid and there's like nothing that that's not validated with your team, right? So that's the Notion style of doc that we're going to share.

7:12 And then the third one is basically going to be the slides. So this is basically like the presentation style that imagine you get 5 minutes in your team meeting or in your like you know, all hands of your org or something, what would you share as part of that in those 5 minutes? So those those will be the slides that you review 5 or 10 minutes. Basically the slides that you review one message per slide, headlines that states the point and charts that carry the proof. So these are the three different type of formats, the two three different jobs based on the audience that you are going to, you know, tackle this messaging with, right? And based on that, you change each of them.

7:55 Which basically means it's the same analysis, but different shape for each reader. Never, you know, the same text that passed three times. It basically has a different flavor based on the type of audience and the type of meeting or if it's async or if it's like in-person meeting that you share. Okay. So, the four things every publish prompt names or like these are the four things, right? I know I kind of explained it to you.

8:27 I'll try to be brief here as well. It's basically uh in your prompt, you want to share these things. You want to share the audience. Who is the audience? Is it the VP? Is it the team wiki? Is it a stakeholder review? And then the second one is the format. What type of uh you know, uh output do you are you looking for? Uh I today in the demo, I'm going to share three types of outputs like I already said, it's going to be a Google Doc, there's going to be a Notion page, and there's going to be a slide deck.

8:56 But guess what? Like Sean joked in the message today, there's so many of these analyses that get shared in a Slack message, right? Um or Teams if you have Teams going on. Uh you have like an insight, there's a question, you share a visualization with like a message. So, that's another style of sharing analysis or sharing insights, right? And also email, our typical traditional way of sharing. Today I I don't uh do the Slack and email, but these are like multiple formats you could share your insights and analysis with, right?

9:28 And another very important thing you want to share in your prompt uh for publishing in Cloud Code is the length and depth, right? You want to understand uh the detail you want to share in your analysis. Like I said, if it's going to be for a VP, you don't need to share all these details, right? You basically need to share the things that the VP cares about, probably a recommendation, and that's it. But, if it's your team, you want to share all the details that they could help review stuff, and you're not sharing something that's flawed.

10:02 And maybe if it's your teammate broader a broader team with the engineers in product, you want to do something in between, right? And yeah, that's follows into the fourth framework piece here. What to include, right? So, exact summary, the data table information, the methodology of how you came about coming up with these things, those are too detailed, right? For a VP. So, you basically ensure and understand what is the amount of detail you need for what type of audience, for what format, what length, and all of these need to be part of your publish prompt, so that it helps you, you know, get the right format for the right amount of people in the right place.

10:54 Okay. So, let's get started. What I want to share with you because we don't have like hours and hours of time with each other, I decided what I'll do is basically what we generally, you know, are at before we publish the analysis. Before we publish the analysis, we have all the charts that are needed, that we think are interesting, right? This is something I've done as a people manager for so many years. If you work, even if you mentor like other data scientists, you see this a bunch, right? Especially with junior data scientists is when you have an analysis, you keep looking at hundreds of things and you have so many visualizations and you think most of them are interesting, some of them are really interesting and when I work with my data scientists, especially juniors, they just put all that stuff in the deck from the get-go. And that's the hardest part, especially as a mentor or a people manager, is to get those 60-slide, 50-slide deck into a 15-slide deck.

11:58 >> I had a Sorry. I had a last year I had This guy is very smart and he's a friend of mine and he does he only did this this one time, but he was out of out of grad school and starting he did his first analysis with us and I was like, "Here's my turn it." And his deck had 200 charts in it. >> Oh, my god. >> 200 charts. And each of the charts were like cut all these different They're like charts within charts.

12:25 And like we got it down to seven charts. Like that's how much stuff was unnecessary there. But I But I got like what he was he was going after. He was like, "Oh, well, if I'm in this meeting and and then they ask me about this, like what if they think about this?" I'm like, "Yeah, totally, but like dude, no one's going to look at 200 charts. Like just have that on your computer and you can pull one up."

12:47 >> The you know what what I've seen is a lot of people, especially even happens with me as well, even after all these years, right? Is the hard part is to figure out what to pick. Uh so, there are like three stages. You start off with the question and you figure out the question, you have the answers. You need to decide out of hundreds of insights, what is the insight you'd like to share? That's the first decision you make as a data person or even a product person, what is something you'd like to share to start with? And then, what visualization uh you'd like to share that insight with because you could have multiple ways of sharing the same thing, right? So, those are the tough and tricky decisions that you need to make at the tail end of your analysis or the tail end of your presentation, whatever it is.

13:36 I I see a question from Prasanna. Prasanna, do you want to ask this uh in the chat the question that you posted in the chat? >> Sure. Uh the question is more like all this information what you're gathering and publishing or showcasing it to the leadership team, right? It's still in my opinion it's a one-way traffic where the information is gathered and being shared. Now that you mentioned about this tool, Google Doc or Notion or Slack or whatever it is, what I'm trying to find out is is there a way to take the feed from each of this uh data points back into your slide deck or whatever the tool that you're using so that you know there's a refinement of information?

14:18 >> Absolutely, Prasanna. Like uh I mean, you're basically talking about you have multiple narratives that you came up with in multiple areas, right? And can we leverage e- the other narrative uh into like, you know, when you probably are sharing with your VP, they would probably want to know this other angle that you already built for a different audience, right? And do we leverage each of them? Uh i- is that your question, Prasanna? Is that >> That is definitely one way of looking at it as and the the question was more towards the you know, feed what we get from leadership or for that matter audience. How do I take it back into my uh s- uh process?

15:04 >> Yeah, you can you can get a feedback directly where you're kind of like I a lot of ways that we iterate on stuff when we're building is we'll do like recorded sessions and then we'll bring that it back in and be like, "Hey, what are all the things we need to update versus then how would that reflect in to we actually do that a lot with our courses. We have, you know, >> Yep. >> yesterday Yes- yesterday we ran like the 3-hour workshop, and the first thing I did afterwards was I had Claude code analyze all of the chat and all of the um the transcript, and later I'll have them do the reviews, too, in the Slack messages to see like, "Hey, what's all the stuff that we can improve for next time?" And there's also like um there is also a personalization aspect to it when you're when you're delivering to like smaller audiences, where something I did at my work was I had uh profiles for each of my my primary stakeholders, um where I actually analyzed the types of documents that they wrote, the types of Slack messages that they had, um then how they like talked to me, and that way I'd have an idea like, "Hey, if I'm pres- if I'm talking to like this guy, John, I know that these are what he cares about."

16:19 >> It's the like a personality MD. If I message it like the way I have a cloud.md, so I can think about if I'm presenting to a specific VP, so we have to sort of understand the tone of style of how they present or what information they consume, and what put it back into the personality MD. Okay. >> Exactly, cuz there's like, you know, you we can think of in in generalities of like, "Hey, we're speaking to a leadership audience," but like your leadership audience at your company could be totally different than a leadership audience at someone at another company.

16:50 So, yeah. It's a really good question. The feedback loop stuff is pretty powerful. >> Absolutely. So, totally agree, Prasanna. I'm sorry I didn't get it fully right the first [laughter] time. So, this is like a V1, right? I'm trying to share with you. You have an opinion, you have a story together. How do you use Claude code, leverage Claude code to share all these insights that you have into multiple styles of presentations, right? Multiple styles of analysis and presentation. So, this is V1. What you're talking about is the most important thing. Once you present, how does that get into the next version, right? So, I I can give my example and then we can move on into the demo. Uh is that uh what I try to do all my uh meetings have a note taker. We used Otter. dot ai and I basically have MCP with Otter in my Claude code as well.

17:46 And as soon as I'm we are done with the presentation, I give the Otter note taker back to Claude and I give my perspective of how the meeting went and give it the transcript of how the meeting went and help me come up with the uh changes that we'd like to do and still the open-ended questions and what the V2 should be about and does it even make sense to have a V2 or not. This is literally something uh that I have in my flow as part of any presentation analysis like any meeting in fact. It doesn't have to be a presentation, right? Anything you share, it's going to be tackled with feedback and how are we handling the feedback? And some of the feedback is actually not live in a meeting. The feedback is actually in comments, right? So, there's so many uh of my presentations, of my team's presentations that go in so like Notion, we have something called Coda. That's what I used to use at my previous place.

18:38 So, all the comments of leadership and all of that uh I I uh make Claude code read through the comments and come up with an idea of either answering within the comments as an answer or come up coming up with another V2 version of the doc that answers those questions in like a a summary. Yeah. >> Morning. That's a great great feedback. Thank you so much. >> No problem at all. Awesome. Okay. So, uh we are dealing with the V1, right? The V1 of the analysis here. And let's say we have the analysis ready. We know what we want to present. We have tons of charts like the 18 charts, the whole chart library.

19:18 I try to make it 18 to be concise for this demo, but you we all know that it could get up to like 200 that Shawn said that we could, you know, have all of those for us at our exposure or at our hand that we could pick for the type of analysis we'd like to we'd like to publish in Google Docs or Slides and others, right? So, I also want to share with you how we came with the analysis well so that you understand a little bit about the system behind this analyst that we built.

19:52 We can start with that in the demo and then we'll go with demoing different, you know, styles of analysis or different places in which we could share and publish the numbers, okay? Let's get started. I'll stop sharing my screen and I'll share my cloud code with you. Hold on, give me a minute. Okay.

20:24 Uh By the way, if you have any questions like Prasanna, feel free to you know, ask the questions. >> I just dropped the link to the open source version of the repo of what Shruti is public in there. It'll appear. >> Yeah. Let me share my screen start with. Okay. Okay, what you're looking at is my VS code IDE and you have this is like an entire repo that we work off of for like everything that we're doing.

21:04 A bunch of courses, lightning lessons, like all of that stuff is in our repo. And right now I'm going to demo you what uh we're like, you know, going to talk about. Okay. So, this is my prompt. I'm basically asking it, "We just finished analyzing Novamart's 2024 data." So, Novamart is like a synthetic uh database that we created. You could take it as like, you know, our um like we try to recreate Amazon to some extent, like a mini version of it.

21:34 Where it is like um a a fictional company called Novamart, and it has all the product-related uh you know, uh issues, metrics, and the funnel, like acquisition to revenue and monetization, all of that part of it. It is It has 13 tables, 6 million records, and like as we try to recreate as much real data as possible real scenarios as possible with this data, right? So, we also use this for all the free workshops to share with you like what we could do with this data with CloudCode.

22:10 And we also share this as uh as part of our free repo uh that Sean shared, you could create the synthetic data yourself. And as part of our paid courses as well, we basically run all our analysis, skills, and agents on top of this data. Okay. So, uh what am I asking it? I'm asking it this we have created this analysis, and can you explain me what could be the headline, what are some of the charts that we created, and what is uh what are the insights so far? I asked it for a summary, right? And this is what it gave me. So, it gave me, "Here's the full read out of Novamart 2024 data. Novamart had a breakout year. Revenue grew 5.4x to 3.15 million, but the growth is leaky and getting more expensive to sustain."

23:02 So, it has some insight looks like already, right? So, acquisition scaled fast uh and it's talking about like, you know, the customers we are paying for con- paying for are converting words and they are not coming back. It's talking about the metric numbers here. What's the top line? How many orders? How many customers? What's AOV and stuff? And it has the top insights. It basically has This is the revenue. This is the uh uh the graph and you know, all of this, right?

23:32 So, it's also asking me what to publish. So, it gave me for a VP one-pager, you could probably publish a not all of it, but some parts of it, right? It is only picking up few charts. But for slides, it's also adding a fuller deck. For notion, it's saying, "Hey, get all the 18 charts that you have that we built." Now, let's do something. Let me ask it um Let me ask it if it could help me understand um how Claude got here, right?

24:07 Um So, let me do that. I'm going to ask for and this is how Can you basically give me a minute? Oops. Oops. Oops. Oops. Okay. Can you share with me I can actually talk to it probably. an ASCII diagram of all the system skills and agents that Claude could used to come up with an this analysis in the first place.

24:47 So, what am I asking for? I'm asking for basically what are the bunch of skills and agents that we have in our repo that it used to come up with this analysis so that you know what is the system behind the system while this probably so this is basically is sharing with me what exactly was the entire you know repo structure the agents the skills that were used to generate it it's still giving me like a shorter version of it I could ask even more detailed version if needed but if you look at it there's an orchestrator and it's cloud code it basically plans delegates validates and writes and it is using all these bunch of things right it is having basically a question framing skill it connects the data and then data profiling understanding what's happening and each of these skills actually invoke a bunch of other skills and agents as well if we have time I can share with you by the end of the demo into the details of what type of skills and agents we have here but I just wanted to share with you like the the system the number of skills and agents that we have that helps create an analysis like this that gives you like you know the top insights oops like the top insights and comes up with visualizations and stuff okay so it basically does question framing it connects the data it does the profiling of the data and then it looks at it comes up with an analysis design which cuts matter which do not and then it queries the sub agents right it we have DuckDB that's the database that we have because it's local we try to use that if you are working with snowflake or databricks you could have MCPs connected to it and have your queries run on databricks and snowflake via MCPs but for our purposes we use a DuckDB local database and we use SQL.

26:56 Um if you see it used eight cards and it started doing all of that parallelly and then it uses skills like triangulation, ensuring that there's some validation and checks um in the type of output and insights we share because we don't want to contradict each insight with each other. So, these are some checks that it does and after that there's you know, after the sanity check and validation and the numbers reconciling with each other, we we'll look at visualization patterns and create some charts and then we'll do the synthesis and then create multiple documents. Like Google Doc export is a skill like that. We have a bunch of skills for Google Slides and Notion. And finally, we come up with these three styles of uh you know, um like output we could do.

27:48 Awesome. Okay. Now, let's try to get this into a Google Doc. Let's see if it could use this and you know, get to a Google Doc. So, I'm basically asking it uh based on all these insights and charts that you have, could you please create a Google Doc for me? And I'm also talking about the Google Doc being uh written for the VP. Like I shared already in my slides, um we are trying to create a Google Doc for the VP. And as soon as we get that going, okay. So, since this is already an analysis that was done, we got this super fast. If this was not an analysis that was already done, we uh the cloud code would definitely take longer time.

28:40 It would take definitely take more time to uh basically come up with stuff and you know, put it together uh of of like the doc and you know put it together put together some of these like charts and all of that stuff. Okay. Let me share what is the document that it created. Okay. So this is the document that it created. It basically came up with No Mar 2024 product review and Q1 priorities. What's the recommendation?

29:11 No Mar grew by so and so and it talks about mobile is leaking money is the number one opportunity. It gives me a deck a graph out of it and then we don't keep the customers we win. If you look at it the title, the graph, all of that is has an insight hidden in it already. Like it not hidden. An insight basically the title talks like to us with an insight. It's not generic like Q1 customers over like you know last 20 entire 2024 where the title is not just a description. The titles of charts, the titles of uh of the you know our sections, they are all directly talking to us with the insight.

30:02 And it basically gave the context enough and then it it gives like a recommendation. So because it's a VP, we are just telling them hey, this is what we need from you. We want to prioritize this. We want to green light this and we want a decision, right? So you wouldn't have something like this for your team because that's not what you'd need from your team. What you need from your team is a review of the doc. But for with a VP, all we want to share is what where is the insight and what is the recommendation and what do we need from them uh to ensure that the recommendations that you have get actually done.

30:42 Okay. So >> Hey sorry, we had a question around um >> Mhm. >> is the doc already created before verifying the analysis? Or is the doc created after like only on upon prompting or is it automated in the workflow to create the doc? >> Yeah. So, I today I'm sharing with you once we have the insights, once we have the charts ready, can how many styles of analysis documents could we create?

31:13 But, the iteration of the analysis, what do we want to present, the insights, the charts, that's an entire different set um of, you know, type of work. We We've shared things like that before in free workshops as well. We call them root cause analysis where we actually went through the step-by-step process of what are, like you know, the decisions that you had to make with CloudCode to ensure that you land on the insight. He today we are taking the insight. We already landed on the insight. We know what what charts we want. We uh all we need we're doing uh in today's is now that you know what are the places that you could publish these insights um and how do we do that, right? Um and another very important thing is we're going to have giveaways for you guys because you signed up, you came all this while, so uh we really appreciate that and we want to make sure that you have get some value. And the value that you're going to get is I'm going to share with you the MCPs uh or in fact CLI. I'm going to share with you the command line interface of Google. How do you do this yourself? I'm going to share with you a PDF on how you connect your CloudCode with the Google uh CLI so that if you have an inside and analysis, all you need to do is just type the prompts that I'll share with you and get the analysis into Google Google Docs, right? Uh, and also Google Slides. Get the deck into the Google Slides because, uh, that's the focus of today's session where we are trying to tell you that not only Cloud Code could give you insights within Cloud Code and you could work with Cloud Code, but you could also take all these insights and publish into a software outside Cloud Code with an MCP with the CLI.

33:10 And that's the intent, yeah. Okay. Awesome. Uh, Prasanna, I'm saying, "What are the inputs data files for us to run those prompts?" Uh, you could get all of those from the free repo. If you're in If you could, like, you know, try it yourself. We also have a bunch of things. We have a course coming up uh, this Saturday Sunday, actually, where we literally walk you through uh, how you get the repo, how do you get it installed, how do you get the data yourself and, you know, run all these analysis yourself from end to end.

33:49 But, we actually also have all of that incorporated in the free repo. If you could do it yourself, uh, and, you know, if you know Cloud Code enough, you could definitely go and, you know, try it out yourself, as well. >> Yeah, we work with some select data in that um, course. We basically have a local database we build. We also connect to a Snowflake um, instance. But, you could do anything, right? If you you can go find a public data set, you can use your company data set. I've been doing a lot of stuff the past week with um, Strava, which is like a fitness running app. Um, and uh, they have an MCP to collect their data, so that's been pretty fun.

34:33 I'll share the link again. >> Awesome. And let me type this. I'm going to ask Now, now that we have Google Docs, let's try Notion, okay? So, the reason why we have all of this is um if you type {slash} MCP, you'll get a bunch of um like MCP servers that are on your Cloud Code. And you would understand if you have MCPs connected, you would see these MCPs. For example, you see that um I have a bunch of MCPs, and I need to keep validating it every time I try to use them, right? So, Notion is something I try to validate. Uh you keep basically need to go and authenticate. It opens a browser, and you authenticate yourself.

35:20 Uh I did that just before the session. So, you have Notion connected here. That's what it says, right? I I don't have Slack connected, Snowflake connected yet. For those things, I basically go and you know, click enter, and then Slack's related authentication kicks off, and I need to say yes, and that's how you get MCPs connected session to session. Uh first, you install MCPs, you go through MCP connections, and later on in every session or between session between couple of sessions, you keep connecting and authenticating with MCPs, right? And today's session, we are basically trying to show you how can Cloud Code connect with all these different types of software that are existing outside and share the information that's sitting in Cloud Code's brain to others, right? So, I just wanted to share with you like some uh like you know, back back of the like the structure or like the um frame uh how do I say? The backstage thing that happens when you have uh publishing an asset to Notion, you need to make sure that your MCP with Notion is connected and mine is connected right now. So, let me escape from this and ask it.

36:34 Can you help me uh create a Notion document? I'm currently talking to it. Sorry. I'm using Whisper Flow to talk to Claude Code right now and everything I speak um is going to get into the terminal and you can see that. This Notion document is something that I'll share with my team um and make sure that it contains all the details from executive summary to different parts of the funnel and also contain uh a bunch of uh details regarding the uh different parts of the funnel and ensure that all 18 charts that you have created for the insights get shared um and make the Notion document as detailed as possible and have multiple tabs in the Notion document as well uh so that we could leverage the multi-page structure of Notion.

37:36 Okay. So, this is something that I'm asking it to create right now. Let's see how long this will take. It's going to If this is going to take a really long time, we have around 14 16 minutes. I want to share with you what it created for me before, right? Because this is something that I ran before and so, it is continuing it. So, in the interest of time, what if possible by the end I can share with you what the um you know, output would be, but let me share with you the Notion doc that it'll come up with that I just ran before our session.

38:17 Ta-da. Where is it? Okay. Yep. Yeah. Okay. So, this is the Notion doc that it got shared, guys. So, if you look at it, I asked for it to give me the full analysis because this is, like I said, something that we'll share with our team. Uh look at these things that Claude code is actually pretty good at leveraging the software's inbuilt uh uh you know, formatting tweaks. Uh especially with Notion and the new age document uh you know, styles like Notion and Coda. It basically created this little summary blob for us. It has headline metrics. It has story and findings. It really nicely embedded the charts.

39:09 It has a recommendation. And look at these emojis. I didn't ask for the emojis. It created the emojis and you know, all of that. And it created all these tabs and you could click on them to go to these tabs. Look at this. Revenue and growth. No amount top line in 2024. What we found. And it gives It's giving me the charts related to it. Ideally, I'd like each of these under the chart under each of these uh you know, text rather than all the text at one go and the charts at one go. Right?

39:43 So, this is something that I would work with Claude code and ensuring, "Hey, uh I like what you have, but can you make sure that the text describing the chart is on top of uh the chart, but don't stack the text together and charts together." I would probably give that feedback to Claude code if I uh you know, was doing this actually real time. And I go back to here and then I could look at all other tabs it created for me. Look at this. Acquisition channel, the so what, and it created the device and the mobile gap. Look at the nice icon it create it used.

40:19 And it also has the so what incorporated here. And it talks about the retention. How many How does it look like for retention? And it also has the categories and margin. Uh what is the so what? Now that we have this. And it also has the operations and risk. Um and the type of uh like you know graphs it chose to share this with us. Uh it basically talks about what the support load is, the Nomad is being US-centric, and all of that. And the one more cool thing that Wait. That it did for me is the appendix. Appendix also has the sequel and methodology. Ideally in my regular work setting, we generally give a Databricks notebook link that people can click on to understand. But here, since it ran in the local local.db, I made it uh you know share the sequel with me that and it also has methodology because we want to share this with your team. You want to make sure that you're querying the right tables and you're using the right filters and stuff, right?

41:32 Awesome. So that's where we So we have 12 minutes. I can literally share with you the Google slide deck as well and we could go probably to answer questions and I'm sure there are tons of questions that you might have. Da da where am I? >> Yeah, if anyone has questions, feel free to start dropping them in. I really like um the Notion one. Also like we we've been talking about you know, share your analysis everywhere. I actually like it for um checking it myself because like you know, when you're working in cloud code like the output a lot of the time that they're creating it's creating for you is like markdown files, which is fine, but it's like I don't know. It's like kind of like tiring on my eyes to like read through those and switch around where it's like have a notion doc where the SQL queries are right under it and everything is right there.

42:23 And it's so fast to be like put this in this other format for me to read. It's like a it's a kind of just like a I don't know, a UX improvement way. >> Absolutely. >> For for reviewing the work myself. >> Yeah. Sometimes Claude code in the terminal is kind of daunting on your eyes. It's too much you get overwhelmed and I do that a bunch too. Almost anything that I read I'd like to summarize, put it in put it put the summary in notion for me and another thing that I do like a trick for you it if something that might help you guys is I comment on my notion and coda docs so that and I tell it to answer my comments within those notion comments as well so that I don't have to work with Claude code and you know the details. If it's really long conversation I do it with Claude code. If it's something simple I try to comment in my docs and Claude code reads those comments and gives me a like an answer to those comments right there.

43:18 Awesome. So let me quickly share a similar prompt that I give it telling that hey can you create a Google slide deck for me? This is what it creates for you. It basically has the insight and the graph uh right away and it picks the most important graphs and it came up with recommendations. Would this be a Google slide deck I'd share? Probably a slightly different one maybe. I'll probably have slightly more text explaining this uh what the you know what this is about because you probably need slightly more detail than what it is maybe a couple of lines underneath.

43:55 I'll work with Claude code to ensure that hey the next version of slide deck could you please add slightly more detail about these you know uh charts. I didn't actually iterate much with this guys. Generally I try to iterate. I wanted to show you what the first version of the prompt output would look like and that's the reason I have it here. Um in real world, I probably will iterate and ask the deck to be lit my perfect version of what I'd like it to be.

44:23 Okay, cool. Yeah, and uh wait, uh let me quickly run through the deck uh and you know >> Yeah, let me tell them a little bit a little bit about what's coming up in the next >> Yes, exactly. >> weekend and then and then also share out the uh after that share out the Yes, as I mentioned. One thing I I one question here, does it update the same doc for iterations? You can do that. You can have it um It depends what you want to do. I I sometimes I have it like not update the same doc cuz I'm not sure what it's going to do, so I have like one like version like two versions of it going at a time.

45:02 Uh so like a backup basically. Although it's pretty good if you say like undo your what you just did. Um but you can give it um you can give it permissions to edit it directly. You Yeah, a lot of the Google stuff is It's annoying to get set up, honestly, which is why I Sravya has created a bunch of instructions around it, but it is nice in terms of like I think it's the most uh it's the one that gives you the most capability but hey, you can do this and you can't do this. Like you can set up like hey, you actually can't edit docs, you can only create copies of them if you wanted that.

45:38 >> Awesome. Yeah, I just wanted to quickly run through what we have coming up. Uh we basically have we completed our first intro to workshop session yesterday. We'll have that in a month from now, I guess. But what's coming is this guys, we have a build it building the system. You remember I shared with you all the skills and agents. We have around 60 skills and you know, 20 and 30 agents that does this work for you. Get the analysis out the door, all the iterations of the analysis, how do we work with Cloud Code to get that out?

46:10 What are the skills and agents that were used to do the analysis? And how do you create those skills and agents yourself? And understand how we build the system, how we build the entire, you know, AI analyst system. We'll share all of that with you in the boot camp that's literally coming this weekend. And we have another version of the boot camp which is way advanced version of it where we basically try to get through multi-agent orchestration. How do you get one agent to verify other agents' work? And how do we do the auto research loops? How do we ensure that you use open source models, code X, and the validation piece and the context piece? How do you get that tighter so that you basically not just, you know, help get Cloud Code to help you do your work, but actually like 10x, 20x, even 30x your work and build systems that helps your entire team as well, right? So that's something that we'll cover in the advanced boot camp. And another parallel track, so the the top two ones are majorly about building systems with Cloud Code, but the we have another course called AI Analytics for Builders that is starting next week as well. You would you could consider this as a crash course for masters in product data science of Silicon Valley tech company where we literally go through entire framework of a what a product data scientist at Meta, at, you know, at Netflix, at all these companies that we all worked at. And we share with you the frameworks of how to frame questions, how to define metrics, how do you do deep dives, how do you design experiments, how do you do causal analysis, and how do you drive decisions and actually get impact. All of that would be part of this five-week course called AI Analytics for Builders.

47:58 We have some really cool discounts and packages. We kind of have a a bundle where we you could uh you know combine basically the you could combine the boot camp with the Analytics for Builders and you would get that uh for $1,800 and we we can actually share more with you, but we have coupons uh the codes as well that gets you 20% off off off these. Okay, so there are a bunch of giveaways that I'm going to give with you uh all.

48:30 The most important thing I would say is connecting Cloud Code to Google Docs. Uh I'll be honest, this is part of our paid boot camp MCPE and CLI connectivity thing, but we decided we'd love to share as many, you know, um things that are of valuable for you. So, that's the reason we decided we would share this with you so that you could start benefiting from connecting with Google Docs today and getting your analysis shipped there.

48:56 Awesome. So, let's jump to questions. >> Uh Emmanuel had a question. Um which of the three boot camps best suits someone that is into analytics engineering and leads a small team? I don't know what your um I opinion is Ravi. I said like uh it kind of depends on your learning goals. Like the two boot camps, the Cloud Computing Analytics boot camp We need a we need a I don't know a better name for it. And the Advanced AI Analytics boot camp, those are about building. We should just we should put that in the in the title back in the title. We used to have it. Um those are about building and then you can take analytics systems and then the five-week thing is about doing analysis. So, it's like using analytic systems to do the analysis. So, it kind of depends like for your team like oh are you focused on um building analytic analytics engineering team uh a system, then maybe that's more like the boot camps, but if your team's also going to be like doing end-to-end analysis of the data, then that's the five-week.

50:02 >> So, one thing I'd like to share to what Shawn added was uh we have We have like sales people. We have product managers. Like all multiple different styles of roles taking all of our courses. They In fact, people who are not in data or people who are adjacent to data science would benefit a lot from AI analytics for builders because they literally understand the entire frameworks of how product data science works, right? And we also have a bunch of product data scientists who took the course themselves because that will basically help them understand their the frameworks that they are used to all their career, but how do you use those frameworks with AI because we teach not just the frameworks, we teach how do you use those frameworks with AI.

50:48 So, that's what the builders course is about. And it's a great refresher and a great AI companion like how do you use frameworks with AI companionship for data folk. For non-data folks, it just gets you opens the door for like doing anything with data. It So, that's the reason we it's been popular in like non-data people as well. And coming to the agentic building agentic systems, the two boot camps, those are for anyone who would like to basically build and work with agentic systems and 10x your productivity thing.

51:24 And even more beneficial if you're actually a data person and you'd like to use the repo that we built, the AI analyst plus repo that we share as part of the boot camp where it contains like multiple myriads of like skills and agents that goes from how do you frame a question better? What type of visualization do you want? How do you like the colors to be, the themes to be? what should be the title? Like, you know, you to actually things like how do you design a metric? How do you validate metrics and stuff like that? Yeah.

51:57 >> Yeah, and actually maybe it's to even speak a little more like kind of like philosophically around like why we created these courses. Well, particularly actually just the AI analyst curriculum of the five-week courses like we are really trying to prepare people for where we believe like the puck is heading. And so, what this came out of is we have this podcast and we interviewed about 10 CEOs, founders, VPs of a genetic analytics company. Like, you know, like the CEO of Hex was on our podcast. The VP of Gen AI for analytics of Tableau was on our podcast.

52:32 And this is back in Q3 of last year and it came very apparent to us that like, "Hey, uh genetic analytics is like this is coming for sure. There's so many people in this market who are creating this. And this is before or like Opus 4.6 and stuff. That's already pretty good. Um and so we saw this. We're envisioning that like, you know, more people data scientists, data analysts, they're going to be able to do faster, more robust, more analysis on their own. And then other people who are kind of like tangential, maybe they're upstream in the workflow or downstream, um they are going to be able to go more end-to-end and leverage data in their own workflows. So, like Stravia said, designers and PMs, but also analytics engineers. So, like if I think about it in terms of like what I've seen with like my team that's worked on it is that um okay, data scientists, data analysts, they're able to get a bunch more analysis done. Now they have free time.

53:28 What are they going to do with that free time? Well, they can do a couple things. They can go more end-to-end, but there's two sides to that. They can either now act on the insights and start doing more kind of like product development work that the decisions from their analysis are making, or they can start doing analysis that they couldn't do before because the data wasn't in a way in a format or available the data foundations weren't built. So, they can start doing some analytics engineering work. Not great analytics engineering work, but they can stretch to that cuz it is just upstream of them.

53:58 Same thing with the AEs on my team is now they are like making our data foundations better than ever cuz they're building a genetic systems to, you know, build their DBT models. And so, they're they're already almost doing the analysis as well. And some analytics engineers like already do a lot of analysis. The ones we I work with, they're not quite doing it, but they're working with the stakeholders. They're building the metric definitions. And so, now they're able to like take it that one step further into like actually doing like data science and analysis.

54:29 Those are I So, I do think that five-week course if if your goal is like to stretch teams into like different types of domains of work, that is an opportunity there for sure. That's why we created at least. >> Yeah. I know we are out of time, but we could take a couple questions or something. If you have anything, you could ask live. >> I think we have some stuff we're going to share out. Are you going to share that out in an email later today?

54:59 >> would share I would share the email. You'll also receive the recording from Maybelline as well. So, like you know, later on if you want to like follow up, I totally suggest you to like try the Google CLI connector. You would be if you don't already have done that with like you know, working with MCPs or connectors, the first few times is definitely magical. I remember it from six months ago or I don't remember now how long ago, but it was it definitely changes how you work.

55:34 >> So, I dropped the builder's course in the chat there. Oh, do you have a promo code on in your slides for the bootcamp for this? >> Uh sorry, the Yeah, I the promo code? Oh, I can give the promo code. Yes, yes. I'll I'll share it in email as well. >> Okay, cool. >> Uh Carol has a question. Do you want to take that, uh Sean, while I get the promo codes? >> Yeah, so there's um Okay, so we run the bootcamp. We're going to try We're trying something new actually this time. So, typically we run it 7:00 a.m. to 11:00 a.m. Pacific time Saturday and Sunday.

56:16 Um so, 4 hours in the morning. It's all live in Zoom. The reason the bootcamp is live is because the in the space is changing so fast, I don't really want to record videos about how to do something in Cloud Code and then it changes 2 months later. So, we're constantly updating the bootcamp. Whereas the 5-week course, it's part async, part live. There's a lot of evergreen information around like, "Hey, how do you do root cause analysis?" That's not going to change.

56:46 Um but for the bootcamp, yeah, 7:00 a.m. to uh 11:00 a.m. Pacific time. But then we are going to have some We're going to try something new this time. We're going to have a bunch of bonus async material um around validation, more data warehouse connections, around context management. That will basically have Monday, Tuesday, Wednesday, Thursday the following week that you can do on your own time. It'll be like a 20-minute video a day, some exercises, and then you can kind of let it sink in through the week, and then Friday um I don't know what time, probably the morning again, Pacific time, we'll do a office hours that's optional and kind of sync with everyone and see how the week went after doing the bootcamp.

57:26 Um and then the other so, we're experimenting with that. And then the other thing we're going to experiment with in July, if you don't want to do a weekend or if if this weekend's too soon, is we're going to do a weekday version of it where we do Monday to Friday 2 hours 7:00 a.m. to 9:00 a.m. Pacific time each day. That'll be recorded. The recordings will go out. Um I'll add an hour later, so around 10:00 a.m. Pacific if you want to do it a day sync. Um So just add some flexibility. So I'm not sure exactly what that time is in Europe. 7:00 a.m. It's like What is it like 8-hour difference or 11-hour difference or something?

58:08 >> it is uh 7:00 a.m. is it on 3:30 p.m.? >> Something like that. Yeah, so it'll be like your your afternoons. >> 4:30 p.m. >> Yeah, and then I think the weekday version will be interesting too because then, you know, you can like take 2 hours in the morning, go try stuff out the rest of the day on your own, do some exercises, do it at work. And then you can come back with questions the next day during that 2 hours and it's not like uh as intense session for 4 hours, but some people like more intense, so we'll try them both out.

58:41 We are definitely up for trying other stuff out in the future, too. I know we don't we don't we're not as Aussie friendly as we should be. We need to do some evening courses Pacific time so our friends in Australia and New Zealand have a better time. Cool. We got two for one with the with the five-weekend debut camp coming up. If you're interested in that, um basically give us a DM on on LinkedIn or or in the Slack channel or we'll send out an email and you can reply there.

59:19 You can email me at um shawn@lambdalabs.ai anytime. if you want to get on a call for 15 minutes and chat about what's best for you, just let me know. I'm pretty free. >> And I just shared the links with the promo codes as well. So that you're all are like you know access them. Yeah. >> I know anyone not in the Slack? I feel like most people are probably in the Slack cuz I recognize a lot of names here.

59:54 But uh we do most updates there. So if you're not in the Slack, use the link there. >> Awesome. >> Cool. Any other questions? Nice. >> Okay. This was great. A nice cozy session. Thank you so much everyone. I'll share with you all the giveaways soon. Yeah.

60:26 See you. >> See you.