220 - How Claude Code Just Replaced All My Coding Tools with Elizabeth Knopf

20 Aug 2025 · 45 min · 17 chapters

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In short

The “model war” cools as new releases converge; then the host demos Claude Code, claiming it can replace many coding/automation tools by letting Claude build and run local projects from natural-language prompts.

Guest backgrounds

Elizabeth Knopf (called “Liz”) is a power user who builds coding/automation workflows (e.g., scraping Indeed via n8n/Appify, processing large CSVs, creating local HTML dashboards). The other host is a frequent model user and podcaster.

Key claims

Claude Code is “universally” praised; it simplifies setup (one terminal install) and reduces friction versus other tools. It can handle multi-step tasks (cleaning, extracting, classifying, analyzing, visualizing) and iteratively fix errors. It uses planning mode and switches between Sonnet/Opus models; outputs run locally via localhost.

Notable examples

Classifying 100k+ job listings from scraped CSVs into categories with confidence scores; generating interactive dashboard-style analysis (hovering charts, filtering, exporting reports) and viewing results in a local browser.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

Tap a time to open that second in VO

The State of AI Models

0:45 to 3:20

Discussion on the current AI model landscape and the performance of various releases.

“I think it's the only thing that has universally been talked about in a positive light.”

ChatGPT vs. Other Models

3:20 to 7:00

Comparative analysis of ChatGPT and its contemporaries based on recent updates.

“Then they were like, and we've improved the way that we actually process your prompts, the way that we batch them.”

User Experience and Stickiness

7:00 to 10:10

Exploring user experiences with AI models and the importance of product stickiness.

“I mean, it probably was in the works because people believe this feedback.”

Introduction to Cloud Code

10:10 to 12:00

Beginning of the hands-on tutorial for installing and using Claude Code.

“And frankly, I still use it the most just because it has so much information on me.”

Step-by-Step Cloud Code Setup

12:00 to 14:01

Detailed walkthrough of setting up Cloud Code, including installation instructions.

“Do you want me to share my screen first?”

Introduction to Node.js and Claude Code

14:01 to 17:42

Learn about Node.js and how to install and use Claude Code effectively.

“So there's all these things, libraries and languages, which is basically like, Just think of it as sets of code that your computer can access and use.”

Building Projects with Claude Code

17:42 to 21:42

Explore how to build projects, manage files, and use features within Claude Code.

“So I don't know if it would be helpful for we could kick off one or I can maybe give a preview of some end states of things I've recently built.”

Using GitHub and Managing Projects

21:42 to 23:18

Understand the importance of GitHub for version control and project management.

“you're going to be eating a ton of tokens one thing and then i want to then i want you to show me your projects do i close this or do i just always keep this open okay so you want to keep this open.”

Project Showcase and Data Analysis

24:14 to 28:01

See examples of projects built with Claude Code and learn about data analysis techniques.

“So this is the end state of some projects I was recently working on.”

Understanding Cloud Code Functionality

28:01 to 29:27

Learn about the initial steps in utilizing Cloud Code for data processing.

“I went to my terminal and I started down this process.”
Show all 17 chapters

Real-World Application of AI in Accounting

29:28 to 30:46

Discover how AI can be applied in accounting for transaction analysis.

“I gave it a lot of information to basically help produce a file.”

Building Context for AI Classification

30:47 to 33:04

Explore the importance of context in training AI for data classification.

“And there's hundreds of thousands of them a month.”

Iterating AI Models for Better Results

33:05 to 35:58

Understand the process of iterating AI models to improve accuracy.

“Wait, so what is this confidence category type?”

Creating Interactive Data Visualizations with AI

35:59 to 38:18

Learn how to leverage AI for creating interactive data analysis tools.

“So literally like, so maybe let's go through a little bit of a process.”

Revolutionizing Data Cleaning and Analysis

38:19 to 41:28

Discover how AI simplifies the data cleaning and analysis process.

“Make sure the data is like, how did you anyways, how did you do the data mapping?”

Automating Data Exploration with Claude Code

41:29 to 42:04

Learn about the automation capabilities of Claude Code in data exploration.

“So I told Claude, I said, go to planning, switch to planning.”

Automating Data Analysis with Claude Code

42:04 to 44:18

Learn how Claude Code automates data analysis tasks effortlessly.

“And it basically gave me like, you know, earlier version of this, which, you know, had a little bit of tweaks, but essentially this, essentially what I just showed.”
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Transcript

Automatic transcript. May contain errors.

0:00The reason I put ChatGPT third is just because I think it's the Honda Accord of the models. You know what I mean? It's like, it's good. It's going to last. It's going to get you to where you need to go. You're not driving an Audi, but you're also, you know, you're not getting a Hyundai accent. All right. Flawed code is the only thing that has come out recently where universally people are like, this is mind-blowing. This is incredible. It's like you're back in the 90s here. Yeah, but don't get intimidated, anyone watching. This is criminal. Like legitimately, I feel overwhelmed. I really do. Please help me, Liz.

0:37Liz, I don't know. It's been a couple of weeks since we've spoken because the summer's crazy. So much has freaking happened. And what I want to talk about today, which we're going to get to is Cloud Code. I think it's the only thing that has universally been talked about in a positive light. You told me just before we hopped on here, that's going to blow my mind and that you're kind of doing away with all of your other tools that you've been working with because Cloud Code is that freaking good. So you're going to walk me through exactly how to open it up, install it, put it on my computer, and then like actually how to use it.

1:07But let's set the table a little bit for people before we get to cloud code. Right now, the model war has not only heated up, it feels like it's kind of cooled down. No? Yeah. Yeah. A little bit. I mean, I don't know if it's the summer doldrums and, you know, people were on vacation just sort of chilling, but But I've just felt like people who are both doomsday folks as well as like hype, you know, EACC, whatever, you know, folks that are just like, it's going to be utopia. We're all automated. Both sides have just sort of simmered. That's probably because of a couple of releases and also people sort of actually seeing the adoption or even the lack thereof of adoption within organizations.

1:53So at least that's the vibe on Twitter. Okay. So the thing that was crazy to me, what we've seen over the last few months, we've seen Gemini 2.5 come out. We've seen just in the last week, Claude Opus 4.1. We've seen Grok 4 come out. And now we've seen ChatGPT 5. And what has been interesting about each one of those model releases is I think everybody was holding their breath for ChatGPT 5. Sam Alton was on the press tour and he's like, GPT 5 is the first time that I actually felt like, wow. You know what I mean? And he's like breathlessly describing GPT-5. And then it comes out and we start looking at this.

2:30So this is my chat GPT. I asked it to compare all of these new models. And the interesting thing is if you look up here on the top, we've got all of these tests that they've used to sort of score these models. This over here, this is humanity's last exam. It's a crazy, crazy hard test. And you can see here GPT-5 scored 25 and 26. I don't know what the text or tools only difference is, but let's just combine those two for right now. So it's a hard test. It scores a 25 out of 100. Grok 4 scores a 50. Okay, that's weird. Grok 4 is a better model. And then I start going and playing with GPT-5. And at first I'm excited because they're like, oh, the persistent memory has been improved.

3:13Oh, cool. Now I can remember all of my chats across all of my projects, which I didn't used to be able to do. So that was a really cool improvement. Then they were like, and we've improved the way that we actually process your prompts, the way that we batch them. Now there's no, there's an equal weight given to your questions as opposed to being more focused on the end of your prompt, as opposed to the beginning of your prompt. The processing is supposed to be faster as well, because of this new batching, I guess, technique that they've implemented. And they've taken away all of the 03, 03 mini, 03 mini high, you know, Chatsby B4, whatever.

3:49And it's just kind of like three models. But I've played around with it. It's okay. It's like a little bit better, but it just kind of feels this time like an iteration and not a step function change. And I think that's what you're talking about. Like the vibe is kind of shifted from you're wowed every six months with a new model to, okay, now it's just iterative. Yep. And I think what's going on, so there's a couple, a number of things you just highlighted. So first off, it does feel like a lot of the models and their pictures are just converging there in terms of being, having parody. So, you know, one will have this feature around video generation, the next will pop out with that sort of what it seems like is happening overall.

4:31But I do think with ChatGPT5, like we were expecting, yes, to have it blow our minds. But one of the things that I think people are at least underappreciating is sort of who the audience is and then what they did with the product. So less so about like the technology behind it, but actually the product. So I think one big challenge was that, you know, you had to select the model. Like most people using chat GPT don't know what three means, 3.5, four, five, like they don't know the difference. Then they started giving, you know, more options. And there's the concept of the paradox of choice. when you have too many options, you're overloaded and you don't know what to use for what.

5:10I personally like that because you could get different outputs and you'd get some interesting nuances when you played around with those different combinations. However, for the average person, like they've simplified and actually sort of reminds me of like Google's first search bar, you know, they had just one box and now that's what ChatGPT is. It's like one box, no choices, there's like, here you go, put in your prompt. And so I think from a knowing that ChatGPT has more of the average consumer across their large distribution. They're removing friction, basically, is like they're making it easier.

5:48And they've made it multimodal. And what I mean by that is typically people talk about multimodal as text, picture, video, sound, AI can do all those things and then spit something out. That's not what I mean. I mean, in this, in this context, it can kind of toggle between the chain of thought that you would get with the O3, the LLM based model that you're getting with 4.0 and the image generation. So it's like making its own judgment based on its own discretion of which model to use based on the question that you're asking. So that, that part is cool. I think the other thing that's cool is it does a much better job but multi-step tasks.

6:27Yes. So for example, if I'm like, go do research on this market, then take the research and run it through this criteria, then take the output from there and rank all of them from best to worst. It used to not be able to do that. It would just, that would be too much for it. It's gotten better at sort of this multi-step execution. So those things are all cool. But to your point, they just made it easier to use, right? Like the iPhone has always just been the iPhone. It's just simple. Everyone knows what they're getting out of an iPhone. And I think they took a product step. I don't know if, do you think that's a Johnny Ive thing?

7:00Like, is this a Johnny Ive fingerprint? Or do you think this was in the works? I mean, it probably was in the works because people believe this feedback. I mean, this was all over the internet from news. We could sort of see this. And I think there's that piece of it. And then there's probably the cost element too, because like, you know, if you run a simple prompt for deep research, you know, that eats up a lot of costs. And while there was a lot of rate funding, everything, you know, they're probably doing it from a cost optimization standpoint. You know, they're probably burning a ton of cash on, you know, GPUs.

7:35So I bet that was also a factor in how they came to doing this. So it was probably some users, some of that, maybe Johnny Ive. I don't know. So we talked about this. Claude code is the only thing that has come out recently where universally people are like, this is mind blowing. This is incredible. We have to do cloud code. So we're going to get to that in a second before we do. Now, after all these models have been released, how would you rank these models best to worst? Let's just use the four horsemen, grok four, Gemini, chat GPT, cloud. How would you rank them? So I will qualify this. It depends on the use case, but I will do a net net in terms of actually how I'm using or which ones I most frequently use.

8:18So I'll use that as metric. So Anthropic, Claude. Then I would say Grok for sure. And Grok, I really like for like real-time information. And then I'm just sort of using it with coding. But again, and getting back to the friction point that we'll get into with Claude Code, there's still just so much friction with all the other ones to like build things. And so when I'm just using it from like conversation and research, that's yeah. And then third, Gemini. And then I'll say Tachibiki is my lower. Wow. Yeah. Okay. So here's my ranking. So video is cool, but I have what scientists call a face for radio.

8:57And so what's even cooler is long form audio via my podcast and my newsletter. Nickonomicspod.com. Go there for free. Subscribe to my newsletter. It's one email per week. Super tactical. And then go to my audio podcast. I do three to five episodes a week, depending on how curious I am. And it's stuff like this. It's all free. No sleazy sales pitches. nickonomicspod.com number one grok i don't use grok the most i do think it's probably the best just general purpose model at this point pretty crazy that they become a frontier model in like two iterations nuts two claude dude like the 4.1 update is it's really good and claude has done a really good job i think making it easy for the user their projects are incredible the artifacts now, the way that you can store artifacts is I'm using it all the time.

9:45Like you were the first one to show me. I use it all the time now. It's incredible. Third is ChatGPT. And then fourth is Gemini. The reason I put ChatGPT third is just because I think it's the Honda Accord of the models. You know what I mean? It's like, it's good. It's going to last. It's going to get you to where you need to go. You're not driving an Audi, but you're also, you know, you're not getting a Hyundai accent. You got a nice car. It does what it's supposed to. It's a good thought partner. And frankly, I still use it the most just because it has so much information on me. It can quickly give me good answers.

10:18And I'm trying to migrate to Claude and Grock, but it's going to take some time. So that's my ranking for it. Well, and I think you bring up a really good point is there is a stickiness to all these products. We're probably power users versus, again, the average person using them. I know I've heard people say, oh, I set things up in Chachupiti, so I'm just sticking with it, or that's the one I want to learn because there's a perception that it's hard to get into the other ones or set up all your contacts. And so I do think that's huge value that Chachupiti has. And so even if they sort of stayed at this level for a while, they're not going to have much churn or attrition or see behavioral changes from people switching.

10:59Power users, yes, maybe. But I think you're starting to see these guys sort of focus on a specific type of user, or at least like consider the majority of their base and cater their iterations and development towards that. Yeah. It's very easy to get lost in the moment and think like, oh, chat GPT is falling behind. Oh, open AI. Oh, Sam Altman. Yes, all that stuff is probably true if you're forward looking. But what they've done in the last two years, three years, however long it's been since they launched 3.0. I think it was 3.0. That was the first one or 3.5. Anyways, it's undeniable. Even if they don't grow at the same rate, even if they lose market prominence, they've got a multi-hundred billion dollar company.

11:39It's a legit company. They've got a good product now. They've got a good team. They've probably lost a lot of people to the brain drain because freaking Mark Zuckerberg is paying a billion dollars to people. It's like sports contracts. It's nuts. But they're entrenched. They're a market leader in this space. And to your point, I think we underestimate how sticky these products are and will be moving forward. So, okay. You're going to show me cloud code. What do I need to do? Do you want me to share my screen first? Or do you want to share your screen? Share your screen so that we can go through like step-by-step.

12:10Here we go. Cloud code. So I click on cloud code over here and I get this like settings. It doesn't just come up. So it's actually not in cloud. You have to open the terminal on your computer. So I'm going to do this step-by-step thing so that people can actually follow along and take advantage of this really, really cool iteration that Claude has released to us. So if you're in Claude, you come over here on the left-hand side, click on Claude code. Now, in order to Claude code, you have to have the max or the pro plan. And I think it's, is it$100 a month? I know one of them I want to say is like, yeah, one of them is$100.

12:45It's not cheap. It's not cheap. But I think after you see what we're about to do, you'll be convinced. So you click here. We're going to click on install instructions and this window pops up. Okay. And this is where I got like overwhelmed. Cause I'm like, I don't know what the, I don't know what I'm supposed to do. I'm not a coder. What's this green screen. I'm not, am I trying to hack into the Pentagon? What am I supposed to do from here? Okay. So actually just back up one sec. So people aren't turned off by the a hundred dollars pro is only$17 a month. So it's accessible. Sorry. Sorry. max is a hundred dollars a month but you will get you probably want to graduate to the max plan once you've utilized the pro but okay so okay good call 17 bucks so it's accessible okay okay so now you're here at this little black box scary looking text so what you want to do is in that upper right hand corner is that little copy icon it's within the black box not copy page oh yep yep copy that All right.

13:43Okay. What you're going to do is go to. So you could also come, is it come here? I think I opened this link. Okay. And you're going to, you'll walk me through it. I know. But this was the link that I just clicked on. And this kind of walks you through is like, all right, step one, install cloud code. And this node.js18, what is that? That's just a language. So there's all these things, libraries and languages, which is basically like, Just think of it as sets of code that your computer can access and use. They're not all set up on your computer when you open up or about to open up. So you do have to copy and paste these lines.

14:20You don't even need to type them. Just copy and paste these lines and I'll include those. All right. So you're looking at my terminal now? Yes. Okay. So for folks at home, make sure you have Node.js 18 or newer. Now we can get into this, which is installing Anthropic. So I'll come here, copy, paste. Yes. Cool. Now actually just see if you just type in Claude what happens. Ready? Yes. Okay. So allow, allow. So is this it? This is it. This is it. You've arrived. It's like really like you're back in the nineties here. And yeah. Don't get intimidated anyone watching. This is like legitimately I feel overwhelmed.

15:06Yeah, I really do. Please help me, Liz. There are better user interfaces that we'll get to maybe in a future video, but we'll keep it some. So pick your dark mode, light mode. Pick your. Oh, do I just click on it or like what do I do? Put in the number. Where do I put it in? You just type. Just start typing in it all. One. Okay. Yep. two what the freak what's happening now it connected to it needs to log in to your actual anthropic account so it opened up the browser on your behalf and now you got to log in to whichever account is associated with your cloud account i've never worked in a console before so i just type i don't like yeah so there are some like things that get annoying like copy and paste like to go back like you have to delete and it's like, like you have to, it's not the best interface and you'll get annoyed at certain points, but for the most part, like this is the best starting just to get a bit.

16:06Do I just need to make sure I remember that it's in users, Nicholas Haluski. Okay. That's my file path. So for your file path, you can create new ones from here as well. So once you get into cloud, you can actually tell it, create new folders. So think about where you want to have all your files that it'll be generated. Interesting. So if you want to have it, like right now I use documents as the main folder and then I have cloud code is literally the name of the folder. And then from there I have objects that I'll name and then. But it's stored locally. It's not, I'm not storing this to like the Google drive or anything.

16:40Right now this is not connected. Well, I shouldn't say it's not connected to the internet because it is, it's not like the files you're generating are not going to be like hosted on a website. They're going to like, when you open up what you've done. It'll be on a local site called localhost colon, and then I'll have a number. It's asking me to, how does blank file path work? So I just enter in a document. Yeah. So just say like, say like what, where would you want, say, create a new folder? Okay. This kind of feels MCP ish. Am I off? Sort of, I guess so. A little bit, a little bit. It actually does use MCP in some ways when you need it to, but I want people just to really, you are just speaking English to it once you're in Claude.

17:26I guess what I meant by MCP is like, I can now have access to my local files on my desktop without having to go like copy, paste, import. Yes. Wow. Okay. Okay. So Claude code has been created successfully. Okay. So now we get to build a project. So I don't know if it would be helpful for we could kick off one or I can maybe give a preview of some end states of things I've recently built. So this is it. I'm in cloud code. You're in cloud code. So now let me actually before we get started with some of my stuff, a couple of things that are important to know when you're about to get started. OK, number one, you if you don't know what to do, you can literally prompt here.

18:07What do I do, Claude? Like just ask Claude, what do I do? The second thing that's super important is say like switch to planning mode. Planning mode helps you actually think through and break down what you're going to be doing in your project. So think of them as a project manager who's like, you're the hyped up CEO that like is like, I want to do this cool thing. Then someone goes and figures out like, how do you do that and implement it? So that's sort of what clients do in planning mode. It'll help break things down into smaller chunks to figure out the best way to build what you want to build.

18:40So again, it is using both Sonnet and Opus models, which is another thing to highlight is you might want to switch back and forth between those. And the only sort of like pseudo code you'll need to know is like slash model and select which one. But if you go to planning mode, it'll optimize and delete Opus, and then you'll switch back to Sonnet because your token will get eaten quickly. And so this is just like a little thing to know. There's a bunch of other, as you can see, other things you'll be able to learn. I don't want to overwhelm people. What's a working directory? Working directory, that's basically like your file folder.

19:19That's a directory like as a folder. So like when you're, again, you know, in code language, it would be like seeing and then, you know, making a directory, M-K-D-I-R, like other little things you would say or type in. I can build an agent? Yeah, you can build an agent in here. Like you can build like anything in here. Like it's really that awesome. So people who maybe were like, I tried NAN or I tried make.com and it just seemed too much like that. And maybe like lovable, maybe replet were just a bit more accessible, but then you would get stuck. This is basically that level of easiness from the replet and lovable type thing, but add the full scope of functionality that like an NAN and make would actually fulfill.

20:01So it is really the best of everything and it does it all in code and it makes it just easy because you're speaking, you're typing or speaking to it in English or your native language and it is building the things. All right. So I just asked a question. I said, I would like to create an agent, but I don't know what type of agent that I want. Can you help me think through what agent I should start with? Cool. I hit enter and then I could see it thinking. And then I saw this tokens. It was like tokens being used. when I went in the beginning and I had to select the$5. Yes. It's you. So I'm now paying on top of what I already pay in a membership or a subscription to use this.

20:43So I'm using the max plan where I don't pay attention. But theoretically, yes. If they're tracking my tokens. Yes. Yes. Okay. Yeah. So that's like something you could maybe build to start is like track my tokens and my usage just to make sure. because then as if you're not on the bigger plan, you're going to want to start optimizing for token usage. Like that will, they'll get eaten quickly. There are some tips and tricks to address that. So you don't do that. Number one, you want to be using Sonnet instead of Opus. Opus for planning, Sonnet for building. And then number two, you're going to want to clear your chats because what's happening right now is it's taking in the full context, text meaning every your whole history of your chat every time and then it'll start taking in all your files so when you start clearing the chats and you start building like a new feature you want to make sure you're sort of summarizing things so that there's enough context to be added in where it's not taking in everything from before because when you're putting in everything you're going to be eating a ton of tokens one thing and then i want to then i want you to show me your projects do i close this or do i just always keep this open okay so you want to keep this open.

21:51This is the other thing. Like I've started building my own little tool for like tracking things because when you close this, it's gone. The chat's gone, but your files will remain. So your project and your files will remain there. So you can still access your project and you'll just have to tell Claude to go access the full. And then, but if you don't have a ton of like history or context, there might be things that are missed. The second thing is like, if you're like it made a change and you didn't like the change, like there's not an easy way to go backwards. So you're going to want to use GitHub.

22:24And this is where you're going to have to set up GitHub. And that's a little bit of, you know, a process. Maybe we could do that next week. I don't know for right now. It will share a little bit of time, but that is something that's super important to do. Isn't it called a markdown as you're like writing code? So you save different versions or no? Okay. So this is, that's a.md file is what you're talking about markdown. So you're going want to create something called claw.md which it now sort of does automatically it'll create this file which is essentially your rules file that's like your master thing to say okay you can tell it okay every time i make a change add it to my update github and i actually think it is doing that relatively automatically or very frequently by on its own okay you're putting the rules there like things you want to remember yeah well we'll look at github later i want to now stop sharing my screen and I'm going to let you do your thing.

23:17Yes. If I did close it, where would, how would I go reopen it? Okay. So you would again, go open terminal and literally blank screen, Claude, and it'll pop up. You'll see that little like sweet. Yeah. It'll show you and then say, what do you want to do with basically? And then you just pop in and then you'll just say, go to folder X, Y, and Z. Yeah. Yeah. Okay. Hey, I don't know if you remember this, but when we started this podcast, we entered into a social contract. I would spend time, energy, and money producing this podcast, interviewing these individuals and giving you insights into how to build, buy, start, grow your business.

23:52And you would like subscribe and leave me five-star review. Now out of that, we both get to talk to really cool people and hear really cool insights. We both get a ton of value, but I just want to help you keep your word. So would you do me a favor? Will you go leave a five-star review for me on Apple or Spotify. It would really help. And if you want, even share this with a friend. All right. So let me share my screen. So this is the end state of some projects I was recently working on. So basically what I started with and how I got here. Well, before you do that, will you just describe what I'm looking at?

24:29Okay. So this, this one right here is a data cleanup tool. This is like, it's very ugly, but it's functional, a data analysis tool. And this is basically the output of Claude doing the full analysis for me. And I had it in a couple of different ways. So where am I? So what am I looking at? Am I looking at like a, I'm so stupid. I'm sorry. A word doc. Is this like an HTML file? What is this? I can't see for some reason when I share my screen the – here, let me see if I cop it out if it shows. No, because then it – Did you open that up from the files that were saved locally on your computer? Yes.

25:07So this is something called – you can't see it, unfortunately, but basically you have something called localhost colon 8000. Rather than having a URL to a website up in my browser in Chrome, I have localhost colon 8000. So when you double click, you open that file, it opens in Chrome, the local host 8 ,000? When I was in Claude, I said, can you provide like an HTML site for me to see this tool? It provided me literally this address, this local host address. So then I copy and pasted it and put it in my browser. And I think I even just was able to click on it from Claude and it popped up. So if you don't know where to locate your thing, ask Claude, say, where do I find this thing you just built for me?

25:55Or how can I view it in X, Y, and Z way? How can I see it in a browser? How can I see it in some manner in a CSV file? It will tell you where to go. So if you don't know what to do again. So this is basically the end of a project, backing up a sec with some context of what this was about. I had an Excel spreadsheet from scraping. Well, actually, first off, I was trying to get an analysis on what geographic locations had certain jobs that there were a lot of job openings, basically to see where there might be gaps in supply and demand for certain types of jobs. So what I did was I had initially built out an NAN scraper that would use Appify and essentially scraped Indeed and a bunch of other websites.

26:42That took forever because the tool I was using was just like throwing in one by one URL, scraping it, it would take some time, blah, blah, blah. Then I ended up with a CSV file that was 300 ,000 rows by about 30 different columns. So that was a ton of data. Then I started adding formulas into that and it just got huge and heavy and I started crashing Excel. I then, it was a it was very text heavy. So I was like, I need to classify the data and I needed to extract some information from that text. So I was like, I cannot do this by hand. I can't do this with any of my like Excel skills because it would just either be take too much time.

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27:25It would take too much. Like it just was getting really frustrating because again, it would take, you know, pinwheel a nightmare for me. So I was like, okay, let me try to build something in NNN. So I went in again and I build something out and I'm like, this is going to take forever. Google spreadsheets I couldn't access from N8N. Doing my local CSV was like a problem. So I updated it to like some, a database by Google called BigQuery, which is then a SQL database. So then it's like getting, you know, levels of complication that, you know, the average person is just not going to do. So I was like, you know, maybe I can use Cloud Code.

28:00So I went back to Cloud Code. I went to my terminal and I started down this process. So what I first thought was, okay, I need to think about this step I set because that's how I've been sort of taught within the context of make.com, Zapier. You have to think through the process. I'm going to stop you for one second. So for people that are listening, when she's saying NAN, make.com, Lindy, Zapier, these are all websites that help you build automations. So they act like the hub, but they've got very specific if this, then that rules set in place. So what she's talking about is she's kind of been trained on thinking through, all right, under the current model of setting up automations, how do I create this software?

28:46But Cloud Code is not like that. So sorry, keep going. Exactly. So I was like, okay, the first step of what I need to do is just clean up the data where I need to basically be extracting information and then I'm gonna be classifying it. So those were the two things, classifying it, and then I'm gonna be analyzing it. So those were sort of the three buckets of things I'm gonna do. So I'm like, okay, I need to build tools specific to each. So I went through, and this was the first one I did, and I wish I had taken some screenshots of what it originally looked like, but essentially what I had was literally uploading a CSV.

29:19I didn't even prompt it really for any sort of UI elements. It just generated this on its own. Okay. And I basically pulled it. Okay, I need to, here's some context about my project. I said, here's what I'm trying to do. I need to upload the CSV. Here's some of the things. And I gave it a lot of like context. I gave it a lot of information to basically help produce a file. And here I can do this real quick. So you upload the CSV. What is it? What did you tell it to pull out? Yeah. Did you say like, go to this column and pull this information from the specific column? Exactly. So I was making specific rules based on like how I was doing about it from, I think this is the right one.

30:00Okay. From like what I would do manually. Like I was thinking, okay, here's this data structure because it was very consistent structures, but there was some variation of like the C first, then the job title first, or like at different parts in the sentences. So I was like breaking that down and trying to give Claude that context rather than having it figure it out on its own. So I don't actually know if this was the right file. So let's see. And we'll only do a test back. I literally had this almost the exact conversation about this two weeks ago. About Claude Code? No, no, no, no. Like about this problem set.

30:34So I was talking to an accountant. Uh-huh. And he's the only one within his organization that reviews all of the credit card transactions and all the transactions that they do through a marketplace. Okay. And there's hundreds of thousands of them a month. Maybe not hundreds, tens of thousands of them a month. And I asked him, I'm like, how do you decide when a red flag or a yellow flag is triggered based on the spending habits? He's like, I kind of just eyeball it. And based on what I've seen month over month over month, if anything looks out of whack, I'll double click into it. And I was like, well, are you coding each one of these expenses?

31:12He's like, nah, not really. Anyways, he gave me his whole explanation. And I'm like, well, why don't you code it? He's like, well, I would have to go in and actually tell it the right rules. Look for this word, and then this word means this, et cetera. And unfortunately, it just doesn't have the context that it needs. And I was like, you realize you just described the use case of AI, right? It uses context to make decisions. and he was like yeah but how would I use AI to go through these massive spreadsheets and then code the expenses based on my experience where they should go and I was like I don't know that's actually a good question I was already thinking through it this is it this is like you just built a tool to do that yes I mean this comes up in in so many situations and this is literally like the the initial I would say use case of like classifying texts holy crap yeah so okay so I just uploaded to csv so as you can see this one has 113 rows 113 000 rows so what i built was then specific to like this use case the first the column that i'm basically extracting the text that i want to categorize and then i just have also like a little unique identifier just for future reference as well we're not going to process the whole thing because it'll take a couple minutes but we're going to do a test batch and here we'll just do this maybe like 50 so through an end this would take like a couple hours to run through to classify through an ai agent which you could do and then you could build out like parallel processing on that an end but it would get very convoluted watch how quickly this is literally i clicked it and it popped up holy crap this is within minutes so essentially what i have here and what i had to do and again this is not minutes this is like like seconds seconds yeah yeah it'd be minutes if i did 100 ,000.

33:01The 50 rows were exactly. Holy crap. Yeah. Wait, so what is this confidence category type? Yep. So it actually came up with this concept. I didn't even prompt it to create a confidence. It is basically how confident is it that it actually produced the right extraction? So I was trying to get this number, this count number in the count column. Yeah. I also was looking for a job title and a category. So as you can see, or you probably can't see too well, in here. I have the text, which is 405 jobs within housing provider. Okay. That was a generic one. Okay. So 10 SLPA home health jobs in Arizona, and there's got cut off a little bit, but that's basically that.

33:43And I think I has one more sentence. So I wanted that 10 number. I wanted this SPL and then I wanted Arizona. Wow. Yeah. So here it has the city. So this basically says it doesn't have a city that's where... So it didn't do the scraping. You still did the scraping through those automation tools. It's doing the analysis. It's doing the analysis. You could do the scraping though. I hadn't, but you could. Tell me about the confidence score. Like why is it 50 %? Why is it 80 %? This is basically saying like, based on the rules I provided, how like accurate it thinks it is based on how it categorized it.

34:15And then I had put in to classify it as like, if you see certain words within this structure of like the number, the job title and then jobs available. So that was like consistent text structure, then extract this. I had some other rules where then we'll classify in general. And basically I created a rule set that I was prompting in and I had to iterate through with Claude to say, this classified it wrong. So here's an example. I took the text, put it back into Claude and said, here's this, what happened, adjust the rule. So when it created this, it saved it to one of your local files and then you said hey i like that tool how do i open it in my browser and it was like oh yeah just copy and paste this into your browser and it pulled up exactly functioning yes functioning the freak so let me actually pull up my folder structure just so you can see what these folders dude i could build an accounting company that does bookkeeping a thousand percent like in 30 minutes yeah yeah okay so let's share my screen again this is all of my cloud code files So a few things I did without actually putting them in the proper place and I'll need to create project folders for these rogue things.

35:26So this was without me saying to put it in a folder. But like here was my the job scraper we were just looking at. Here's all the files it created. It created test folders. It created forth data, the exports that I've generated. OK, I don't know why they're not there. Data that it's using, templates and sessions, config, checkpoints. So this is everything. I did not ever come in here at all. The only thing I ever did was even ask it, like, where's my CSV file I can download just to like check things out. And then it even just gave me the link or location. Oh, my gosh. Liz, you're right. You're right.

36:01This is freaking incredible. Clear my calendar. Clear my calendar. Jeez. Yeah. So literally like, so maybe let's go through a little bit of a process. Let me show a couple other quick examples on like what I ended up getting to because I think this was a useful lesson that people can learn from. I went down again this path of, okay, great. Then I got this to a point where I felt good with the data. Then I was like, I need to analyze it. So then I had created a prompt of like, do this analysis. I said, use like concept because it's simple analysis. It's like pivot tables and other things, but it's just over a large data set.

36:37There's going to be weird quirks. So I like, I gave it a bunch of what I thought it needed to do. So again, like what I have here is literally like, okay, so this is like thinking about my file. It had, this is super ugly, but like this, all the rows and basically says, okay, what do I want to do? This is actually the wrong file, but basically here's the things with data and you like calculate them. And then I like click through to like a couple other prompts that'll do, and it'll generate an analysis. Wait, wait, wait. Do you click on these? Are you like, yeah, I want this. I want this. I want this.

37:07this is like a pick and choose job number like really the only thing i want here are like these numbers and that and let me go see what you can click would you click on it like are you clicking on it saying yep these are the things i want so it's saying selected and it's going right there so jobs this this this is all the holy crap yep so it's like this is my little user interface even though it's ugly job number who cares if it's ugly right it works okay so hold on holy crap this is what i selected and then it populates what it's already there it's already there so it's shown it's like live filtering yeah it's live filtering and showing like a subset of the sum the average the min the max i can export this you know basically pivot table blah blah the next step was then i basically said okay i i told it to like create context and goals we'll just say you know job listing results from google identify total jobs per category by city.

38:05And then basically I had some data mapping, which I recommended. So we're going to just do it by a state level job title. I forgot which one it's going to be. Now, again, it created all this based off a natural language prompt that you said, all right, now I want you to look at it. Ask me what my goals are. Ask me what my analysis goals are. Make sure the data is like, how did you anyways, how did you do the data mapping? I don't even get. Yeah. So I just hold it like literally here. I want when you create this data analysis, make sure I can identify which column is the geography and which is the job title.

38:43And like, you don't even have to be specific on that because I'll show you in the next one, what worked even better was giving it less context. So like you can craft it specifically to what you want in the workflow. But what I found, and I don't know if this is going to fail or work. So let's see, because it's, it was failing before, which is yeah. Analysis failed. Okay. So it does work with a few of them. It did work out on a few. I have to iterate this, but when I like hadn't had some bottlenecks here, I'm like, you know what, can I just give it my files and have it build me what I need? And it did.

39:15So all the freak. So it looks, oh my gosh, this looks incredible. Don't, don't get concerned or worried about all the stuff I just said. Like all the stuff I just said was like, you have to think, think really hard. Like don't Like be dumb and just say, I want this answer. Say, Claude, I am looking for, I have data that has jobs and cities. I want to figure out how to analyze which cities have the most jobs opening. And then you might have to iterate through and like whatever. This was basically showing job categories, statistical analysis, showing the average jobs. And it's interactive. It's interactive.

39:55it hover and i didn't prompt it or tell it to do this this even build i mean it looks like a tableau or like power bi dashboard totally and i i didn't tell it to do any of this stuff like it just did it for me like at one point it was like color code it because the text was like overlapping and it was hard but like those are small things but yeah like this is pretty this i thought was pretty cool like this shows state statistic summaries like top five categories it did for me so i didn't have to think about that the concentration based on the location which i i added i added it in to say like what are the closest oh my gosh how far away it's from major metros because like it had a whole bunch of like cities that i'm like i can't look at thousand cities and figure out so sites so basically you did a prompt is like i'm trying to figure out how many whatever openings there are per city it gives its first iteration then you're like i didn't really the color is kind of overlapping here change the color actually i want to see how far it is from a major airport can you add that?

40:49Like that's how this, that's how you iterated to this. That's how I iterated to this. And I didn't give it the CSV file for it. Analyzed it. So it basically said, okay, here's what it is. So I gave it that context, but yeah, I figured it all out and it did a better job than what I was coming up with. And so it did the data cleaning. It did the job extraction and it did the analysis without going through these two things. So I started from scratch. Cause you thought, okay, I have all this data. First I got to clean it. Then I got to put it in a format that the AI is going to like, and then I can give it the prompt that I really want to get out of it.

41:21Yeah, that's what I thought. I was wrong. You're wrong. Claude Code was like, nah, I got you. I'll figure that part out. So I first got into planning mode. So I told Claude, I said, go to planning, switch to planning. So all this grayed out text is me. The white is Claude. It says, I understand you want to go to planning mode. And this is exactly what I said. I have a CSV file with 100 ,000 rows and around 13 or so columns. I didn't even give it exact numbers. I want to identify patterns and trends and insights based on the job title and geographies, and I even had a spelling error, of the title.

41:56For context, I scraped search results based on the pattern of indeed the job title, city, state. That's all I gave it. And it basically gave me like, you know, earlier version of this, which, you know, had a little bit of tweaks, but essentially this, essentially what I just showed. And so as you can see, just so you can see what, how this played out and my interactions with it. Okay. I need to do data exploration and understanding. So it loaded the CSV, you know, the structure. So it actually went and looked for the CSV file in the folder. Cause I remember this happened. I, at first I hadn't loaded it in and it said I couldn't find a CSV file.

42:34like so it like found a different one and I was like no and then I put it in that file folder and it found the csv file I was talking about geez yeah so then it went through and broke down what the plan would be so again I didn't tell it to do data cleaning it figured that out on its own I didn't say do geographic analysis it figured it out then figured out like pattern analysis and then the visualization and how I could export it so then it created a to-do list of what I needed to do. And then literally I was just pressing enter. It'll go through these steps and then you can iterate. And so then it gave me, let's see where the local host is.

43:13Okay. So it processed it first. Okay. Reference the CSV file. It wrote a bunch of code. There were some errors. It fixed its errors. I didn't even like notice it because it was dealing with it. Perfect. He said, now it has a clear understanding. Updated the to-dos. Wrote it. Great. Great. My goodness. So let me see where some endpoints here. So again, it's showing you the to-do list and it's going through what it's doing, what it's next, all that. And then here's, okay, it successfully completed the job listing analysis project. It provided a summary of what it did. I didn't ask it, it just does this automatically.

43:50Shared key insights for me. It initially created a bunch of, I remember, okay, before it created the HTML, it actually created a bunch of images of charts and then it created reports in like JSON and MD and CSV files. So I was like, okay, but I really want to see this in an app. Oh, and then that saved it in that local folder. Saved it in local. And then it said - Oh, can you make this interactive on a local host HTML UI? Oh, okay. So then it basically created that dashboard for me. All right. Hopefully you liked that episode. And if you've made it this far, you're either really committed or you're stuck doing yard work and you can't actually skip on your phone.

44:26So while I have you, the show is growing, but I have a favor to ask of you. will you please help me grow the show i want to reach more people there's a couple things that you can do like and subscribe is the simplest thing obviously you want to get notifications for when the next episode is coming out but if you go the next step will you leave me a review five star on spotify or apple what that does is it tells the algorithm that oh hey this is a high value podcast because more people are leaving reviews for it and it then pushes it out to more people. So that's why when people are like, well, you log and subscribe and put the five-star rating, it's not just to make themselves feel better.

45:02It's actually to get more exposure for the show. So if you do that for me, I would greatly appreciate it. And I'll see you next time.

From the publisher

🚨MY NEWSLETTER https://nikolas-newsletter-241a64.beehiiv.com/subscribe 🚨


Join me, Nik (https://x.com/CoFoundersNik), as I interview Elizabeth Knopf (https://x.com/@leveragedupside) about the one AI tool everyone is raving about: Cloud Code! We dive deep into why this new offering from Claude is a game-changer for business and automation, even if it looks a bit old-school in the terminal. 


We also discuss the latest in the model war, including the releases of ChatGPT-5, Claude Opus 4.1, Grok 4, and Gemini, and share our personal rankings. Elizabeth gives a mind-blowing demonstration of how she uses Cloud Code to tackle massive data analysis automation cleanup and analysis tasks, like processing hundreds of thousands of job scraping entries, all by simply speaking English to the AI.


Learn how this tool can remove friction, simplify complex multi-step tasks, and help you build powerful agents for your business.


Enjoy the conversation!


Questions This Episode Answers:

• How has the AI model war evolved recently, and what's the general sentiment?

• What are the key differences and improvements in new LLMs like ChatGPT-5 and Claude Opus 4.1?

• How do leading AI models like Grok, Claude, ChatGPT, and Gemini rank in terms of utility and performance?

• What is Cloud Code, and how can it simplify complex data analysis and automation tasks for business users?

• How can you install and begin using Cloud Code to manage and analyze local files without extensive coding knowledge?

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Love it or hate it, I'd love your feedback.

Please fill out this brief survey with your opinion or email me at nik@cofounders.com with your thoughts.

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This week we covered:

00:00 Introduction to AI Models and Cloud Code

07:48 Ranking AI Models: A Comparative Analysis

12:08 Exploring Cloud Code: Installation and Setup

18:19 Building Projects with Cloud Code

23:10 The Social Contract of Podcasting

24:08 Project Overview and Data Cleanup Tool

25:56 Data Analysis and Job Market Insights

28:42 Building Automation Tools with Claude

31:58 Classifying Text and Job Data

36:03 Interactive Data Analysis and Visualization

41:06 Automating Data Cleaning and Insights Generation

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