💥 Part 4 | ChatGPT Canvas or Claude Artifacts: How to Create SQL Database & Python Code from Simple Images

Introduction

This part is where things get exciting because you and I are diving into the tools that take your writing and coding to the next level. Let’s talk about why these advanced functionalities matter and how they can change the way you work.

Advanced doesn’t mean complicated. Let me tell you, after years of experimenting with tools like OpenAI Canvas and even competitors, these advanced features aren’t just “nice to have”; they’re game-changers.

When I first started using OpenAI Canvas, I thought, “How much more can this actually do?” Turns out, a lot. Now, let me break down what you’ll get from this part:

• Comparison: OpenAI Canvas or Anthropic Artifacts.
  I’ll share my honest experience of working with both platforms and why OpenAI Canvas stands out for real-world use cases, like in our AI Fire ecosystem projects.

• Create SQL Databases from Architecture Images.
  Ever turned an image of a database design into working SQL code? You will. I’ll show you exactly how this works, step by step.

• Generate Python Code for Visualizations from UI Mockups.
  Here’s a trick I’ve used: upload a screenshot of a UI mockup, and within minutes, get functional Python code. This isn’t just theory, I’ve done this with the same tools we teach in our playbooks.

• Best Tips for ChatGPT Canvas.
  I’ve spent hundreds of hours testing and tweaking workflows in OpenAI Canvas. In this section, I’ll share the most practical, time-saving tips that’ll make you feel like a pro.

By the end of this section, you’ll see why these tools aren’t just “fancy extras”. They’ll change how you approach every project, from the simplest email draft to building a full-scale application.

I. Comparison: OpenAI Canvas or Anthropic Artifacts

Before we get into the details, let’s set the stage. ChatGPT 4.0 Canvas and Claude 3.5 Artifacts both aim to improve how we interact with AI, but they do so in different ways. It’s like comparing apples and oranges, if apples could write code and oranges could create interactive dashboards. Each has its own strengths, so let’s explore how they differ and what they can offer.

1. What is Anthropic Artifacts

Anthropic Artifacts is a tool designed by Anthropic, similar to OpenAI Canvas in its purpose of enhancing collaborative work. It focuses on creating a workspace for generating, refining, and reviewing text and code. Artifacts aims to provide users with a streamlined and efficient way to interact with AI for complex projects like writing detailed reports or debugging lengthy codebases.

Claude will create an Artifact when the content it generates meets the following criteria:

• Significant and self-contained: The content is typically longer than 15 lines.

• Editable and reusable: It’s something the user is likely to want to edit, iterate on, or reuse outside of the current conversation.

• Complex and independent: The content is a complete piece on its own, without needing extra context from the ongoing conversation.

• Referenceable: The content is something the user will probably want to refer back to or use later.

Some common examples of Artifact content include:

• Documents (Markdown or Plain Text)

• Code snippets

• Websites (single page HTML)

• Scalable Vector Graphics (SVG) images

• Diagrams and flowcharts

• Interactive React components

One more special thing is that Artifacts are now generally available for all Claude.ai users across Free, Pro, and Team plans. You no longer need to manually enable this feature as it’s now a standard part of the Claude experience. Additionally, Artifacts can now be created and viewed on Claude’s iOS and Android mobile apps, making the feature accessible on-the-go.

But the experience can differ depending on the subscription plan. Free and Pro plan users can publish and remix Artifacts with the wider community, which allows for collaboration and the ability to build on materials shared by users from around the world. Team plan users enjoy the added advantage of creating Artifacts and sharing them within Projects, providing a secure environment for teamwork.

Since its preview launch in June 2024, users have created tens of millions of Artifacts, showing its popularity and usefulness across a variety of use cases.

2. Key Differences Between Canvas and Artifacts

When comparing OpenAI Canvas and Anthropic Artifacts, I’ve noticed a few critical differences based on personal experience and testing both platforms.

ChatGPT 4.0 Canvas: The Digital Whiteboard

• ChatGPT’s Canvas is essentially an advanced digital whiteboard. It’s a workspace designed for writing, coding, and collaborating with AI in real-time. You can think of it as a space for your ideas, where you can freely write, erase, and refine everything without leaving the chat interface.

Claude 3.5 Artifacts: The Content Generator

• Claude's Artifacts, on the other hand, works like a magic window. It generates standalone pieces of content such as code, documents, or interactive elements that you can view and manipulate alongside your conversation with Claude. It focuses more on the output rather than the process.

Writing:

• After asking ChatGPT Canvas to write an article about a rye bread recipe, I found that I had multiple options for editing. I could adjust the reading level, add emojis, and even change the sentence length. The tool also allowed me to highlight specific sections of the text I wanted to edit, making the process quite flexible.

  

• On the other hand, while I prefer Claude’s interface, I found Claude Artifacts a bit limited for editing. I could write out what I wanted to edit, but I didn’t have as many controls as I did with ChatGPT Canvas. Because of this, I’m giving the edge to ChatGPT Canvas for writing and editing capabilities.

  => Besides writing, you can also use ChatGPT Canvas for other tasks, like translating languages, further expanding its usefulness.

  

  => Winner: ChatGPT Canvas

Context is King: One of the biggest differences between these tools is how they handle context

• ChatGPT Canvas, powered by GPT-4o, has a smaller context window, about half the size of what Claude can handle. This means that after roughly 75,000 tokens, ChatGPT may start to lose track of the conversation.

• In contrast, Claude can handle longer interactions, maintaining its performance even as the conversation stretches across more tokens.

  For those of us who tend to go off on tangents (guilty as charged), Claude's ability to keep up with longer, more wandering conversations is definitely an advantage.

Memory Function:

• While Claude does have a memory function, it's important to note that it only retains context within a single conversation, meaning its memory resets when you start a new chat; it does not have a persistent long-term memory across different conversations like ChatGPT.

Editing Experience:

• ChatGPT Canvas excels in real-time editing and collaboration. It’s like Google Docs met an AI. You can edit, format, and refine your work on the go, with the AI providing suggestions as you work.

• Claude Artifacts, on the other hand, is more of a “look, don’t touch” experience when it comes to editing. You can view your creations in their full form, but if you want to make changes, you’ll need to export the content and edit it outside the platform.

User Experience: At the end of the day, it all comes down to user experience

• ChatGPT Canvas feels like a natural extension of the ChatGPT interface we’re already familiar with (even if we sometimes love it or curse at it). It’s a comfortable experience with some added features and improvements.

• On the other hand, Claude Artifacts, which is integrated into Claude’s general interface, feels more like a separate tool within a tool. While it’s powerful, it might take some time to adjust to, especially for those used to more traditional chat interfaces.

Real-Time Previews:

• Canvas has a clear advantage here, it offers live previews. This feature is incredibly helpful for developers working on visual projects, like converting HTML/CSS/JavaScript into WebGL. You can see how changes look and function without leaving the editor.

• Artifact, unfortunately, doesn’t yet include live previews. This means you have to copy the output into another environment to test and refine. While it’s not a dealbreaker, it adds extra steps, especially for coding-heavy workflows.

Performance and UX

• Speed and Iteration: Both tools perform well under different conditions. CLA’s previews are fast, but its full rewrites slow things down. Canvas, by contrast, is better for iterative workflows, where pinpoint edits keep the process efficient.

• Error Handling: Canvas stands out by allowing you to feed error messages back into the system for debugging. For example, in the video, Canvas searched the internet for missing imports and made specific fixes. CLA doesn’t currently support this.

So, which one is better? Like most things in tech, it really depends on what you want to achieve.

In my view, Canvas is a more powerful tool for iterative, collaborative workflows. Would I choose Canvas? Absolutely, for its adaptability and user-focused design. But for tasks requiring immediate visual feedback, CLA still holds its ground.

II. Create SQL Database from Architecture Images

Now, let's explore some of the real-world use cases that we can use ChatGPT Canvas for. I would like to create an SQL database from the image of a architecture. So, let's go to one of the articles where we can use the example of the entity-relationship model.

Here’s the article: Dimensional Modeling - Part 1: Basic Fact Table Techniques

Imagine you're scrolling down the article about dimensional modeling and trying to learn a little bit more about different data models. You stumble upon the concept of the entity-relationship model, and you would like to explore it further. ​

The image was of an architecture diagram. So, let's go a little bit deeper into the different data elements captured in the screenshot of this diagram. You can ask ChatGPT to convert it into a database. Based on the image above, you can populate it with some data to give a more interactive feel to the database. Now, you can see it creates an SQL file with some of the popular fields.

Create the SQL database based on the image and populate with the data, use canvas

We also have this website called SQL Fiddle that will try to compile the information:

And as you can see, we are getting some errors that we can try to resolve. The model is already working to fix some of them. First, it didn't need to address everything initially to create the solution, but now we can focus on the specific issues. Let's see. There is a syntax highlighting error here:

You can copy and paste it, then ask the model to fix it. You just need to put the groups in quotation marks:

Now we can ask the model to fix the same issue with other tables, and it will try to create proper quotation marks in every table. The bug in the code has been addressed by carving and pasting. The model has finally figured it out, and now we can easily render and query the database. And now, the error has been resolved, let’s find all the teachers.

So the way we will do this is to type:

SELECT *
FROM Teachers

You can see it gave us an output of all the teachers available. Now, I'm going to ask the model to add more teachers, subjects, and students. Next, I'm going to ask the model to create a query for additional tasks: a query for students who took math:

Now we can see all the students who took the math subject. I'm going to ask them all to show the teachers for the student as well. Let's make the query, and you can see the model in action. We can copy and paste the query. For instance, you can see that only John Smith is a math teacher.

I encourage you to try out more complicated SQL queries, create your own databases for your applications, or even generate a game from scratch with your own game mechanics.

III. Generate Python Code for Visualizations from UI Mockups

Let me take you through how OpenAI Canvas can make turning UI mockups into Python code feel like second nature.

You’ve got a UI mockup like a bar graph, pie chart, or a dashboard sketch. Manually writing Python code to replicate that visualization can take hours. OpenAI Canvas makes this quick and efficient. Imagine saving 50% of your coding time or getting the output right in the first three tries instead of ten. That’s the power we’re talking about here.

Here’s how you can generate Python code for visualizations from a simple mockup.

• Upload or Describe the UI Mockup

  • Upload a screenshot or a drawing of the graph (e.g., from Figma or even a rough sketch):

    

  • You can also type a detailed description: “A bar graph showing sales figures for Q1 to Q4.”

• Ask Canvas to Analyze It

  • Use a prompt like, “Generate Python code to create a website similar to this using Matplotlib.”

    

  • Canvas analyzes the image or description to identify key elements, like labels, axis ranges, colors, and data points.

• Review the Initial Code

  • Canvas will generate Python code using popular libraries like Matplotlib. But I think it’s better to change to HTML file to easily render on website.

    

• Iterate and Refine

  • You can tweak the output by asking Canvas for specific changes:
    “Make each box more outstanding and add data labels on top of each.”

    

  • Canvas will refine the code based on your input, saving you from manual edits.

• Run and Test

  • Copy the code into your Python IDE (like VS Code or Jupyter Notebook). Or just simply click on Run in your chat, but I realize it often makes errors.

    

  • Run it to see the visualization. If needed, go back to Canvas for further refinements.

I know this mockup is not good enough cause I’m not a UI/UX designer. I just want to show you how you could save hours by using Canvas. By the end of this, you’ll have not just Python code but a clear, professional-looking visualization ready to impress. And the best part? You’ll feel like a pro without the usual frustrations of debugging and trial-and-error coding.

Let me know if you want deeper insights or additional examples!

Conclusion

You’ve reached the end of this part and also the end of this playbook, but let’s pause for a moment and look at what you and I just accomplished. You’ve unlocked some of the most powerful ways to use OpenAI Canvas and change how you write, code, and problem-solve every single day.

I’ve spent years testing tools like this, and I’ll tell you, most people don’t use even half the potential of what’s in front of them. But not you. You’re here to push boundaries and make the most of every feature.

This is your call to action: don’t stop here. Try out the features you learned, experiment, and create something that truly feels like your own. Use OpenAI Canvas to create a game app or build a complete database system for a personal project.

Remember, your learning doesn’t end with this playbook. So go ahead, dive deeper, explore further, and make this tool your own. You’ve got this 💪 BYEEEE