In this CodeYam Academy session, CodeYam CEO and Cofounder shares a practical snapshot of AI tools as of September 2026, primarily for people who are newer to AI, technology, and/or building software.

This session covers:

• What terms like models, agents, automations, memory, and context actually mean

• How to compare AI tools based on where they work and what they can access

• The differences between working in a browser, desktop app, editor, mobile app, and terminal

• How to give AI useful context and improve your results through iteration

• Why you should not expect one “perfect prompt” to produce a perfect one-shotted result

• How to diagnose problems and escape rabbit holes when you get stuck

• What to consider around privacy, training, customer data, and file permissions

• What you can build, including websites, apps, automations, reusable skills, and connections to other tools

• How to choose the smallest, simplest version of an idea to start with

Nadia is not an engineer by training, but she now uses AI to build websites, mobile apps, Chrome extensions, Claude skills, and other software that she could not previously build on her own. This session draws on that experience to offer an approachable starting point for anyone curious about doing more with AI.

You do not need an engineering degree or a perfectly formed idea to build software today. You just need curiosity, a willingness to experiment and learn, and enough patience to keep iterating when the first result is not what you imagined.

Open a note and list every feature your app idea includes, everything, the ones you’re most excited about and the ones that seem little. Now look at the list and ask: if I removed this feature, would the whole idea still make sense? Cross out every feature where the answer is yes. What’s left is the one or few features that have to exist for your idea to mean anything. That’s your MVP, not later, not version two, just that. If you’re stuck between two, pick the one that’s easier to test in a week, not the one that’s more impressive.

Here’s a sneak peek at what you’ll learn:

1. What do we mean when we say “hosting” and “domains”? Can you have one without the other?

2. How is a hosted website different from what I see when I double-click a file on my computer or visit a URL with “localhost” in it?

3. How do I choose a hosting provider? Which hosting providers do you recommend?

4. Do I need a domain, or can I launch without buying one? What would you recommend for a personal website?

5. How should I choose a domain name and extension? Should I buy a .com, .ai, or another domain extension?

6. What should I do if the domain I want isn’t available?

7. Where and how do I buy a domain? Am I stuck with the provider I choose?

8. When I buy a domain, what have I actually purchased? Do I own it forever?

9. What’s the realistic cost of hosting and a domain for a simple site?

10. When has someone outgrown free hosting? What are the signals?

11. What hidden costs should I watch out for?

Before generating anything, CodeYam asks her a few questions to narrow down the design how many variants to generate (she asks for six, so she can compare), and whether she already has any design assets of her own (she has none).

With those answers submitted, she lets CodeYam do its work. The process takes about 8 minutes and results in six different mockups of the home dashboard. Dani reviews them, rules out a few, and tries new color combinations and changing fonts.

She then asks for some changes and after a second round of designs, she chooses one and CodeYam moves into the next stage: generating the code for that screen and automatically creating the rest of the app’s screens.

This installation method is best for builders who already use terminal-based coding agents. It is currently compatible with Claude Code, Codex, and Gemini.

For newer or non-technical builders, she shows how to get started with the CodeYam MCPB (Model Context Protocol Bundle), a desktop extension that works directly with Claude Desktop.

For users who are comfortable working in the terminal, she also covers installing the CodeYam CLI, which can be used with Claude Code and other AI agents.

Nadia also shares where to find help if you run into issues during installation, including CodeYam’s debugging prompts and ways to reach the team.

In this quick CodeYam tutorial, CEO & Cofounder Nadia Eldeib breaks down the basics. Static websites use fixed content and are often simpler, faster, and easier to build, while dynamic websites can support features like user accounts, databases, personalization, and interactive experiences.

If you’re new to building software, this is a useful starting point for understanding what you’re actually trying to build before you start building it.

Resources mentioned

1. Other reading:

   1. Figma: Static vs. dynamic websites: Which website is right for you?

   2. Wix blog: Static vs. dynamic websites: the key differences and which to use

   3. Reddit (e.g. this thread and this thread and the learn programming subreddit)

   4. Quora: What are the pros and cons of having a dynamic website vs a static website?

2. Online courses:

   1. edX: Take Harvard’s CS50 online

   2. Stanford: Code in Place

3. CodeYam example:

   1. CodeYam: Prompt Library static website example

She walks through some of the final details and testing that went into the site, from responsive images and animations to checking different devices, browsers, and screen sizes. She also discusses how to use AI as a copy editor to tighten writing without losing a personal tone and voice.

Finally, Nadia reflects on a question that comes up whenever you build software: when is your project or product good enough to ship?

For a relatively simple static personal website, the cost of a small bug or typo is relatively low.

For software involving sensitive data, security, or more complex systems, the stakes can be significantly higher. Learning to understand and evaluate potential risks and tradeoffs is an important part of the software development journey.

While the personal site will almost certainly keep evolving over time, this wraps up this how to build a personal website with AI (using CodeYam and Claude Code) series.

Revisit the rest of this series here:

• Part 1: Rebuilding a Personal Website with AI

• Part 2: Brainstorming with the CodeYam Team

• Part 3: A Design Critique

• Part 4: Exploring Four Design Directions

• Part 5: Going Live on a Custom Domain

We’ll continue to share CodeYam tutorials and educational content for a wide range of software builders, from experienced developers to non-technical folks with an idea completely new to building with these tools.

The most important word in MVP isn’t “minimum.” It’s “viable.”

If you’re working on something new, you don’t actually have an app idea yet. You have a belief.

At best, you have a hypothesis that a group of people has a problem and that your solution could make their lives better.

An MVP is the fastest, cheapest way to find out if that hypothesis is true, before you spend three months building something nobody asked for.

It’s not a stripped-down version of your dream app. It’s an experiment. And an experiment only needs to be good enough to get you an honest answer and teach you something.

Before you open a single tool, ask yourself:

What is the one belief I need to test first?

Write it down. That question is what this whole series is about.

This is Lesson 1 in a series on building Minimum Viable Products (MVPs). The series covers how to turn an idea (or belief or hypothesis) into something testable, decide what to build first, and learn from real users without overbuilding.

The redesigned site is now live on a secondary custom domain, which Nadia is using to continue testing and refining the site before moving the changes over to her primary personal website.

She walks through what has changed since the earlier design explorations, including updates to the photos and copy, a new contact form (hello, scope creep), mobile testing, and some of the issues she found after getting the site live.

This is a quick look at the later stages of building a website with AI: going from designs and a working build to a live site, testing in production, and continuing to make changes and iterate.

Follow the full series to see the website evolve from the original site through design exploration, critique, development, and launch.

Catch up on the series here:

• Part 1: Rebuilding a Personal Website with AI

• Part 2: Brainstorming with the CodeYam Team

• Part 3: A Design Critique

• Part 4: Exploring Four Design Directions

Stay tuned for the next update!

Our mission is to empower individuals and teams to build beautifully designed, highly maintainable software with CodeYam.

After exploring different approaches and getting the team’s thoughts on the original redesigns as well as her current personal website, Nadia has narrowed things down to four variants that all riff on similar themes but with slightly different aesthetics, composition, and typography.

In this quick update, she walks through each direction, going from a version that stays relatively close to the existing site to a more editorial, magazine-inspired and visually dynamic concept and some other wilder variations.

She also shares how she’s iterating with CodeYam and Claude Code: incorporating ‘s design feedback, getting more specific with prompts, adding real images and content, and sharing work back with the CodeYam team for design and development feedback.

Rather than trying to arrive at the final design in one shot, the process is an example of using AI to explore, critique, and iteratively refine a real website.

Catch up on the series:

Part 1: Rebuilding a Personal Website with AI
Part 2: Brainstorming with the CodeYam Team
Part 3: A Design Critique

Stay tuned for the next iteration.

If you’ve been using Claude in your web browser, the Claude Desktop app gives you another way to work with Claude and makes it possible to connect Claude to additional tools like connectors and Model Context Protocol Servers (MCP) and local files and resources on your computer.

Claude Desktop when paired with a paid subscription account also gives you access to Claude Cowork, a tool similar to Chat and less technical-feeling than Claude Code that many folks seem to love.

This video covers:

1. How to find and download the official Claude Desktop app

2. How to choose the right version for your computer

3. How to install and open Claude Desktop app

4. How to log in and get started.

Once you have Claude Desktop installed, you can also extend what Claude can do by adding desktop extensions. Here are some instructions from the team at Anthropic on how to “Get started with the desktop app” for Claude Code.

Next, you can watch our tutorial on How to Install the CodeYam Desktop Extension for Claude.

The CodeYam Desktop Extension is an MCPB (Model Context Protocol Bundle). Put simply, an MCPB packages up everything Claude Desktop needs to connect to another tool or service (CodeYam, in this case) so you can install and integrate the tool or service more easily. In this case, it lets you connect Claude Desktop with CodeYam and launch CodeYam’s Editor tool directly from your conversations within Claude Desktop.

In this video, walks through ’s original personal website, first built using Vercel’s v0 and next.js sharing what’s working, what could be improved, and opportunities to explore as part of the redesign.

This is the third part of our second static website example, showing how design critique and expert feedback can inform the process of (re)building an existing website with CodeYam and Claude Code. Find the first part here and second part here.

In this video, the CodeYam team reviews early explorations for CEO & Cofounder ’s personal website redesign, using CodeYam with Claude Code as the AI agent, brainstorming different possible approaches to design iteration and sharing perspectives across product, design, and engineering.

This is the second video on Nadia’s website redesign, showing how we move from an initial build toward a design direction we’re excited to keep developing. You can watch the first video here.

In this video, CodeYam CEO & Cofounder works through a refresh of her personal website, exploring new design directions using CodeYam and Claude Code. This is an example of (re)building a static website.

This is the second video on Nadia’s website redesign, showing how we move from an initial build toward a design direction we’re excited to keep developing. You can watch the second video here, where our team brainstorms on ideas to improve my designs.

Step-by-Step Installation Guide

1. Download the Extension.

   Begin by downloading the CodeYam desktop extension, known as the MCP bundle (MCPB). This bundle acts as a local version of the MCP servers you may already use in the Claude Desktop app.

2. Install the Extension.
   After downloading, double-click the file to open it in the Claude Desktop application. You may need to search for and open your Claude desktop application. Once found, you’ll see a prompt to install the extension.

3. Enable the Extension.
   Ensure that the CodeYam extension is enabled, which it should be by default. You can also check the version to confirm everything is up to date.

4. Create a New Project.
   Navigate to the code tab and create a new project. It might take a moment to load, especially if you’re recording your screen. Just trust the process!

5. Open CodeYam.
   Set up a new folder and trust the workspace. This will allow you to open the CodeYam Desktop extension and launch the CodeYam editor tool for software development.

If you have questions or get stuck, you can always reach out to our team in our community Discord or by emailing us at hello@codeyam.com with the relevant context.

Our mission is to empower individuals and teams to build beautifully designed, highly maintainable software with CodeYam.

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At CodeYam, we’ve become strong believers in AI workflows. They are a core part of the CodeYam experience. Workflows provide a robust and consistent development experience that gives us confidence that the code being written is not introducing technical debt that will make it harder and harder to make changes to the codebase in the future.

One of the biggest complaints we hear from non-developers who are vibe coding is that eventually, as the application becomes more complex, it also becomes more brittle. It becomes harder to make changes confidently. Eventually the AI is unable to make the requested change as the code is such a mess. Workflows can ensure that the coding, even when vibe coding, is done in a manner that better ensures you will be able to make changes to your application as it grows in complexity.

So what are workflows exactly? How do they differ from other AI tools or techniques such as skills?

We define a workflow as follows:

This differs from AI skills in that a workflow breaks down a complex challenge into many steps and delivers the instructions for each step only after the previous step is completed.

You could do this with skills by having many skills that you apply as you move through the challenge, but a workflow seeks to automate this process and might have additional checks and audits at the end of each step. This keeps each step in a workflow small and easy to understand.

This is not complex technologically, but deconstructing a complex task into smaller sub-tasks is an often overlooked strategy when dealing with complex challenges. The book The Checklist Manifesto provides ample evidence for the value of taking complex processes, breaking them down, and creating checkpoints for each well-understood stage of the process. Steadily working through each checkpoint ensures that nothing is forgotten and frees up mental capacity for the more challenging, creative parts of the work.

Making a change to a codebase, such as building a feature, is a complex process. You don’t want to just write the code sufficiently to make the change work. You want to write tests, ensure the code is well-organized and easy to find, ensure that nothing else breaks unexpectedly from the change, etc. This is in addition to making sure that the change performs as expected and that you like how it behaves once the change is in place.

A primary goal of software development, beyond building the product, is to ensure that you can make changes to the product quickly and effectively even as the complexity of the product grows. This is no easy feat.

Software explodes in complexity as you build out additional features. Ensuring the code remains well-tested and well-organized as you quickly iterate on the product has always been a challenge for software teams.

AI introduces new benefits and challenges toward achieving this goal. AI can go very fast and allows non-developers to vibe code a product without any understanding of the underlying code. As mentioned before, though, this often results in a brittle application that becomes harder to change over time and frequently must be abandoned.

Workflows can take the AI large language models that exist today and guarantee a much more reliable process for ensuring a codebase remains maintainable regardless of the AIs ability to think through such a challenge itself. And since AIs can work so fast the extra effort to ensure the codebase remains maintainable is not a significant cost.

Unlike humans, AI has no problem following a strict workflow, regardless of its length or how tedious each step is. So in this manner AI + workflows are often more reliable than even a team of senior engineers. It is difficult and tedious for the most talented and diligent software engineer to follow a robust workflow every time. AI does not have this problem.

At CodeYam we have a workflow that, at a high level, looks like this:

1. Plan

2. Prototype

3. Demo

4. Deconstruct

5. Glossary

6. Present

7. Save

The goal of this workflow is simple. Start by planning the feature. Then rapidly prototype and demo the prototype to ensure that there is a shared understanding of the desired outcome. At this point, it’s easy to iterate on the code to make changes if needed.

Once the prototype is approved we can deconstruct the code to ensure all functionality is organized and test-driven into small, well-defined functions with clear boundaries that are well-tested.

The next step is to document in a glossary that records every function, every function’s usage, and how it is tested. This glossary serves as the organizational step so that every aspect of the codebase is easily discoverable, encourages reusing existing functions, and acts as an audit itself, ensuring that every entry in the glossary is recorded properly, matching the code, with tests to demonstrate its functionality.

We then break these fundamental steps down even further into a workflow that has over 25 steps (and is constantly evolving) with a number of sub-tasks at each step. Many of these steps have audit gates that help ensure that the AI has not forgotten anything and that the system remains clean, well-tested, and well-organized.

This workflow allows us to kick off any feature knowing that the AI will check in at predetermined times to allow the user to confirm the work is being done as expected, with robust steps and audit checks along the way that essentially guarantee the work is done in an organized and reliable manner. This creates a nice rhythm to the work as well as you can easily get a sense as to how far along in the workflow the AI is and when it is likely to complete the work.

Is this completely necessary? Will AIs be able to handle this on their own and reliably ensure that the codebase is always clean, well-tested, well-organized, and maintainable? Possibly. Right now, all of the top models we have tested struggle to do this effectively. Even if they were able to without a strict workflow, though, there still may be advantages to workflows.

Much as the research in The Checklist Manifesto revealed, checklists are valuable tools in any complex work, especially work that is complex and creative.

Why ask any intelligence to keep in memory dozens of to dos when it can externalize that work to a tool? And why not provide that intelligence with tools that help it double check its work or that can automatically handle tedious aspects of the workflow?

We’ve written about this before in To Tool Or Not To Tool, but the idea that an intelligence would avoid using tools that make its life easier because it is intelligent enough to do the work without the tools is ridiculous.

If anything, a sign of higher intelligence is that it uses more tooling, not less. The only situation where it makes sense to avoid a tool is when the task at hand is so easy that even engaging with the tool is a waste of time.

So the better question becomes, why wouldn’t you use workflows and tooling to make the job an AI is trying to accomplish easier and more reliable?

It’s unlikely that the challenge of maintaining a robust, clean, well-tested, and well-organized codebase will become so easy for AIs that tooling is not helpful. Tests themselves are tooling in this same manner. Tests provide a fast and reliable way to ensure that the application works properly in a wide range of situations. So you’d have to believe that AIs would be so powerful as to not need tests at all to completely avoid the benefits of tooling, which seems highly unlikely.

Either way, right now we are seeing great benefits from workflows and the accompanying tooling we have created. They work with any technology stack and at any level of complexity. They give us a lot of confidence that no matter how complex the application we are building gets, it will continue to be easy to make changes to it.

Don’t take our word for it. Try CodeYam yourself. It’s free to use and takes just a few minutes to get started. Simply follow the instructions on the CodeYam homepage.

If you’re building something more ambitious, are new to building software, or want help establishing AI-native workflows for your team, get in touch. We’d be happy to help you get started.

Our mission is to empower individuals and teams to build beautifully designed, highly maintainable software with CodeYam.

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