Building small, specialized utilities to streamline a daily workflow has become significantly more accessible with the rise of generative AI tools. After demonstrating several custom-built tools recently, I received numerous inquiries regarding the specific process of creating them. Many of these utilities are small enough to be developed using a standard Claude Pro account (or even a local model) rather than more expensive enterprise tiers. By using a tool like Claude Code, it is possible to move from a conceptual idea to a functional application in a matter of minutes.
In my latest video, we build a simple application from scratch!
To demonstrate this process, I demo building a Markdown reader that runs natively in Windows. Markdown is a document format frequently used in technical documentation because it remains readable as plain text while also allowing for rich formatting.
Using Claude’s desktop application in its code mode allows the AI to write files directly to the local disk and compile them, which is necessary for creating executable files.
For this project, I utilized Claude’s Opus 5.5 model with the effort setting set to high. While this consumes more of the allotted usage limits than other models, it tends to produce functional code on the initial attempt. After pointing the tool to a new project folder and providing a prompt detailing the desired C# functionality, the system began planning and executing the code. Within approximately six minutes, the application was compiled and running. The resulting reader successfully formatted Markdown text, included a source code toggle, and even added a word-wrap feature that hadn’t been explicitly requested but proved useful.
But I didn’t complete the app’s development with it just being a basic reader. I subsequently asked the tool to add printing capabilities and a PDF export feature. During this stage of development, the AI agent actually interacts with the desktop to test its own work, opening the application and navigating menus to ensure the functions operate as intended. Because the AI takes temporary control of the interface during these tests, it is often more efficient to run these sessions on a secondary computer to avoid interruptions.
After verifying that the printing and PDF exports worked correctly, I introduced a more complex requirement: a WYSIWYG (What You See Is What You Get) editor. This allows for live editing of the Markdown file with a toggle to prevent accidental overwrites. This addition took about seven minutes to implement. Throughout this entire process—building the initial app, adding printing, and creating an editor—I consumed only about 13 percent of my five-hour usage limit and a negligible portion of my weekly allocation.
For those who prefer not to use a paid subscription, there are free alternatives available through platforms like OpenCode. Using one of OpenCode’s free open-weight models, I was able to generate a functional Notepad clone, which it called ASCIIPAD. While these free models can be slower and subject to unpredictable usage caps depending on server traffic, they are capable of handling simpler coding tasks like this.
A practical strategy for managing costs and usage limits involves a hybrid approach. I often use a local AI model to handle the foundational coding of an application. If the local model struggles with a specific advanced feature, I then point Claude Code at the existing project folder to finish the task. This minimizes the workload for the paid model and allows for more complex projects to stay within the bounds of a standard professional plan.
These tools have reached a point where the barrier between an idea and a functional piece of simple utility software is almost non-existent. Whether you are looking to build a specific text processor or a custom data utility, the ability to summon a tailored application out of thin air has fundamentally changed how I approach my own technical needs. I look forward to seeing how others apply these methods to solve their own workflow challenges.