The video showcases how Pythagora 2.0, an AI-powered coding assistant, can rapidly build a full-featured sleep tracking app—including frontend, backend, and AI-driven features—based on a detailed prompt, with minimal manual coding required. The creator demonstrates the ease of designing, testing, and debugging the app, highlighting Pythagora’s ability to automate complex development tasks and streamline the entire process.
In this video, the creator demonstrates building a full-featured sleep tracking application using Pythagora 2.0, an AI-powered coding assistant. The goal is to create an app that tracks sleep data, analyzes patterns, and provides personalized recommendations through an AI sleep coach. The app includes a homepage, sleep coach page, login and registration, and an account page where users can input their OpenAI API key and select an LLM model. The creator emphasizes the importance of crafting a detailed prompt or specification, as this guides the AI in generating the application’s structure and features.
The process begins by pasting the detailed prompt into Pythagora 2.0, which reformats and clarifies the requirements before starting the build. The AI then generates the application’s front end, creating all necessary pages and components in just a few minutes. The creator is able to preview the app live, test the UI, and make adjustments as needed. The interface is similar to VS Code, allowing for easy navigation and code inspection, while the AI handles the bulk of the coding work.
Once the front end is complete, the workflow moves to backend development. Pythagora builds out authentication, API endpoints, and data models, prompting the creator to test each feature as it’s implemented. The app supports uploading sleep data via screenshots or voice memos, with AI-powered parsing and transcription using OpenAI’s Whisper. The creator demonstrates how errors are detected and fixed automatically by the AI, streamlining the debugging process and minimizing manual intervention.
Throughout the build, the creator tests features such as user registration, login, API key management, data uploads, and AI-driven recommendations. When issues arise, the AI provides logs and suggestions, and the creator can simply click “fix all” to resolve most problems. The app successfully processes sleep data, displays it in graphs, and offers personalized recommendations and chatbot interactions through the sleep coach page.
By the end of the video, the creator has a fully functional, sophisticated sleep tracking application built in under an hour and a half, with thousands of lines of code generated by Pythagora 2.0. The process showcases the power and efficiency of AI-assisted “vibe coding,” where developers can focus on high-level design and testing while the AI handles implementation and troubleshooting. The creator recommends Pythagora for anyone looking to rapidly prototype or build complex applications with minimal coding effort.