Imagine building a complex, fully functional sleep tracking application in under an hour and a half, with thousands of lines of code generated almost effortlessly. That’s exactly what Matthew Berman demonstrates using Pythagora 2.0, the cutting-edge AI development platform designed to revolutionize how we build software. In this article, I’ll walk you through the entire process of creating a sleep tracker app powered by AI, from initial concept to a working application, highlighting the powerful features and workflow of Pythagora 2.0 along the way.
Whether you’re a developer curious about AI-assisted coding or someone interested in sleep health technology, this deep dive will showcase how modern AI tools can dramatically speed up development and open doors to smarter, more personalized apps.
Table of Contents
- 🚀 Introduction to the Sleep Tracker Project
- 📝 Crafting the Perfect Prompt and Spec
- 💻 Using Pythagora 2.0: A New Era of Vibe Coding
- 🛠️ Building the Frontend: Rapid UI Creation
- 🔐 Implementing Authentication and User Settings
- 🎤 Handling Audio and Screenshot Uploads
- 🐞 Iterative Bug Fixing and AI-Assisted Debugging
- 📊 Displaying and Analyzing Sleep Data
- 🧠 AI-Powered Sleep Coach and Recommendations
- ⚙️ The Power of Pythagora 2.0: A Developer’s Perspective
- 🔍 Summary of Key Features Built
- ❓ FAQ about Building AI-Powered Apps with Pythagora 2.0
- 🌟 Final Thoughts: The Future of AI-Powered Development
🚀 Introduction to the Sleep Tracker Project
Sleep is a fundamental part of our health, yet many of us struggle to understand and improve our sleep quality. As someone who has personally wrestled with sleep challenges, I wanted to create an application that not only tracks sleep but also leverages AI to analyze patterns, offer personalized recommendations, and provide a sleep coaching experience.
The goal was ambitious: build a robust sleep tracking app featuring a home page, sleep coach interface, user authentication (login and registration), and an account page where users can input their own OpenAI API keys and select the large language model (LLM) powering the AI features.
This app wouldn’t just collect raw data; it would intelligently parse natural language inputs (like voice memos describing sleep hours), process screenshots from popular sleep tracking devices like Whoop and Eight Sleep, and extract meaningful insights to guide users toward better sleep habits.
📝 Crafting the Perfect Prompt and Spec
The first—and arguably most important—step in this AI-powered development journey was creating a detailed prompt for Pythagora 2.0. This prompt acts as the project’s blueprint, specifying every page, feature, and interaction in precise terms. I spent considerable time here, ensuring that the AI understood exactly what I wanted.
The prompt outlined:
- Main navigation: Home, Sleep Coach, Login, Registration, and Account pages.
- Detailed UI elements and their behaviors—what buttons do, what the user can input, and how data flows.
- Data structures, including JSON formats for storing sleep data and user settings.
- Authentication requirements to keep user data private and secure.
- Two data input methods: natural language voice memos and screenshot uploads from third-party devices.
- The sleep coach’s ability to answer questions with context from the user’s own sleep data.
This level of specificity is crucial because it guides the AI in generating code that matches your vision. I also used AI itself to help define the JSON data structures—making the process even smoother.
💻 Using Pythagora 2.0: A New Era of Vibe Coding
Pythagora 2.0 is an evolution of the original AI coding assistant, now fully hosted in the cloud with a seamless Visual Studio Code-like interface. It’s designed for “vibe coding,” where you collaborate with AI to build sophisticated apps with minimal manual coding.
Once I pasted in the prompt, Pythagora began refining it—reformatting, clarifying, and presenting it as an editable markdown spec. This live document lets you tweak requirements as you go, allowing for dynamic adjustments and improvements without losing track of the big picture.
From there, the AI broke down the project into “epics” and “tasks,” giving me a clear view of what was being built and what was coming next. For example, the first epic was writing the spec itself, which was immediately marked done. The next was building the frontend UI skeleton, followed by backend APIs and data storage.
🛠️ Building the Frontend: Rapid UI Creation
Within minutes, Pythagora generated the frontend components based on the spec. Files like app.tsx, sleep.ts, and chat.ts appeared in the project explorer, along with APIs for settings, sleep data, and chat functionalities.
I was able to preview the live app hosted on the cloud, logging in with any email address (since authentication was still in testing). The UI was already interactive, letting me enter daily sleep data, upload screenshots, or record voice memos.
The interface was intuitive, with graphs showing total sleep, REM, and deep sleep, plus sleep hygiene indicators like late meals or screen time before bed. The sleep coach page resembled a chatbot, ready to answer questions once hooked up to the AI backend.
🔐 Implementing Authentication and User Settings
Next came the critical task of authentication. Pythagora built login and registration pages with email, username, and password fields, plus forgot-password functionality. Testing these pages was straightforward, with clear success messages and redirects.
For the account page, I could enter my OpenAI API key and choose which LLM to use (I opted for GPT-4o, a powerful model suited for the app’s needs). Once saved, these settings enabled the AI features like transcription and personalized recommendations.
🎤 Handling Audio and Screenshot Uploads
One of the app’s standout features is the ability to upload sleep data in natural language or via device screenshots.
- Voice Memos: Users can record voice memos describing their sleep hours. Pythagora built frontend audio recording components and backend APIs to handle file uploads, storage, and transcription using OpenAI’s Whisper model.
- Screenshot Uploads: Users can upload images from Whoop or Eight Sleep apps. The AI parses these screenshots, extracting detailed sleep metrics like light, deep, and REM sleep, then stores the data in the database.
Testing these features was smooth. I uploaded a Whoop app screenshot showing my sleep stages and confirmed it was stored on the server and in the database. Voice memos were recorded, transcribed, and saved successfully after a few minor AI-driven bug fixes.
🐞 Iterative Bug Fixing and AI-Assisted Debugging
As with any software project, issues cropped up during development. Pythagora’s continuous error detection and self-fixing capabilities made debugging a breeze. When errors appeared—like failed voice memo processing or missing data in graphs—I simply clicked “fix all,” and the AI analyzed logs, identified root causes, and patched the code.
This iterative process felt like having a smart pair programmer constantly reviewing and improving the app. It saved me hours of manual troubleshooting and let me focus on testing and refining the user experience.
📊 Displaying and Analyzing Sleep Data
Once data upload and processing were stable, the next challenge was visualizing the sleep insights correctly. Initially, the sleep patterns graph didn’t update after submitting new data. The AI quickly added empty state handling and logging to diagnose the issue.
It turned out the AI was defaulting to outdated dates in the data, so I instructed it to assume the upload date matched the previous night’s sleep. After this fix, the graphs displayed REM, total, and deep sleep data accurately, providing a clear overview of sleep quality trends.
🧠 AI-Powered Sleep Coach and Recommendations
The app’s AI sleep coach is a chatbot interface where users ask questions about sleep health and hygiene. It accesses the user’s data context to offer personalized advice. Initial testing showed the coach responding with general recommendations like maintaining a consistent schedule and dimming lights before bed.
While this was a great start, future iterations could deepen personalization by integrating the user’s full sleep history and habits into the AI’s responses. The coach can evolve to become a truly interactive wellness assistant, guiding users toward better rest.
⚙️ The Power of Pythagora 2.0: A Developer’s Perspective
Pythagora 2.0 strikes a unique balance in the AI coding landscape. It is more hands-on than no-code tools like Lovable and Bolts, yet more guided than lower-level coding assistants such as Cursor or Windsurf. This middle ground offers developers:
- Control: You can review, edit, and steer the AI’s work at every step.
- Speed: Thousands of lines of code generated in under 90 minutes.
- Collaboration: The AI handles routine coding and bug fixing, while you focus on design, testing, and refinement.
The fully hosted VS Code-like environment makes it easy to explore the codebase, run the app, view logs, and interact with tasks and specs—all in one place.
🔍 Summary of Key Features Built
- Multi-page app with Home, Sleep Coach, Login, Registration, and Account pages.
- User authentication with email/password and secure API key storage.
- Voice memo recording, transcription, and natural language parsing of sleep data.
- Image upload and AI parsing of sleep tracking device screenshots.
- Sleep data visualization including REM, deep, and total sleep graphs.
- AI-powered sleep coach chatbot for personalized recommendations.
- Robust backend APIs for data storage, processing, and retrieval.
- Continuous AI-assisted debugging and error fixing.
❓ FAQ about Building AI-Powered Apps with Pythagora 2.0
How much coding experience do I need to use Pythagora 2.0?
Pythagora 2.0 is designed for developers who want to collaborate with AI rather than fully automate development. Basic familiarity with JavaScript/TypeScript and web development concepts helps, but the AI handles most of the heavy lifting. You still get full control to review and tweak the code.
Can I customize the AI’s behavior during development?
Absolutely. The prompt and spec documents are live and editable throughout the process. You can refine requirements, add new features, or change UI behavior at any time, and the AI adapts accordingly.
Is Pythagora 2.0 suitable for complex applications?
Yes. This sleep tracking app is one of the most complex projects built with Pythagora so far. Its task and epic management system helps organize and execute multi-faceted projects efficiently.
How does Pythagora handle errors and bugs?
The platform continuously monitors for errors during builds and runtime. When issues are detected, you can trigger automatic fixes where the AI analyzes logs, identifies root causes, and patches the code. This iterative debugging process saves time and improves reliability.
Can I integrate other AI models besides GPT-4o?
Yes, the app includes a model selection feature allowing users to pick different large language models (LLMs) depending on their needs and API keys. This flexibility enables experimenting with various AI capabilities.
Is the app secure and private?
All user data is protected behind authentication. API keys and sensitive information are stored securely. Since the app is personal and private, users control their data and AI interactions.
🌟 Final Thoughts: The Future of AI-Powered Development
Building this sleep tracking app with Pythagora 2.0 was an eye-opening experience into the future of software development. What once took days or weeks can now be accomplished in under two hours, with AI handling the grunt work while developers focus on creativity and user experience.
Pythagora 2.0’s blend of control, speed, and collaborative AI coding makes it an ideal platform for building sophisticated applications that integrate AI seamlessly. From parsing natural language inputs to managing complex data flows and delivering personalized insights, the possibilities are vast.
If you’re interested in exploring AI-assisted development, I highly recommend checking out Pythagora.ai. It’s a powerful tool that can transform how you build apps, whether you’re working on personal projects or professional software.
In the realm of health and wellness technology, apps like this sleep tracker can empower users with actionable insights tailored to their unique data—making better sleep and healthier lives more achievable than ever.
Thanks for reading, and here’s to building smarter, faster, and better with AI!
This article was created from the video This AI Coder is MIND BLOWING (Pythagora 2.0 Tutorial) with the help of AI.
