This AI Coder Is On Another Level (Pythagora Tutorial)

Introduction

In the world of full-stack development, the capabilities of artificial intelligence are changing how we approach building applications. In this article, I'll share my experience of using Pythagora, an AI-powered coding assistant, to create a benchmarking application for large language models (LLMs). I built this application in under two hours with my AI partner, and it consisted of 1,637 lines of code across 37 files. Here's how the process unfolded.

Getting Started with Pythagora

Before we dive in, ensure that you have the necessary tools installed, particularly the Pythagora extension in Visual Studio Code and the code framework, which includes Node.js. Once everything is in place, we can create a new app. When prompted for a project name, I decided to call it "Benchmark."

Project Overview

The main goal for this project was to develop a robust benchmarking application that tests various LLMs against a set of predefined questions. Users should have the capability to create tests, run them, and publish the results. The project would require several features:

A homepage with a welcome message and a list of published tests
User authentication, including roles for admin users
An admin dashboard to manage tests
A test creation page and a test view page
Sharing capabilities for the results

With a detailed project specification ready to go, I clicked "Send" to initiate the build process.

Building the Application

Pythagora employs various agents to manage different stages of development, such as a spec writer and an architect agent. The architect agent proposed using Node.js and several other technologies, such as MongoDB and Express. Following this, I had to input my API keys into an environment file, which Pythagora highlighted for me.

As the development process continued, I was guided through a variety of tasks, including:

Implementing user authentication
Creating an admin dashboard
Adding functionality to change user roles
Developing a test creation form
Implementing back-end functionality for executing tests

With each task, the developer agent created corresponding code and prompted me for any necessary actions or input. Pythagora made it incredibly easy to navigate through updating and testing the application iteratively.

Testing and Debugging

Testing was a fundamental part of this process. Each time I added new components or functionality, I could start the application and use the browser to verify that each feature was working correctly. Pythagora provided a seamless flow to debug issues as they arose, whether it was a problem with user authentication or issues with the test view page.

Adding Details

During the development cycle, I also implemented a real-time progress tracker that displayed updates about LLM test executions. I utilized various LLMs, including OpenAI’s GPT-4, for which I set up sample tests. I also integrated a bar chart to visualize the results, improving the overall user experience.

Final Touches

Eventually, I completed the process of displaying published tests on the homepage. This allows non-authenticated users to view tests easily.

Deployment

The final step was deploying the application with a single click. Pythagora made this process simple and fast. The deployment link was shared, allowing others to access the application live on the web.

Conclusion

In conclusion, working with Pythagora has transformed the way I view coding—what was once a manual and complex process has become efficient and user-friendly with the help of AI. This video showcases how powerful Pythagora is, and I highly recommend it for anyone looking to streamline their development processes. If my experience intrigued you, consider giving Pythagora a try for your next project!

Keywords

Pythagora
AI-powered coding assistant
Benchmarking application
Full-stack development
LLMs
User authentication
Admin dashboard
Test creation
Real-time progress tracking
Deployment

FAQ

1. What is Pythagora?
Pythagora is an AI-powered coding assistant that helps developers streamline the process of building applications. It manages various development tasks and assists with debugging and testing.

2. What kind of applications can you build with Pythagora?
You can build full-stack applications utilizing various technologies such as Node.js and MongoDB, just like the benchmarking application for testing large language models.

3. How long did it take to build the app?
It took less than two hours to build a complete benchmarking application with 1,637 lines of code, thanks to the assistance from Pythagora.

4. Does Pythagora require manual coding?
While Pythagora does automate many coding tasks, developers may still need to provide input or make adjustments throughout the development process.

5. Is the application live after deployment?
Yes, the application can easily be deployed live on the web with one click, making it accessible to users immediately after building.