AI Coding Comparison Challenge. 4 AIs build an HTTP Server in Python. See how they do.

Introduction

In today’s tech landscape, AI has become a powerful tool for many tasks, including software development. However, how effective are the current AI coding assistants in real-world programming scenarios? This article focuses on a hands-on comparison of four prominent AI code generation tools: GitHub Copilot, JetBrains PyCharm, Cursor, and Codium. The challenge? Each AI will attempt to build a simplified HTTP server in Python, and we will assess their performance step-by-step using Codecrafters as a benchmark.

Introduction to the Challenge

The demand for effective AI-based code generation tools is at an all-time high. Software professionals are increasingly turning to AI to increase productivity and reduce bugs. However, the hype surrounding AI often overshadows the reality of its capabilities. This comparison aims to evaluate four different AI tools without cherry-picking problems, evaluating their performance based on a real-world coding task.

A Brief Overview of the Four AIs

1. GitHub Copilot - A widely-known AI tool that integrates directly with coding environments.
2. Cursor - An editor with built-in AI integration designed for seamless coding experiences.
3. Codium - Similar to Copilot, but offers a free plan that allows for extensive testing without financial commitment.
4. JetBrains PyCharm - A powerful IDE with its own AI integration feature.

Setting Up the Test

To ensure impartiality, the Codecrafters platform was selected for this coding challenge. Codecrafters offers programming challenges that come with clear, step-by-step instructions. This particular test involves building a simple HTTP server in Python, making it a reasonable expectation for any professional programmer.

The selected challenge from Codecrafters requires respondents to handle tasks such as managing client sockets, returning responses, and processing HTTP headers.

Testing Workflow

The workflow for this comparison involved having each AI attempt to code solutions for each step of the challenge. After receiving response codes from Codecrafters, errors were provided back to the AI for modification. This iterative process aimed to simulate a real coding environment where developers must frequently debug and revise their code.

Results of the Challenge

Step 1: Modify the Response Handling

All four AIs failed to complete the first challenge successfully on their initial attempts. PyCharm’s AI was closest by correctly pointing the response to the client socket, although it ultimately failed once it did not set up the client socket correctly. Cursor, Codium, and Copilot also struggled in this initial step, with Copilot showing particular weakness.

Step 2: Managing Paths

In checking the paths for requests, Cursor and Codium performed well. PyCharm tried to introduce complexity, leading to complications in its architecture. Copilot became mired in an irrelevant ‘if’ statement that made no logical sense in the context.

Step 3: Handling Echo Requests

In this step, PyCharm managed to lay out its logic but struggled due to the complexity of its structure, which confused the debugging process. Cursor and Codium executed the task with minimal issues, whereas Copilot continued to show significant deficiencies, often requiring manual intervention.

Step 4: User-Agent Handling

When processing headers, Codium and Cursor addressed the requests accurately, while Copilot and PyCharm faced significant hurdles. PyCharm’s vision of utilizing dictionaries caused unnecessary confusion and failed to provide practical results.

Final Thoughts on Performance

By the end of the test, it was clear that the order of effectiveness among the four AIs was predominantly as follows:

1. Cursor: Performed best and required minimal adjustments.
2. Codium: A close second, only slightly less accurate than Cursor.
3. JetBrains PyCharm: Mediocre performance, often overcomplicating solutions.
4. GitHub Copilot: Consistently marked as the least effective, often requiring complete rewrites or manual corrections.

Cursor and Codium emerged as promising solutions for AI-assisted coding, while the more established entities, GitHub and JetBrains, lagged behind.

Conclusion

The quest for efficient AI in coding continues, and this challenge exposed some critical insights about their current limitations. While these AI tools are advancing, none successfully completed all the tasks laid out in this relatively simple scenario, raising questions about their effectiveness in actual coding environments.

I encourage programmers to continue experimenting with these tools in their workflows, as my own experience suggests that even imperfect AI can offer substantial productivity boosts.

Keywords

AI coding, GitHub Copilot, JetBrains PyCharm, Cursor, Codium, HTTP server, Python, Codecrafters, code generation, software development.

FAQ

Q: What is the primary focus of this article?
A: The article compares the performance of four AI coding tools in creating an HTTP server in Python.

Q: Which AI tool performed best in the coding challenge?
A: Cursor emerged as the best-performing AI in this challenge, with Codium closely following.

Q: Did any of the AIs complete all tasks successfully?
A: No, none of the four AIs managed to complete all six tasks successfully. They struggled with various components of the challenge.

Q: What is Codecrafters?
A: Codecrafters is an online platform that provides programming challenges designed to be used as benchmarks for coding tools.

Q: Are GitHub Copilot and JetBrains PyCharm worth using after this comparison?
A: Based on the results of the comparison, both tools struggled significantly, and I cannot recommend either for productive use at the moment.