GPT with Me - Ep 28: Co-Creating Code with OpenAI's 4o1-Preview

Introduction

Welcome back to another episode of GPT with me, Just See. It’s been a while since I last explored coding using Cursor and OpenAI’s latest model, the 4o1-Preview. Today, I want to investigate how well this new model can tackle coding challenges, especially focusing on its capabilities in testing.

I've often found earlier models lacking when it comes to testing—an essential aspect of AI assisting developers rather than replacing them. While speculation exists about AI fully replacing certain roles, I think there's immense value in developing tools that assist instead.

So, let’s recap: I've been busy juggling a job, podcasting, and blogging in the few weeks since our last discussion. I'm diving back into a familiar project: a memory cache kata which I initially struggled to solve with earlier models. I believe these challenges aren't overly complicated and should be in the grasp of the latest AI advancements.

Some background: a memory cache is a straightforward storage paradigm, often represented as a dictionary where you can store and retrieve values using keys. The challenge we're focusing on requires handling a cache that can hold a maximum of 100 entries. It’s a testing-focused project meant to clarify that effective design can facilitate proper testing.

I’ve had success with a different project in the past and have recently made some backend improvements to it. However, today I’m primarily focused on seeing how the new model tackles specific coding tasks.

Using Cursor, I prompted the model to assist with the memory cache coding challenge in Python. Its initial response provided a reasonable implementation, but I recognized a need for improvements in the test cases, specifically addressing style and clarity.

As the model worked on this, I noticed a significant difference in how it handled explanations and instructions. Not only did it provide functioning code, but I appreciated the detailed reasoning it provided along the way.

Now, I’ve created a test setup to accompany this memory cache code. I craftily restructured it to run the implementation and validate test cases effectively. The model generated initial solutions quickly, which is a notable enhancement compared to previous experiences.

However, after running into a few errors, I realized there were still gaps—certain configurations weren't being addressed, and a few tests were missing. I went back to the model to ask for these additional tests related to capacity and Time To Live (TTL).

To my delight, the AI was able to update the memory cache implementation effectively. It provided clear reasons for changes, which aided my understanding and operational flow in the code.

We explored various interactions, from converting the code into Python to the C language while keeping our caching logic intact. At times, I encountered delays—indeed, some of the smaller models didn't perform as well for programming tasks when compared to larger previews. However, the overall experience was enlightening, highlighting the key attributes of the coding process and AI collaboration.

The adventure offered a unique opportunity to witness how the 4o1-Preview could manage complex coding structures and longer explanations of its internal processes. It was essential to understand which model to use based on the task requirements, hinting that we have much to look forward to in the evolution of AI aid for developers.

In conclusion, I’m thrilled with the promising capabilities of the new model, particularly its potential in testing workflows, which I found previously lacking. If you’re interested in exploring coding challenges, I invite you to visit my website, tddb. bud.com. You can try out various katas or use AI to generate solutions across different programming languages. Stay curious and keep experimenting!

Keywords

• OpenAI
• Memory Cache
• Coding Challenges
• Testing
• Co-Creation
• Model Comparison
• Python
• C Language
• AI Assistance
• Development Tools

FAQ

Q1: What is the focus of the episode?
A1: The episode centers around testing capabilities in coding with OpenAI's 4o1-Preview model, focusing specifically on a memory cache coding challenge.

Q2: How does the 4o1-Preview compare to previous models?
A2: The 4o1-Preview has shown significant improvements in generating valid code solutions and explanations, making it easier for developers to co-create code effectively.

Q3: What type of coding problems did you explore in this episode?
A3: I explored a memory cache problem and incorporated testing strategies to enhance the implementation and validate its performance.

Q4: Where can I find more coding challenges?
A4: You can visit my website, tddb. bud.com, where you can explore different katas and utilize AI for coding tasks across various programming languages.