OpenAI-o1 on Cursor | First Impressions and Tests vs Claude 3.5

The speaker expresses excitement over OpenAI's release of their new reasoning model, called '01'. They plan to conduct a first impression test using the model for debugging and compare its performance with other models like GPT-3.5 and GPT-4. Before diving into testing, the speaker reviews the new model's features, highlighting its ability to think before responding and solve complex tasks in science, coding, and more. The model uses 'reasoning tokens' to break down prompts and generate responses. The speaker also mentions the limitations of the model, such as fixed temperatures and the inability to stream or use system messages. They briefly touch on the different versions available, like '01 mini' for coding and math tasks and '01 preview' for general knowledge problems. Pricing information is provided, with '01 preview' being more expensive than '01 mini'. The speaker then sets up a Python script to interact with the model via API, showcasing the initial steps in coding and the response time of the model.

The speaker moves on to practical testing of the '01' model by setting up a development environment with Cursor, an AI development tool. They demonstrate how to select the '01' models within Cursor and use them to create a simple game. The game development serves as a testbed to compare the '01' models with the existing GPT-3.5 model. The speaker creates a space game using Next.js and assets like sprites and sounds. They provide a prompt for the AI to follow, which includes instructions for creating the game. The speaker then runs the prompt through GPT-3.5 and observes the output, noting the compilation errors and the need for fixes. Despite the issues, the speaker is excited about the potential of integrating the new AI models into their workflow.

The speaker evaluates the performance of the '01 mini' model by using it to develop the same space game. They encounter issues with the model's response, noting the lack of instructions and the slow speed of execution. Despite these challenges, the speaker appreciates the detailed explanation provided by the model. They attempt to run the game but face further problems with the score not updating and the absence of sound. The speaker tries to debug the issues but concludes that the '01 mini' model failed to produce a working game, unlike the GPT-3.5 model. They express disappointment but remain open to further exploration of the '01' model's capabilities.

The speaker attempts to use the '01 preview' model to develop a more complex system involving an API endpoint and a Bitcoin price tracking simulation. They set up the project structure and begin implementing the code. However, they encounter numerous errors and slow response times, leading to a lack of confidence in the model's ability to handle the task. Despite the model's detailed explanations and suggestions for code changes, the speaker is unable to get the system running successfully. They compare this experience unfavorably to the more straightforward and efficient process they had with the GPT-3.5 model.

The speaker outlines a plan to build a system that uses an API to fetch Bitcoin prices and backtest different trading algorithms. They start by using the GPT-3.5 model to create the system, which includes setting up Docker and using a composer. The speaker is impressed with the ease of setup and the successful execution of the strategies, which yield positive results. They then attempt the same task using the '01 preview' model, noting the significant increase in response time. Despite following the model's instructions, the speaker faces issues with the strategies not performing as expected and the system crashing. They conclude that the GPT-3.5 model provided a better solution for this task.

In the final paragraph, the speaker reflects on their first impressions of the '01' model. They express excitement about the potential of the model but also note the challenges they faced during testing. The speaker acknowledges the need for further exploration and understanding of the model's capabilities. They invite viewers to share their experiences and use cases for the new model and express their intention to continue experimenting with it. The speaker concludes by stating their preference for the GPT-3.5 model for now but remains open to incorporating the new model into their workflow as they gain more experience with it.