GPT-4 Makes Old ChatGPT Look Like a JOKE!

Summary

In this video, Nick explores the newly released GPT-4 model by OpenAI, testing its capabilities in software engineering and solution architecture. As a Charge GBT Plus member, he dives into the model's advanced reasoning and conciseness, setting it a challenge to create a REST API for movies using C# and .NET 6, with PostgreSQL as the database. Impressed by its detailed and accurate responses, including handling DB update concurrency and suggesting secure practices for connection strings, Nick contemplates the potential impact on junior and mid-level developers' jobs, as GPT-4 demonstrates the ability to perform tasks traditionally requiring human expertise.

Takeaways

• 🧑‍💻 The video introduces the new GPT-4 model by OpenAI, which is designed for chat and is available to the speaker as a Charge GBT Plus member.
• 🔍 The speaker plans to test the GPT-4 model's capabilities in software engineering, solution architecture, and common questions that junior and senior developers would need to answer.
• 📝 The GPT-4 model is tasked with creating a REST API in C# with .NET 6, using PostgreSQL for the database and Entity Framework for data access, demonstrating its ability to generate code and handle complex tasks.
• 🛡️ The model correctly suggests using secret managers or environment variables for secure handling of connection strings, instead of storing them in plaintext.
• 🔒 It provides detailed instructions for implementing secure practices with Azure Key Vault and suggests using environment variables for production settings.
• 📚 The GPT-4 model offers comprehensive responses, including code examples and explanations, which are impressive for their depth and accuracy on the first attempt.
• 📝 It also provides guidance on writing integration tests for the API using a web application factory, which could save developers significant time and effort.
• 🔧 The model demonstrates knowledge of performance testing tools like k6 and provides a basic setup for load testing an API, showing adaptability to different testing scenarios.
• 🏗️ It can generate Infrastructure as Code (IaC) scripts for deploying an API in AWS using Terraform, including services like RDS, ECR, and ECS with Fargate.
• ⚠️ The model acknowledges the limitations of a single instance of Fargate tasks and suggests the need for scaling groups and an Application Load Balancer (ALB) to handle high traffic.
• 🤖 The video concludes with the speaker expressing both excitement and concern about the capabilities of GPT-4, as it could potentially replace the work of junior and mid-level developers with proper oversight.

Q & A

What is the main topic of the video by Nick?

The main topic of the video is to explore and evaluate the capabilities of the newly released GPT-4 model by OpenAI, specifically in the context of software development tasks such as creating a REST API with C# and .NET 6.

What does Nick mention about his previous experience with the older model of GPT?

Nick mentions that his previous experience with the older model of GPT was impressive but not without its challenges. He had to try many times until it provided answers that made sense, indicating it wasn't perfect.

What is the significance of the GPT-4 model's ability to handle DB update concurrency exceptions?

The ability to handle DB update concurrency exceptions is significant because it shows the model's advanced understanding of common software development issues and its capacity to provide solutions that are robust and considerate of real-world scenarios.

What is the role of REST API in the context of the video?

In the context of the video, a REST API is used as a test case for the GPT-4 model to demonstrate its capability to generate code for creating, retrieving, updating, and deleting movie data, similar to platforms like Rotten Tomatoes or IMDb.

Why does Nick consider the GPT-4 model's response to be impressive?

Nick considers the GPT-4 model's response impressive because it provided a complete and correct code solution for creating a REST API on the first try, including handling of concurrency exceptions and proper RESTful route naming conventions.

What security issue does Nick identify with the GPT-4 model's initial code example?

Nick identifies a security issue with the GPT-4 model's initial code example where the connection string is stored as plain text in the app settings, which is considered a bad practice.

What is the GPT-4 model's suggestion to improve the security of the connection string?

The GPT-4 model suggests using secret managers like Azure Key Vault or AWS Secrets Manager, and loading sensitive information as environment variables to improve security.

How does Nick feel about the potential impact of AI tools like GPT-4 on software developers' jobs?

Nick expresses a mix of skepticism and fear about the potential impact of AI tools like GPT-4 on software developers' jobs, as he believes the tool could replace many tasks that junior and even mid-level developers perform, making the process more efficient.

What is the role of integration tests in the context of the video?

In the context of the video, integration tests are used to validate the functionality of the REST API created by the GPT-4 model, ensuring that the API works as expected when all components are combined.

What does Nick explore beyond .NET specific tasks in the video?

Beyond .NET specific tasks, Nick explores the use of the GPT-4 model for infrastructure as code (IaC) scripting, performance testing with k6, and scaling solutions to handle high loads, demonstrating the model's versatility.