GPT-4o is Magical: Experts Shared Fantastic Coding Use Cases

GPT-4o is a big upgrade from OpenAI that is changing the generative AI scene worldwide, especially for coders. The unmatched natural language interactions and vision capabilities make it more powerful than any other current AI Model out there. Let’s find out how experts have tested GPT-4o and what they shared about its capabilities for Coding.

5 Use Cases of GPT-4o for Coding-Related Tasks

The good news is that OpenAI’s GPT-4o can be used for a variety of coding tasks for developers who are looking to improvise on their personal and industrial projects. Here are some use cases of it only for programming. These tasks include coding fully functional apps, debugging erroneous codes and also solving complex Python problems.

Let’s look at them one by one!

1) A Fully Functional Web App

Karthik Pasupathy tried the free version of GPT-4o to develop a web app. He designed a Webinar Planner Web App with the help of the chatbot, using the OpenAI API for the whole process. Watch the full thing here:

He began by giving natural language instructions to GPT-4o. He asked the web app to design the webinar planner app based on the event details and requirements.

To his surprise, GPT-4o had successfully developed a Webinar Planner Web App, coded from scratch. The whole web app was developed by it except just the written content and the logo which was designed by the user on Canva.

He also further states in the video how he also asked for instructions from it to deploy the web app on Replit. It responded amazingly to this query stating all the dependencies needed to be installed to run the web app on replit. This shows that natural language instructions are enough for an LLM now to code such powerful web apps.

2) Fixing Coding Errors

GPT-4o can even solve major coding issues and debug them to provide fresh error-free code. Look at this video shared on X, where the user first provides the image of a code containing an error. GPT-4o responds amazingly to the error and states the portion of the code containing the error.

The best part comes when it provides the correct code also with all errors debugged, and this whole process was carried out really fast without any interference.

3) Fine-tuning AI Models

Richard Rizk, another AI enthusiast, fine-tuned Llama 3 side-by-side on both GPT-4o and Blackbox. Although Blackbox was better at solving this operation, GPT-4o was quite impressive as well.

When the user gave the prompt to fine-tune the Llama 3 model, GPT-4o began by providing instructions on the required dependencies needed to be installed. It further continued by providing code blocks to train the model and prepare the model datasets accordingly. After each code block, GPT-4o explained the code as well.

Although Blackbox was better in providing the whole code solution in one go, GPT-4o is nothing less. This shows Developers can now go one step beyond training AI models with the help of OpenAI’s most powerful LLM.

4) Solving Python Problems of Varying Difficulties

GPT-4o can be used to solve Python problems of varying difficulties starting from very easy ones to even the expert level ones.

This was put to test by Mervin Praison, a site reliability engineer and AI enthusiast. He used the OpenAI API to test their latest AI model for Python codes. (You can see the video from the timestamp 5:40 to view the whole process in action.)

We are not going to discuss the easy problems as any model nowadays can solve those types of Python problems. Let’s jump to some hard problem!

The problem was about a virtual DAC, in which GPT-4o was asked to create a function to convert digital input to analog. The OpenAI chatbot did well in providing the Python Code solution and it even passed all the test cases when it was run.

The next Python problem was a hard-level challenge in finding a domain name from a DNS pointer. Basically, the task for it was to write a function that takes an IP address and returns the domain name using PTR and DNS records. It impressed here again and the code passed both test cases.

Next was the very hard problem of generating an identity matrix. It was successful here as well and the identity matrix code passed all the six test cases.

Lastly, there was this expert-level Python problem which asked to create a Python function to generate an ECG sequence. It was given the problem and it responded well with the perfect solution.

However, when the code was run to be checked, the compiler was unable to process it. Maybe this was because GPT-4o used an updated version of Python to code the problem.

5) Creating a Coding Chatbot Using GPT-4o Chatbot!

Aditya Saxena created a coding chatbot powered by GPT-4o. For this, he used pmfm.ai itself and he selected the GPT-4o model as a standard model to develop the coding chatbot.

After the Coding Chatbot was created and deployed, the user put it to the test. At first, he ran a simple Python ‘Hello World’ problem to which it responded at a lightning-fast pace, obviously.

He then continued to provide a bit more complicated problems namely Python Problem to Check Palindromes followed by a problem for finding the longest Palindromic Sequence.

The chatbot provided correct solutions with proper insights for each of these problems, this shows that GPT-4o’s coding knowledge base is so efficient that it can be used to train complex coding assistants in one go. The level of recreation and capabilities is beyond wonders.

Conclusion

GPT-4o comes with highly powerful and that can be said for coding jobs too. With time more use cases will unfold but already the developer community have started to get their hands on it and enjoy its never-ending benefits.