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As someone who has always been fascinated by the capabilities of artificial intelligence, I was excited to get my hands on GPT-4, the latest AI language model developed by OpenAI. In my experiments with GPT-4, I've been amazed by the power of conversational AI and the endless possibilities it offers for automating tasks and streamlining workflows. Here are some of the things I've been learning with GPT-4:
One of the first things I wanted to test with GPT-4 was its ability to generate code for simple Three.js projects. I started by giving it a basic prompt to generate HTML for a Three.js object that spells out "hello world" in space with a red lighting floor and casts shadows. I was impressed with the specificity of the code that GPT-4 generated based on my prompts and how it explained every step conversationally. GPT-4 made it easy for me to tweak the code and generate different variations until I had the exact Three.js project I envisioned.
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Next, I decided to see if GPT-4 could help me write a Chrome extension. I gave it a prompt to create a Chrome extension that finds the word "basketball" on any webpage and replaces it with a photo of an owl. GPT-4 generated the necessary manifest and JavaScript files easily, and I had to change a file reference to add an image of an owl. Seeing how quickly GPT-4 generated a working Chrome extension was incredible, which could be a valuable tool for automating tasks or personalizing browsing experiences.
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One of the most powerful applications of AI language models like GPT-4 is their ability to query APIs and extract data. I gave GPT-4 a prompt to write some JavaScript code to query a weather API, display the 10-day forecast in a full-screen table with a large type, and return the data in Celsius for a given location. With a few minor tweaks to the code to add an API key and specify a city name, GPT-4 generated a working query that displayed the 10-day forecast for Atlanta in just a few minutes. This is just one example of how GPT-4 can automate data extraction tasks and save time for developers and data analysts.
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Finally, I wanted to explore GPT-4's ability to generate 3D visualizations with Three.js. I gave it a prompt to create a cube made of spheres that could be rotated and zoomed in on, and it generated the necessary code within minutes. I was impressed by the speed and accuracy of the 3D visualization GPT-4 created. I can see how this could be a valuable tool for architects, designers, and engineers who need to create 3D visualizations quickly and efficiently.
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Overall, my experience with GPT-4 has been incredibly positive. It has allowed me to create working code in minutes, instead of spending hours or even days writing it myself. Using natural language prompts to generate code has saved me a lot of time and effort.
Of course, GPT-4 is not perfect. Sometimes, it generates code that doesn't work quite as expected or needs tweaking. However, in many cases, it can produce exactly what I need with minimal adjustments.
I believe that GPT-4 has the potential to revolutionize the way we write code. Its ability to understand natural language and generate working code based on our requests could drastically reduce the time and effort required to develop software.
As more developers begin to explore the possibilities of GPT-4, I'm excited to see what new innovations will arise. This technology can potentially transform the way we think about programming, and I can't wait to see where it goes next.
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