The Use of GPT Chat AI in Blender Code Creation
In this experiment, I aimed to explore the potential of GPT chat AI in creating code for the Blender program. The goal was to utilize code to create geometry in Blender, and see if the process could be made easier through the use of artificial intelligence.
I must admit, I am far from being a proficient programmer, and even closer to being a novice. However, that did not discourage me from trying to utilize GPT chat AI in this experiment. I treated myself as a typical user who is unfamiliar with programming, and relied solely on the AI to generate code for me.
The outcome was surprising. The code generated by the AI was consistently close to what I had requested, showcasing the AI’s ability to interpret my requests accurately. The key takeaway from this experiment is that you are no longer the one writing the code, but rather the operator who asks for the code to be written. The aim is to learn how to be an effective operator and to utilize the AI’s capabilities to your advantage.
I attempted to challenge the AI by asking it to create more complex forms, such as creating a chamfer on a model. The result was successful, and the code generated by the AI was able to accomplish the task.
In addition, I also asked the AI to simplify and make the code more concise. As a non-programmer, I cannot accurately judge the AI’s performance in this task. However, it is worth noting that the AI was able to provide a comprehensive explanation for each of its decisions and the working of the scripts. This feature is particularly useful for both beginners and experienced programmers, as it helps to understand the reasoning behind a particular decision.
The experiment was a resounding success, and I am pleased with the outcome. A full video of the experiment can be found below.
In conclusion, the use of GPT chat AI in Blender code creation has the potential to revolutionize the way we create code for programs like Blender. The AI’s ability to interpret and execute requests accurately, as well as its capacity to explain its decisions, make it a valuable tool for both beginners and experienced programmers.
import bpy
WIDTH = 10
HEIGHT = 40
LENGTH = 10
CHAMFER = 0.05
class Cube:
def __init__(self, width=WIDTH, length=LENGTH, height=HEIGHT, chamfer=CHAMFER):
self.width = width
self.length = length
self.height = height
self.chamfer = chamfer
def create(self):
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
bpy.ops.mesh.primitive_cube_add(size=1, location=(0, 0, 0))
cube = bpy.context.object
cube.scale = (self.width, self.length, self.height)
cube.location[2] = self.height / 2
cube.select_set(state=True)
bpy.ops.object.editmode_toggle()
bpy.ops.mesh.select_all(action='SELECT')
bpy.ops.mesh.bevel(offset=self.chamfer, segments=1, profile=0.5)
bpy.ops.object.editmode_toggle()
cube.select_set(state=False)
cube = Cube()
cube.create()
You can watch the full video of my experiment with GPT chat AI and Blender on here: