I was working an album cover last night and got these really cool images in midjourney so made a video out of it. Animated using Pika. Song made using Suno Full version on my bandcamp. It’s called Static.
sRGB: A standard “web”/computer-display RGB color space defined by IEC 61966-2-1. It’s used for most monitors, cameras, printers, and the vast majority of images on the Internet.
Rec. 709: An HD-video color space defined by ITU-R BT.709. It’s the go-to standard for HDTV broadcasts, Blu-ray discs, and professional video pipelines.
Why they exist
sRGB: Ensures consistent colors across different consumer devices (PCs, phones, webcams).
Rec. 709: Ensures consistent colors across video production and playback chains (cameras → editing → broadcast → TV).
What you’ll see
On your desktop or phone, images tagged sRGB will look “right” without extra tweaking.
On an HDTV or video-editing timeline, footage tagged Rec. 709 will display accurate contrast and hue on broadcast-grade monitors.
This 2025 I decided to start learning how to code, so I installed Visual Studio and I started looking into C++. After days of watching tutorials and guides about the basics of C++ and programming, I decided to make something physics-related. I started with a dot that fell to the ground and then I wanted to simulate gravitational attraction, so I made 2 circles attracting each other. I thought it was really cool to see something I made with code actually work, so I kept building on top of that small, basic program. And here we are after roughly 8 months of learning programming. This is Galaxy Engine, and it is a simulation software I have been making ever since I started my learning journey. It currently can simulate gravity, dark matter, galaxies, the Big Bang, temperature, fluid dynamics, breakable solids, planetary interactions, etc. The program can run many tens of thousands of particles in real time on the CPU thanks to the Barnes-Hut algorithm, mixed with Morton curves. It also includes its own PBR 2D path tracer with BVH optimizations. The path tracer can simulate a bunch of stuff like diffuse lighting, specular reflections, refraction, internal reflection, fresnel, emission, dispersion, roughness, IOR, nested IOR and more! I tried to make the path tracer closer to traditional 3D render engines like V-Ray. I honestly never imagined I would go this far with programming, and it has been an amazing learning experience so far. I think that mixing this knowledge with my 3D knowledge can unlock countless new possibilities. In case you are curious about Galaxy Engine, I made it completely free and Open-Source so that anyone can build and compile it locally! You can find the source code inGitHub
When you’re working with binary data in Python—whether that’s image bytes, network payloads, or any in-memory binary stream—you often need a file-like interface without touching the disk. That’s where BytesIO from the built-in io module comes in handy. It lets you treat a bytes buffer as if it were a file.
What Is BytesIO?
Module:io
Class:BytesIO
Purpose:
Provides an in-memory binary stream.
Acts like a file opened in binary mode ('rb'/'wb'), but data lives in RAM rather than on disk.
Ideal for testing code that expects a file-like object.
Safety
No temporary files cluttering up your filesystem.
Integration
Libraries that accept file-like objects (e.g., PIL, requests) will work with BytesIO.
Basic Examples
1. Writing Bytes to a Buffer
from io import BytesIO
# Create a BytesIO buffer
buffer = BytesIO()
# Write some binary data
buffer.write(b'Hello, \xF0\x9F\x98\x8A') # includes a smiley emoji in UTF-8
# Retrieve the entire contents
data = buffer.getvalue()
print(data) # b'Hello, \xf0\x9f\x98\x8a'
print(data.decode('utf-8')) # Hello, 😊
# Always close when done
buffer.close()
The AI is being trained using a mix of Shutterstock 2D imagery and 3D models drawn from the TurboSquid marketplace. However, it’s only being trained on models that artists have approved for this use.
People cannot generate a model and then immediately sell it. However, a generated 3D model can be used as a starting point for further customization, which could then be sold on the TurboSquid marketplace. However, models created using our generative 3D tool—and their derivatives—can only be sold on the TurboSquid marketplace.
TurboSquid does not accept AI-generated content from our artists
As AI-powered tools become more accessible, it is important for us to address the impact AI has on our artist community as it relates to content made licensable on TurboSquid. TurboSquid, in line with its parent company Shutterstock, is taking an ethically responsible approach to AI on its platforms. We want to ensure that artists are properly compensated for their contributions to AI projects while supporting customers with the protections and coverage issued through the TurboSquid license.
In order to ensure that customers are protected, that intellectual property is not misused, and that artists’ are compensated for their work, TurboSquid will not accept content uploaded and sold on our marketplace that is generated by AI. Per our Publisher Agreement, artists must have proven IP ownership of all content that is submitted. AI-generated content is produced using machine learning models that are trained using many other creative assets. As a result, we cannot accept content generated by AI because its authorship cannot be attributed to an individual person, and we would be unable to ensure that all artists who were involved in the generation of that content are compensated.
Stable Diffusion is a latent diffusion model that generates AI images from text. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space.
Stable Diffusion belongs to a class of deep learning models called diffusion models. They are generative models, meaning they are designed to generate new data similar to what they have seen in training. In the case of Stable Diffusion, the data are images.
Why is it called the diffusion model? Because its math looks very much like diffusion in physics. Let’s go through the idea.
Open-source fonts packaged into individual NPM packages for self-hosting in web applications. Self-hosting fonts can significantly improve website performance, remain version-locked, work offline, and offer more privacy.
