• 1 Post
  • 26 Comments
Joined 10 months ago
Cake day: October 19th, 2024
  • kayzeekayzee@lemmy.blahaj.zonetoSteam Deck@sopuli.xyz•Replacing the Delta fan IS VERY worth itEnglish
    17·
    26 days ago

    Interesting! My lcd deck hasn’t had any issues with fan sounds. I don’t think I’ve ever even noticed the fan while playing. My sd slot is a little here and there though.

    Speaking of which… everyone remember to REMOVE THE MICRO SD BEFORE REMOVING THE BACK PLASTIC PLATE!! For some reason it’s designed to snap the poor sd card right in half if you don’t. Lost a good 512 GB from that mishap 🥲

  • kayzeekayzee@lemmy.blahaj.zonetoProgramming@programming.dev•Can somebody explain the graphics stack? Vulkan, OpenGL, Magma, DirectX, SDL, Metal, Mesa, ... wat?English
    6·
    27 days ago

    My only experience is with gpu-side OpenGL, so here goes:

    Your gpu is a separate device designed to run simple tasks with a staggering amount of parallelization. What does that mean? Basically every vertex and pixel on your screen needs to be processed before it can be displayed, and the gpu has a bunch of small cores that do all of that for every single frame your monitor outputs. A programmer defines all this using shaders. In OpenGL, the shader language is called GLSL.

    In the opengl graphics pipeline, the cpu-side code defines which effects apply to which geometry in what order. For example, you may want to render every opaque object first, and then draw the transluscent objects on top with semi-transparency (this is called deferred rendering, and is a very common technique). Maybe you’d want a different shadow map for each light-emitting object. Maybe you’d want a setting to define how much bloom to draw to the screen. Maybe you want to provide textures for the gpu to access. The possibilities are endless.

    On the gpu-side, we write code in shaders. The shaders, written in GLSL, get compiled by your device-specific drivers into the machine code your hardware uses. In OpenGL there are several types of shader, but there are two main ones: Vertex and Fragment shaders.

    Vertex shaders run first. They run on every vertex in the scene and do that math that puts each vertex in the correct location. You can also assign varying values specific to each vertex that get passed down the pipeline to the next shaders.

    Between the vertex and fragment shaders, the gpu automatically saves performance by removing any vertex that ends up off-screen, or any triangle that’s definitely not visible to the camera (this is called culling), and then fills in each triangle with pixels called fragments (in a process called rasterization). Each fragment will also have access to the varying value of it’s three vertices interpolated across the face of the triangle (ie the closest triangle will have the most influence).

    After this, the fragment shaders are run on every pixel/“fragment” on screen - this is where you’d render effects like lightning and shadows and apply textures. The fragment shaders determine the color of the pixel as it appears on your screen.

    There are other specialized shaders you can add too! But your gpu needs to be new enough to support them:

    • Compute shaders let you define work groups of threads to do parallel math that’s not directly related to the pixels on screen.
    • Tesselation shaders let you break larger geometry down into smaller pieces before rasterization.
    • Geometry shaders process each triangle and let you do things like clone geometry, which isnt possible in vertex shaders.
  • kayzeekayzee@lemmy.blahaj.zonetoTechnology@lemmy.world•Kids are making deepfakes of each other, and laws aren’t keeping upEnglish
    11·
    1 month ago

    I agree with the other comments, but wanted to add how deepfakes work to show how simple they are, and how much less information they need than LLMs.

    Step 1: Basically you take a bunch of photos and videos of a specific person, and blur their faces out.

    Step 2: This is the hardest step, but still totally feasable for a decent home computer. You train a neural network to un-blur all the faces for that person. Now you have a neural net that’s really good at turning blurry faces into that particular person’s face.

    Step 3: Blur the faces in photos/videos of other people and apply your special neural network. It will turn all the blurry faces into the only face it knows how, often with shockingly realistic results.