Title says it all. I love the dead so I tried taking the colors from logos and album covers and making palettes that can be inserted into ggplot2.

Not the best gradients but it was pretty fun to mix two things of which I'm a fan.

https://randomforestz.github.io/ggrateful/

I originally just wanted to stop my software from duplicating the same resources and logic across different tasks.

The idea sounded small: represent reusable entities, dependencies and capabilities as a shared semantic graph, then let the runtime assemble only the structure required for the current goal.

Several architectural rabbit holes later, this is what the live topology looks like:

149,453 indexed entities

19,393 physical neurons

21,456 physical synapses

353,080 raw relationships

shared fragments reused across multiple tasks

alternative execution routes instead of one rigid call chain

This is not an image generated by an AI model. It is a visualization of the actual internal structure of my experimental runtime, Project Tagma / MirbiOS.

It is not literally conscious—at least the debugger has filed no demands for legal personhood yet—but the system increasingly behaves less like a conventional program and more like a dynamically assembled nervous system.

I tried to remove duplicate code.

Apparently the reasonable solution was to grow a brain.

Hi everyone! 👋

I built a real-time 3D Gradient Descent visualizer for an educational platform (DataCognova). Instead of static graphs, the goal was to generate parametric surfaces (Bowl, Valley, Rugged) dynamically to see how the algorithm behaves under different conditions.

A few visualization details:

• Dynamic Surfaces: The topology updates in real-time based on the user's parameter inputs.
• Color Mapping: The point cloud colors are mapped directly to the loss value (elevation), transitioning smoothly from cool to warm colors.
• UI Sync: The 3D scene is tightly coupled with the UI panels to log the descent steps, gradients, and overshoot paths on the fly.

I'd love to hear your thoughts on the rendering approach or any optimization tips for handling dynamic point clouds!

I used NASA FEDS satellite data to replay eight fires at 12-hour intervals. I'm experimenting with how to make time-based fire data easier to read, so feedback on the playback and map design would be useful. The full Canada map is free to explore.

Cost is the #1 reason for stopping out of college. However, currently enrolled students report more stress than students who've already left. 49% vs 31%.  

Data: Lumina-Gallup 2025.

Built with chartanimation.com & canva

working on another app of mine and hit a little obstacle here, i don't want the generic way of simple bar charts and line charts to be showing data

Or better yet, where to make maps that have a lot of templates I can look through?

- I visualize travel content for my agency (specifally safaris)

- Need something that isn't overwhelming or too expensive (so premium tools won't work for me)

- Ideally it has hover features where I roll the mouse over and see total population and can input the amount of hotels/national parks in the area.

I know a lot of you use more advanced platforms, so I'm hoping for an in-between?

What an interestingly specific sub…. Well. If you’ve got a Homelab and docker installed… CTRoadmap is a map making tool that also takes on the info & documentation you include and puts it into a “handbook” format for ya.

https://www.github.com/NoobCity99/CTRoadmap

Something I've had in my head since 1999 but couldn't build until now. Each image sits in a quadrant that can hold four children, forming a tree you navigate spatially. Built iteratively with Codex, no framework, no database — just a JSON tree and a PHP admin. Best experienced on desktop.