👋 Hi, I'm Serhii — aka xcont
I'm a self-taught developer working at the intersection of optimization, pattern formation, and cellular automata.
I build experimental systems where complexity emerges from simple rules — using code, recursion, and a bit of imagination.
Genetic algorithm that evolves cellular automaton rules to maximize specific behavior (like flickering, stability, or growth).
A genetic algorithm evolves the initial field state of a cellular automaton while keeping the rules fixed.
The goal: observe what kinds of behavior or structures can emerge from a Turing-complete substrate.
It’s a search for dynamics, complexity, or stability — without touching the rules themselves.
Exploring fractals generated from symbolic billiards, irrational discretization, and curved surface slicing. A bridge between number theory and emergent geometry.
Fractal patterns generated via recursive substitution, then evolved using a genetic algorithm.
Focus: visual symmetry, self-similarity, and how simple logic creates structured complexity.
→ fractals
Fractals from a single point — grown using either cellular automata or convolution kernels. This project explores 6 minimal systems that all follow the same basic idea: Start with a tiny seed, apply a deterministic transformation, pad or copy, repeat. Fractals emerge.
But here’s the twist — CNNs (convolutional neural nets) do the same. They copy, convolve, repeat. So what happens when we apply the same kernel over each iteration? You get... fractals. Turns out, when a neural net "draws a cat", it draws a fractal that looks like a cat.
Fractals generated by recursively permuting pixel arrays via a quadrant-based shuffle operation.
This deterministic process reveals deep structural symmetries in both binary and full-color images.
The method echoes spatial structure-modification routines found in convolutional architectures (e.g., padding and upsampling),
though it diverges from convolution by applying no local aggregation — only recursive, deterministic reordering.
A generative MIDI tool that evolves musical patterns using deterministic shuffling and genetic algorithms. Built with Node.js + MIDI export.
Artificial life on a grid: each particle is controlled by a neural network.
They move, sense, and fight — evolving through a genetic algorithm.
The goal isn’t predefined — just survival, competition, and strange emergent behaviors.
A minimalist encryption scheme based on recursive, invertible spatial shuffling.
Pure, deterministic logic with visual twist.
Below is an example of this process applied to a sample image:
| Original Image | Shuffled Image | Wrong Key Attempt |
|---|---|---|
 |
 |
 |
→ perfect-shuffle-cryptography
Interactive 2D particle system with electric-like forces and edge wrapping.
Built in JavaScript with live controls for mass, charge, and interaction strength.
Tiny sandbox to create layered fractals by scaling and overlaying a simple pattern.
Playful and deterministic — showing how recursion builds structure from almost nothing.
- Emergence, recursion, and transformation
- Genetic algorithms and search-space exploration
- Cellular automata as minimal computational universes
- Visual representations of abstract systems
- Metaphysical math (no mysticism — just weird consequences of logic)
JavaScript · Node.js · Canvas · HTML
Comfortable with C++, Python, Arduino, and some lower-level stuff if needed.
- Originally from Ukraine 🇺🇦 — currently in Ireland
- Former Habr.com author (writing deep-dive articles on algorithms and visual systems)
- Reformatting my past work for GitHub
- Fluent in math, logic, and frustration-driven debugging
Serhii Herasymov
📧 sergeygerasimofff@gmail.com
🔗 github.com/xcontcom
Complexity isn't magic — it's just logic repeated until it surprises you.