Alongside my game projects, I'm interested in digging deeper into some of the technical foundations behind games and simulations.

These are areas I'm currently experimenting with. Some will become standalone projects, while others will find their way into my games.

C / C++

Memory & data structures
Pointers and manual memory management
Data-oriented programming
Performance and profiling
Modern C++
Parallelism

Game AI

Steering behaviours
Pathfinding
A*
Decision systems
Tactical AI
Learning

Simulation

Particle systems
N-body simulation
Springs and constraints
Cloth simulation
Flocking / Boids
Fluid simulation
Terrain erosion

Optimisation

Bin packing
Scheduling
Spatial partitioning
Heuristics
Genetic algorithms
Parallelisation

Data Analysis

NumPy
Pandas
Matplotlib
Statistics
Experimental design
Performance analysis
Visualisation

Why This?

I've spent a lot of time working on conventional web and backend software, so I'm particularly interested in problems where the computer is doing something computationally interesting.

Roadmap

Current priority

Continue developing Legends of Rahnok and complete the core-feature port to Godot 4.

The technical experiments below support this work

Status	Project	Technical focus	GamePasta output
Current	Godot Terrain	Procedural terrain, noise, terrain generation	Terrain article + mini game + terrain tech demo
Current	C Text RPG	C, data structures, game architecture	Terminal dungeon crawler
Future	Simulation	Godot, systems, components, decisions	Tavern simulation game, Rahnok NPCs
Future	Simulation	emergent behaviour	Faction system Legends of Rahnok
Future	Pathfinding	C, Graphs, BFS, Dijkstra, A*	Pac-Man-style experiment + pathfinding article
Future	Procedural Generation	Godot, graph rewrite algorithm	Dungeon exploration game, Legends of Rahnok
Future	Dynamic Worlds	Mutable terrain, dynamic pathfinding	Digging / destructible-world experiment
Future	Dynamic Worlds	Obstacle placement, NPC memory	Rahnok feature / article
Future	2D Physics	C, Raylib, vectors, physics, collision	Physics article + Flappy Bird clone
Future	Physics & Gravity	Forces, gravity, integration, simulation	Asteroids / Spacewar / Lunar Lander experiment
Future	Game AI	Game trees, minimax, agents	Tic-Tac-Toe / turn-based AI experiment
Future	Simulation	Cellular automata, emergent behaviour	Conway's Game of Life experiment
Future	Ballistics	Projectile motion, trajectory solving	artillery game, 3D bow physics into Legends of Rahnok
Future	Autonomous Agents	Steering, Seek, Arrive, Pursuit, waypoints	Racing / agent behaviour experiment
Future	Raycasting	C, Raylib, spatial computation, rendering	Small raycast dungeon

Current Projects

C Text RPG

Goal: Build a small text-based RPG from scratch in C, using the project to learn C fundamentals and implement my own data structures. Later, add a Raylib frontend and publish it on my website.

[ ✓ ] Set up the C project and build system
[ ✓ ] Create the terminal command loop
[ ] Implement player, rooms, enemies and basic combat
[ ] Build and use a dynamic array for inventory
[ ] Build and use a hash table
[ ] Represent the dungeon as a graph
[ ] Separate game logic from the terminal interface
[ ] Add a Raylib frontend
[ ] Build the game for WebAssembly
[ ] Publish the finished game on my website

Godot Terrain

Goal: Build a re usable terrain generation resource in Godot for use in Legends of Rahnok, Write an article / tutorial about how I implemented it. Build a tech demo showcasing the terrain and deploy it to github page, publish a stand alone mini game featuring terrain.

[ ✓ ] Build terrain generation algorithm in Godot
[ ✓ ] Write an article about how it works
[ ✓ ] Implement into Legends of Rahnok
[ ] Deploy a tech demo
[ ] Stand alone terrain exploration game

Learning goals

C Programming

[ ] Build a command-line program from scratch
[ ] Build a dynamic array using malloc / realloc
[ ] Build a linked list
[ ] Build a stack and queue
[ ] Build a hash table
[ ] Build a binary tree
[ ] Build a priority queue
[ ] Build a graph representation
[ ] Implement a memory-safe resource cleanup pattern
[ ] Debug a deliberate memory leak
[ ] Debug a deliberate buffer overflow
[ ] Build a small C program using multiple source/header files

Game AI

Relevant resources:
Artificial Intelligence for Games — Ian Millington & John Funge
CS50 — Introduction to Artificial Intelligence

Search

[ ] Build a DFS visualiser
[ ] Build a BFS visualiser
[ ] Build a Dijkstra visualiser
[ ] Build an A* visualiser
[ ] Compare search algorithms on the same maps
[ ] Add weighted terrain to the pathfinding demo
[ ] Add multiple agents using the pathfinder

Movement

[ ] Build a Seek demo
[ ] Build an Arrive demo
[ ] Build a Pursue / Evade demo
[ ] Build a Wander demo
[ ] Build a Separation demo
[ ] Combine multiple steering behaviours
[ ] Build a simple crowd simulation

Simulation

Relevant resources:
Artificial Intelligence for Games — Ian Millington & John Funge
Numerical Methods in Scientific Computing — TU Delft

Particles

[ ] Build a basic particle simulator
[ ] Add gravity
[ ] Add boundary collisions
[ ] Add particle lifetime and emitters
[ ] Add attractors
[ ] Add multiple interacting forces

Physics

[ ] Build an N-body gravity simulator
[ ] Compare Euler and Verlet integration
[ ] Measure energy conservation
[ ] Experiment with timestep size
[ ] Build a spring system
[ ] Build a cloth simulation
[ ] Build a Boids simulation
[ ] Add predators to the Boids simulation

Performance

[ ] Profile the N-body simulation
[ ] Measure scaling as particle count increases
[ ] Implement spatial partitioning
[ ] Compare brute-force and spatial partitioning
[ ] Implement Barnes-Hut
[ ] Compare accuracy vs performance

Python & Data Analysis

Relevant resource:
University of Helsinki — Data Analysis with Python Link

Data Analysis with Python

[ ] Complete the NumPy section
[ ] Complete the Matplotlib section
[ ] Complete the Pandas section
[ ] Complete the Data Manipulation section
[ ] Complete the Statistical Analysis section
[ ] Complete the Regression section
[ ] Complete the Classification section
[ ] Complete the Clustering section
[ ] Complete the Machine Learning section
[ ] Complete the Data Visualisation exercises

SQL

[ ] Work through SQL window functions and CTEs
[ ] Complete CMU Advanced SQL lectures
[ ] Analyse electricity data using window functions and CTEs
[ ] Analyse logistics data with joins, aggregation, CTEs and window functions.
[ ] Optimise several slow queries using indexes and EXPLAIN ANALYZE
[ ] Combine SQL + Python to analyse a real dataset

Operations Research & Optimisation

[ ] Build a school timetable generator
[ ] Build a delivery route planner
[ ] Build a backpack/item packing optimiser
[ ] Build a worker/job scheduling simulator
[ ] Build a resource allocation experiment

Resources

Books

Artificial Intelligence for Games - Ian Millington & John Funge
The C Programming Language, 2nd Edition - by Kernighan & Ritchie
LearnCpp - Modern C++ programming
C++ Book — cppreference - C++ language and standard library reference
Numerical Methods in Scientific Computing - TU Delft Open Books
Numerical Recipes - Numerical algorithms and scientific computing
TU Delft — From Theory to Application: Learning to Optimize with Operations Research

Courses

CS50's Introduction to Artificial Intelligence
University of Helsinki — Data Analysis with Python

data sets

Tools

C
C++
Python
NumPy
Matplotlib
Raylib
CMake

The eventual goal is to develop a useful combination of skills,

And, most importantly, build interesting things along the way.