Developer who thinks in algorithms recursion logic automation efficiency _
Building everything from backends to mobile apps to neural networks.
If it can be automated, I've probably already built it.
I'm a developer who lives for logic. While my room might look like chaos, my codebase is immaculate—every variable named, every function documented, zero redundancy. // ironic, I know
Sure, a beautiful UI is nice—but nothing compares to a perfectly optimized, fully automated algorithm. That moment when O(n²) becomes O(log n)? Gorgeous. I build across the entire stack: web, mobile, APIs, neural networks—but my heart belongs to the logic underneath.
What I love about coding is the freedom. No barriers. Build something as big as Google from your bedroom—no money, fame, or permission required. Everyone starts equal. In your own codebase, you're basically a god of your own universe, shaping reality one function at a time.
Outside of code, I'm probably debating the Ship of Theseus, questioning whether true randomness exists, or going down a rabbit hole about the universe. I'm a firm believer that everything has an explanation—we just haven't found the right math yet. // determinism.exe loading...
interface Developer {
name: "Ruven Witzig";
passion: "Optimized Algorithms";
loves: [
"Problem Solving",
"Automation",
"Philosophy",
"The Universe"
];
believes: "Everything is explainable";
}
const me: Developer = {
// Simulating the Big Bang...
};// where the real magic happens
// how machines think fascinates me
// the rabbit holes I'm currently in
// yes, someone still knows these exist
I love visualizing the beauty of algorithms. Here are a few interactive demos—tap to play!
AI-Powered Project Inspiration
Stuck on what to build next? CodeSpark generates personalized project ideas tailored to your skill level, interests, and available time—then creates your GitHub repository with a pre-populated README to get you coding faster.
Shared Living Made Simple
A streamlined home management system for housemates and families. Features a beautiful 10-day calendar view, real-time event syncing, and a minimalist interface that makes coordination effortless.
Talent Showcase & Booking Platform
A comprehensive platform for people to share their talents with the world. Features an automatic booking system, availability management, payments, reviews, and everything needed to turn talent into opportunity.
AI Legal Assistant for Swiss Law
An AI-powered legal assistant built with a custom knowledge base containing the entire Swiss law. Provides anonymous, secure legal guidance in plain language.
Online Exam Management
A comprehensive exam tool that enables schools to create, manage, and conduct online exams. Built for scalability and ease of use across educational institutions.
Swiss Secondary School Platform
The intranet platform used by most Swiss middle schools—Gymnasiums, vocational schools (Berufsschulen), and BMS. A large-scale system serving thousands of students and teachers daily.
AI-Powered Enterprise Intranet
Custom intranet solution for a Swiss health insurance company, featuring AI integration with custom knowledge bases for intelligent document search and internal communication.
Generate beautiful release notes from GitHub repos. Filter by milestone, tags, or date range—then export as Markdown or JSON.
AST-based tool to detect hardcoded strings in React/Expo projects. Auto-generates translation files and validates existing keys.
Visualize and build decision workflows interactively. Perfect for mapping out complex logic and branching scenarios.
Modern UI overlay for Redmine. Dashboard analytics, smart filtering, and a calendar view—making issue tracking actually enjoyable.
My actual love. LeetCode, work challenges, whatever—give me a complex problem and watch me disappear for hours finding the elegant solution. favorite
Fascinated by how AI actually thinks. Not just using models—understanding architectures, activation functions, and why backpropagation works.
Building intelligent search with embeddings and retrieval-augmented generation. Making AI actually useful with your own data.
Privacy-first development with Swiss-hosted infrastructure. Using DSGVO-compliant LLMs and servers—because data protection isn't optional.
If it can be automated, I've probably already built it. Turning manual processes into scripts that run themselves.
Building tools that make development faster, easier, and more accessible. Good DX isn't a luxury—it's a multiplier.
I believe truly understanding something means you can explain it simply. Here's how I'd explain some fascinating concepts to a friend over coffee.
Imagine your brain as billions of tiny switches connected by wires. Each switch decides whether to pass a signal forward based on the signals it receives. That's essentially what a neural network does—but with math.
Each circle is a neuron. It takes numbers in, does some math, and spits a number out. Simple as that.
The connections between neurons have weights—like volume knobs. They control how much influence one neuron has on another. These are the numbers the network learns.
After summing inputs, neurons use an activation function (like ReLU or Sigmoid) to decide: "Should I fire or stay quiet?" This adds non-linearity—letting networks learn complex patterns, not just straight lines.
The network makes a guess, checks how wrong it was (the loss), then works backwards adjusting weights to be less wrong next time. Repeat this millions of times and it "learns."
Neural networks are layers of math functions that adjust their internal numbers through trial and error until they get good at recognizing patterns.
Traditional search looks for exact words: search "car" and it finds "car"—not "automobile" or "vehicle." Vector search understands meaning. It knows these words are related, even if spelled completely differently.
"fast car" → LIKE '%fast%' AND '%car%'
❌ Misses "speedy automobile"
"fast car" → [0.8, -0.2, 0.5, ...]
✓ Finds similar meanings
Text gets converted into a list of numbers called a vector (or embedding). Words with similar meanings end up with similar numbers. "King" and "Queen" are close together; "King" and "Banana" are far apart.
To find similar content, we measure the distance between vectors. Closer vectors = more similar meaning. It's like comparing coordinates on a map, but in hundreds of dimensions.
Regular databases aren't optimized for "find the 10 closest vectors." Vector databases (Pinecone, Weaviate, Qdrant) use special indexing to make this lightning-fast, even with billions of vectors.
LLMs have a knowledge cutoff and can hallucinate. RAG fixes this: before answering, we search a vector database for relevant documents, then give those to the LLM as context. Now it answers based on your actual data—not just what it memorized during training.
Convert text to numbers, store in a special database, search by mathematical similarity. RAG uses this to give AI assistants access to your own documents.
To understand recursion, you must first understand recursion. // This is the only acceptable recursion joke
Seriously though: recursion is when a function calls itself to solve smaller pieces of the same problem until it hits a simple case it can solve directly.
function factorial(n: number): number {
// Base case: stop here!
if (n <= 1) return 1;
// Recursive case: call yourself
return n * factorial(n - 1);
}
// factorial(4) = 4 * 3 * 2 * 1 = 24The base case is your exit door. Without it, the function calls itself forever (stack overflow!). It's the simplest version of the problem that you can solve directly.
Each recursive call adds a "frame" to the stack—like stacking plates. When a base case is hit, the plates start coming off: each call returns its result to the one that called it.
Recursion shines for problems that are self-similar: tree traversal, nested structures, divide-and-conquer algorithms (merge sort, quicksort), mathematical sequences. If the problem contains smaller versions of itself, recursion is your friend.
Recursion can be memory-hungry (each call uses stack space) and sometimes slower than loops. But for the right problems, it's so elegant that it's worth it. // readability > micro-optimization
Recursion breaks big problems into smaller identical problems until they're trivial to solve, then combines the answers back up. Elegant, powerful, and occasionally mind-bending.
Got an interesting problem to solve? A system to architect?
Or just want to chat about the beauty of recursive algorithms?