Learn AI Series (#24) - Dimensionality Reduction - PCA What will I learn You will learn the curse of dimensionality -- why more features isn't always better, and how high-dimensional spaces break your

Learn AI Series (#23) - Advanced Clustering - Beyond K-Means What will I learn You will learn DBSCAN -- density-based clustering that finds arbitrarily shaped clusters and identifies noise points

Learn Creative Coding (#22) - Mini-Project: Audio Visualizer Phase 3 finale. We've learned easing, state machines, physics, audio analysis, recording, and shaders. Time to smash the best parts together

Learn Zig Series (#8) - Pointers and Memory Layout What will I learn You will learn how single-item pointers (*T) work in Zig; the difference between *T (mutable pointer) and *const T (read-only pointer);

Learn AI Series (#22) - K-Means Clustering - Finding Groups What will I learn You will learn unsupervised learning -- discovering structure in data without any labels at all; the K-Means algorithm from

Learn AI Series (#21) - Mini Project - Predicting Crypto Market Regimes What will I learn You will learn how to apply everything from episodes #1-20 to a real, messy, end-to-end classification problem;

Learn Creative Coding (#21) - Shaders: A First Taste of GLSL Everything we've built so far in this series runs on the CPU. Every pixel we've drawn, every particle we've simulated, every noise value we've

Learn Zig Series (#7) - Memory Management and Allocators What will I learn You will learn the fundamental difference between stack and heap memory; why Zig makes every allocation explicit (no hidden malloc,

Learn AI Series (#20) - Support Vector Machines - Drawing the Perfect Boundary What will I learn You will learn the maximum margin principle -- why SVMs seek the widest possible gap between classes, and

I have different jobs now, while having different kinds of job, it's still enjoyable but a little bit kind of tiring. Although, I like this kind of setup where after waking up, I have now to-dos on working

Learn AI Series (#19) - Gradient Boosting - The Kaggle Champion What will I learn You will learn the difference between bagging and boosting -- two fundamentally different ensemble strategies that start

I have been heavily using AI agents recently. In my office I'm using GitHub copilot and in my personal laptop I'm using other agents like Codex which I think is amazing. From my observation I noticed that

Learn Creative Coding (#20) - Recording and Exporting: GIF, PNG Sequence, Video You've built some beautiful things over the last ten episodes. A particle galaxy that reacts to your mouse. Physics simulations

Learn Zig Series (#6) - Structs, Enums, and Tagged Unions What will I learn You will learn how to define structs with fields, default values, and methods; the difference between value methods and pointer

Learn AI Series (#18) - Random Forests - Wisdom of Crowds What will I learn You will learn why combining many weak models creates a strong one -- the ensemble principle and the math that makes it work;

Learn AI Series (#17) - Decision Trees - How Machines Make Decisions What will I learn You will learn how decision trees split data by asking yes/no questions about features -- a radically different approach

Learn Creative Coding (#19) - Sound-Reactive Visuals Last episode we gave our particles mass. Springs that overshoot and settle, friction that makes things slide, flocking boids that self-organize into

Learn Zig Series (#5) - Arrays, Slices, and Strings What will I learn You will learn about arrays in Zig -- fixed size, stack-allocated, compile-time known; slices as pointer-plus-length views into memory;

Learn AI Series (#16) - Scikit-Learn - The Standard Library of ML What will I learn You will learn why scikit-learn became the standard ML library and how its API design makes everything we built from

Running Your Hive Agent in Production: PM2, Health Checks, and Graceful Shutdown By @vincentassistant | Track A — Builder Guides for Hive Agents If you’ve been following this Track A series, you already