Your development environment should feel like **one cohesive tool**, not a collection of unrelated windows with clashing colors. I theme everything with the same palette: Catppuccin Mocha . The result is a workspace where context-switching between tools is effortless. Why One Palette Everywhere? # Most developers pick a theme for their editor and call it a day. Their terminal is one color, their editor another, their tmux status bar a third, and their Git diffs something else entirely. Every time they switch contexts, their brain spends a fraction of a second recalibrating.
Imagine you're **blindfolded on a mountain** and you need to find the lowest valley. You can't see anything, but you *can* feel the ground under your feet. What would you do? You'd feel which direction slopes downward, take a small step that way, and repeat. Congratulations. You just invented **gradient descent**, the algorithm behind nearly every modern AI system. ML Fundamentals · Part 1 of 3 1 2 3 Why Should You Care? # Optimization is everywhere. When your GPS finds the fastest route, when Netflix recommends a movie, when your phone recognizes your face, behind all of these is an algorithm trying to find the best possible answer from a sea of possibilities. Gradient descent is the workhorse algorithm that makes this happen.
Imagine you're playing **20 Questions**. You're trying to guess what animal your friend is thinking of. Would you start with "Is it a golden retriever?" or "Does it live in water?" The second question is obviously smarter, because it eliminates roughly half the possibilities in one shot. Decision trees in machine learning work exactly the same way, and they use **entropy** and **information gain** to figure out what the smartest question is. ML Fundamentals · Part 2 of 3 1 2 3 What’s the Big Idea? # When a machine learning algorithm builds a Decision Tree , it needs to decide which question to ask first. Should it split the data by color? By size? By temperature? The answer comes from a beautifully simple concept: ask the question that reduces uncertainty the most.
When a factory produces a defective product, how do you trace the problem back through the assembly line to find which worker made the mistake? Neural networks face the exact same challenge. They have layers of "workers" (neurons), and when the final output is wrong, they need to figure out **who's responsible** and by how much. The algorithm that solves this is called **backpropagation**, and it's the reason deep learning works at all. ML Fundamentals · Part 3 of 3 1 2 3 Neural Networks Are Everywhere # Neural networks are behind the phone in your pocket: face recognition, voice transcription, photo enhancement, text prediction. Self-driving cars, medical image analysis, language translation. All of it.
After years of refining my terminal workflow, I've landed on a stack I genuinely enjoy using every day: **Ghostty** as the terminal emulator, **tmux** with **sesh** for session management, and **Neovim** with **LazyVim** for editing. Everything runs on macOS (Apple Silicon) with a consistent Catppuccin Mocha theme across all tools.
I've been gradually replacing classic Unix tools with modern alternatives, mostly written in Rust . After a year of daily use, these aren't experiments anymore. They're muscle memory. The Replacements # Classic Modern Why cat bat Syntax highlighting, line numbers, git integration ls eza Icons, git status, tree view, color-coded grep ripgrep 10x faster, respects .gitignore, smart case find fd Simpler syntax, respects .gitignore, colored output cd zoxide Learns your habits, fuzzy matching sed sd Intuitive regex syntax, no escaping nightmare du dust Visual directory size with a tree view df duf Colorful, filterable disk usage top btop Beautiful TUI with mouse support, per-core graphs ps procs Colorized, searchable, tree view history atuin Encrypted sync, full-text search, workspace filtering Setting Up Aliases # In my .zshrc, I alias the classics to their replacements so the transition is invisible:
Every developer eventually reaches the point where their configs become too valuable to lose. Here's how I use **yadm** to manage my macOS dotfiles with automated testing, daily maintenance, and a pre-commit workflow that keeps everything in check. For me, the turning point was spending a weekend setting up a new MacBook and realizing I couldn’t reproduce my environment reliably. That’s when I started managing my dotfiles properly.