Obsidian + Claude Code is everywhere right now. But pointing an AI at a folder of markdown files and hoping for the best doesn’t work. What matters is how you structure the knowledge base. Get that right, and Claude becomes genuinely useful. Get it wrong, and you get confident garbage. There’s been a wave of posts about this combo lately: James Bedford’s full walkthrough, Greg Isenberg’s “personal OS” approach, kepano (Obsidian’s CEO) sharing Claude Skills. They’re all worth reading.
Boris Cherny created Claude Code. When he shared how he actually uses it day-to-day, the setup was surprisingly simple. I went through every tip, tried most of them, and have opinions about all of them. The original thread is on Boris’s X account. A good companion site is howborisusesclaudecode.com which compiles everything in one place.
I spent five years on the AWS Billing team. The hardest problem I tackled was detecting when customers used AWS services but weren’t charged correctly. This post walks through how I designed a system that reduced charge discrepancies by 300x and eliminated 230 million monthly false positives. The Problem # AWS billing is trickier than it looks. When a customer launches an EC2 instance, writes to S3, or queries DynamoDB, each action generates a usage record. These records flow through a pipeline that calculates charges based on the customer’s pricing plan, region, and service tier.
At Twitter, I was responsible for kernel updates across 5,000+ production servers. Updating a kernel is risky on one machine. Doing it across a fleet, without downtime, without data loss, and without breaking the services that millions of people depend on, is a different problem entirely. The Problem # Twitter’s production infrastructure ran on thousands of bare-metal servers across multiple data centers. Each server ran a Linux kernel that needed regular updates for security patches, performance improvements, and hardware compatibility.
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
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. 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 — 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. 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. Neural Networks Are Everywhere # Before we dive into how neural networks learn, let’s appreciate what they do. The phone in your pocket uses neural networks for face recognition, voice transcription, photo enhancement, and text prediction. Self-driving cars, medical image analysis, language translation — all neural networks.
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.
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.