Nick Liu on Nick Liu - Software Engineer

AWS Billing Unbilled Usage Auditor

nickboy@users.noreply.github.com (Nick Liu) — Mon, 01 Jan 0001 00:00:00 +0000

Designed and built a distributed system that detects unbilled usage across all AWS services — reducing charge discrepancies by 300x and eliminating 230 million monthly false positives.

Key Metrics
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300x Reduction in Discrepancies

$125,000 → $432

230M False Positives Eliminated

~95% Alert Actionability

Architecture
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flowchart LR
    A["Usage Records\n(Billions/day)"] --> B["Smart Sampling\n& Aggregation"]
    B --> C["Multi-Signal\nValidation"]
    C --> D["Automated\nResolution"]
    D --> E{Real issue?}
    E -- Yes --> F["Alert with\nDiagnosis"]
    E -- No --> G["Auto-resolve\n& Log"]
    style B fill:#6366f1,color:#fff
    style C fill:#6366f1,color:#fff
    style D fill:#6366f1,color:#fff

Technical Deep Dive
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Aggregation over Brute-Force
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Instead of checking every individual usage record (which generated 230M false positives), the system aggregates at the service-account-period level.

Twitter Fleet-Scale Kernel Automation

nickboy@users.noreply.github.com (Nick Liu) — Mon, 01 Jan 0001 00:00:00 +0000

Built the automation and validation tooling to manage kernel updates across 5,000+ production servers at Twitter — with zero-downtime progressive rollouts and automated canary validation.

Key Metrics
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5,000+ Production Hosts

Weeks → Days Rollout Time

140+ Tickets in One On-Call Week

Zero-Downtime Updates

Architecture
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flowchart LR
    A["New Kernel\nVersion"] --> B["Canary\nValidation"]
    B --> C["Wave 1\n1% Fleet"]
    C --> D["Wave 2\n5% Fleet"]
    D --> E["Wave 3\n25% Fleet"]
    E --> F["Full Fleet\nRollout"]
    C -- anomaly --> G["Pause &\nAuto-Alert"]
    D -- anomaly --> G
    E -- anomaly --> G
    style B fill:#6366f1,color:#fff
    style C fill:#6366f1,color:#fff
    style D fill:#6366f1,color:#fff
    style E fill:#6366f1,color:#fff
    style F fill:#6366f1,color:#fff

Technical Deep Dive
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Validate Before You Roll
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A Python library that validates kernel safety before fleet-wide rollout:

Building a Knowledge Base That AI Can Actually Use

nickboy@users.noreply.github.com (Nick Liu) — Sat, 21 Mar 2026 00:00:00 +0000

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.

What I Learned from How Claude Code's Creator Uses Claude Code

nickboy@users.noreply.github.com (Nick Liu) — Sat, 21 Mar 2026 00:00:00 +0000

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.

Case Study: Building AWS Billing's Unbilled Usage Auditor

nickboy@users.noreply.github.com (Nick Liu) — Sun, 22 Feb 2026 00:00:00 +0000

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
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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.

Case Study: Fleet-Scale Kernel Automation at Twitter

nickboy@users.noreply.github.com (Nick Liu) — Sun, 22 Feb 2026 00:00:00 +0000

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
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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.

Catppuccin Mocha: Why I Theme Everything the Same Color

nickboy@users.noreply.github.com (Nick Liu) — Sun, 22 Feb 2026 00:00:00 +0000

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

Finding the Bottom of a Valley Blindfolded: Understanding Gradient Descent

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

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?
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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.

How Machines Ask Smart Questions: Entropy & Information Gain

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

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?
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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.

How Neural Networks Learn from Mistakes: Backpropagation Explained

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

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
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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.

My Terminal Setup in 2026: Ghostty, tmux, and Neovim

nickboy@users.noreply.github.com (Nick Liu) — Sun, 15 Feb 2026 00:00:00 +0000

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.

Modern CLI Tools That Replaced My Unix Classics

nickboy@users.noreply.github.com (Nick Liu) — Sun, 01 Feb 2026 00:00:00 +0000

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.

Managing Dotfiles Like a Pro with Yadm

nickboy@users.noreply.github.com (Nick Liu) — Thu, 15 Jan 2026 00:00:00 +0000

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.

Nick Liu on Nick Liu - Software Engineer

AWS Billing Unbilled Usage Auditor

Key Metrics #

Architecture #

Technical Deep Dive #

Aggregation over Brute-Force #

Twitter Fleet-Scale Kernel Automation

Key Metrics #

Architecture #

Technical Deep Dive #

Validate Before You Roll #

Building a Knowledge Base That AI Can Actually Use

What I Learned from How Claude Code's Creator Uses Claude Code

Case Study: Building AWS Billing's Unbilled Usage Auditor

The Problem #

Case Study: Fleet-Scale Kernel Automation at Twitter

The Problem #

Catppuccin Mocha: Why I Theme Everything the Same Color

Finding the Bottom of a Valley Blindfolded: Understanding Gradient Descent

Why Should You Care? #

How Machines Ask Smart Questions: Entropy & Information Gain

What’s the Big Idea? #

How Neural Networks Learn from Mistakes: Backpropagation Explained

Neural Networks Are Everywhere #

My Terminal Setup in 2026: Ghostty, tmux, and Neovim

Modern CLI Tools That Replaced My Unix Classics

Managing Dotfiles Like a Pro with Yadm

Key Metrics
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Architecture
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Technical Deep Dive
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Aggregation over Brute-Force
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Key Metrics
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Architecture
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Technical Deep Dive
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Validate Before You Roll
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The Problem
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The Problem
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Why Should You Care?
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What’s the Big Idea?
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Neural Networks Are Everywhere
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