The hype says anyone can code now. The reality: vibe coding changes *what* senior engineers do, not *whether* we're needed. And the gap between experienced and inexperienced developers is getting wider, not narrower. Collins Dictionary named “vibe coding” their Word of the Year for 2025. Search interest spiked over 6,000%. The narrative is seductive: describe what you want, AI writes the code, programming becomes as easy as having a conversation.
Claude Code calls a custom statusline command every turn with a JSON payload on stdin. The payload includes the current context-window fill percentage, model, cost, and cwd. Nothing in the contract says you can only read it — you can fork it to anything you want, and the command stays a statusline. (Quick framing for anyone new to Claude Code: it’s Anthropic’s terminal CLI for Claude, and the statusline is the configurable line of text it prints under your prompt every turn — like a shell prompt for the agent. You point at any script in settings.local.json, Claude pipes a JSON object to it on stdin, and whatever the script writes to stdout becomes the visible line.)
I wrote a daemon to listen to Claude Code hooks. My first version read `$CLAUDE_HOOK_PAYLOAD` and logged empty bodies for two days straight. The payload was sitting on stdin the whole time. This post is the five gotchas I hit while wiring up ClaudeDeck — a Stream Deck plugin (a small program that runs inside Elgato’s Stream Deck app on the USB grid of programmable LCD keys) that talks to Claude Code over its hooks system. Claude Code is Anthropic’s terminal CLI for Claude — claude in your shell — and its hooks are user-defined scripts it spawns at certain points in a session (before a tool call, on session start, on prompt submit). My daemon is a long-running background process the plugin and the hooks both talk to over a local socket. None of the gotchas are exotic. All of them cost me hours. Each one is a place where the docs were either silent, ambiguous, or contradicted by tribal knowledge I picked up from other people’s projects.
I rebuilt my portfolio site in a weekend using vibe coding with Claude Code . This is the real workflow — no hype, with honest tradeoffs. Andrej Karpathy coined “vibe coding” in early 2025. The pitch: describe what you want in natural language, let the AI write the code, spend your time directing instead of typing. Collins Dictionary named it Word of the Year. Most takes on it are either breathless hype or dismissive eye-rolls. Here’s what it looks like when a senior engineer uses it for a real project.
I built a Claude Code permission gate that holds an HTTP response open until a Stream Deck key is pressed. Then I needed to inject a keystroke into Claude Code's own TTY so a key press could write `1\r` straight into Claude's stdin. Bun can hold HTTP open all day. Bun cannot reliably wrap a child PTY through `node-pty` and capture the parent shell's PID. So I split my daemon: HTTP and WebSocket stay on Bun, and a Node CommonJS subprocess owns the PTY that runs Claude. (Quick grounding before the story: a PTY — pseudo-terminal — is the kernel object every interactive shell talks to. It’s a pair of file descriptors, master and slave; the program reads/writes the slave end as if it were a real terminal, and anything you write to the master end looks to that program like a human typing. The TTY is the slave end seen from the child’s side. node-pty is Microsoft’s library that gives a JavaScript parent process a writable handle to the master. Bun is a JavaScript runtime — Node’s faster sibling — and Node CommonJS is plain old require()-based Node, no transpile step. The story below is about which runtime owns the PTY.)
My daemon logged 111 consecutive HTTP 429s against `https://api.anthropic.com/api/oauth/usage` over an 18-hour stretch, with zero successful responses ever in its lifetime. The poller was reading `Retry-After: 272` and ignoring it. While I was arguing with the backoff, Claude Code was pushing the same `rate_limits.five_hour` and `rate_limits.seven_day` numbers to my statusline command every turn, on stdin, for free. (Quick framing: Claude Code is Anthropic’s terminal CLI for Claude; Claude Max is the higher-tier subscription plan with weekly and 5-hour usage windows. HTTP 429 is “Too Many Requests” — the server’s polite way of saying “back off.” Retry-After is the response header that tells the client how long to wait. OAuth is the auth protocol Claude Code uses to talk to Anthropic on behalf of a logged-in user. And the statusline — the same one I covered in the statusline side-channel post — is the script Claude Code spawns every turn with a JSON blob on stdin.)
Claude Code wants to run a shell command. I want to press a physical Stream Deck key — the YES key, two inches to the left of my keyboard — to approve it. The hook gets exactly one HTTP response to decide allow vs deny. The key press might land in 200 milliseconds; it might land seven minutes later, after I've been pulled into a meeting and come back. The trick is that Claude Code's hook timeout is 600 seconds, which turns out to be just enough headroom to hold the HTTP response open the whole time and let a hardware button write the answer. (Setup, for anyone who hasn’t seen this stack before: Claude Code is Anthropic’s terminal CLI for Claude, and one of its hook events — PreToolUse — is a script Claude spawns and waits on before running a tool like Bash or Edit. The script’s stdout decides “allow” / “deny” / “ask”. Stream Deck is Elgato’s USB grid of programmable LCD keys. The plumbing I’m describing here lives in a daemon — a background process at 127.0.0.1:9127 — that the hook script POSTs to and that the Stream Deck plugin connects to over WebSocket. For the hooks docs themselves and the four other gotchas in that layer, see the hooks-reality post.)
The AI coding tool landscape in 2026 has finally settled into four serious players: Claude Code , Cursor , GitHub Copilot , and Windsurf . I've used all four on real work. This is the honest comparison. Forget feature checklists. What matters is how each tool feels under real engineering work — the kind I do every day as a senior software engineer at Meta. I built this site primarily with Claude Code, but I’ve put serious hours into the others.
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.
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.