Hooks in Claude Code are automated actions that fire on specific events during AI sessions. They range from simple bash scripts to agent-powered validators. The difference between exit code 1 and exit code 2 will save you hours of frustrated debugging.
Anthropic added ultraplan to Claude Code, offloading plan generation to Opus 4.6 in the cloud with up to 30 minutes of deep reasoning. It is brilliant for complex refactors but fails outside git repos. Here is how it works and when local plan mode is the better choice.
Manual terminal screenshots are tedious and inconsistent across tutorials. A headless tmux session with pyte ANSI parsing and Pillow rendering produces pixel-perfect 1070x625 PNGs, MP4 recordings at 4 FPS, and direct Vimeo uploads. One Python CLI with 12 subcommands replaces all the manual screenshotting and cropping.
Claude models now run inside GitHub Copilot at no extra cost. That does not mean Copilot replaces a direct Claude subscription. The context window shrinks, extended thinking disappears, and MCP support is missing. Most mid-size teams end up needing both. Here is what to buy and why.
Every article about Claude Code automation stays surface-level. Here is the production pattern for running non-interactive mini prompts with queue processing, quality gates, and auto-restart wrappers, built from running dozens of automated jobs across multiple repos every day.
Your team runs entirely on AWS with Enterprise Support credits making Amazon Q Developer seem like the obvious choice. But when developers actually test both tools, they keep switching to Claude Code. The 1 million token context window versus 200K, code quality improvements, and better handling of complex legacy codebases make the decision clear despite AWS integration advantages.
For mid-size development teams, Claude Code costs significantly more than Cursor Teams. But the real cost difference extends far beyond license fees - GitClear found AI code duplication grew 4x across 211 million changed lines. Factor in integration setup complexity, training cycles, ongoing support, and productivity losses during adoption and tool migration.