A former Meta L8 engineer shares his end-to-end agentic workflow, from terminal setup to prompt strategies. He outlines the tools (WezTerm, tmux, Neovim, Claude Code/OpenCode), voice input, and delegation techniques that let him manage AI agents like a dev team.

• A former Meta L8 produces 30+ high-quality PRs in a single day by managing AI agents instead of writing code himself
• His entire workflow runs in a terminal (WezTerm + tmux + Neovim) using Claude Code for Anthropic models and OpenCode for everything else, avoiding vendor lock-in features like auto-memory
• He pairs local, offline speech recognition with terminal prompts to iterate faster, treating each prompt like a spec refined against a "golden set" of expected outputs
• He's built custom CLI tools to automate context loading, versioning, and error handling so prompts plug into a repeatable pipeline

This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.

• Boris Cherny landed 259 PRs in 30 days by having loops handle all the prompting instead of him
• Loops have evolved through five stages, from ReAct/AutoGPT to today's self-scheduling, crash-recoverable multi-agent systems
• The real difference from plain cronjobs is a built-in decision engine: loops read state, decide next steps, and validate results rather than just firing a fixed script
• Cherny's practical advice centers on self-verification, auto-permissions, and using /loop or /goal commands so agents catch their own mistakes