Former Meta L8 Kun Chen published a ByteByteGo guide that details his personal agentic engineering stack. The post walks readers through a four‑phase framework for evaluating AI search, a golden query set, and code that measures accuracy. By exposing the exact metrics he uses, Chen lets teams move from “looks good” to provable quality.

Chen’s workflow relies on a terminal‑centric environment: WezTerm for a frameless console, Neovim with plugins like oil.nvim and neogit, and tmux to orchestrate sessions. He pairs Claude Code for Anthropic models with Open Code for others, keeping the stack model‑agnostic. He also uses open‑source agents that avoid vendor lock‑in, swapping models as performance improves. Voice input, powered by local speech models, replaces typing and accelerates prompting.

Since going solo, Chen claims the agentic setup has let him ship 30+ PRs daily, maintaining his own quality bar and staying in a flow state. He demonstrates the process by building an image‑input feature for his “Hi Bit” AI tutor, showing how deterministic code hands off to GPT‑4o only when rule‑based logic fails. The guide offers a reproducible template for engineers seeking measurable AI‑assisted productivity.