GitHub’s uptime has fallen sharply, with hundreds of incidents—including dozens of major outages—largely fueled by AI‑driven code generation, prompting high‑profile users to migrate, leadership to prioritize availability, and a costly overhaul of capacity and architecture.
This article explains how GitHub Spec Kit implements Spec‑Driven Development to replace ad‑hoc “vibe coding” with a structured workflow, detailing its core principles, six guiding rules, a seven‑step process, and a hands‑on example building a Todo app using Claude Code.
The article introduces BMad Method, a universal AI‑agent framework for agile, human‑AI collaborative development, detailing its core features, dual deployment modes, installation steps, project structure, agent team roles, workflow examples, a hands‑on tutorial for building a To‑Do app, extension ecosystem, and key advantages.
Vibe Coding, introduced by Andrej Karpathy in 2025, lets developers describe software goals in natural language while large language models generate the code, reshaping the developer’s role, outlining the workflow, discussing tools, risks, and future prospects of this AI‑driven programming paradigm.
Baidu Comate, built on the Wenxin large model and embedded as an IDE plugin, delivers a 24/7 AI coding assistant that helps developers think, write, and review code through context‑aware generation, error fixing, test case creation, rapid sub‑600 ms responses, fill‑in‑the‑middle editing, prompt‑engineered personalization, AutoWork automation, and an open Comate+ platform for extensibility.
The article describes how the author used ChatGPT to generate over 5,000 lines of code for a WeChat mini‑program community app, outlining the AI‑Driven Development (AIDD) workflow, detailed login and post‑list module implementations, multiple iteration cycles, code snippets, and reflections on efficiency and reuse.
