#工程实践
7 related articles
Tutorials·2 min
The Five-Step Rhythm Method for AI Programming: A Practical Strategy Where Slow Is Fast
A methodology for rhythm control in AI programming: use a five-step strategy (identify, plan, minimal change, test, risk assessment) to prevent AI from going off-track and causing rework.
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Tutorials·2 min
Orchestrating AI Agents as State Machines: Stop Being a Human Confirmation Button
Explore the next evolution of AI coding: applying CI/CD engineering practices to orchestrate Agents as state machines with YAML templates, Gates, and Dashboards for autonomous multi-Agent progression.
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Product Reviews·3 min
Superpowers vs GStack In-Depth Comparison: How to Choose Your Claude Code Plugin
In-depth comparison of Claude Code's top open-source plugins Superpowers and GStack — their skills, workflows, and use cases to help developers choose the best AI coding assistant setup.
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Tutorials·2 min
Superpowers: Deconstructing the Quality Assurance Architecture for AI Coding Agents — An Open-Source Project with Nearly 200K Stars
Deep dive into Superpowers, a ~200K Star GitHub project that uses 5 phases and 14 Scale modules to systematically improve AI Coding Agent code quality.
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Deep Dives·2 min
CLAUDE.md: How One Markdown File Cut AI Coding Rework Rates from 41% to 11%
How CLAUDE.md — a 14k-star GitHub file — uses four golden rules to cut Claude Code rework rates from 41% to 11%, based on Karpathy's analysis of AI coding failures.
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Tutorials·2 min
AI Programming Post-Mortem: 6 Engineering Rules That Determine Project Success or Failure
Post-mortem from a real Android project built with AI tools, distilling 6 engineering rules—from cross-project context to local environment loops and API contracts—that determine AI programming success.
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