AI Coding Hub: Tool Selection, Workflows, and Code Quality
An AI coding hub with comparisons and teardowns of Claude Code, Cursor, Codex, and GitHub Copilot, plus real development workflows, code review checklists, capability tests, and local model options.
- 14 articles
- 3 wiki entries
- 6 tools
The gap in AI-assisted coding is no longer whether you use it but how. The same tool completes a few lines for one developer and refactors a module for another; the difference is a repeatable workflow and a review standard. This hub organizes our content around tools, workflows, and quality control.
Who is this hub for?
On this page
Start here: what actually changed
From completion to agentic coding — get the overall picture first.
- Article
Practical Guide to AI Coding Assistants: From Code Completion to Architecture Support
Covers practical usage tips for mainstream tools such as Cursor, GitHub Copilot, and Claude Code, along with best pract…
- Wiki
Vibe Coding
Wiki entry: what vibe coding means
- Wiki
Harness Engineering
Building the harness around a coding agent
- Article
How Code Agents Modify Projects: Read, Plan, Execute, Verify, and Rollback
What enables tools like Claude Code and Cursor Agent to independently read codebases, modify multiple files, and run te…
The main tools: Claude Code, Cursor, Codex, Copilot
Use product teardowns and workflow guides to choose an interaction style for your tasks.
- Article
Why Claude Code Is Well-Suited for Large-Scale Project Refactoring: A Product Breakdown
Like other AI-powered coding tools, why does Claude Code stand out in legacy projects spanning hundreds of thousands of…
- Article
Why Cursor Is Taking Off: A Product Breakdown from AI Autocomplete to Agent-Based Programming
What sets Cursor apart among the crowded field of AI coding tools? This article dissects its product logic: why it was …
- Article
Customizing the Codex Desktop Theme: Official Appearance Settings and Dream Skin
A tool you stare at for ten hours a day deserves to look good. This guide covers two levels of Codex desktop customizat…
Real development workflows
Combining tools, onboarding an existing project, and isolating changes.
- Article
How to Build Projects with Claude Code, Cursor, and Codex: A Real-World Development Workflow
A practical guide to AI-assisted development: assign roles to Claude Code, Cursor, and Codex; manage context; review co…
- Article
How to Integrate Claude Code, Cursor, and Codex into an Existing Project
AI coding tools shine on toy projects but can stumble in a real codebase with tens of thousands of lines, established c…
- Article
Stop Expecting One Tool to Do It All: A Cursor + Claude Code + Greptile + NotebookLM Workflow
The most efficient approach to AI-assisted programming is not finding a single 'best' tool. It is letting Cursor or Cop…
- Article
How to Isolate and Review AI Code Changes with Git Worktree and Diff
When AI edits code in your main workspace, its changes become hard to separate from your own. Git Worktree gives each A…
Quality and risk control
Why AI breaks projects, a review checklist, and automated review tools.
- Article
Why AI-Driven Code Changes Often Break Projects—and How to Mitigate the Risk
When AI breaks a project, it's not due to incompetence but rather its lack of global context, confident hallucinations,…
- Article
How to Review AI-Generated Code: A Practical Checklist
AI-generated code is unusually good at looking correct, which is precisely what makes it dangerous. This review checkli…
- Article
How Should You Test AI Coding Ability? From Puzzle Scores to Real Projects
Perfect LeetCode results and high benchmark scores do not prove that a model can improve your codebase. Learn the limit…
Local models and private deployment
Keep coding assistance inside your network when the cloud is not an option.
- Wiki
Local Model
Wiki entry: the local model trade-off
- Article
Local LLM Offline Assistant Guide: Build a Private AI Workspace with Ollama or LM Studio
A practical local AI assistant guide for individuals and small teams, covering model selection, hardware, document Q&A,…
- Article
Running DeepSeek Locally: Distilled Models, Full-Size Access, and GPU Selection
A practical explanation of which DeepSeek models can actually run on local hardware, how much VRAM they need, how to in…
Tools and related sections
Coding tools in our directory, rankings, and related topics.
- Tool
Cursor
A popular AI coding IDE with an agent-first workspace and in-house Composer models; its parent company was acquired by …
- Tool
Claude Code
Anthropic's coding agent that reads and edits codebases, runs commands and tests, and shares one setup across terminal,…
- Tool
OpenAI Codex
OpenAI's coding agent, bundled with every ChatGPT plan, running on the web, CLI, IDE extension and iOS, with cloud task…
- Tool
GitHub Copilot
GitHub's official AI coding assistant, covering IDE completion, chat, agent mode, a CLI, and a cloud coding agent; sinc…
- Tool
Windsurf
The AI IDE from the former Codeium team, renamed Devin Desktop by Cognition in June 2026, with ACP support that puts se…
- Tool
Ollama
An open-source tool for running large models locally; pull and execute Llama, Qwen, and others with a single command, o…
- Section
AI coding tools directory
Browse every coding tool in the directory.
- Section
AI coding for everyone
A use-case topic for readers without a coding background.
FAQ
- Q: Claude Code, Cursor, Codex, or Copilot — which should I pick?
- A: Choose a working style, then try it on your own tasks. Start with Cursor or Copilot in your existing IDE for editing and review in one place; evaluate Claude Code or Codex for delegated multi-file work. Check current official documentation rather than judging a product solely by its original interface.
- Q: Why does AI help on small projects but stumble on large ones?
- A: Large projects carry constraints that live outside the code: directory conventions, legacy decisions, cross-module effects. The fix is to write those constraints down for the tool and to limit the scope of each change. See our articles on AI breaking projects and on large-project refactoring.
- Q: What extra checks does AI-generated code need?
- A: At least three: whether the dependencies and APIs it used actually exist, whether error handling and edge cases were skipped, and whether the diff went beyond the requested scope. Our code review checklist is ready to reuse.
- Q: Can a team that cannot send code outside still use AI coding tools?
- A: Evaluate local execution with Ollama or LM Studio and an editor plugin that accepts custom endpoints. Suitability depends on the model, hardware, and coding task, so test project examples first. Disable cloud models and external tools when data must stay local. Start with the offline assistant guide.