Learning Path · Intermediate
AI Coding Roadmap
Become 3x more effective with AI in your daily coding loop. Aimed at working engineers.
Outcome
You're proficient with at least one agent (Cursor or Claude Code), you've shipped real code with AI help, and you know where AI helps vs. hurts.
Key takeaways
- 1Solid working knowledge of one stack — pick the one you ship with daily
- 2Comfort with command line and git
- 3Willingness to abandon old habits if AI replaces them
Recommended tools
The roadmap
- 01
1. Pick your editor
Week 1Spend a week with each. Commit to one.
- 02
2. Get the autocomplete loop right
Week 2Most engineers leave 50% of AI's value on the table by not learning the basics of tab autocomplete and context.
- 03
3. Agent loops
Week 3Move from typing-with-help to delegating-and-reviewing. This is where the 3x is.
- 04
4. Pick the right model per task
Week 4Don't pay Opus to lint. Don't ask Haiku to design a system.
- 05
5. Ship something real
Week 5–6Use AI to take a real project from 0 to deployed. Then write up what worked and what didn't.
Frequently asked questions
Related on AIKnowHub
Tool Guide
Cursor AI Guide
The most popular AI-native IDE. A VS Code fork with deep model integration, multi-file edits, and a strong agent loop.
Concept
AI Agents Explained
Agents are LLMs that can take actions in a loop. Here's what that actually means, where they shine, and where they fall over.
Tool Guide
Claude Code Guide
The definitive guide to Anthropic's terminal-native AI coding agent — install, configure, MCP, hooks, skills, sub-agents, plan mode, cost, security, limitations.
Comparison
Best AI Model for Coding
Which model actually writes the best code? Benchmarked against real tasks — refactors, bug fixes, new features, code review.
Directory
Claude Code
Terminal-native agent that reads, writes, runs, and reviews your codebase.
Directory
Cursor
AI-native VS Code fork with multi-model support, tab autocomplete, and an agent mode.