Resources for building with AI, not just using it
Instructor-curated tools, articles, and frameworks. No hype, no sales pressure — just what's actually useful when you're building real AI projects.
class AIToolkit:
# Curated. No fluff.
tools = [
"cursor", # AI-first editor
"claude", # reasoning + code
"copilot", # autocomplete
]
approach = "workflow-first"
noise = False
# Build with it. Don't just use it.
✓ 4 tools · frameworks · articles
AI Coding Tools
These are the tools worth learning. Each entry includes what it's actually for, when to reach for it, and the mistake most beginners make.
Cursor
AI-first code editor built for pair-programming with LLMs. Understands your codebase and assists inline.
When to use
When building features, refactoring, or navigating a large codebase with AI suggestions inline.
Common mistake
Using it as autocomplete instead of a thinking partner. Ask it to explain tradeoffs, not just generate code.
Claude
Conversational AI assistant with strong reasoning, code generation, and long-context understanding.
When to use
Architecture decisions, code review, explaining complex systems, and working through design tradeoffs.
Common mistake
Accepting the first output without verifying logic. Always ask it to justify its approach.
GitHub Copilot
In-editor AI code completion trained on public repositories. Integrates directly into VS Code and JetBrains.
When to use
Boilerplate generation, writing test cases, and filling in familiar patterns you would write yourself.
Common mistake
Trusting completions in security-sensitive or data-handling code without review. Copilot doesn't know your context.
Codeium
Free AI code completion and chat assistant that works across most editors and IDEs.
When to use
When budget is a constraint but you still want in-editor AI assistance during development.
Common mistake
Treating it as equivalent to Copilot. Its context window is shorter, so complex multi-file tasks need more guidance.
Learning Resources
How to Think About AI Systems (Not Just AI Tools)
A practical introduction to the difference between using AI features and designing AI workflows.
Prompt Engineering vs. System Design
Why better prompts are not the same as better systems, and what that means for your projects.
Frameworks & Approaches
Structured ways to think about AI-assisted development that hold up beyond tutorials.
Workflow-First Design
Define the steps, decisions, and data flows before writing a single prompt. Prevents brittle systems.
Structured Output Validation
Techniques for making LLM outputs reliable in production: schema validation, retry logic, and fallback paths.
Want to apply these to a real project?
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