The AI Developer Toolkit: What Actually Helps

AI coding tools have moved from novelty to essential infrastructure for many professional developers. Knowing which tools to use for what is the starting point.

The Landscape

• [GitHub Copilot](https://github.com/features/copilot)
• Inline code completion in your editor (VS Code, JetBrains, Neovim, and others)
• Autocompletes lines, functions, and sometimes entire blocks
• Copilot Chat for in-editor conversational assistance
• $10/month for individuals; included in GitHub Pro and some enterprise plans

• [Cursor](https://www.cursor.com)
• A code editor built on VS Code with AI deeply integrated
• Stronger than Copilot for multi-file edits and complex refactoring
• Can edit multiple files simultaneously based on a single instruction
• Features: inline edit (CMD+K), chat, and agent mode
• Growing rapidly in professional developer adoption

• [Claude](https://claude.ai) and [ChatGPT](https://chat.openai.com) for code
• Not in-editor, but excellent for architecture discussions, debugging complex problems, and explaining concepts
• Claude 3.5 Sonnet consistently performs well on coding benchmarks
• Useful for long, complex coding conversations that in-editor tools handle less well

• [Codeium](https://codeium.com)
• Free alternative to Copilot
• Solid autocomplete; less context-aware than Copilot for complex codebases

What AI Coding Tools Actually Do Well

Boilerplate and repetitive code AI excels at writing code that follows patterns you have already established. CRUD operations, standard data transformations, utility functions, tests for obvious cases — all of these benefit greatly from AI assistance.

Unfamiliar syntax and APIs When working in a language or framework you know less well, AI dramatically reduces the lookup time. "Write a Pandas operation that groups a DataFrame by category and calculates mean, min, and max for each numeric column" — this is faster than documentation hunting.

First drafts of functions Describe what a function should do; AI writes a plausible first implementation. You review and refine. This is faster than writing from scratch even when significant editing is needed.

Code explanation Paste unfamiliar code and ask "explain what this does" — particularly valuable when onboarding to a codebase or understanding legacy code.

Test generation Ask AI to write unit tests for a function. It handles the obvious cases quickly; you add edge cases it misses.

What AI Coding Tools Do Poorly

Novel algorithms For genuinely new algorithmic work, AI is a weak partner. Its suggestions will be based on existing patterns, which may not apply.

Understanding your specific codebase context In-editor tools have limited context. They often suggest code that would work in isolation but conflicts with your architecture, naming conventions, or existing abstractions.

Correctness AI-generated code looks correct more often than it is. It compiles. It may even pass tests. But subtle bugs — particularly around edge cases, error handling, and state management — are common.

Security-sensitive code Authentication, authorisation, cryptography — these require extra scrutiny. AI may produce code that works but has security vulnerabilities it does not flag.

The Non-Negotiable: You Must Review Everything

• The most dangerous pattern with AI coding tools is accepting generated code without review. AI code:
• Compiles but may have logic errors
• May introduce security vulnerabilities
• May use deprecated APIs or outdated patterns
• May not handle edge cases correctly

Treat AI-generated code exactly as you would treat code from a junior developer: review it, understand it, and test it before it goes anywhere near production.