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Tutorial 1: Pick an AI Coding Tool

  • Contributor
  • 5 days ago
  • 3 min read

There's no "best" tool. Match to your workflow, language, and trust level.

Step 1: The 2026 Landscape (10 min)

Major players:

  • Cursor — VS Code fork; tight AI integration; tab-complete + chat

  • GitHub Copilot — extension for many editors; inline suggest

  • Claude Code — terminal-first; agentic; multi-step tasks

  • Windsurf — Codeium's IDE; similar to Cursor

  • Continue — open source; bring-your-own-model

  • Aider — terminal; git-aware

  • Cody (Sourcegraph) — code search + AI

  • Tabnine — older; focused on completion

The space changes quarterly. Re-evaluate every 6 months.

Step 2: Inline Suggest vs. Chat vs. Agent (10 min)

Three modes:

  • Inline suggest — types ahead; you accept (Tab)

  • Chat — ask questions; get explanations

  • Agent — does multi-step tasks autonomously

Most tools offer 1-2 of these. Pick by what you need most.

For most engineers in 2026: chat + agent matter more than inline suggest.

Step 3: Cursor (10 min)

  • VS Code fork

  • Excellent tab-complete

  • Chat in side panel

  • "Compose" for multi-file edits

  • Agent mode for tasks

Strengths: integrated experience; fast.

Trade-offs: another editor to install; potential VS Code-update lag.

Popular default in 2026.

Step 4: Claude Code (10 min)

  • Terminal-based

  • Strong at agentic tasks

  • Reads your codebase; edits files

  • Uses Claude models

  • CLI workflow

Strengths: multi-file work; thinks like a teammate.

Trade-offs: not in editor; learning curve.

Increasingly popular for "give me a task; check the result."

Step 5: GitHub Copilot (10 min)

  • Available in VS Code, JetBrains, others

  • Inline suggestions strongest

  • Chat improving

  • Enterprise-friendly (GitHub-managed)

  • Lowest friction adoption

Strengths: works everywhere; org-friendly.

Trade-offs: chat / agent less powerful than competitors.

For Copilot-only teams: works. For more: consider others.

Step 6: Match to Your Language (10 min)

  • JS / TS / Python: every tool excels

  • Go / Rust: most work well

  • Niche languages (Elixir, OCaml): test before betting

  • Mainframe / legacy: limited support

Tools learned from public code. Less public code = weaker performance.

Step 7: Match to Your Codebase Size (10 min)

  • Small repo: any tool handles

  • Medium (10k files): need good code search (Cursor, Cody, Claude Code)

  • Large (100k+ files): only some scale (often: managed paid tiers)

Test the tool against your actual repo before committing.

Step 8: Cost (5 min)

Roughly:

  • Personal: $10-30/month

  • Team: $20-60/user/month

  • Enterprise: negotiate

Tools that look free use limited models. Real productivity needs frontier models.

Worth it if the tool saves 30+ minutes a day.

Step 9: Privacy and IP (10 min)

Concerns:

  • Does the tool train on your code?

  • Does it send code outside your network?

  • Does it have on-prem option?

Read the privacy policy:

  • Copilot for Business: doesn't train on your code

  • Cursor: similar options

  • Cody / Continue: can self-host with own LLM

For sensitive codebases: verify.

Step 10: Try Before Committing (10 min)

For two weeks:

  • Use the tool on real work

  • Track: time saved, frustration moments

  • Compare against your baseline

Trial periods are free for most tools. Use them.

The "best" tool is the one your brain clicks with.

What You Just Did

AI coding tool selection: landscape, modes, individual tools, language fit, codebase size, cost, privacy, trial. Choosing well.

Common Failure Modes

Pick the most-hyped without trying. Doesn't fit your workflow.

Free tier; weak model. Disappointing experience.

Ignore privacy policy. Compliance / IP issues.

Commit to one tool forever. Space changes; you fall behind.

No metrics on impact. Can't justify spend.

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