Claude Code, Cursor, and GitHub Copilot take three very different shots at AI-assisted coding: a terminal-native agent, an AI-first IDE, and a multi-IDE plugin. Claude Code leads on raw skill and complex multi-file work, scoring highest on SWE-bench at about 74-81%. Cursor offers the best editor experience with background agents and cloud automation. GitHub Copilot has the lowest entry price at $10/month and the widest IDE support. Most pro developers now mix two or more tools, with Claude Code plus Cursor as the top pair per the JetBrains AI Pulse survey from January 2026.

Git worktrees let you attach many working directories to a single repo. Each one has its own branch checked out. Claude Code ships a native --worktree (-w) flag that handles the setup in one command. It creates a worktree, checks out a new branch, and launches Claude inside it. Run the same command in another terminal and you’ve got a second agent. Scale to five, ten, or more sessions and none of them clash on disk.

I get better output from Claude Code by adding fewer tools, not more. Piling on MCP servers rarely helps, but the right CLI tools and skills do. With picks like Supabase CLI, Playwright, GitHub CLI, and the GSD framework, I built a stack that handles code, deploys, research, and browser work on its own.

When I first found Claude Code, I tried to connect every MCP server I could find. Within a week, the agent felt slower and less decisive, and it often picked the wrong tool for the job. The fix was almost always a smaller, more careful toolset.

Claude Code Agent Teams is an experimental feature - available since v2.1.32 (February 2026) - that lets you run 2-16 Claude Code sessions coordinated by a single team lead. Each teammate operates in its own context window with full tool access, while communicating through a shared task list and direct peer-to-peer messaging. You enable it with one config change, describe the team you want in natural language, and Claude handles spawning, assignment, and coordination. The feature works best for parallelizable work like multi-file refactors, cross-layer feature builds, and research-and-review workflows, but it costs 3-7x more tokens than a single session and has no session resume capability.

The single most important file in any Claude Code project is CLAUDE.md - a persistent instruction set that loads every session and shapes how the agent reads, writes, and verifies code. But CLAUDE.md alone is not what separates productive setups from fragile ones. The real productivity stack in 2026 combines CLAUDE.md conventions with on-demand skills, deterministic hooks, and git worktree isolation for running 10-15 parallel sessions against a single repository. Each session is scoped to one task, operating in its own branch, turning a solo developer into a small engineering team .

You can build a fully local, sandboxed code interpreter agent by pairing Ollama (running a reasoning model like Llama 4 Scout or DeepSeek R1) with a Docker container that executes the generated Python code. The agent sends a user prompt to the local LLM, which produces Python code; that code gets injected into a locked-down Docker container with no network access and strict resource limits; the stdout/stderr output is captured and fed back to the LLM for reflection and iteration. The entire loop runs on your machine with zero cloud API calls, giving you a private, free, ChatGPT Code Interpreter-style experience.