AI

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 official marketplace plugins, CLI tools, and skills do. Start with /plugin and picks like typescript-lsp and security-guidance, then add Supabase CLI, Playwright, GitHub CLI, and the GSD framework. That stack 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, live since v2.1.32. It lets you run 2-16 Claude Code sessions under one team lead. Each teammate gets its own context window and full tool access. They talk through a shared task list and direct peer-to-peer messages. You turn it on with one config change, then describe the team you want in plain language. Claude handles the spawning, the assignment, and the coordination. The feature shines on work you can split up: multi-file refactors, cross-layer feature builds, and research-and-review jobs. The catch is that it costs 3-7x more tokens than a single session, and it cannot resume a session.

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. You pair Ollama (running a reasoning model such as Scout, the smallest Llama 4 variant , or DeepSeek R1) with a Docker container that runs the generated Python code. The agent sends a prompt to the local LLM, which writes Python. That code goes into a locked-down container with no network and strict limits. The output feeds back to the LLM so it can fix and retry. The whole loop runs on your machine with zero cloud calls.

Agentic RAG replaces the standard “retrieve-then-generate” pattern. The LLM gets tool-use powers to decide when to retrieve, which sources to query, how to rewrite queries, and whether the result is enough. Instead of fetching docs on every query, the model acts as an orchestrator. It runs targeted searches across vector stores, SQL databases, and web sources, then checks its own answers. This pattern lifts answer accuracy by 15-25% on multi-hop benchmarks and cuts wasted retrieval calls by about 35%.