Pydantic v3 shipped in late 2025. It has a new Rust-backed core and a fresh model system. With FastAPI 0.115+, you get auto request checks, fast JSON output, and OpenAPI 3.1 docs. No manual schema work. Data errors get caught at the API edge. Client SDKs come from the live spec. The check overhead that used to be a bottleneck is now mostly gone.

This guide walks through what changed in v3, how to lay out a production project, the validation patterns to know, and what deployment looks like when you care about speed.

git worktree lets you check out multiple branches of the same repository simultaneously into separate directories - no stashing, no cloning, no context switching overhead. Each worktree shares the same .git object store, so you get independent working trees instantly without re-downloading any history. Run git worktree add ../my-repo-hotfix hotfix/urgent-fix and you have a fully functional working tree on a separate branch, ready to build and test while your feature branch stays untouched in the original directory.

Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) aren’t rivals. They solve different layers of the same problem. MCP sets how an AI agent connects to tools and data. A2A sets how agents talk to each other and pass off tasks. Together they form the base plumbing of the agentic web.

If you’re building past a single chatbot in 2026, you need to grasp both.

The Fragmentation Problem

Before these protocols, the AI tooling space was a mess of clashing integrations. Every major framework had its own way to plug into outside tools: LangChain , CrewAI , and AutoGen . Giving a LangChain agent access to the Slack API meant writing a LangChain-only tool wrapper. Wanting the same in a CrewAI workflow meant starting over. None of the adapters carried across.

Python is excellent for most of what developers throw at it - API servers, data pipelines, automation scripts, machine learning glue code. But CPU-bound work is a different story. When you’re parsing 500MB log files, running simulation loops, or crunching millions of rows in a tight inner loop, you’re going to hit a wall. Not always, but often enough that it becomes a real problem.

The solution is not to rewrite your entire application in Rust. That’s dramatic and usually unnecessary. The better approach is to profile your code, find the 5-10% that consumes most of the CPU time, and rewrite just that part in Rust. The rest of your codebase stays Python. Your interfaces stay Python. You just swap out the slow function for a fast one.

OpenAI Codex CLI is an open-source (Apache 2.0), Rust-built terminal coding agent. It has over 72,000 GitHub stars. It pairs GPT-5.4’s 272K default context window, which you can push to 1M tokens, with OS-level sandboxing. That sandbox runs on Apple Seatbelt on macOS and Landlock plus seccomp on Linux. Here is the key point: Codex CLI is the only major AI coding agent that enforces security at the kernel level, not through application-layer hooks. With codex exec for CI pipelines, MCP client and server support, and a GitHub Action for PR review, it is the most infrastructure-ready rival to Claude Code in 2026.

The three best ergonomic split keyboards for programmers in 2026 are the MoErgo Glove80 ($399, best overall comfort with contoured key wells and aggressive tenting), the ZSA Voyager ($365, best portable option with a low-profile design and magnetic tenting legs), and the Kinesis Advantage360 Pro ($499, best for deep key well fans with wireless ZMK firmware). All three offer full Linux support, open-source firmware tweaks, and columnar stagger layouts that cut finger strain on long coding days.