Three Tools That Actually Make AI Coding Production-Ready
MCPorter, dmux, and Base Skills solve the boring but essential workflow problems that demos ignore.
Three Tools That Actually Make AI Coding Production-Ready
The demo phase is over. Developers are building production-ready infrastructure around AI coding tools, solving the workflow problems that make the difference between "cool demo" and "daily driver."
MCPorter makes Anthropic's Model Context Protocol actually usable with TypeScript runtime and CLI. It auto-discovers MCP servers from popular AI tools and generates typed clients so you can compose richer automations without configuration hell.
dmux enables parallel AI coding agent management with isolated git worktrees. Finally, you can run different agents on separate branches simultaneously, then merge changes in an organized workflow instead of chaos.
Base Skills provides pre-built blockchain capabilities for AI agents — contract deployment, wallet integration, node operation. Skip the boilerplate and get agents building on Base immediately.
These aren't flashy. They're the infrastructure that makes AI coding teams actually productive rather than just impressive in screenshots.
Featured Tools
dmux
dmux enables developers to manage multiple AI coding agents in parallel using isolated git worktrees and tmux sessions. It allows different agents to
Base Skills
Agent skills collection that enables AI agents to interact with the Base blockchain network. Provides pre-built capabilities for deploying contracts,
MCPorter
A TypeScript runtime, CLI, and code generation toolkit for the Model Context Protocol (MCP) that helps developers discover, call, and compose automati
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