Claude Code

TL;DR — AI-Native Workspace is an open-source framework for running a real research or engineering workspace with AI coding agents. Its core idea is simple: separate the Model, Agent, Protocol, and Application layers so you can swap Claude, DeepSeek, Codex, Cursor, MCP servers, project docs, and knowledge workflows without rewriting everything. Most AI coding workflows start as a pile of useful fragments: a CLAUDE.md, a few prompts, some shell scripts, maybe a .cursorrules file, and a note that says “remember to update the TODO.” That works for one project. It starts breaking when the workspace grows to five projects, three model providers, a wiki, an inbox, a handful of local services, and months of accumulated decisions. ...

TL;DR — DeepSeek ships an Anthropic-compatible API endpoint that lets Claude Code treat DeepSeek V4 Pro like a drop-in replacement for Claude Opus. The setup is eight environment variables, and it works — including tool calling, sub-agent spawning, and native web search. At $0.435/M input tokens (permanent price after the initial launch promo), it’s roughly 4–17× cheaper than Claude Opus 4.7. This is a practical guide based on a real setup we run daily. ...

TL;DR — We reverse-engineered Claude Code’s agent architecture from its TypeScript source to understand how it handles security, complex tasks, and tool permissions. Then we applied those patterns to an open-source Terraria AI bridge that lets players talk to an LLM inside the game. Here’s what we found, what we built, and what we learned about practical agent design. Why We Cracked Open Claude Code’s Source Claude Code isn’t just a coding assistant. Under the hood it’s an agent runtime — it spawns sub-agents, manages file permissions, runs bash commands, and decides when to ask the user vs. just doing the thing. We wanted to understand how it works so we could apply the same ideas to a completely different domain: a Terraria game server. ...

TL;DR — RAG answers from documents. Agents take actions. Most real systems use both: RAG provides context, agents act on it. The hard part isn’t picking one — it’s knowing which layer of your problem belongs to which pattern. Why This Comparison Matters Right Now Two things happened in the last six months that make this comparison less academic than it used to be. First: coding agents crossed a quality threshold around November 2025. Simon Willison’s five-minute PyCon talk describes it as the moment agents went from “often-work” to “mostly-work” — usable as daily drivers, not just demos. The “best model” title changed hands five times between Anthropic, OpenAI, and Google in a single month. ...