Composer: A real-time markdown editor where Claude Code agents edit alongside you
Composer (usecomposer.md) is a real-time markdown editor designed to solve a specific pain: after a Claude Code agent generates a document, the agent gets cut off from human feedback. You paste the doc into Slack, people comment, but the agent can't see those comments or fix the paragraph everyone is arguing over.
Key Details
- Live collaborative editing — humans and agents edit the same document in real time. The Claude Code agent connects via the Model Context Protocol (MCP).
- Agent capabilities — the agent can read the document, reply to comments, and leave suggestions, just like a teammate.
- Push docs directly — you push a doc straight out of your Claude Code session, no copy-paste dance.
- Comments and suggestions — work today. Access controls are also available.
- Team collaboration — invite teammates into the session; they can also bring their own agents.
- Public docs — free, unlimited, no sign-in required to try.
Who It's For
Developers who use Claude Code to generate plans, specs, or meeting notes, and then need to iterate on those documents with human collaborators without losing the agent's context.
📖 Read the full source: r/ClaudeAI
👀 See Also
OnUI: Browser Extension for Precise UI Feedback to Claude Code
OnUI is a browser extension that lets you annotate webpage elements and export structured reports for Claude Code via local MCP, eliminating ambiguous UI descriptions. Built primarily with Claude Code, it's free, open-source, and available for Chrome, Edge, and Firefox.
Measuring Claude Code MCP Stack: Cache Friendliness vs. Byte Savings, and a 2-Line Fix for Prompt Cache
Greg Shevchenko benchmarks MCP compressors and retrieval layers on two axes: byte savings and cache friendliness. A 2-line fix (sort rg hits, sort map entries) boosts cache from ~0% to 100% with no byte-savings loss. Open-source harness included.
Exploring the Claude Code Guidelines: A Minimalist Approach in 65 Lines
The Claude Code extension encapsulates essential AI coding principles in just 65 lines of Markdown, emphasizing 'Think Before Coding'. Despite its simplicity, it has gained notable traction among developers.
Deterministic Compiler Architecture for Multi-Step LLM Workflows Shows Strong Benchmark Results
A deterministic compilation architecture for structured LLM workflows uses typed node registries, parameter contracts, and static validation to compile workflow graphs ahead of time. Benchmarks show it outperforms GPT-4.1 and Claude Sonnet 4.6 across workflow depths from 3-12+ nodes.