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pulpie-mcp lets coding agents crawl whole doc sites into clean local Markdown

The new Apache-2.0 MCP server gives Claude Code, Codex, and other clients four tools to fetch, save, and crawl docs as full Markdown (tables and code intact, not summarized), using a local 210M-parameter extraction model in about 420 MB of VRAM; the author crawled 60 pages in about 49 seconds. Install with uv tool install git+https://github.com/pinkpixel-dev/pulpie-mcp, but note the bundled model is CC BY-NC 4.0, so non-commercial use only by default.

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Show HN: Phonebook lets one Claude Code session ask another repo's agent a question

Phonebook ( npx agent [email protected] setup ) connects running Claude Code sessions, so an agent in one project can ask a session you've registered in another repo (yours or an approved teammate's) and get the answer back where you're working; it needs Node.js 24 and runs on macOS or Linux, not Windows. Sign up is paused for now, and questions and replies pass through Phonebook without end to end encryption and are kept for up to 7 days, so stick to non sensitive projects…

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EverMind open-sources Raven, a host agent that orchestrates Claude Code, Codex, and its own specialists

Raven (Apache 2.0, pre alpha) has a Host Agent split a goal into a dependency graph and hand each piece to its built in research, code, design, and on call agents or to 13 preset third party agents like Claude Code, Codex, and OpenCode, while a separate Evolver keeps only harness changes that pass benchmark checks. Install with curl fsSL https://raven.evermind.ai/install.sh | bash , or run it in Docker and open localhost:18793…

Show HN: Pi pod runs pi coding-agent sessions in isolated sandboxes on your own server

The open source pi pod puts each pi agent session in its own container on a VM you host, with agent config defined at the org, user, and project level so a team can share it, and a locally rendered TUI so typing doesn't lag over SSH. Native mobile apps are built from source for now, a hosted version is coming, and HN commenters note containers alone aren't a hard security boundary…

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Microsoft's Agensh: leaderless coding-agent teams keep improving from 1 to 1,024 agents

Agensh drops the central orchestrator: each worker claims its own sub task, builds and tests it, and merges into a shared Git repo while posting findings to a shared board, all on top of a Copilot single agent harness with a 6 hour budget. On ProgramBench's five hardest tasks with GPT 5.6 sol, going from 1 to 128 agents lifted the mean test pass rate from 19.31% to 28.78%, and 1,024 agents pushed pandoc from 33.89% to 55.06%, though the paper doesn't report what large teams cost…

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