linggen

same here. i usually only notice fonts when something feels off, not when it's working. the article made the hidden defaults feel a lot less arbitrary.

the hard part probably isn't search over 500k emails, it's keeping the generated wiki from becoming a second messy inbox. i'd want to see how it handles stale facts and conflicting versions of the same project. email has a lot of "this was true for two weeks in 2019" buried inside it.

For coding agents, the biggest improvement for me wasn't a different editor, it was making the tool/context path inspectable. If a skill or memory block gets injected, I want to see exactly why it was selected and what text it added. Otherwise the agent can look “smart” for one run and be impossible to debug the next time it takes a weird detour.

yes, continue it. I archived my agent, but now revived it, keep building it, not just for selling it, the building itself is the real fun, learn a lot, build while changing direction, will be something someday.

a good mcp, just curious about the reason you pick the MCP instead of a skill, can it done by a skill as well?

Linggen is an agent with a WebUI and P2P architecture, which sets it apart from tools like Claude Code and OpenClaw. The name is inspired by the Chinese fantasy novel Fanren Xiuxian Zhuan. It started as a fan project but is designed for practical daily use.

Built in Rust, it is fast to install (seconds) and remains lightweight under heavy workloads, including multiple sessions, extensive tool usage, and sub-agent execution.

  Author here. Built this because I wanted one system where I could drop a markdown file and get a new agent
   — for coding, but also for scheduled code reviews, architecture checks, or anything else.

  The file-based approach is the core idea. An agent is 15 lines of YAML + markdown. A skill is a SKILL.md
  directory. A mission is a cron entry pointing at an agent. No SDKs, no plugins, no code changes — just
  files.

  Happy to go deep on the Rust runtime, multi-agent delegation, or anything else.

I’m the author. This came out of watching AI tools subtly rewrite system boundaries while still passing tests. The post is about documentation topology and keeping intent close to code—not about a specific tool.

I do have a local model path (Qwen3-4B) for testing.

The tradeoff is simply model quality vs locality, which is why Linggen focuses on controlling retrieval rather than claiming zero data ever leaves the device. Using a local LLM is straightforward if that’s the requirement.

That’s true — Linggen can’t control the behavior of Claude or any other cloud LLM.

What it can control is the retrieval boundary: what gets selected locally and exposed to the model. If nothing is returned, nothing is sent.

If a strict zero-exfiltration setup is required, then a fully local model would indeed be the right option.

Yes, that’s correct — the model only sees the retrieved slices that the MCP server explicitly returns, similar to pasting selected context into a prompt.

The distinction I’m trying to make is that Linggen itself doesn’t sync or store project data in the cloud; retrieval and indexing stay local, and exposure to the LLM is scoped and intentional.

Good question. Linggen itself always runs locally.

When using Claude Desktop, it connects to Linggen via a local MCP server (localhost), so indexing and memory stay on-device. The LLM can query that local context, but Linggen doesn’t push your data to the cloud.

Claude’s web UI doesn’t support local MCP today — if it ever does, it would just be a localhost URL.

Compared to plain docs, Linggen indexes project knowledge into a vector store that the LLM can query directly.

The key difference is that it works across projects. While working on project A, I can ask: “How does project B send messages?” and have that context retrieved and applied, without manually opening or loading docs.

Hi HN, I’m the author.

Linggen is a local-first memory layer that gives AI persistent context across repos, docs, and time. It integrates with Cursor / Zed via MCP and keeps everything on-device.

I built this because I kept re-explaining the same context to AI across multiple projects. Happy to answer any questions.