nnlens 0.1.1

Explain neural-network architectures layer by layer in five linked views (structure, plain words, math, naive code, optimized code). An MCP server + local renderer driven by your own LLM subscription.

nnlens

Throw in a paper, a GitHub repo, or just the name of a technique — get back a layer-by-layer explanation of a neural network, in five linked views.

nnlens is an MCP server + local renderer. You connect it to an MCP host you already use (Claude Desktop, Claude Code, Cursor, …). The host's model — driven by your own subscription — does the explaining; nnlens gives it the methodology, fetches the real sources, runs the code, and renders the result to a local web page.

nnlens never calls an LLM itself and never handles an API key. The reasoning happens in your MCP host, on your existing plan. That is the whole point: no metered API, no shared credentials, no hosted service borrowing your subscription.

The five views

Every component (a layer, block, or technique) is explained five ways, and the views are linked by a shared concept ledger so the same idea keeps the same everyday word, symbol, and formal name across all of them:

1. 構造 — a Mermaid diagram of the data flow, plus a short note.
2. 言葉での説明 — plain language only. No symbols, no jargon.
3. 数式 — the real notation, carrying over the everyday words from view 2 and attaching each to its symbol (hover any highlighted word to see the mapping).
4. 素の実装 — a from-scratch implementation that is literally the math (pure Python / numpy, no torch), actually executed so the output is real.
5. 最適化された実装 — the fast version, excerpted from the official repository (with a source link) or written from scratch when none exists — numerically cross-checked against the naive view when it's locally runnable.

Beyond a single page:

• Any language — explanations are written in whatever language you ask in, and the page chrome follows: ja/en labels are built in, and the host supplies ui_labels translations for anything else. Nothing about your language is hardcoded.
• Library — every explanation you generate is saved locally (~/.nnlens/store) and listed in the sidebar; delete with the hover ✕.
• Cross-links — explanations reference each other (related chips and [[slug]] wikilinks in the prose). Links to explanations you haven't generated yet show up greyed out — a built-in "what to explain next" list.
• Contract lint — render returns warnings when the views drift apart (a ledger term never marked, symbols leaking into the plain-words view, an uncited optimized view, an unverified naive run), so the host fixes them.
• Self-healing pages — pages are stamped with a template hash and rebuilt automatically when nnlens updates its renderer.

Install

pip install nnlens        # or: pipx install nnlens

Or from source:

git clone https://github.com/tsuzakiii/nnlens
cd nnlens
pip install -e .

Connect it to your MCP host

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "nnlens": { "command": "nnlens" }
  }
}

Claude Code:

claude mcp add nnlens -- nnlens

(If the nnlens script isn't on your PATH, use "command": "python", "args": ["-m", "nnlens"] instead.)

Use it

In your host, invoke the explain prompt (e.g. type /nnlens / /explain) or just ask:

nnlens で Scaled Dot-Product Attention を説明して

The host will fetch the paper/repo, write the five views, run the naive code to verify it, and hand you a URL like http://127.0.0.1:8787/e/… — open it for the rendered page with diagrams, math, and hover-linked terms.

Try the renderer without a host

python scripts/build_example.py     # (re)build the bundled example, runs its code
python scripts/demo_render.py --open

How it fits together

MCP host (your subscription) ── drives ──► nnlens tools
        │                                    ├─ fetch_paper / find_official_repo / fetch_repo_code
        │  writes the 5 views                ├─ run_python   (proves view 4 runs)
        └───────────────────────────────────► render        (→ local web page URL)

• Tools = the deterministic work (retrieval, code execution, rendering).
• explain prompt = the methodology the host follows to assemble the views.

Limitations (read before trusting it)

• Correctness is not guaranteed. The prose and math are written by the host model. Diagrams are model-generated and are the weakest link — treat view 1 as a sketch. View 4 is executed, so its output is real; the rest is best-effort.
• run_python is not a hardened sandbox. It runs code your host produced, on your machine, with a timeout — not untrusted third-party code. Don't point it at untrusted input.
• View 5 excerpts are fetched from public repos at view time and shown with attribution; nothing is redistributed. Respect each source repo's license.
• The renderer loads Markdown/Mermaid/KaTeX from a CDN, so viewing needs internet.

License

MIT. See LICENSE.