Lumai 发布九层 AI 分层理解模型与七层新闻地图

Lumai

Layered Understanding Model of AI
Lumai (say "loo-my", from lumen, light) brings light into AI's black box. It teaches the Lumai Model,
short for the Layered Understanding Model of AI: nine layers, named the way the OSI Model names network layers
(Lumai Layer 1, Physical & Energy, up to Lumai Layer 9, Applications & Interfaces).
Every model, tool, technique and chip on one layered map, each explained with a diagram,
with the latest news tagged onto it every three hours.

Open the site → · Atom feed · Contribute

The homepage is the Lumai Model: a visual, zoomable explainer of nine layers (landscape → stack → layer → concept, at depths from "story" to "frontier"). Its content model is documented in docs/content-model.md. Pages marked draft haven't had a human review yet. The seven-layer news map described below now runs the AI news feed at /news/; old #c= and #l= links and /next/ links redirect automatically.

Why

Newsletters and feeds tell you what happened. They rarely tell you where it fits. If you work with LLMs, it is easy to feel permanently behind: new frontier models, agent harnesses, quantisation formats, desktop AI boxes, routers and protocols every week.

Lumai is a map first and a news board second. Every story lands on a concept, and every concept sits on a layer, so a headline like "new 4-bit MoE runs on a DGX Spark" becomes three things you can click: quantisation (L2), mixture of experts (L3) and desktop AI machines (L1).

The seven-layer news map: an OSI-style map for AI

The news feed still files stories on this earlier seven-layer map. The explainer on the homepage uses the nine layers of the Lumai Model.

#	Layer	≈ OSI	What moves	Example threat
7	Agents & Apps	Application	goals & tasks	excessive agency, the lethal trifecta
6	Tools & Protocols	Presentation	tool calls (JSON, MCP)	tool poisoning, injection via tool output
5	Context & Memory	Session	context windows	RAG and memory poisoning
4	Inference & Access	Transport	token streams	exposed inference servers, key leakage
3	Models	Network	tokens → predictions	jailbreaks, backdoors
2	Weights & Numerics	Data link	tensors & bytes	malicious model files
1	Compute	Physical	FLOPs & bytes/second	GPU memory leakage

Security, safety and governance run across all seven. Remember it top-down: All Tools Can Invoke Models With Compute.

What's inside


The tower	The seven layers at a glance, each with its key technologies and threats (toggle Technologies / Threats / Both). Click a layer for a plain-words explanation, a diagram, threat cards with real examples and defences, and every concept on that layer.
Follow a prompt	An animated walkthrough of one request travelling down the stack to the silicon and back up as an answer.
Learning paths	Guided sequences such as Run models locally, Understand agents, Pick the right model, Secure AI systems and Ground models in your data, with progress saved in your browser.
News board	About 25 sources: lab blogs, Hacker News, r/LocalLLaMA, Hugging Face papers, arXiv, respected newsletters, tech press, AI-security research and release notes. Stories are auto-tagged onto the map; filter by layer, source type and time window, or show only stories new since your last visit.
Trending & Radar	Which concepts are heating up this week versus last, and a Radar of terms trending across several sources that the map doesn't cover yet. A weekly workflow turns the Radar into a GitHub issue, so the map keeps pace with the field.
Atom feed	The tagged stream is published at `/feed.xml` for any feed reader.

Principles

Zero third-party requests. No CDNs, web fonts, analytics or cookies. The page ships a strict Content-Security-Policy (default-src 'self') and no-referrer.
Zero dependencies. Plain HTML, CSS and ES modules; the fetcher is standard-library Python. There is nothing to npm install and nothing to audit but this repo.
Private by default. "Learned" marks and your last-visit time live only in your browser's localStorage.
Hardened automation. GitHub Actions are pinned to commit SHAs with least-privilege permissions per job; feed responses are size-capped, links are restricted to http(s), and feed text is escaped everywhere it is rendered.

How it works

flowchart LR
  subgraph Actions["GitHub Actions · every 3 h"]
    F[fetch_news.py<br/>RSS · Atom · JSON] --> T[tag against the concept map<br/>keywords, optional LLM]
    T --> H[heat · radar · source health]
  end
  H --> N[(news.json + feed.xml)]
  C[(concepts.json<br/>layers → categories → concepts)] --> T
  N --> P[GitHub Pages · static site]
  C --> P
  N --> W[weekly map-review issue]
  W -. contributors update .-> C

Run it yourself

git clone https://github.com/hmussana/Strata && cd Strata
python3 scripts/fetch_news.py            # pull feeds into site/data/news.json
python3 -m http.server -d site 8000      # open http://localhost:8000
python3 -m unittest discover -s scripts  # offline tests with fixtures

After editing concepts.json, run python3 scripts/fetch_news.py --reindex to re-tag existing stories without touching the network.

Deploy your own copy

Fork the repo and enable Settings → Pages → Source: GitHub Actions.
Run Actions → Update news & deploy → Run workflow once. It then runs every 3 hours.
Set a repository variable SITE_URL if your Pages URL differs (used for the Atom feed), and update the og:, twitter: and canonical URLs in site/index.html and site/news/index.html, plus site/sitemap.xml and site/robots.txt.

Optional: LLM summaries and smarter tagging

Keyword tagging works out of the box. For one-line summaries and LLM-assigned tags, point the fetcher at any OpenAI-compatible endpoint (OpenRouter, LiteLLM, Ollama, llama.cpp):

Setting	Type	Example
`LLM_BASE_URL`	repository variable	`https://openrouter.ai/api/v1`
`LLM_MODEL`	repository variable	any inexpensive model your endpoint serves
`LLM_API_KEY`	repository secret	your key

Only new stories are sent (at most LLM_MAX_ITEMS, default 40 per run), and model output is validated against the concept list and escaped before display.

Repository layout

site/                   static site, deployed as-is
  index.html
  manifest.webmanifest  app name and icons
  sitemap.xml, robots.txt
  assets/icon.svg       placeholder "L" mark (final logo goes here, plus icon-192.png and icon-512.png)
  assets/app.js         stack map, drawer, learning paths, news board, search
  assets/diagrams.js    diagram DSL → inline SVG
  assets/styles.css     design tokens, light/dark themes
  data/concepts.json    the knowledge map (layers, concepts, paths)
  data/sources.json     news sources and noise filters
  data/news.json        generated
  feed.xml              generated
scripts/
  fetch_news.py         fetch → tag → heat/radar → news.json + feed.xml
  map_review.py         weekly "what's missing" report
  test_fetch_news.py    offline tests (fixtures/)
.github/workflows/      update-and-deploy · ci · map-review

Contributing

The most valuable contributions are content: new concepts, sharper explainers, better diagrams, missing keywords and trustworthy sources. The concept format, diagram types and review checklist are in CONTRIBUTING.md.

License

Code is MIT. The concept map content (site/data/concepts.json) and diagrams are CC BY 4.0. News headlines and snippets belong to their publishers and always link back to the source.