Merge pull request #963 from domiscd/feat/landing-page-update

feat(website): update landing page

website/.vitepress/config.mts

@@ -22,10 +22,12 @@ export default withMermaid(
       ['link', { rel: 'icon', href: `${docsBase}mempalace_logo.png` }],
       ['link', { rel: 'preconnect', href: 'https://fonts.googleapis.com' }],
       ['link', { rel: 'preconnect', href: 'https://fonts.gstatic.com', crossorigin: '' }],
-      ['link', { href: 'https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap', rel: 'stylesheet' }],
+      ['link', { href: 'https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@300;400;500&family=Cormorant+Garamond:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400&family=Geist:wght@300;400;500;600&display=swap', rel: 'stylesheet' }],
       ['meta', { property: 'og:title', content: 'MemPalace — AI Memory System' }],
       ['meta', { property: 'og:description', content: '96.6% LongMemEval recall. Zero API calls. Local, free, open source.' }],
       ['meta', { property: 'og:image', content: `${docsBase}mempalace_logo.png` }],
+      ['script', { async: '', src: 'https://www.googletagmanager.com/gtag/js?id=G-PPQE4Z7P1K' }],
+      ['script', {}, `window.dataLayer = window.dataLayer || [];\nfunction gtag(){dataLayer.push(arguments);}\ngtag('js', new Date());\ngtag('config', 'G-PPQE4Z7P1K');`],
     ],
 
     themeConfig: {

website/.vitepress/theme/index.ts

@@ -1,6 +1,10 @@
 import DefaultTheme from 'vitepress/theme'
+import Landing from './Landing.vue'
 import './style.css'
 
 export default {
   extends: DefaultTheme,
+  enhanceApp({ app }) {
+    app.component('Landing', Landing)
+  },
 }

website/index.md

@@ -1,88 +1,6 @@
 ---
-layout: home
-
-hero:
-  name: MemPalace
-  text: Give your AI a memory.
-  tagline: "Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls."
-  image:
-    src: /mempalace_logo.png
-    alt: MemPalace
-  actions:
-    - theme: brand
-      text: Get Started
-      link: /guide/getting-started
-    - theme: alt
-      text: Architecture →
-      link: /concepts/the-palace
-    - theme: alt
-      text: GitHub ↗
-      link: https://github.com/MemPalace/mempalace
-
-features:
-  - icon:
-      src: /icons/file-text.svg
-      alt: Verbatim Storage
-    title: Verbatim Storage
-    details: Store source text directly instead of extracting facts up front. The raw benchmark result comes from retrieving verbatim content.
-  - icon:
-      src: /icons/building-2.svg
-      alt: Palace Structure
-    title: Palace Structure
-    details: Wings and rooms give retrieval useful structure. In the project benchmarks, narrowing search scope outperformed flat search.
-  - icon:
-      src: /icons/search.svg
-      alt: Semantic Search
-    title: Semantic Search
-    details: Vector search over verbatim content lets the model retrieve past discussions by topic, project, or room. Backend is pluggable.
-  - icon:
-      src: /icons/git-merge.svg
-      alt: Knowledge Graph
-    title: Knowledge Graph
-    details: Temporal entity-relationship triples in SQLite. Facts can be added, queried, and invalidated over time.
-  - icon:
-      src: /icons/wrench.svg
-      alt: 19 MCP Tools
-    title: 19 MCP Tools
-    details: MCP tools expose search, filing, knowledge graph, graph navigation, and diary operations to compatible clients.
-  - icon:
-      src: /icons/shield-check.svg
-      alt: Zero Cloud
-    title: Zero Cloud
-    details: Core storage and retrieval run locally. Optional reranking features can add an API dependency but are not required for the benchmark path.
+layout: page
+pageClass: mempalace-home
 ---
 
-<style>
-:root {
-  --vp-home-hero-name-color: transparent;
-  --vp-home-hero-name-background: linear-gradient(
-    135deg,
-    #4f46e5 0%,
-    #06b6d4 50%,
-    #8b5cf6 100%
-  );
-}
-</style>
-
-<div style="max-width: 688px; margin: 0 auto; padding: 48px 24px 0;">
-
-## Verbatim Retrieval First
-
-MemPalace stores source text and retrieves it with semantic search. The benchmarked raw mode does not require an LLM at any stage — no extraction, no rerank, no summarisation.
-
-**LongMemEval retrieval recall (500 questions):**
-
-| Mode | R@5 | LLM required |
-|---|---|---|
-| Raw (semantic search over verbatim text) | **96.6%** | None |
-| Hybrid v4, held-out 450q | **98.4%** | None |
-
-The raw 96.6% reproduces on any machine with the committed dataset: result JSONLs, the `seed=42` train/held-out split, and the `--mode raw` / `--held-out` runners are all in the `benchmarks/` directory of the repo.
-
-We deliberately do not publish a side-by-side comparison against other memory systems on this page. Retrieval recall (R@5) and end-to-end QA accuracy are different metrics and are not comparable; where MemPalace can be fairly compared on the same metric, we link to the other project's published source.
-
-<div style="text-align: center; padding-top: 16px;">
-  <a href="./reference/benchmarks" style="color: var(--vp-c-brand-1); font-weight: 500;">Full benchmark methodology →</a>
-</div>
-
-</div>
+<Landing />