feat: add real-time collaborative shopping list at /cospend/list

Real-time shopping list with SSE sync between multiple clients, automatic
item categorization using embedding-based classification + Bring icon
matching, and card-based UI with category grouping.

- SSE broadcast for live sync (add/check/remove items across tabs)
- Hybrid categorizer: direct catalog lookup → category-scoped embedding
  search → per-category default icons, with DB caching
- 388 Bring catalog icons matched via multilingual-e5-base embeddings
- 170+ English→German icon aliases for reliable cross-language matching
- Move cospend dashboard to /cospend/dash, /cospend redirects to list
- Shopping icon on homepage links to /cospend/list

scripts/embed-shopping-icons.ts

@@ -0,0 +1,55 @@
+/**
+ * Pre-compute embeddings for Bring! catalog items to enable icon matching.
+ * Maps item names to their icon filenames via semantic similarity.
+ *
+ * Run: pnpm exec vite-node scripts/embed-shopping-icons.ts
+ */
+import { pipeline } from '@huggingface/transformers';
+import { readFileSync, writeFileSync } from 'fs';
+import { resolve } from 'path';
+
+const MODEL_NAME = 'Xenova/multilingual-e5-base';
+const CATALOG_PATH = resolve('static/shopping-icons/catalog.json');
+const OUTPUT_FILE = resolve('src/lib/data/shoppingIconEmbeddings.json');
+
+async function main() {
+  const catalog: Record<string, string> = JSON.parse(readFileSync(CATALOG_PATH, 'utf-8'));
+
+  // Deduplicate: multiple display names can map to the same icon
+  // We want one embedding per unique display name
+  const uniqueItems = new Map<string, string>();
+  for (const [name, iconFile] of Object.entries(catalog)) {
+    uniqueItems.set(name, iconFile);
+  }
+
+  const items = [...uniqueItems.entries()];
+  console.log(`Loading model ${MODEL_NAME}...`);
+  const embedder = await pipeline('feature-extraction', MODEL_NAME, { dtype: 'q8' });
+
+  console.log(`Embedding ${items.length} catalog items...`);
+  const entries: { name: string; icon: string; vector: number[] }[] = [];
+
+  for (let i = 0; i < items.length; i++) {
+    const [name, icon] = items[i];
+    const result = await embedder(`passage: ${name}`, { pooling: 'mean', normalize: true });
+    const vector = Array.from(result.data as Float32Array).map(v => Math.round(v * 10000) / 10000);
+    entries.push({ name, icon, vector });
+
+    if ((i + 1) % 50 === 0) {
+      console.log(`  ${i + 1}/${items.length}`);
+    }
+  }
+
+  const output = {
+    model: MODEL_NAME,
+    dimensions: entries[0]?.vector.length || 768,
+    count: entries.length,
+    entries,
+  };
+
+  const json = JSON.stringify(output);
+  writeFileSync(OUTPUT_FILE, json, 'utf-8');
+  console.log(`Written ${OUTPUT_FILE} (${(json.length / 1024).toFixed(1)}KB, ${entries.length} entries)`);
+}
+
+main().catch(console.error);