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diff --git a/scripts/day1_demo.py b/scripts/day1_demo.py
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+#!/usr/bin/env python3
+"""
+Day 1 Demo: End-to-end Minimal Memory RAG.
+1. Load MemoryCards + Embeddings.
+2. Receive a query.
+3. Retrieve top-k memories.
+4. Generate answer with QwenInstruct.
+"""
+
+import json
+import numpy as np
+import torch
+import sys
+import os
+
+# Add src to sys.path so we can import personalization
+sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
+
+from typing import List
+
+from personalization.config.settings import load_local_models_config
+from personalization.models.embedding.qwen3_8b import Qwen3Embedding8B
+from personalization.models.llm.qwen_instruct import QwenInstruct
+from personalization.retrieval.preference_store.schemas import MemoryCard
+
+def load_memory_store(cards_path: str, embs_path: str):
+    print(f"Loading memory store from {cards_path}...")
+    cards = []
+    with open(cards_path, "r", encoding="utf-8") as f:
+        for line in f:
+            cards.append(MemoryCard.model_validate_json(line))
+    
+    embs = np.load(embs_path)
+    return cards, embs
+
+def cosine_similarity(E: np.ndarray, e_q: np.ndarray) -> np.ndarray:
+    # E: [M, d], e_q: [d]
+    # Assumes vectors are normalized
+    return np.dot(E, e_q)
+
+def dense_retrieve(
+    query: str,
+    embedder: Qwen3Embedding8B,
+    cards: List[MemoryCard],
+    E: np.ndarray,
+    topk: int = 3
+) -> List[MemoryCard]:
+    
+    # Encode query
+    # encode returns list[list[float]] or tensor
+    e_q_list = embedder.encode([query], normalize=True, return_tensor=False)
+    e_q = np.array(e_q_list[0], dtype=np.float32)
+    
+    # Sim
+    sims = cosine_similarity(E, e_q)
+    
+    # Top-k
+    # argsort is ascending, so take last k and reverse
+    if len(cards) == 0:
+        return []
+    
+    k = min(topk, len(cards))
+    idx = np.argsort(sims)[-k:][::-1]
+    
+    results = [cards[i] for i in idx]
+    return results
+
+def main():
+    cards_path = "data/corpora/memory_cards.jsonl"
+    embs_path = "data/corpora/memory_embeddings.npy"
+    
+    try:
+        cards, embs = load_memory_store(cards_path, embs_path)
+        print(f"Loaded {len(cards)} memory cards.")
+    except FileNotFoundError:
+        print("Error: Memory store not found. Please run scripts/migrate_preferences.py first.")
+        sys.exit(1)
+        
+    cfg = load_local_models_config()
+    
+    print("Initializing models...")
+    embedder = Qwen3Embedding8B.from_config(cfg)
+    llm = QwenInstruct.from_config(cfg)
+    
+    # Demo Query
+    # Let's try to pick a query that should trigger a retrieval if we have relevant memories.
+    # Since we processed pilot_study, let's assume we might have some "python code" or "formatting" prefs.
+    # If the pilot study didn't yield many prefs, we might just query something generic.
+    query = "Please write a function to calculate fibonacci numbers. Remember my preferences."
+    
+    # Or let's allow user input or command line arg
+    if len(sys.argv) > 1:
+        query = sys.argv[1]
+
+    print(f"\nQuery: {query}")
+    
+    # Retrieve
+    hits = dense_retrieve(query, embedder, cards, embs, topk=3)
+    
+    print(f"\nRetrieved {len(hits)} memories:")
+    notes = []
+    for h in hits:
+        print(f" - [{h.kind}] {h.note_text} (from user: {h.user_id})")
+        notes.append(h.note_text)
+        
+    # Generate
+    print("\nGenerating answer...")
+    # Mock history: just the current turn
+    history = [{"role": "user", "content": query}]
+    
+    answer = llm.answer(history, notes)
+    
+    print("-" * 40)
+    print("Answer:")
+    print(answer)
+    print("-" * 40)
+
+if __name__ == "__main__":
+    main()
+