diff --git a/README.md b/README.md
index 5cf689c..bf89284 100755
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 
 LEANN is a revolutionary vector database that makes personal AI accessible to everyone. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
 
-RAG your  **[file system](#📚-process-any-documents-pdf-txt-md)**, **[emails](#🕵️-search-your-entire-life)**, **[browser history](#🌐-time-machine-for-the-web)**, **[WeChat](#💬-wechat-detective)**, or 60M documents on your laptop, in nearly zero cost. No cloud, no API keys, completely private.
+RAG your  **[file system](#-process-any-documents-pdf-txt-md)**, **[emails](#-search-your-entire-life)**, **[browser history](#-time-machine-for-the-web)**, **[WeChat](#-wechat-detective)**, or 60M documents on your laptop, in nearly zero cost. No cloud, no API keys, completely private.
 
 LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Read more →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
@@ -125,7 +125,7 @@ print(results)
 
 LEANN supports RAGing a lot of data sources, like .pdf, .txt, .md, and also supports RAGing your WeChat, Google Search History, and more.
 
-### 📚 Process Any Documents (.pdf, .txt, .md)
+### Process Any Documents (.pdf, .txt, .md)
 
 Above we showed the Python API, while this CLI script demonstrates the same concepts while directly processing PDFs and documents.
 
@@ -142,7 +142,7 @@ Uses Ollama `qwen3:8b` by default. For other models: `--llm openai --model gpt-4
 
 **Works with any text format** - research papers, personal notes, presentations. Built with LlamaIndex for document parsing.
 
-### 🕵️ Search Your Entire Life
+### Search Your Entire Life
 ```bash
 python examples/mail_reader_leann.py
 # "What did my boss say about the Christmas party last year?"
@@ -181,7 +181,7 @@ Once the index is built, you can ask questions like:
 - "Show me emails about travel expenses"
 </details>
 
-### 🌐 Time Machine for the Web  
+### Time Machine for the Web  
 ```bash
 python examples/google_history_reader_leann.py
 # "What was that AI paper I read last month?"
@@ -236,7 +236,7 @@ Once the index is built, you can ask questions like:
 
 </details>
 
-### 💬 WeChat Detective
+### WeChat Detective
 
 ```bash
 python examples/wechat_history_reader_leann.py