diff --git a/README.md b/README.md
index 75bf469..b62a058 100644
--- a/README.md
+++ b/README.md
@@ -105,4 +105,13 @@ op.save_model('onnx', 'test.onnx')
     PosixPath('/Home/.towhee/operators/audio-embedding/nnfp/main/test.onnx')
 
 ## Fine-tune
-To fine-tune this operator, please refer to [this example guide](https://github.com/towhee-io/examples/blob/main/fine_tune/7_fine_tune_audio_embedding_operator.ipynb).
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+To fine-tune this operator, please refer to [this example guide](https://github.com/towhee-io/examples/blob/main/fine_tune/7_fine_tune_audio_embedding_operator.ipynb).
+
+# More Resources
+
+- [Scalar Quantization and Product Quantization - Zilliz blog](https://zilliz.com/learn/scalar-quantization-and-product-quantization): A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
+- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
+- [Audio Retrieval Based on Milvus - Zilliz blog](https://zilliz.com/blog/audio-retrieval-based-on-milvus): Create an audio retrieval system using Milvus, an open-source vector database. Classify and analyze sound data in real time.
+- [Vector Database Use Case: Audio Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/audio-similarity-search): Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus).
+- [Neural Networks and Embeddings for Language Models - Zilliz blog](https://zilliz.com/learn/Neural-Networks-and-Embeddings-for-Language-Models): Exploring neural network language models, specifically recurrent neural networks, and taking a sneak peek at how embeddings are generated.
+- [Understanding Neural Network Embeddings - Zilliz blog](https://zilliz.com/learn/understanding-neural-network-embeddings): This article is dedicated to going a bit more in-depth into embeddings/embedding vectors, along with how they are used in modern ML algorithms and pipelines.
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