diff --git a/README.md b/README.md
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 * [AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics](https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics): A tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
 * [SKBEL](https://github.com/robinthibaut/skbel): A Python library for Bayesian Evidential Learning (BEL) in order to estimate the uncertainty of a prediction.
 * [NannyML](https://bit.ly/nannyml-github-machinelearning): Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
-* [cleanlab](https://github.com/cleanlab/cleanlab): The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. 
+* [cleanlab](https://github.com/cleanlab/cleanlab): The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
 * [AutoGluon](https://github.com/awslabs/autogluon): AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
 
 
@@ -1381,7 +1381,7 @@ be
 <a name="python-spiking-neural-networks"></a>
 #### Spiking Neural Networks
 
-* [Rockpool](https://github.com/synsense/rockpool) - A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware. 
+* [Rockpool](https://github.com/synsense/rockpool) - A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
 * [Sinabs](https://github.com/synsense/sinabs) - A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
 * [Tonic](https://github.com/neuromorphs/tonic) - A library that makes downloading publicly available neuromorphic datasets a breeze and provides event-based data transformation/augmentation pipelines.
 
@@ -1731,6 +1731,7 @@ be
 <a name="tools-misc"></a>
 #### Misc
 
+* [Humanloop](https://humanloop.com) – Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
 * [Qdrant](https://qdrant.tech) – Qdrant is [open source](https://github.com/qdrant/qdrant) vector similarity search engine with extended filtering support, written in Rust.
 * [milvus](https://milvus.io) – Milvus is [open source](https://github.com/milvus-io/milvus) vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
 * [Weaviate](https://www.semi.technology/developers/weaviate/current/) – Weaviate is an [open source](https://github.com/semi-technologies/weaviate) vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
@@ -1758,7 +1759,7 @@ be
 * [Flyte](https://flyte.org/) - Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
 * [Chaos Genius](https://github.com/chaos-genius/chaos_genius/) - ML powered analytics engine for outlier/anomaly detection and root cause analysis.
 * [MLEM](https://github.com/iterative/mlem) - Version and deploy your ML models following GitOps principles
-* [DockerDL](https://github.com/matifali/dockerdl) - Ready to use deeplearning docker images. 
+* [DockerDL](https://github.com/matifali/dockerdl) - Ready to use deeplearning docker images.
 
 <a name="books"></a>
 ## Books