diff --git a/_data/openprojectlist.yml b/_data/openprojectlist.yml
index 17bb2dc2..99f0a543 100644
--- a/_data/openprojectlist.yml
+++ b/_data/openprojectlist.yml
@@ -80,8 +80,45 @@
     * Extended: To be able to execute on GPU using CUDA or OpenMP
     * Optional: Extend the magics for the wasm use case (xeus-cpp-lite)
     * Present the work at the relevant meetings and conferences
-    
-- name: "Integrate Clad to PyTorch and compare the gradient execution times"
+
+- name: "Enhancing LLM Training with Clad for efficient differentiation"
+  description: |
+    This project aims to leverage Clad, an automatic differentiation (AD)
+    plugin for Clang, to optimize large language model (LLM) training primarily
+    in C++. Automatic differentiation is a crucial component of deep learning
+    training, enabling efficient computation of gradients for optimization
+    algorithms such as stochastic gradient descent (SGD). While most modern LLM
+    frameworks rely on Python-based ecosystems, their heavy reliance on
+    interpreted code and dynamic computation graphs can introduce performance
+    bottlenecks. By integrating Clad into C++-based deep learning pipelines,
+    we can enable high-performance differentiation at the compiler level,
+    reducing computational overhead and improving memory efficiency. This will
+    allow developers to build more optimized training workflows without
+    sacrificing flexibility or precision.
+
+    Beyond performance improvements, integrating Clad with LLM training in C++
+    opens new possibilities for deploying AI models in resource-constrained
+    environments, such as embedded systems and HPC clusters, where minimizing
+    memory footprint and maximizing computational efficiency are critical.
+    Additionally, this work will bridge the gap between modern deep learning
+    research and traditional scientific computing by providing a more robust
+    and scalable AD solution for physics-informed machine learning models. By
+    optimizing the differentiation process at the compiler level, this project
+    has the potential to enhance both research and production-level AI
+    applications, aligning with compiler-research.org's broader goal of
+    advancing computational techniques for scientific discovery.
+  
+  tasks: |
+    * Develop a simplified LLM setup in C++
+    * Apply Clad to compute gradients for selected layers and loss functions
+    * Enhance clad to support it if necessary, and prepare performance benchmarks
+    * Enhance the LLM complexity to cover larger projects such as llama
+    * Repeat bugfixing and benchmarks
+    * Develop tests to ensure correctness, numerical stability, and efficiency
+    * Document the approach, implementation details, and performance gains
+    * Present progress and findings at relevant meetings and conferences
+ 
+- name: "Integrate Clad in PyTorch and compare the gradient execution times"
   description: |
     PyTorch is a popular machine learning framework that includes its own
     automatic differentiation engine, while Clad is a Clang plugin for