diff --git a/_releases/release-64000.md b/_releases/release-64000.md
index bd36ccb5..8f8f1375 100644
--- a/_releases/release-64000.md
+++ b/_releases/release-64000.md
@@ -15,7 +15,7 @@ This release represents a massive leap toward the future of ROOT 7. We’ve unlo
 
 But we didn't stop there. We’ve streamlined the entire codebase for peak efficiency and addressed over 160 items in our trackers to ensure rock-solid stability and cutting-edge features ([see the full list here](https://root.cern/doc/v640/release-notes.html#items-addressed-for-this-release)). Dive into the full [release notes](https://root.cern/doc/v640/release-notes.html) and explore the highlights below!
 
-🔓**Opt-out of automatic class registration** Break free from histograms and objects automatically attaching to the current (T)directory! Enable this game-changing capability invoking ROOT::Experimental::DisableObjectAutoRegistration(): unlock more control by [exploring the documentation](https://root.cern.ch/doc/master/namespaceROOT_1_1Experimental.html#a74fae8f88965b8c79dfbd25bebbce3a4).
+🔓**Opt-out of automatic class registration** Break free from histograms and objects automatically attaching to the current (T)directory! Enable this game-changing capability invoking ROOT::Experimental::DisableObjectAutoRegistration(): unlock more control by [exploring the documentation](https://root.cern/doc/master/namespaceROOT_1_1Experimental.html#a74fae8f88965b8c79dfbd25bebbce3a4).
 Histograms More features in the new histograms! Concurrent filling is now available, also to save memory in highly multithreaded applications. This feature is even integrated into RDataFrame: [check out the tutorials](https://root.cern/doc/v640/group__tutorial__histv7.html)!
 
 🤖 **Machine Learning** The Data Loader has been reimagined as ROOT::Experimental::ML::RDataLoader! This powerful tool now performs cluster-aligned reads with a sophisticated shuffling strategy and supports multiple RDataFrames as input. Seamlessly output to NumPy, PyTorch, or TensorFlow, while leveraging new under- and oversampling support for dual-class eager loading.