From e274a352551ec4c6055b7fc0086db7a516863578 Mon Sep 17 00:00:00 2001
From: Jeremy Bernstein <14089157+jxbz@users.noreply.github.com>
Date: Tue, 20 Aug 2024 08:25:36 -0400
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e044214..1920879 100644
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 Modula is a deep learning framework designed for graceful scaling. Neural networks written in Modula automatically transfer learning rate across scale.
 
-We are slowly writing [the Modula docs](https://jeremybernste.in/modula/). Check them out for an accessible introduction to scaling theory and the Modula API. Also, here are some [slides](https://docs.google.com/presentation/d/12pykmY3KT1vP_25zFYISvVMA4KQbaVdeo6cRF8SMGXM/edit?usp=sharing) for a talk that Jeremy gave, that provide a more visual introduction to Modula. And here is [Modulax](https://github.com/GallagherCommaJack/modulax/) by Jack Gallagher. And here is a barebones implementation of [Modula in NumPy](https://colab.research.google.com/drive/1lKS15RJilGsstYP5JDQKSn3Z7TUUYIDQ?usp=sharing).
+We are slowly writing [the Modula docs](https://jeremybernste.in/modula/). Check them out for an accessible introduction to scaling theory and the Modula API. Also, here are some [slides](https://docs.google.com/presentation/d/1mCp6weIty9BzFFmx7LUGk2MPmNi-m-yKjigQ9wnycng/edit?usp=sharing) for a talk that Jeremy gave, that provide a more visual introduction to Modula. And here is [Modulax](https://github.com/GallagherCommaJack/modulax/) by Jack Gallagher. And here is a barebones implementation of [Modula in NumPy](https://colab.research.google.com/drive/1lKS15RJilGsstYP5JDQKSn3Z7TUUYIDQ?usp=sharing).
 
 Modula is an experimental framework based on our research paper: [Scalable Optimization in the Modular Norm](https://arxiv.org/abs/2405.14813).