diff --git a/content/projects/formula1tenth.md b/content/projects/formula1tenth.md
index aa9b0cc..1c218ee 100644
--- a/content/projects/formula1tenth.md
+++ b/content/projects/formula1tenth.md
@@ -1,38 +1,26 @@
 ---
-title: "F1TENTH"
-subtitle: "A small-scale, autonomous racing competition"
+title: "Cruz Control"
+subtitle: "Autonomous Vehicle Research"
 categories: [about]
 promoimg: "f1tenth.jpg"
 priority: 2
 ---
 
-# What is F1TENTH?
-F1TENTH is an autonomous robotics competition in which teams compete by
-developing self-driving algorithms for a one-tenth scale autonomous race car.
-Since teams use the same cars, the competition is centered around optimizing
-these algorithms to navigate around a randomized racetrack in minimum time. In
-partnership with UCSC’s Cyber-Physical Systems Research Center, Slugbotics
-members in the F1TENTH competition apply technologies such as LIDAR/SLAM, ROS,
-Rviz, and Gazebo to develop their car.
+# What is Cruz Control?
+Originally, this team was focused on competing in the F1Tenth competition for autonomous racing, but now we have pivoted to working on Autonomous Driving research.
 
 {{< image-block "/images/about/promo/f1tenth2.jpg" >}}
 
-# How does the F1TENTH Competition work?
-To prepare for competition students learn about ROS through interactive labs
-which primes them for learning about the perception, planning and control of the
-car. Using that knowledge, members develop complex algorithms that take
-advantage of the various sensors available to reliably race around a given
-track. Competitions take place around the world, where teams compete in time
-trials to qualify and seed for the Grand Prix where the vehicles are pit against
-each other in head to head battles. 
+# What do we research?
+We are currently researching the use of Spiking Neural Networks with Prof. Jason Eshragian, and using Deep Reinforcement Learning and LLMs to create more robust and explainable driving models with Prof. Leilani Gilpin. Currently we are learning to use the nuScenes dataset to train a simple lane-following model which we can then test on our 1/10th scale autonomous car.
 
 {{< image-block "/images/about/promo/f1tenth3.jpg" >}}
 
-# What do students gain from the F1TENTH competition?
-Through participating in F1TENTH, students will gain experience with tools that
+# What do students gain?
+Through participating in Cruz Control, students will gain experience with tools that
 are fundamental to the robotics and autonomous sensing industries, such as ROS
 and Gazebo, and discover the mathematical underpinnings behind these
 technologies. Further, members can delve deeper into core autonomous systems
 concepts such as perception through sensors, path planning,  autonomous
 navigation, control systems, robotics simulation, and applying simulation to
-hardware projects.
+hardware projects.