diff --git a/README.md b/README.md
index fab8ad1b..01db42cd 100644
--- a/README.md
+++ b/README.md
@@ -19,19 +19,6 @@ Multiple scripts are used to collect and pre-process the data. This data is info
 ### Identifying Heroes
 Currently a neural network ([Contributed by Autopoietico](https://github.com/thearyadev/top500-aggregator/pull/1)) is being used for image classification. The dataset used to train this model is located in `./assets/top_500_mnist_images`, and the model and params files are located in `./neural_network/`. This model will need to be re-trained for each hero release, or top 500 hero image change. The tools for training are located in `./train.py`
 
-
-## Tools & Technologies Used
-
-### MySQL
-MySQL is used to store all the leaderboard data for each season. This database archive is stored in the `./archive` directory and is available in this github repository.
-
-### Web
-#### FastAPI / Hypercorn
-FastAPI and Hypercorn are used to serve the web page. 
-
-#### Highcharts
-Highcharts is used on the frontend to display all the data collected and generated by the application. 
-
 ## Building from Source
 
 ### Data Collection
@@ -44,17 +31,7 @@ Highcharts is used on the frontend to display all the data collected and generat
 3. Configure the correct settings for the season identifier in `./utils/generator.py`
 4. Run `python utils/generator.py`
 
-
-### Deploy
-The deployment for this project is done using docker. 
-
-You will require `docker`, `docker compose` and `mysql-client` installed. 
-1. Clone this repository
-2. Run `docker compose up -d` in the project directory.
-
-Once complete, the stack will be serving Top 500 Aggregator. The issue is, the data is not populated by default. You will need to use `mysql` cli tool to insert the `./archive/latest.sql` file into the database. 
-
-
+*Note: Some of the paths have been moved. Please see project file tree.*
 
 ### Development 
 
@@ -78,7 +55,6 @@ Install all dependencies using poetry. There are some dev dependencies for code
 `train.py` is used to train the model. See inline documentation for more details. 
 
 #### Neural Network 
-
 The neural network is trained on the dataset located in `./assets/top_500_mnist_images`. This dataset is a collection of images of the top 500 leaderboard for each hero. The images are 49x50 pixels. Labels are numbered and indexed in line 50 of `./heroes/her0_comparison.py`. 
 
 The images in the dataset are processed using `./process_mnist.py`. This scriipt converts the images to grayscale, and then converts it to an array of 8 bit signed integers. This array is then saved to its same path, except in in `./assets/top_500_mnist_images/`.
diff --git a/frontend/app/components/topmatter/topmatter.tsx b/frontend/app/components/topmatter/topmatter.tsx
index c2011c16..258ba124 100644
--- a/frontend/app/components/topmatter/topmatter.tsx
+++ b/frontend/app/components/topmatter/topmatter.tsx
@@ -44,10 +44,7 @@ const TopMatter = ({ seasonNumber }: { seasonNumber: string }) => {
         <strong>When is the data updated?</strong>
       </p>
       <p>
-        The dataset is updated once per season. Starting in season 8, the most
-        recent season will be updated weekly, overwritten each week until the
-        end of the season
-      </p>
+        The dataset is updated once per season.</p>
       <hr className="m-5" />
     </div>
   );