diff --git a/README.md b/README.md
index fc53482..ba4bfaa 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# I Paid for 4 Gigabytes of RAM So I Will Use All 4 Gigabytes of RAM and Probably More
+# I Paid for 4 GB of RAM So I Will Use The Whole 4 GB of RAM (and Probably More)
 Submission for Stitch-A-Ton Contest.
 
 ## Prerequisites
@@ -14,10 +14,18 @@ Submission for Stitch-A-Ton Contest.
   If the test cases does not request a large portion of the canvas ***AND*** resizes it to a relatively still large ratio (i,e, ~0.9) **at the same time**, this might not be necessary.
 - **Access to** the `mkfifo` command, satisfied by default bar very special cases
 - **OpenCVSharp4 Runtime for Raspberry Pi 5**
-  A copy exists in this repository's release page. 
+  
+  If running via dotnet run, Download the `.nupkg` file [here](https://null.formulatrix.dev/reinardras/stitch_something/releases/tag/0.0.0) and save on `LocalNuget` before running. Otherwise it shouldn't be necessary.
+
+  If there's still a problem with library being missing, download the `.so` file and put it on `/usr/local/lib`.
 
 
 ## Running
+
+Either of these method works.
+
+### Via dotnet run
+
 On root directory:
 ```
 ASSET_PATH_RO=<path> dotnet run --project StitchATon --profile deploy
@@ -31,6 +39,17 @@ The API is accessible at `:5255`, providing the following api:
 
 To browse the API, prepend `ASPNETCORE_ENVIRONMENT=Development` to the command and go to `/swagger/index.html`.
 
+
+### Via releases
+
+Download [here](https://null.formulatrix.dev/reinardras/stitch_something/releases/).
+
+Run with the same parameters: 
+
+```
+ASSET_PATH_RO=<path> ./StitchATon --profile deploy
+```
+
 ## Writeup
 
 This submission contains no specific magic in the image processing, just OpenCVSharp stretched to the best of its ability according to my knowledge. This section contains a brief overview of the main features.
@@ -72,7 +91,7 @@ The status "Currently being loaded" is relevant when multiple requests requiring
 
 **This mechanism enables the shared canvas to serve multiple processes.**
 
-### Serving and Encoding cropped image
+### Encoding and Serving Cropped Image
 After the needed chunks are certain to be loaded to the main canvas, next is cropping it; which is a trivial operation in OpenCV, not requiring any extra memory since it still refers to the main canvas.
 
 What's not trivial is *encoding* it, which after some quick tests shows to take longer than reading and decoding multiple images from the canvas.