updated README to better explain the status of the project

1 files changed, 3 insertions, 1 deletions

diff --git a/README.md b/README.md
index 6709c57..3519e11 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,8 @@ Extremely lossy image compression with neural networks
 
 Uses the limited, slow, and inefficient (but super simple and easy-to-use!) feed-forward neural network [Zoltar](https://github.com/matthewsot/zoltar).
 
+*Note:* While this technique does seem to work rather well for specific photos and with specific configurations, it's mostly meant as a proof of concept. The image compression algorithms that power most JPEG compression will probably give you a better result for most applications and images. Feel free to check this out though! Maybe you can make it even better :)
+
 # Show me the numbers
 Original PNG image, **106,638 bytes** uncompressed and 106,795 bytes when compressed with the default Windows "send-to" compression:
 
@@ -111,4 +113,4 @@ nimg reconstruct image.nimg
 To convert ``image.nimg`` back into a PNG with the 'missing' pixels highlighted in red (as ``image.reconstructed.png``):
 ```
 nimg reconstruct image.nimg demo
-```
\ No newline at end of file
+```