diff --git a/README.md b/README.md
index 1b0fd16..88802ec 100644
--- a/README.md
+++ b/README.md
@@ -6,4 +6,5 @@ I made it because I want to try to break it.
 This will work iff I succeed in building a PPT-discriminator for sha256 from randomness  
 As my first approach this discriminator will be based on an LSTM-network.  
 Update: This worked out way better than expected; given long enought sequences (128 Bytes are more than enough) we can discriminate successfully in 100% of cases.  
-Update: I did an upsie in the training-code and the discriminator is actually shit.
+Update 2: I did an upsie in the training-code and the discriminator is actually shit.  
+Update 3: Turns out: sha256 produces fairly high quality randomness and this project seems to have failed...  
diff --git a/discriminate.py b/discriminate.py
index c8fadf8..2bdff23 100644
--- a/discriminate.py
+++ b/discriminate.py
@@ -10,7 +10,7 @@ import random
 import shark
 from model import Model
 
-bs = int(256/8)
+bs = shark.bs
 
 class Model(nn.Module):
     def __init__(self):
diff --git a/model.py b/model.py
index a42fec5..ee20b42 100644
--- a/model.py
+++ b/model.py
@@ -3,6 +3,8 @@ from torch import nn
 from torch import nn, optim
 from torch.utils.data import DataLoader
 
+import shark
+
 class Model(nn.Module):
     def __init__(self):
         super(Model, self).__init__()
diff --git a/shark.py b/shark.py
index 06e37fd..ef4f747 100644
--- a/shark.py
+++ b/shark.py
@@ -3,12 +3,6 @@ import math
 import os
 import random
 
-# Shark is a sha256+xor based encryption.
-# I made it because I want to try to break it.
-# (Precisely: Show it does not provide semantic security, because it is not IND-CPA-secure)
-# This will work iff I succeed in building a PPT-discriminator for sha256 from randomness
-# As my first approach this discriminator will be based on an LSTM-network.
-
 bs = int(256/8)
 
 def xor(ta,tb):