diff --git a/experimentalresults.typ b/experimentalresults.typ
index f647fb4..8b662bd 100644
--- a/experimentalresults.typ
+++ b/experimentalresults.typ
@@ -1,4 +1,4 @@
-#import "utils.typ": todo
+#import "utils.typ": todo, inwriting
 #import "@preview/subpar:0.1.1"
 
 = Experimental Results <sectionexperimentalresults>
@@ -101,5 +101,7 @@ One could use a well established algorithm like PatchCore or EfficientAD for det
   label: <comparisonnormal>,
 )
 
+#if inwriting [
 == Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
 #todo[Maybe don't do this]
+]
diff --git a/introduction.typ b/introduction.typ
index f495d8c..134a7b4 100644
--- a/introduction.typ
+++ b/introduction.typ
@@ -1,4 +1,4 @@
-#import "utils.typ": todo
+#import "utils.typ": todo, inwriting
 
 = Introduction
 == Motivation
@@ -29,8 +29,10 @@ Does giving the Few-Shot learner more good than bad samples improve the model pe
 How much does the performance improve if only detecting an anomaly or not?
 How does it compare to PatchCore and EfficientAD?
 
+#if inwriting [
 === Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
 // I've tried different distance measures $->$ but results are pretty much the same.
+]
 
 == Outline
 This thesis is structured to provide a comprehensive exploration of Few-Shot Learning in anomaly detection.