rm some todos

app.py

@@ -103,8 +103,7 @@ This may seem daunting at first, but in reality, DL researchers have already mad
 # with other users. If there are too many active peers, take a look at alternative starter kits here <b>TODO</b>
 # """)
 
-content_title("How does collaborative training work?")
-content_text("In order to train a model this way, you need to s")
+content_title("What happens inside?")
 
 make_tabs()
 

static/tabs.html

@@ -143,7 +143,7 @@ a:visited {
                     the moderators remove them from the list and revert the model to the latest checkpoint unaffected by the attack.
                     </p>
 
-                    <p><b>Spoiler (TODO): How to implement authentication in a decentralized system efficiently?</b></p>
+<!--                    <p><b>Spoiler (TODO): How to implement authentication in a decentralized system efficiently?</b></p>-->
 
                     <p>
                     Nice bonus: using this data, the moderators can acknowledge the personal contribution of each participant.
@@ -156,7 +156,7 @@ a:visited {
                     suggested such a technique (named CenteredClip) and proved that it does not significantly affect the model's convergence.
                     </p>
 
-                    <p><b>Spoiler (TODO): How does CenteredClip protect from outliers? (Interactive Demo)</b></p>
+<!--                    <p><b>Spoiler (TODO): How does CenteredClip protect from outliers? (Interactive Demo)</b></p>-->
 
                     <p>
                     In our case, CenteredClip is useful but not enough to protect from malicious participants,