diff --git a/atomic-charges/learning_atomic_charges.demoinfo.yaml b/atomic-charges/learning_atomic_charges.demoinfo.yaml
index 21a77b7b0389fc68dc5c702a763c23f5b7166bc1..01b9d14077eccf7a400771986b5ec72c455b7603 100644
--- a/atomic-charges/learning_atomic_charges.demoinfo.yaml
+++ b/atomic-charges/learning_atomic_charges.demoinfo.yaml
@@ -1,7 +1,7 @@
 {
 "title": "Learning atomic charges",
 "authors": ["Csányi, Gábor", "Kermode, James R."],
-"editLink": "/jupyter/notebooks/shared/afekete/tutorial/learning_atomic_charges.ipynb",
+"editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/learning_atomic_charges.ipynb",
 "isPublic": true,
 "username": "tutorialsNew",
 "description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.", 
diff --git a/demos/creedo.demoinfo.yaml b/demos/creedo.demoinfo.yaml
index 5c2af48cadd4a90bd711f71e0d4cabf7450d7f61..e4486783dff5a1eb10ba4333f98a113f9d664c4f 100644
--- a/demos/creedo.demoinfo.yaml
+++ b/demos/creedo.demoinfo.yaml
@@ -3,7 +3,7 @@
 "type": "demos",
 "attributes": {
 "title": "Discovering simple descriptors for crystal-structure classification",
-"logicalPath": "/Creedo/index.htm",
+"logicalPath": "/Creedo/cM/start/",
 "authors": ["Boley, Mario", "Goldsmith, Bryan", "Kariryaa, Ankit", "Ghiringhelli, Luca"],
 "editLink": "/Creedo/index.htm",
 "isPublic": true,
diff --git a/grain-boundary/GB_tutorial_Berlin_2017.demoinfo.yaml b/grain-boundary/GB_tutorial_Berlin_2017.demoinfo.yaml
index b634392bf9a6f5890a3007eefb6354c29a7e2a6d..4da7228f4824e5ae4ea4120899593ff31551c178 100644
--- a/grain-boundary/GB_tutorial_Berlin_2017.demoinfo.yaml
+++ b/grain-boundary/GB_tutorial_Berlin_2017.demoinfo.yaml
@@ -1,7 +1,7 @@
 {
 "title": "Grain boundaries of alpha-Fe tutorial",
 "authors": ["Fekete, Ádám", "Stella, Martina", "Lambert, Henry", "De Vita, Alessandro", "Csányi, Gábor"],
-"editLink": "/jupyter/notebooks/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
+"editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
 "isPublic": true,
 "username": "tutorialsNew",
 "description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",