diff --git a/02_Presentation/AI_that_grows/AI_grow.md b/02_Presentation/AI_that_grows/AI_grow.md
index 06c0ca511ea39b761383d277b7eff4db154b6000..4d0be6d9ad62f786c13197ce048663b13dc7c1ec 100644
--- a/02_Presentation/AI_that_grows/AI_grow.md
+++ b/02_Presentation/AI_that_grows/AI_grow.md
@@ -8,10 +8,11 @@ Research and development of workflows for the co-design reconfigurable AI softwa
 - "focus on finding minimal architectures".
 - "By deemphasizing learning of weight parameters, we encourage the agent instead to develop ever-growing networks that can encode acquired skills based on its interactions with the environment".
 
-![](./WANN_schematic.png)
-![](./WANN_operators.png)
 
-<img src="./square_biped.png" width="50%" /><img src="./square_biped.gif" width="50%" />
+<img src="./WANN_schematic.png" width="70%" />
+<img src="./WANN_operators.png" width="70%" />
+
+<img src="./square_biped.png" width="35%" /><img src="./square_biped.gif" width="35%" />
 
 ## Case Study Implementation: Cart-Pole Swing Up
 
@@ -21,7 +22,7 @@ One of the most famous benchmarks of non-linear control, there is lots of approa
 
 WANN is interesting as it tries to get the simplest network that uses the input sensors (position, rotation and their derivatives) to the output (force). It focuses on learning **principles** and **not only tune weights**. 
 
-<img src="./swing_best.png" width="100%" />
+<img src="./swing_best.png" width="75%" />
 
 This is one of the outputs of the network and you can see because of it's simplicity it's not a black box and one can deduce the principles learnt [[1]](https://towardsdatascience.com/weight-agnostic-neural-networks-fce8120ee829):
 - the position parameter is almost directly linked to the force, there is only an inverter which means that if the cart is on right or left of the center (+- x), it always try to **go to in the opposite direction** to the center.
@@ -30,9 +31,9 @@ This is one of the outputs of the network and you can see because of it's simpli
 
 ## Evolution and DICE Integration
 
-<img src="./wann_run.png" width="100%" />
+<img src="./evol1.gif" width="50%" />
 
-<img src="./evol1.gif" width="75%" />
+<img src="./wann_run.png" width="50%" />
 
 
 <img src="./quick.gif" width="100%" />
diff --git a/02_Presentation/macro_dice/macro_dice.md b/02_Presentation/macro_dice/macro_dice.md
index e588fdce4d6ef5c0ea86dc2b09ec6dbbd84182f1..910535d873122fff7e5c24d1111875b3de46c780 100644
--- a/02_Presentation/macro_dice/macro_dice.md
+++ b/02_Presentation/macro_dice/macro_dice.md
@@ -12,23 +12,22 @@ Hardware Implementation:
 
 Using the [MetaVoxel simulation tool](https://gitlab.cba.mit.edu/amiraa/metavoxels):
 
-![](./200114_simulation.PNG)
+<img src="./200114_simulation.PNG" width="75%" /><br/>
 
 ## Robotics Design
 
-<img src="./dice_assembly.gif" width="100%" /><br/>
+<img src="./dice_assembly.gif" width="75%" /><br/>
 
 ## Assembly
 - Option for different kinds of assembly (swarm assembly)
-<img src="./assembly.gif" width="100%" /> <br/>
+<img src="./assembly.gif" width="75%" /> <br/>
 
 ## Control
-<img src="./rover1.gif" width="75%" />
-<img src="./rover2.gif" width="75%" /><br/>
+<img src="./rover1.gif" width="50%" />
+<img src="./rover2.gif" width="50%" /><br/>
 
-<img src="./r.gif" width="75%" /><br/>
-
-<img src="./rr.gif" width="75%" /><br/>
+<img src="./r.gif" width="50%" /><br/>
+<img src="./rr.gif" width="50%" /><br/>
 
 
 ## Optimization