Move notes into boxes

README.md

@@ -215,10 +215,15 @@ jitted_vmapped_frame_fk = jit(vmapped_frame_fk)
 joints_batch = jnp.tile(joints, (1024, 1))
 w_H_b_batch = jnp.tile(w_H_b, (1024, 1, 1))
 w_H_f_batch = jitted_vmapped_frame_fk(w_H_b_batch, joints_batch)
-# Note that the first call of the jitted function can be slow, since JAX needs to compile the function. Than it will be fast.
+
 
 ```
 
+> [!NOTE]
+> The first call of the jitted function can be slow, since JAX needs to compile the function. Than it will be fast!
+
+```python
+
 ### CasADi interface
 
 ```python
@@ -260,7 +265,6 @@ joints = cs.MX.sym('joints', len(joints_name_list))
 M = kinDyn.mass_matrix_fun()
 print(M(w_H_b, joints))
 
-
 ```
 
 ### PyTorch interface
@@ -295,6 +299,9 @@ print(M)
 
 ### PyTorch Batched interface
 
+> [!NOTE]
+> When using this interface, note that the first call of the jitted function can be slow, since JAX needs to compile the function. Than it will be fast!
+
 ```python
 import adam
 from adam.pytorch import KinDynComputationsBatch
@@ -325,7 +332,6 @@ joints_batch = torch.tensor(np.tile(joints, (num_samples, 1)), dtype=torch.float
 
 M = kinDyn.mass_matrix(w_H_b_batch, joints_batch)
 w_H_f = kinDyn.forward_kinematics('frame_name', w_H_b_batch, joints_batch)
-# Note that the first call of the jitted function can be slow, since JAX needs to compile the function. Than it will be fast.
 ```
 
 ## 🦸‍♂️ Contributing