diff --git a/Documentation/Pages/core-concepts.md b/Documentation/Pages/core-concepts.md
index 92a0d4f7190dc55a2c77e087f20aed7ca368a882..17e383fc335269183073008dcc3ef5724a466f44 100644
--- a/Documentation/Pages/core-concepts.md
+++ b/Documentation/Pages/core-concepts.md
@@ -18,7 +18,7 @@ TNL is based on the following core concepts:
    - Classes for general data structures.
      (TODO: alternatively use "Dense" and "Sparse", because a dense matrix can
      be an extended alias for 2D array)
-   - `Array`, `Vector` (also `VectorOperations`), `NDArray`, ...
+   - `Array`, `Vector`, `NDArray`, ...
 5. Views
    - Views wrap only a raw pointer to data and some metadata (such as the array
      size), they do not do allocation and deallocation of the data. Hence, views
diff --git a/Documentation/Pages/main-page.md b/Documentation/Pages/main-page.md
index cf595614c9411995ac5668d52ed4751dc7cf6bac..8a1685b9c4a413f5bc9ab72912e3e991289023ad 100644
--- a/Documentation/Pages/main-page.md
+++ b/Documentation/Pages/main-page.md
@@ -11,8 +11,6 @@ and distributed systems, which can be managed via a unified interface.
 Similarly to the STL, features provided by the TNL can be grouped into
 several modules:
 
-> TODO: Each topic in this list should have a separate page or tutorial.
-
 - _Core concepts_.
   The main concepts used in TNL are the _memory space_, which represents the
   part of memory where given data is allocated, and the _execution model_,