Mention the super-simple format

Author: FrancescAlted

doc/python-blosc2.rst

@@ -28,41 +28,37 @@
     :column: col-lg-4 col-md-6 col-sm-12 mb-4
     :container: + gap-3
 
-    **Excellent compression capabilities**
+    **N-dimensional, compressed arrays**
 
 
-    Blosc2 combines `advanced codecs and filters <https://www.blosc.org/posts/bytedelta-enhance-compression-toolset/>`_ for efficient compression, reducing storage space while maintaining high performance.
+    `NDArray objects <https://www.blosc.org/python-blosc2/getting_started/tutorials/01.ndarray-basics.html>`_ allow for efficient storage and manipulation of multidimensional data, making it easy to work with complex data sets.
 
     ---
 
-    **Optimized compute engine**
+    **Excellent compression capabilities**
 
 
-    Blosc2 accelerates complex mathematical operations and reductions with an
-    `optimized compute engine <https://www.blosc.org/posts/ndim-reductions/>`_,
-    achieving high-performance for computing and data analysis.
+    Combines `advanced codecs and filters <https://www.blosc.org/posts/bytedelta-enhance-compression-toolset/>`_ for efficient compression, reducing storage space while maintaining high performance.
 
     ---
 
-    **N-dimensional, compressed arrays**
+    **Optimized compute engine**
 
 
-    `NDArray objects <https://www.blosc.org/python-blosc2/getting_started/tutorials/01.ndarray-basics.html>`_ allow for efficient storage and manipulation of multidimensional data, making it easy to work with complex data sets.
+    For accelerating complex mathematical operations and reductions with an `optimized compute engine <https://www.blosc.org/posts/ndim-reductions/>`_,  achieving high-performance for computing and data analysis.
 
     ---
 
-    **Binary data optimization**
-
+    **Super simple format**
 
-    Designed to compute with compressed binary data (integers, floats, bools), Blosc2 is specially meant for
-    `high-performance numerical applications <https://nbviewer.org/github/Blosc/python-blosc2/blob/main/bench/lazyarray-expr-small-dask.ipynb>`_.
+    Blosc2's `simple format <https://github.com/Blosc/c-blosc2/blob/main/README_FORMAT.rst>`_, with specs taking less than 1000 lines, makes it easy to integrate with other systems and tools.
 
     ---
 
     **Flexible storage**
 
 
-    By offering storage in `memory, disk <https://github.com/Blosc/python-blosc2/blob/main/doc/getting_started/tutorials/01.ndarray-basics.ipynb>`_, or `network <https://ironarray.io/caterva2>`_, Blosc2 adapts to your needs and facilitates integration into various systems.
+    Can store data in `memory, disk <https://github.com/Blosc/python-blosc2/blob/main/doc/getting_started/tutorials/01.ndarray-basics.ipynb>`_, or `network <https://ironarray.io/caterva2>`_, adapting to your needs and facilitates integration into various systems.
 
     ---
 

src/blosc2/lazyexpr.py

@@ -1670,7 +1670,7 @@ class LazyExpr(LazyArray):
 
     This is not meant to be called directly from user space.
 
-    Once the lazy expression is created, it can be evaluated via :func:`LazyExpr.eval`.
+    Once the lazy expression is created, it can be evaluated via :func:`LazyExpr.compute`.
     """
 
     def __init__(self, new_op):  # noqa: C901
@@ -2726,7 +2726,7 @@ def lazyudf(
     kwargs: Any, optional
         Keyword arguments that are supported by the :func:`empty` constructor.
         These arguments will be used by the :meth:`LazyArray.__getitem__` and
-        :meth:`LazyArray.eval` methods. The
+        :meth:`LazyArray.compute` methods. The
         last one will ignore the `urlpath` parameter passed in this function.
 
     Returns