diff --git a/pep-0211.txt b/pep-0211.txt
index 4d55a8930..8c518f0a4 100644
--- a/pep-0211.txt
+++ b/pep-0211.txt
@@ -20,7 +20,7 @@ Introduction
     definitive historical record.
 
 
-Proposal
+Summary
 
     Add a single new infix binary operator '@' ("across"), and
     corresponding special methods "__across__()", "__racross__()", and
@@ -141,38 +141,12 @@ Proposal
 
     A new operator '@' (pronounced "across") will be added to Python,
     along with special methods "__across__()", "__racross__()", and
-    "__iacross__()", with the usual semantics.
-
-    NumPy will overload "@" to perform mathematical multiplication of
-    arrays where shapes permit, and to throw an exception otherwise.
-    Its implementation of "@" will treat built-in sequence types as if
-    they were column vectors.  This takes care of the cases MM and MV.
-
-    An attribute "T" will be added to the NumPy array type, such that
-    "m.T" is:
-
-    (a) the transpose of "m" for a 2-dimensional array
-
-    (b) the 1xN matrix transpose of "m" if "m" is a 1-dimensional
-        array; or
-
-    (c) a runtime error for an array with rank >= 3.
-
-    This attribute will alias the memory of the base object.  NumPy's
-    "transpose()" function will be extended to turn built-in sequence
-    types into row vectors.  This takes care of the VM, VD, and VO
-    cases.  We propose an attribute because:
-
-    (a) the resulting notation is similar to the 'superscript T' (at
-        least, as similar as ASCII allows), and
-
-    (b) it signals that the transposition aliases the original object.
+    "__iacross__()", with the usual semantics.  (We recommend using
+    "@", rather than the times-like "><", because of the ease with
+    which the latter could be mis-typed as inequality "<>".)
 
     No new operators will be defined to mean "solve a set of linear
-    equations", or "invert a matrix".  Instead, NumPy will define a
-    value "inv", which will be recognized by the exponentiation
-    operator, such that "A ** inv" is the inverse of "A".  This is
-    similar in spirit to NumPy's existing "newaxis" value.
+    equations", or "invert a matrix".
 
     (Optional) When applied to sequences, the "@" operator will return
     a tuple of tuples containing the cross-product of their elements
@@ -293,7 +267,8 @@ Related Proposals
 
     0225 :  Elementwise/Objectwise Operators
 
-            A (much) larger proposal that addresses the same subject.
+            A larger proposal that addresses the same subject, but
+            which proposes many more additions to the language.
 
 
 Acknowledgments
@@ -313,19 +288,37 @@ References
     [7] http://www.python.org/pipermail/python-list/2000-August/112529.html
 
 
-Appendix: Other Operations
+Appendix: NumPy
 
+    NumPy will overload "@" to perform mathematical multiplication of
+    arrays where shapes permit, and to throw an exception otherwise.
+    Its implementation of "@" will treat built-in sequence types as if
+    they were column vectors.  This takes care of the cases MM and MV.
 
-    We considered syntactic support for three other operations:
+    An attribute "T" will be added to the NumPy array type, such that
+    "m.T" is:
 
-                         T
-    (a) transposition:  A   => A[j, i] for A[i, j]
+    (a) the transpose of "m" for a 2-dimensional array
 
-                         -1
-    (b) inverse:        A   => A' such that A' * A = I (the identity matrix)
+    (b) the 1xN matrix transpose of "m" if "m" is a 1-dimensional
+        array; or
 
-    (c) solution:       A/b => x  such that A * x = b
-                        A\b => x  such that x * A = b
+    (c) a runtime error for an array with rank >= 3.
+
+    This attribute will alias the memory of the base object.  NumPy's
+    "transpose()" function will be extended to turn built-in sequence
+    types into row vectors.  This takes care of the VM, VD, and VO
+    cases.  We propose an attribute because:
+
+    (a) the resulting notation is similar to the 'superscript T' (at
+        least, as similar as ASCII allows), and
+
+    (b) it signals that the transposition aliases the original object.
+
+    NumPy will define a value "inv", which will be recognized by the
+    exponentiation operator, such that "A ** inv" is the inverse of
+    "A".  This is similar in spirit to NumPy's existing "newaxis"
+    value.