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Maciej Fijalkowski committed 1be9e40

start writing the release announcement

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File pypy/doc/release-1.8.0.rst

+============================
+PyPy 1.7 - business as usual
+============================
+
+We're pleased to announce the 1.8 release of PyPy. As became a habit, this
+release brings a lot of bugfixes, performance and memory improvements over
+the 1.7 release. The main highlight of the release is the introduction of
+list strategies which makes homogenous lists more efficient both in terms
+of performance and memory. Otherwise it's "business as usual" in the sense
+that performance improved roughly 10% on average since the previous release.
+You can download the PyPy 1.8 release here:
+
+    http://pypy.org/download.html
+
+What is PyPy?
+=============
+
+PyPy is a very compliant Python interpreter, almost a drop-in replacement for
+CPython 2.7. It's fast (`pypy 1.8 and cpython 2.7.1`_ performance comparison)
+due to its integrated tracing JIT compiler.
+
+This release supports x86 machines running Linux 32/64, Mac OS X 32/64 or
+Windows 32. Windows 64 work is ongoing, but not yet natively supported.
+
+.. _`pypy 1.8 and cpython 2.7.1`: http://speed.pypy.org
+
+
+Highlights
+==========
+
+* List strategies. Now lists that contain only ints or only floats should
+  be as efficient as storing them in a binary-packed array. It also improves
+  the JIT performance in places that use such lists. There are also special
+  strategies for unicode and string lists.
+
+* As usual, numerous performance improvements. There are too many examples
+  which python constructs now should behave faster to list them.
+
+* Bugfixes and compatibility fixes with CPython.
+
+* Windows fixes.
+
+* NumPy effort progress, for the exact list of things that have been done,
+  consult the `numpy status page`_. A tentative list of things that has
+  been done:
+
+  xxxx # list it, multidim arrays in particular
+
+* Fundraising XXX
+
+.. _`numpy status page`: xxx
+.. _`numpy status update blog report`: xxx