+The PyPy project has recently gathered a lot of attention for its
+progress in speeding up the Python language -- it is the fastest,
+most compatible and most stable 'alternative´ Python interpreter. No
+longer merely a research curiosity, PyPy is now suitable for production
+The speed comes from a custom Just-in-Time compiler (JIT). It is the
+first Virtual Machine to have a JIT generated automatically from the
+interpreter of the language, which makes it complete by construction.
+The JIT itself is a tracing JIT, roughly similar to SpiderMonkey.
+* most Python benchmarks run much faster than with CPython or Psyco
+* the real-world PyPy compiler toolchain itself (200 KLocs) runs twice as fast
+* supports x86 (32 or 64 bit), ARM (v7), and soon POWER64
+* full compatibility with CPython (more than Jython/IronPython)
+* ctypes, CFFI and C++ support to call C/C++ libraries from Python (fast)
+* supports Stackless Python (in-progress)
+* integrates existing CPython C extensions (slowly)
+In this talk we will see examples of what PyPy is best at (pure Python
+code that runs for a while), what compatibility issues you may run into
+(very few), how to use CPython C extension modules (you can more or
+less, but it's slow right now), as well as dig a bit below the surface
+and use some tools to view the x86 machine code that was produced by the
+I will end the talk with an overview of Software Transactional Memory
+(STM) and how it promizes to give a PyPy without the Global Interpreter
+Lock (GIL), i.e. able to run a single process using multiple cores.