#21: PyPy - The JIT Compiled Python Implementation

1

Introduction

00:00 • 3min

2

Python: A Research Variable

03:09 • 3min

3

The Different Interpreters for Python

05:41 • 2min

4

Python and Dropbox: A Comparison

08:01 • 2min

5

How to Avoid Using the C API in Python

10:00 • 2min

6

How to Use CFFI in PyPy

11:41 • 2min

7

Why I Use PyPy Over CPython

13:26 • 2min

8

How to Run a Python Application Faster

15:28 • 2min

9

Python Performance Comparisons

17:33 • 2min

10

How to Speed Up Your Python Web Application

19:15 • 3min

11

PyPy: A Language for Writing Interpreters

22:34 • 4min

12

The Importance of Compiling Python Code

26:08 • 2min

13

PyPy: Better Memory Consumption

27:58 • 2min

14

The Garbage Collector: A Mark in Sweep Garbage Collector

30:23 • 2min

15

How to Write Code Like With Python Three Coroutines Without the Yield Keyword

31:55 • 2min

16

Python and Twisted

33:44 • 2min

17

Software Transaction Memory for the Listeners

35:51 • 2min

18

The Importance of Parallel Programming

37:38 • 3min

19

Python's Race Condition and Timing Threading Problems

40:52 • 2min

20

Pi 3K in Pi Pi

43:03 • 3min

21

PyPy and the JIT Viewer

45:36 • 2min

22

How to Make Your Application Faster on PyPy

47:54 • 2min

23

How to Write Code in Python

49:59 • 2min

24

Talk Python to Me: Macha Falkowski

51:39 • 2min