python

Alex Martelli's Google Developers Day talk on classic design patterns (creational, structural and behavioral) and how they appear in Python. He stresses that patterns must be studied in the context of the implementing language.

Joshua Bloch's Google Tech Talk on how to design good APIs and why it matters, with concrete principles and examples. It treats every programmer as an occasional API designer.

Douglas Crockford's 2009 Google tech talk on the good and bad parts of JavaScript, based on his book. He argues for using a reliable subset of the language and avoiding its flawed features.

Cal Henderson's DjangoCon 2008 keynote, "Why I Hate Django", is a funny critique of Django from a Flickr architect. It touches on scaling topics such as database replication.

Robert Martin's RailsConf 2009 talk looks at why Smalltalk lost out despite its technical strengths. He draws lessons about language communities and arrogance that he argues could hurt Ruby too.

Cliff Click of Azul Systems gives a Google Tech Talk on running Java business applications on a machine with about a thousand cores. He covers hardware and software codesign, including garbage collection hardware and the JIT.

Larry Wall speaks at Google's Chicago office in 2008 about the Perl community, the language, and Perl 6. The talk touches on grammars, macros and error messages.

Paul Biggar's Google Tech Talk on compiling and optimizing scripting languages, drawn from his work on the phc ahead-of-time PHP compiler. He covers runtime typing, aliasing and code generation challenges, and argues AOT can compete with JIT.

Cuong Do of YouTube describes how a handful of engineers scaled the site, covering hardware, software and database scalability challenges during its rapid growth. The talk was given at the 2007 Seattle Conference on Scalability.

In this TED talk, Hans Rosling uses animated statistics to debunk myths about the developing world, showing how health and income have improved across countries.

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.