data-driven programming

A 2008 Google Tech Talk in which Slava Pestov introduces Factor, a concatenative language influenced by Forth, Lisp and Smalltalk. It covers the interactive environment, metaprogramming, and the language's modern high-level features and libraries.

Sean Parent of Adobe argues that object-oriented frameworks ultimately fail to deliver scalable, high quality software and proposes combining generic and declarative programming instead. It draws on Stepanov's Elements of Programming.

Van Jacobson's 2006 Google Tech Talk argues that networking should be rethought around named data rather than host-to-host conversations. He contrasts the circuit and datagram views of the internet and sketches content-centric networking as a replacement for the location-centric TCP/IP model.

Van Jacobson's 2006 Google Tech Talk argues for rethinking networking around named data rather than host-to-host connections. He gives the history and the unsolved problems of the current internet architecture.

The first lecture of MIT's 1986 6.001 Structure and Interpretation of Computer Programs, given by Harold Abelson, introducing Lisp and the idea of computer science as the study of process.

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.

Harvard physicist Eric Mazur explains how he realized his students were memorizing rather than understanding, and how he changed his teaching. He presents peer instruction and data showing improved student performance.

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.

Ian Piumarta's 2007 Stanford EE380 lecture on building a dynamic programming environment from the ground up, from the Viewpoints Research Institute's work. It covers the design of a language and system with a recursive design, parsing through code generation, and the COLA architecture.

The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.