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Factor: an extensible interactive language1:36:40

Factor: an extensible interactive language

59K views • 18 years ago
Google TechTalks
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.
A Possible Future of Software Development1:01:33

A Possible Future of Software Development

82K views • 18 years ago
Google TechTalks
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.
Lecture 1A | MIT 6.001 Structure and Interpretation, 19861:12:56

Lecture 1A | MIT 6.001 Structure and Interpretation, 1986

995K views • 17 years ago
MIT OpenCourseWare
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.
RailsConf 09:  Robert Martin, "What Killed Smalltalk Could K1:00:56

RailsConf 09: Robert Martin, "What Killed Smalltalk Could K

87K views • 17 years ago
O'Reilly
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.
Building Your Own Dynamic Language1:14:32

Building Your Own Dynamic Language

21K views • 18 years ago
Stanford
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.
Lecture 1 | Convex Optimization I (Stanford)1:20:33

Lecture 1 | Convex Optimization I (Stanford)

753K views • 18 years ago
Stanford
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.