Steve Yegge on GROK (YouTube) — The Grok Project is an internal Google initiative to simplify the navigation and querying of very large program source repositories. We have designed and implemented a language-neutral, canonical representation for source code and compiler metadata. Our data production pipeline runs compiler clusters over all Google’s code and third-party code, extracting syntactic and semantic information. The data is then indexed and served to a wide variety of clients with specialized needs. The entire ecosystem is evolving into an extensible platform that permits languages, tools, clients and build systems to interoperate in well-defined, standardized protocols.
Deep Learning for Semantic Analysis — When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effect of contrastive conjunctions as well as negation and its scope at various tree levels for both positive and negative phrases.
Fireshell — workflow tools and framework for front-end developers.
SICP.js — lots of Structure and Interpretation of Computer Programs (the canonical text for higher-order programming) ported to Javascript.
