The Realities of Docker in Production

“Docker seems like a fundamentally better abstraction for packaging and deploying applications in a persistent way across multiple environments. Among individual developers and ops engineers it’s praised, but at a recent Docker in Production meetup held at Heavybit, engineers from, ClusterHQ, RelateIQ and Docker discussed the realities of running Docker in Production — namely what does and doesn’t work…”

O Simples e Elegante Algoritmo Que Torna o Google Maps Possível

“Algoritmos são uma ciência baseada em engenhosidade. Uma manifestação natural do raciocínio lógico – indução matemática, em especial – um bom algoritmo é como um retrato passageiro e condenatório da alma de um problema. Uma selva de propriedades e relações torna-se uma simples relação de recorrência, um passo recursivo de linha única produzindo caos e complexidade sem limites. E para ver através desta complexidade, é necessário engenhosidade.

Foi o pioneiro da programação Edsger W. Dijkstra quem realmente sacou isso, e o algoritmo que leva seu nome segue como uma das coisas mais engenhosas na informática. Um defensor ferrenho da simplicidade e elegância na matemática, ele meio que acreditava que todo problema complicado tinha uma parte acessível, uma entrada, e a matemática era uma ferramenta a ser usada para encontrá-la e explorá-la…”

Why Racket? Why Lisp?

“In prac­ti­cal pro­gram­ming projects, Lisps are rare, and Racket es­pe­cially so. Thus, be­fore I em­barked on my Lisp ad­ven­ture, I wanted to un­der­stand the costs & ben­e­fits of us­ing a Lisp. Why do Lisps have such a great rep­u­ta­tion, but so few users? Was I see­ing some­thing every­one else missed? Or did they know some­thing I didn’t? To find out, I read what­ever I could find about Lisps, in­clud­ing Paul Gra­ham’s Hack­ers & Painters and Pe­ter Seibel’s Prac­ti­cal Com­mon Lisp. (OK, parts. It’s a big book.)

What I found was plenty of Lisp flat­tery from ex­pert Lisp pro­gram­mers. (Also plenty of Lisp kvetch­ery from its de­trac­tors.) What I didn’t find were sim­ple, per­sua­sive ar­gu­ments in its fa­vor. So here’s why Racket was the right tool for this project, and what I see as the prac­ti­cal virtues of Lisps in general...”

Deep Learning, NLP, and Representations

“In the last few years, deep neural networks have dominated pattern recognition. They blew the previous state of the art out of the water for many computer vision tasks. Voice recognition is also moving that way.

But despite the results, we have to wonder… why do they work so well?

This post reviews some extremely remarkable results in applying deep neural networks to natural language processing (NLP). In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it’s a very elegant perspective…”