Travis Reeder thinks performance, memory, concurrency, reliability, and deployment are key to exploring Go and its value in production. Travis describes how it’s worked for Iron.io.
Brian Holt talks about React, performance issues, some general web performance tips, lessons learned while helping write m.reddit.com using React.
Roy Clarkson and Greg Turnquist discuss using Spring Data REST to build a back-end for a startup, exemplifying with Spring-A-Gram, an app built with Spring Data REST and secured by Spring Security.
Scott Hickey works through a solution to the Bank OCR kata, using Groovy and functional programming techniques. The code uses recursion plus Groovy methods that support functional programming.
Nick MacDonald discusses a project’s transformation using hypermedia APIs, which has provided them with a simplified backbone to evolve internally and across boundaries.
Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app.
William Markito Oliveira and Fred Melo discuss the architecture and implementation details of a stock prediction solution built entirely on top of open source code and some R and a web interface.
Lucian Vlad Lita focuses on the next step in personalization: well-designed software architectures for storing, computing, and delivering responsive, accurate in-product predictions and experiments.
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
Simon Ferguson describes how MBIE is using APIs, initiatives that are underway to increase use of APIs, and the considerations that are involved with providing governmental services as APIs.
Matt Ranney covers the evolution of Uber's architecture and some of the systems they built to handle the current scaling challenges.