Wednesday, 31st October 2018
matthewp/haunted: React’s Hooks API implemented for web components (via) It’s been fascinating over the past few days watching various frontend web stacks start playing with the new ideas introduced by the proposed React hooks API. lit-html is one of my favourite React alternatives—it’s built on web components and makes really clever use of ES6 template literals (in place of React’s JSX, which requires an additional compilation step). With Haunted Matthew Phillips explores the combination of lit-html, web components and hooks-style state management.
This Is How We Radicalized The World (via) Don’t be put off by the click-baity title: this article by Ryan Broderick is absolutely worth your time. Ryan has been traveling the world covering the global rise of populism, which has been driven in a great part by new patterns of social media usage and distrust of the news media. Ryan ties together stories from a bunch of different countries over the last few years and make a compelling case that we need to come to terms with social media radicalization as a global problem and figure out how to respond to it and deal with the fallout.
Making Sense of React Hooks (via) Dan Abramov provides the most comprehensive justification I’ve seen so far for the new React hooks API.
October 21 post-incident analysis (via) Legitimately fascinating post-mortem by GitHub. They run database masters in multiple data centers with raft for leader election... but when they had an unexpected network split between east and west coast they ended up with several seconds of write that had not been correctly replicated. Cleaning up the resulting mess took the best part of 24 hours! Distributed systems are hard.
Reinforcement Learning with Prediction-Based Rewards (via) Fascinating result: by teaching a reinforcement learning agent that plays video games to optimize for “unfamiliar states”—states where it cannot predict what will happen next—the agent does a much better job of playing some games. “... for the first time exceeds average human performance on Montezuma’s Revenge. RND achieves state-of-the-art performance, periodically finds all 24 rooms and solves the first level without using demonstrations or having access to the underlying state of the game.”