Reminds me of The Newtonian Casino.

An app to help you use less apps.

Kevin Rutherford:

What follows is my personal, opinionated, idiosyncratic approach to React/Redux development, forged in half a dozen attempts to pursue test-driven development at a good pace.

Some interesting approaches in here. I like the use of more classic terms for some of the Redux concepts. Commands instead of Action Creators. Events instead of actions. Read models instead of reducers or selectors.

A podcast about Watergate.

Tips to visualise and limit context switching.

A useful service for improving the security of your users’ accounts.

A useful overview.

From a couple years ago. The section dispelling dogma that’s arisen around the approach was particularly interesting (from 22:40).

Camille Fournier:

If you are (or were) a highly opinionated engineer, practicing making space for information rather than quickly jumping in and sharing your conclusions is a must for leadership growth.

Joel Spolsky:

I don’t know what the original idea of Twitter was, but it succeeded because of natural selection. In a world where the tech industry was cranking out millions of dumb little social applications, this one happens to limit messages to 140 characters and that happens to create, unintentionally, a subtlety-free indignation machine, which is addictive as heck, so this is the one that survives and thrives and becomes a huge new engine of polarization and anger.

DHH:

Creativity, progress, and impact does not yield easily or commonly to brute force.

The Case for Learned Index Structures:

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes.

…

Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets.

Not the weather, but the climate.

He didn’t know whether to fix it or sell tickets.

I used to find it odd that these hypothetical AIs were supposed to be smart enough to solve problems that no human could, yet they were incapable of doing something most every adult has done: taking a step back and asking whether their current course of action is really a good idea. Then I realized that we are already surrounded by machines that demonstrate a complete lack of insight, we just call them corporations.