A useful service for improving the security of your user’s 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).
Creating an event sourced, CQRS application is simple enough conceptually but there is a lot of hidden detail when it comes to building them. There are a couple of event sourcing libraries I’ve used that can help.
The first, Event Sourcery, is in Ruby and created by my colleagues at Envato. You can use Postgres as your data store and it gives you what you need to build aggregates and events and projectors and process managers.
The immutability and process supervision baked into Elixir makes it a compelling option for implementing these kind of applications as well. Commanded is written in Elixir and follows a very similar approach to Event Sourcery and works a treat.
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.
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.
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.
A deep dive into the feature progression of Google Maps and how far ahead of the pack they are.
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.
Feel the pain.
I’ve been waiting eagerly for this episode to air — it’s my favorite of the season. As I looked through my notes, I was surprised to find that Kor and I first started working on scenes for “eps3.4_runtime-err0r.r00” as far back as January. The attacks against E Corp’s Hardware Security Modules (HSMs) are among the most complex hacks we’ve depicted on the show
Rich Hickey explained the design choices behind Clojure and made many statements about static typing along the way. I share an interesting perspective and some stories from my time as a Haskell programmer. I conclude with a design challenge for the statically typed world.
People often talk about a Person class representing a person. But it doesn’t. It represents information about a person. A Person type, with certain fields of given types, is a concrete choice about what information you want to keep out of all of the possible choices of what information to track about a person. An abstraction would ignore the particulars and let you store any information about a person. And while you’re at it, it might as well let you store information about anything. There’s something deeper there, which is about having a higher-order notion of data.
It was emotional.
Having a formal system means we can better support the growth of our engineers. We’re able to have more honest, open conversations about progress, promotions, and opportunity. While the framework is still relatively new, it is showing early promise at incentivising the kinds of behaviours we want to see in the team, and recognising the different kinds of value that people add.
We already have ideas on how to improve the framework further, and plan to continue iterating on it over time. We are releasing it publicly now, in the hope that it can help other companies that are thinking about how to grow and support their employees.
Medium’s growth framework looks well thought out and nicely structured.
A series of posts on options for creating a unified UI over disparate services.
The L16 replaces one big lens with 16 small ones and combines the images via software.
The old clustering of commodity hardware with software approach keeps on popping up in different contexts.
Some handy tips in here.
The real story in this mess is not the threat that algorithms pose to Amazon shoppers, but the threat that algorithms pose to journalism. By forcing reporters to optimize every story for clicks, not giving them time to check or contextualize their reporting, and requiring them to race to publish follow-on articles on every topic, the clickbait economics of online media encourage carelessness and drama. This is particularly true for technical topics outside the reporter’s area of expertise.
However there are general five general areas of interest that are always worth examining because they reveal mistakes with such surprising regularity. Specifically it’s worthwhile to find out how any system handles inputs, math, text, time and system resources.
Plenty of good testing advice.
I have long felt there is a shadow org chart, much like a shadow economy, where employees trade ideas, give direction, offer help, and spread culture. This shadow org chart is built bottom up by employees and is very different from the top down hierarchical org chart set by me.
I wanted to map this shadow org chart and find employees who have disproportionate levels of influence relative to their hierarchical position. I also wanted to see the influence centers and decision makers, and the directional current between them and the rest of the company.
Some fascinating analysis and data visualisation of a graph of influence.
An algorithm for splitting and sharing secrets.
The researchers found quite simply that the more people use Facebook, the more unhappy they are.
His idea was that if the price is falling that means the market is working, and no questions of monopoly need be addressed. This philosophy still shapes regulatory attitudes in the US and it’s the reason Amazon, for instance, has been left alone by regulators despite the manifestly monopolistic position it holds in the world of online retail, books especially.
I’ve spent time thinking about Facebook, and the thing I keep coming back to is that its users don’t realise what it is the company does. What Facebook does is watch you, and then use what it knows about you and your behaviour to sell ads.
Facebook is in the surveillance business. Facebook, in fact, is the biggest surveillance-based enterprise in the history of mankind.
A look at the internal data structure Emacs uses to represents buffers.
Management and leadership lessons.
They kill me.
Our goal should be to reduce these issues—we’ll never be truly rid of them. The first step is self-awareness. Ask yourself:
- “Do I know what I’m doing?”
- “Do I have a plan?”
- “Is this the simplest thing I could do?” (note: Simple ain’t easy)
- “What is the problem I’m trying to solve?”