I still firmly hold the belief that iOS applications are either loss leaders or loss generators, that iOS devices themselves are thick terminals, and that a proper iOS execution strategy must be backed with a useful service either involving real world consequences (i.e. get a ride or get groceries delivered), or a wider cross-platform strategy (i.e. build your document on one platform, revise on another).
Airtime uses AWS Simple Notification Service (SNS) and AWS Simple Queue Service (SQS) to power the pub/sub event backbone that connects producer services to consumer services.
A loosely-coupled, event-based architecture based on AWS services.
A collection of practices and patterns out in the wild.
Zsh does so much.
Nice tour of Unix and zsh.
I don’t understand a bunch of this but it has me intrigued.
The versioned, forkable, syncable database
A nice example Elixir/Phoenix application.
A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources.
Soundcloud’s experience building an application using React Native.
A look at using queues for submitting commands.
A nice outline of VPCs subnets and friends.
Feel the Burr.
Sandi Metz being awesome. Highlighting extraction of variants and null objects.
Getting my guitar back on.
Micheal Lewis on a 14 year old stock manipulator.
Take it. It’s yours.
For the security conscious and paranoid.
A list of everything that could go in the <head> of your HTML document.
What is the CAP theorem for teams? Moving together balanced against moving forward.
Decent coverage of private and public subnets, NATs, and Internet gateways.
A wish list of future features for Rails. Some nice ideas in here.
curl wrapper that uses chrome cookies.
First time played in seven years.
Advice for us all.
A nice run down of how SSL/TLS works and its vulnerabilities.
A good template for elaborating site performance.
A deep dive into aspects of building an Event Store.
Guidelines for using rspec.
Music makes repetitive tasks more enjoyable.
Recreating shots from films.
Lots of great tips in here.
Models are used to save money when designing. i.e. The maths came second as a cheaper way to build stuff. When your end medium is so malleable you can design in it.
Good to see this kind of information shared.
Mathmetical tools to aid software development.
Recycle your infrastructure frequently to minimise security holes.
Strategies for dealing with Slack.
Nice concept summary.
Add placeholder image colours to images.
Computers and how we suck at it all.
Lots of meaty stuff.
A look at what engineering means, its history, and how software fits in.
Your own index is something you put in the back of the book (or the front if you prefer). It’s a list of the book’s themes and topics that most resonate with you, and the pages which have the best quotes and ideas around those topics.
I’ll use an index card to do this for the ebooks I read.
Amazing what’s happened in ten years.
An extensive look at what it takes to be a senior engineer.
Rackt is a GitHub organization that was created to ensure critical open source projects in the React community receive long-term support and maintenance.
We believe these four principles will provide a strong ecosystem of our open source projects by ensuring projects have active and enthusiastic owners and contributors.
Jesper Louis Andersen:
I’m very fond of Erlang creator Mike Williams point: “If you don’t make experiments before starting a project, then your whole project will be an experiment”
If we instead ask about prototyping, then we need a programming language with certain traits. The team is usually small, so we need an expressive language, and we need to address the core kernel of the system in isolation, first. We don’t need a lot of interfacing to foreign systems and in general we won’t care too much if the system we build is fast. Also, we usually won’t need to operate the prototype in production, since it is simply a proof of concept.
There are more interesting points about language design in part one and two of this broad reaching interview.
At work this past quarter, we painstakingly started three new projects at work. I say “painstakingly” because every project required decisions to be made around tooling depending on the scope & needs.
Ultimately, the problem is that by choosing React (and inherently JSX), you’ve unwittingly opted into a confusing nest of build tools, boilerplate, linters, & time-sinks to deal with before you ever get to create anything.
the React ecosystem have, largely, opted for discrete modularization at the cost of terse APIs by offloading their architectural underpinnings to the user and, as a result, worsen the developer experience in aggregate.
2016 will likely involve a serious, focused conjoining of projects, tools, and language features to merge the best and brightest packages/tools/boilerplates into more formalized projects. — Matt Keas in State of the Union.js
The approach that I now use for releasing code into the wild is governed by an approach called the “100:10:1 method,” a term coined by Nick Bentley.
I think I will give this a try.
One of Elm’s goals is to change our relationship with compilers. Compilers should be assistants, not adversaries. A compiler should not just detect bugs, it should then help you understand why there is a bug. It should not berate you in a robot voice, it should give you specific hints that help you write better code. Ultimately, a compiler should make programming faster and more fun!
I’ve enjoyed stretching my brain a bit whilst learning Elm. These changes will improve the learning process tremendously.
If you use PostgreSQL this is a nice native Mac OSX client.
A properly formed git commit subject line should always be able to complete the following sentence:
If applied, this commit will your subject line here
- If applied, this commit will refactor subsystem X for readability
- If applied, this commit will update getting started documentation
- If applied, this commit will remove deprecated methods
- If applied, this commit will release version 1.0.0
- If applied, this commit will merge pull request #123 from user/branch
The purpose of a self-imposed deadline is to sharpen the edge of your prioritization sword and stake a flag of coordination for the team. It’s not a hill to die on. It’s not a justification for weeks of death marching. It’s a voluntary constraint on scope.
Yes, deadlines are wonderful! They’re the tie-breaker on feature debates. They suck all the excess heat out of the prioritization joust: “Hey, I’d love to get your additional pet feature into the first release, but, you know: THE DEADLINE”.
A more recent and less fictitious example is electronic logging devices on trucks. These are intended to limit the hours people drive, but what do you do if you’re caught ten miles from a motel?
The device logs only once a minute, so if you accelerate to 45 mph, and then make sure to slow down under the 10 mph threshold right at the minute mark, you can go as far as you want.
So we have these tired truckers staring at their phones, bunny-hopping down the freeway late at night.
Of course there’s an obvious technical countermeasure. You can start measuring once a second.
Notice what you’re doing, though. Now you’re in an adversarial arms race with another human being that has nothing to do with measurement. It’s become an issue of control, agency and power.
You thought observing the driver’s behavior would get you closer to reality, but instead you’ve put another layer between you and what’s really going on.
These kinds of arms races are a symptom of data disease. We’ve seen them reach the point of absurdity in the online advertising industry, which unfortunately is also the economic cornerstone of the web. Advertisers have built a huge surveillance apparatus in the dream of perfect knowledge, only to find themselves in a hall of mirrors, where they can’t tell who is real and who is fake.
Facebook’s struggle pivoting to mobile illustrates the potential for trouble. Facebook’s speedy, individualistic and iterative way of designing and shipping software was deeply embedded in product team culture. If you worked in the web tier, the cost of releasing was pretty close to zero and literally everything else about the way you worked was optimized to take advantage of that assumption. As the company’s focus shifted to native mobile apps, the engineers hired for their mobile expertise insisted on a heretofore unknown process like feature and UI freeze, functional specs and QA.
PSYC 4410: Obsessions of the Programmer Mind
Identify and understand tangential topics that software developers frequently fixate on: code formatting, taxonomy, type systems, splitting projects into too many files. Includes detailed study of knee-jerk criticism when exposed to unfamiliar systems.
During internal testing, his team realized that if you don’t recognize a single artist in a playlist, you might question if it’s actually geared for you. That’s why the playlist is intended to have a mix of mostly new tracks with a few songs you’ve heard before.
“There’s something compelling about this humans versus robots narrative: a lovingly curated playlist versus an algorithm screwing up your sexy time,” says Ogle. “That whole distinction no longer really describes how we work. Discover Weekly is humans all the way down. Every single track that appears in Discover Weekly is because other humans being have said, ‘Hey this is a good song, and here’s why.’”
My Discover Weekly playlist has been hitting the spot and it’s interesting to get an insight into how they are put together.
Although Spotify has had humans making playlists for years, its efforts got a major boost last year with the introduction of Truffle Pig, an internal tool from The Echo Nest that breaks music down into thousands of categories like “wonky,” “chillwave,” “stomp and holler,” or “downtempo.”
What you hear from everyone at Spotify is that humans using data insights are key to curating music on a large scale. Naturally, they’re also using data to evaluate how well playlists are working.
An example of data-centric tools aiding human decision makers.
This implies that the problem with enterprises is not the stupidity of its buyers. They are no less smart than the average person–in fact, they are as smart with their personal choices for computing as anybody. The problem is that enterprises have a capital use and allocation model which is obsolete. This capital decision process assumes that capital goods are expensive, needing depreciation, and therefore should be regulated, governed and carefully chosen. The processes built for capital goods are extended to ephemera like devices, software and networking.
It does not help that these new capital goods are used to manage what became the most important asset of the company: information. We thus have a perfect storm of increasingly inappropriate allocation of resources to resolving firms’ increasingly important processes. The result is loss of productivity, increasingly bizarre regulation and prohibition of the most desirable tools.
If you think about innovation as a scarce resource, it starts to make less sense to be on the front lines of innovating on databases. Or on programming paradigms.
The point isn’t really that these things can’t work. Of course they can work. But exciting new technology takes a great deal more attention to work than boring, proven technology does.
Some fantastic points about what your job is when building software and the trade-offs that are constantly in front of you.
The best — maybe the only? — real, direct measure of “innovation” is change in human behaviour. In fact, it is useful to take this way of thinking as definitional: innovation is the sum of change across the whole system, not a thing which causes a change in how people behave. No small innovation ever caused a large shift in how people spend their time and no large one has ever failed to do so.
This is the best definition for an overused term I’ve heard in a while.
The whole piece is an enthralling call to arms to a group of people building a product.
“Dashboard”, “Big Data”, “Data visualization”, “Analytics” — there’s been an explosion of people and companies looking to do interesting things with their data. I’ve been lucky to work on dozens of data-heavy interfaces throughout my career and I wanted to share some thoughts on how to arrive at a distinct and meaningful product.
I’ve been doing a lot of data-driven interface work over the last few years and the advice in this article is spot on.
It’s funny. I can’t count the number of times I’ve seen organizations with large monolithic software applications move toward SOA. It’s easy to think that we’ve learned a lesson in the industry and that services are just the new “best practice.” Surely we would’ve started with them if we were just starting development now. Actually, I think the truth is a bit more jarring. Different architectures work at different scales. Maybe we need to recognize that and understand when to leap from one to another.
The process space has similar issues. If we cargo-cult anything from the small team space into the large organization space, we’re falling prey to a blind spot. We’re missing an opportunity to re-think things and figure out what works best for small teams, interacting teams, and large-scale development. They may be quite different things.
I agree that the rules are different for different organisation sizes. I like Michael’s comparison of moving across organisation sizes to state transitions in physics.
At Grammarly, the foundation of our business, our core grammar engine, is written in Common Lisp. It currently processes more than a thousand sentences per second, is horizontally scalable, and has reliably served in production for almost 3 years.
Haskell isn’t a common choice for large production systems like Sigma, and in this post, we’ll explain some of the thinking that led to that decision. We also wanted to share the experiences and lessons we learned along the way. We made several improvements to GHC (the Haskell compiler) and fed them back upstream, and we were able to achieve better performance from Haskell compared with the previous implementation.
Terminology for iOS is based on WordNet, a great semantic lexical reference. We do not offer a full Mac app for Terminology, but have prepared a dictionary using this same great data for use in the built-in OS X Dictionary app.
So, interesting: an enormous amount of effort is spent on apps that convert map/vector-structured data into map/vector-structured data.
However, because of the mindshare dominance of object-oriented programming that came because of Java - “Smalltalk for the masses” - it’s hard for most people to think of an approach other than a pipeline of structured data -> objects -> structured data. The tools and frameworks and languages push you toward that. So: a programming model tailored to objects with serious lifetimes has been used for data that lasts only for one HTTP request.
We’ve given up on “fat models, skinny controllers” as a design style for our Rails apps—in fact we abandoned it before we started. Instead, we factor our code into special-purpose classes, commonly called service objects. We’ve thrashed on exactly how these classes should be written, so this post is going to outline what I think is the most successful way to create a service object.
Some good advice for building service objects in Rails.
As of today Front Row uses Haskell for anything that needs to run on a server machine that is more complex than a 20 line ruby script. This includes most web services, cron-driven mailers, command-line support tools, applications for processing and validating content created by our teachers and more. We’ve been using Haskell actively in production since 2014.
An experience report on using Haskell in production.
In this talk, I will explain to you how list folds work using an explanation that is very easy to understand, but most importantly, without sacrificing accuracy.
I particularly like the constructor replacement analogy for foldr.
- Recovery over Perfection
- Predictability over Commitment
- Safety Nets over Change Control
- Collaboration over Handoffs
Ultimately all these statements are about creating responsive systems.
When we design processes that attempt to corral reality into a neat little box, we set ourselves up for failure. Such systems are brittle. We may feel in control, but it’s an illusion. The real world is not constrained by our imagined boundaries. There are surprises just around the corner.
I have told this story to different audiences. Programmers as a rule are delighted by it and managers, invariably, get more and more annoyed as the story progresses; true mathematicians, however, fail to see the point.
The Lisp model is that programming is a more general kind of interaction with a machine. The act of describing what you want the machine to do is interleaved with the machine actually doing what you have described, observing the results, and then changing the description of what you want the machine to do based on those observations. There is no bright line where a program is finished and becomes an artifact unto itself.
Having deployed a variety of Ruby apps (Rails and non-Rails) over the course of many years, here are some lessons I’ve learned to keep things afloat.
- In Rust, as in garbage collected languages, you never explicitly free memory
- In Rust, unlike in garbage collected languages, you never explicitly close or release resources like files, sockets and locks
- Rust achieves both of these features without runtime costs (garbage collection or reference counting), and without sacrificing safety.
This post contains a nice summary of Rust’s ownership model.