If you use PostgreSQL this is a nice native Mac OSX client.

Chris Beams:

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

For example:

• 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

DHH:

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”.

Maciej Cegłowski:

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.

Paul Irish:

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout. This is also called reflow or layout thrashing, and is common performance bottleneck.

Jocelyn Goldfein:

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.

James Hague:

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.

Alex Heath:

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.

Asymco:

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.

Dan McKinley:

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.

Stuart Butterfield:

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.

Ville Immonen:

JavaScript is evolving and new ES2015 and ES2016 editions (previously known as ES6 and ES7, respectively) pack a bunch of new features and Babel makes it very easy to use of them today. These features make some previously essential functions from utility libraries obsolete.

Truth Labs:

“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.

Michael Feathers:

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.

Vsevolod Dyomkin:

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.