- Understanding Understanding Source Code with Functional Magnetic Resonance Imaging (PDF) — we observed 17 participants inside an fMRI scanner while they were comprehending short source-code snippets, which we contrasted with locating syntax error. We found a clear, distinct activation pattern of five brain regions, which are related to working memory, attention, and language processing. I’m wary of fMRI studies but welcome more studies that try to identify what we do when we code. (Or, in this case, identify syntax errors—if they wanted to observe real programming, they’d watch subjects creating syntax errors) (via Slashdot)
- Oobleck Security (O’Reilly Radar) — if you missed or skimmed this, go back and reread it. The future will be defined by the objects that turn on us. 50s scifi was so close but instead of human-shaped positronic robots, it’ll be our cars, HVAC systems, light bulbs, and TVs. Reminds me of the excellent Old Paint by Megan Lindholm.
- Google Readying Android Watch — just as Samsung moves away from Android for smart watches and I buy me and my wife a Pebble watch each for our anniversary. Watches are in the same space as Goggles and other wearables: solutions hunting for a problem, a use case, a killer tap. “OK Google, show me offers from brands I love near me” isn’t it (and is a low-lying operating system function anyway, not a userland command).
- Most Winning A/B Test Results are Illusory (PDF) — Statisticians have known for almost a hundred years how to ensure that experimenters don’t get misled by their experiments […] I’ll show how these methods ensure equally robust results when applied to A/B testing.
Getting apps into the store is a non-deterministic process
One of the major topics of my Enterprise iOS book is how to plan release schedules around Apple’s peril-filled submission process. I don’t think you can count yourself a truly bloodied iOS dev until you’ve gotten your first rejection notice from iTunes Connect, especially under deadline pressure.
Traditionally, the major reasons that applications would bounce is that the developer had been a Bad Person. They had grossly abused the Human Interface standards, or had a flakey app that crashed when the tester fired it up, or used undocumented internal system calls. In most cases, the rejection could have been anticipated if the developer had done his homework. There were occasional apps that got rejected for bizarre reasons, such as perceived adult content, or because of some secret Apple agenda, but they were the rare exception. If you followed the rules, your app would get in the store.
Can explanation contribute to technology creation?
“If you’re explaining, you’re losing.”
That gem of political wisdom has always been hard for me to take, as, after all, I make my living at explaining technology. I don’t feel like I’m losing. And yet…
It rings true. It’s not that programs and devices shouldn’t need documentation, but rather that documentation is an opportunity to find out just how complex a tool is. The problem is less that documentation writers are losing when they’re explaining, and more that creators of software and devices are losing when they have to settle for “fix in documentation.”
I was delighted last week to hear from Doug Schepers of webplatform.org that they want to “tighten the feedback loop between specification and documentation to make the specifications better.” Documentation means that someone has read and attempted to explain the specification to a broader audience, and the broader audience can then try things out and add their own comments. Writing documentation with that as an explicit goal is a much happier approach than the usual perils of documentation writers, trapped explaining unfixable tools whose creators apparently never gave much thought to explaining them.
It’s not just WebPlatform.org. I’ve praised the Elixir community for similar willingness to listen when people writing documentation (internal or external) report difficulties. When something is hard to explain, there’s usually some elegance missing. Developers writing their own documentation sometimes find it, but it can be easier to see the seams when you aren’t the one creating them.
Remember, even a failure can serve as an example of what not to do
The first highly visible component of the Affordable Health Care Act launched this week, in the form of the healthcare.gov site. Theoretically, it allows citizens, who live in any of the states that have chosen not to implement their own portal, to get quotes and sign up for coverage.
I say theoretically because I’ve been trying to get a quote out of it since it launched on Tuesday, and I’m still trying. Every time I think I’ve gotten past the last glitch, a new one shows up further down the line. While it’s easy to write it off as yet another example of how the government (under any administration) seems to be incapable of delivering large software projects, there are some specific lessons that developers can take away.
Cryptanalysis Tools, Renaissance Hackers, MakerCamp Review, and Visual Regressions
- bletchley (Google Code) — Bletchley is currently in the early stages of development and consists of tools which provide: Automated token encoding detection (36 encoding variants); Passive ciphertext block length and repetition analysis; Script generator for efficient automation of HTTP requests; A flexible, multithreaded padding oracle attack library with CBC-R support.
- Hackers of the Renaissance — Four centuries ago, information was as tightly guarded by intellectuals and their wealthy patrons as it is today. But a few episodes around 1600 confirm that the Hacker Ethic and its attendant emphasis on open-source information and a “hands-on imperative” was around long before computers hit the scene. (via BoingBoing)
- Maker Camp 2013: A Look Back (YouTube) — This summer, over 1 million campers made 30 cool projects, took 6 epic field trips, and met a bunch of awesome makers.
- huxley (Github) — Watches you browse, takes screenshots, tells you when they change. Huxley is a test-like system for catching visual regressions in Web applications. (via Alex Dong)
Retreading old topics can be a powerful source of epiphany, sometimes more so than simple extra-box thinking. I was a computer science student, of course I knew statistics. But my recent years as a NoSQL (or better stated: distributed systems) junkie have irreparably colored my worldview, filtering every metaphor with a tinge of information management.
Lounging on a half-world plane ride has its benefits, namely, the opportunity to read. Most of my Delta flight from Tel Aviv back home to Portland lacked both wifi and (in my case) a workable laptop power source. So instead, I devoured Nate Silver’s book, The Signal and the Noise. When Nate reintroduced me to the concept of statistical overfit, and relatedly underfit, I could not help but consider these cases in light of the modern problem of distributed data management, namely, operators (you may call these operators DBAs, but please, not to their faces).
When collecting information, be it for a psychological profile of chimp mating rituals, or plotting datapoints in search of the Higgs Boson, the ultimate goal is to find some sort of usable signal, some trend in the data. Not every point is useful, and in fact, any individual could be downright abnormal. This is why we need several points to spot a trend. The world rarely gives us anything clearer than a jumble of anecdotes. But plotted together, occasionally a pattern emerges. This pattern, if repeatable and useful for prediction, becomes a working theory. This is science, and is generally considered a good method for making decisions.
On the other hand, when lacking experience, we tend to over value the experience of others when we assume they have more. This works in straightforward cases, like learning to cook a burger (watch someone make one, copy their process). This isn’t so useful as similarities diverge. Watching someone make a cake won’t tell you much about the process of crafting a burger. Folks like to call this cargo cult behavior.
How Fit are You, Bro?
You need to extract useful information from experience (which I’ll use the math-y sounding word datapoints). Having a collection of datapoints to choose from is useful, but that’s only one part of the process of decision-making. I’m not speaking of a necessarily formal process here, but in the case of database operators, merely a collection of experience. Reality tends to be fairly biased toward facts (despite the desire of many people for this to not be the case). Given enough experience, especially if that experience is factual, we tend to make better and better decisions more inline with reality. That’s pretty much the essence of prediction. Our mushy human brains are more-or-less good at that, at least, better than other animals. It’s why we have computers and Everybody Loves Raymond, and my cat pees in a box.
Imagine you have a sufficient amount of relevant datapoints that you can plot on a chart. Assuming the axes have any relation to each other, and the data is sound, a trend may emerge, such as a line, or some other bounding shape. A signal is relevant data that corresponds to the rules we discover by best fit. Noise is everything else. It’s somewhat circular sounding logic, and it’s really hard to know what is really a signal. This is why science is hard, and so is choosing a proper database. We’re always checking our assumptions, and one solid counter signal can really be disastrous for a model. We may have been wrong all along, missing only enough data. As Einstein famously said in response to the book 100 Authors Against Einstein: “If I were wrong, then one would have been enough!”
Database operators (and programmers forced to play this role) must make predictions all the time, against a seemingly endless series of questions. How much data can I handle? What kind of latency can I expect? How many servers will I need, and how much work to manage them?
So, like all decision making processes, we refer to experience. The problem is, as our industry demands increasing scale, very few people actually have much experience managing giant scale systems. We tend to draw our assumptions from our limited, or biased smaller scale experience, and extrapolate outward. The theories we then tend to concoct are not the optimal fit that we desire, but instead tend to be overfit.
Overfit is when we have a limited amount of data, and overstate its general implications. If we imagine a plot of likely failure scenarios against a limited number of servers, we may be tempted to believe our biggest odds of failure are insufficient RAM, or disk failure. After all, my network has never given me problems, but I sure have lost a hard drive or two. We take these assumptions, which are only somewhat relevant to the realities of scalable systems and divine some rules for ourselves that entirely miss the point.
In a real distributed system, network issues tend to consume most of our interest. Single-server consistency is a solved problem, and most (worthwhile) distributed databases have some sense of built in redundancy (usually replication, the root of all distributed evil).
Velocity 2013 Speaker Series
If you’re a System Administrator, you’re likely all too familiar with the 2:35am PagerDuty alert. “When you roll out testing on your infrastructure,” says Seth Vargo, “the number of alerts drastically decreases because you can build tests right into your Chef cookbooks.” We sat down to discuss his upcoming talk at Velocity, which promises to deliver many more restful nights for SysAdmins.
Key highlights from our discussion include:
- There are not currently any standards regarding testing with Chef. [Discussed at 1:09]
- A recommended workflow that starts with unit testing [Discussed at 2:11]
- Moving cookbooks through a “pipeline” of testing with Test Kitchen [Discussed at 3:11]
- In the event that something bad does make it into production, you can roll back actual infrastructure changes. [Discussed at 4:54]
- Automating testing and cookbook uploads with Jenkins [Discussed at 5:40]
You can watch the full interview here:
User research you can do now
There’s a lot of advice about how to do great user research. I have some pretty strong opinions about it myself.
But, as with exercise, the best kind of research is the kind that you actually DO.
So, in the interests of getting some good feedback from your users right now, I have some suggestions for Tiny Tests. These are types of research that you could do right this second with very little preparation on your part.
What is a Tiny Test?
Tiny Tests do not take a lot of time. They don’t take a lot of money. All they take is a commitment to learning something from your users today.
Pick a Tiny Test that applies to your product and get out and run one right now. Oh, ok. You can wait until you finish the post.
Dozens of companies now exist that allow you to run an unmoderated test in a few minutes. I’ve used UserTesting.com many times and gotten some great results really quickly. I’ve also heard good things about Loop11 and several others, so feel free to pick the one that you like best.
Regular Expressions, Mobile Diversions, UX Pitfalls, and DIY Keyboarding
- RE2: A Principled Approach to Regular Expressions — a regular expression engine without backtracking, so without the potential for exponential pathological runtimes.
- Mobile is Entertainment (Luke Wroblewski) — 79% of mobile app time is spent on fun, even as desktop web use is declining.
- Five UX Research Pitfalls (Elaine Wherry) — I live this every day: Sometimes someone will propose an idea that doesn’t seem to make sense. While your initial reaction may be to be defensive or to point out the flaws in the proposed A/B study, you should consider that your buddy is responding to something outside your view and that you don’t have all of the data.
- Building a Keyboard: Part 1 (Jesse Vincent) — and Part 2 and general musings on the topic of keyboards. Jesse built his own. Yeah, he’s that badass.