Software architecture failing: tech writing is biased towards what the big ones do, which usually doesn’t fit most other contexts – but, who got fired for choosing IBM, right? Although I feel connected to this rant at an emotional level, I do think it’s necessary to elaborate more and make a positive contribution: help to create and spread that alternate history of software development. How do you do it? Hat tip: Fran.
On November 2016 I had a free month between jobs. Apart from some resting, reading, and general preparations for my new adventure, I still had quite a bit of free time to do new things or build good habits. It was while cleaning my office that I found a keyboard I had bought a couple of years back:
Its layout was a beautiful matrix -which is good for your fingers- and came with Dvorak by default. So it struck me: how about improving my typing during the coming weeks?
As a programmer, typing is an essential skill for me. I had been doing it for more than 15 years in a learn-by-doing way, and I plan to keep typing for years to come. I thought it would be fun to spend a couple of hours a day training in touch-typing and give Dvorak a second try. And so I did.
How it felt
Before I switched, I recorded about 15 typing sessions at TypeRacer using the QWERTY layout, which logs typing speed (words per minute) and accuracy (% characters right over the total). I was at 67 wpm and about 95% accuracy at the time.
Progress was very humbling at the beginning; it felt like learning to walk again, and I swear that, sometimes, I could even hear my brain circuits being reconfigured! After a few weeks, though, I was at 40 wpm and, by the end of the month, I was at 50 wpm. I stopped quantifying myself by then: as I started working, I had a lot of typing to do anyway.
During the first months, real-time communication -chat, slack- was the only moment I struggled and felt like perhaps the switch wasn’t a good idea. I don’t know what people thought of me, but my writing at the time was typing-bounded – I was certainly a very slow touch-typist by my own standards. But time passed and I improved.
Spáñish Dvorak and symbols
Throughout the process I changed my setup quite a bit:
- I started by using the Programmer Dvorak layout with a TypeMatrix keyboard.
- After a few months, I switched back to my good old ThinkPad keyboard because having to use a mouse again after years without it was painful.
- A few months later, I switched to the Dvorak international layout, because the Programmers Dvorak didn’t quite suit me.
- Then, I tweaked the common symbols I use for programming so they were more ergonomic for my daily tasks.
- Although the bulk of my typing is in English, I still need to write decent Spáñish, which basically means using tildes on vowels and ñ so I switched to the Spanish Dvorak.
- Finally, Spanish Dvorak wasn’t the improvement I was looking for, so I’ve ended up accommodating tildes, ñ, and other symbols in the Dvorak international as I see fit.
This is how my layout looks like today:
All these changes through the year have affected my ability to build muscle memory – sometimes I still need to look at some specific symbol on the keyboard. However, the current version has been unchanged for months, so I only need a bit more time for them to stick.
Performance to date
Given that I was a QWERTY user for 15 years, I thought I would give the new layout a year before comparing any numbers. The fair thing to do would be comparing after 15 years, but I’m a bit impatient for that. So I went to TypeRacer and noted down the results for about 20 races:
In terms of speed, it looks like I’m
In terms of accuracy, I’ve improved a bit. My median accuracy has increased by 1,5 points, and I had only 2 sessions below 95%.
My accuracy has improved, and having fewer mistakes to correct will help me become a faster typist as time passes. By learning to touch-type I also have grown more endurance.
This experiment was very humbling. I believe it increased my brain plasticity by an order of magnitude. Although I hope to improve my numbers, what’s
I spent the weekend reorganizing things, including my blog. I’ve got a new WordPress theme (independent publisher) which looks a lot more lightweight. I’ve consolidated the essays section with stuff that grew out of individual posts (I keep thinking that someday I’ll have the time to publish them as independent e-books), polished the about, fixed some links in the glossary, and started to reorganize the archives.
I’m also going to try a different approach in the following months: instead of having separate blogs for music, lifestream, thoughts, etc I’m going to publish everything here – I do not publish that much anyway, and I like the idea of this having a more personal touch.
When we want to acquire a new skill, we are faced with two choices: trial-error, or instruction. One is experience-driven or practice-based, the other is concept-driven or theory-based.
Trial-error is the built-in mechanism humans come with to acquire knowledge and skills – our thinking processes are optimized for that. However, it may be expensive and impractical in some situations. For instance, learning to pilot an aircraft by trial-error is risky should you want to keep the chances of learning in the future high. We have developed systems that lower the cost of trial-error, though, such as pilot simulators. It can also be time-consuming: we just don’t have the time to trial-error every piece of knowledge our society is based upon!
Learning by instruction appears to be more efficient: we are presented with models and recipes that work, saving us a lot of time that we can use to advance our knowledge further. Nevertheless, the instruction is not always possible; sometimes the map of knowledge of a certain domain isn’t built yet, so we need to rely on the trial-error approach. Even most important is the fact that internalizing abstract knowledge not based on direct experience seems to be more difficult for humans.
This poses a question: how shall we learn?
The Dreyfuss model
In February 1980, Dreyfuss brothers published a seminal paper on how to teach: «A five-stage model of the mental activities involved in directed skill acquisition». This work was supported by the US Air Force, which was interested in improving their training programs.
What they said is 1) we should recognize the role of the first-hand experience in acquiring knowledge and 2) to become an expert it is necessary to learn the rules, guidelines, and maxims of the particular skill we are interested in.
The rules are the principles that always apply, they don’t depend on anything so they are context-free or non-situational. Examples of rules are the valid movements of a piece in the go game, the set of instructions in programming, the techniques in the Aikido martial art.
The guidelines are the principles that only apply in specifics contexts, so they are context-bound or situational. Things like josekis in the go game (sequences of moves in a specific part of the board), the design patterns in programming, or the katas in Aikido.
The maxims are principles that guide us towards achieving our long-term goal, they help us by assigning a value to guidelines: is this joseki worth it if I’m playing for territory in go? Is the ability to grow new features necessary for this specific part of the application? What specific throw should I use if I want to face the next adversary?
For one to become an expert, rules, guidelines, and maxims should be second nature.
Dreyfuss defines a 5-step process someone goes through to gain knowledge: novice, competence, proficiency, expertise, mastery. Others outline different systems that include three stages. What’s important is to realize that the learning process is at its best when we take a practical approach and theory is presented to the learner as they are prepared to assimilate the next artifact – rules, guidelines, maxims.
Learning to learn is probably one of the more important skills when we no longer know what’s coming next. The real world TM tends to be more chaotic and intertwined than the sequential process outlined by Dreyfuss. Realizing where are you at a particular skill will help you in making decisions about what focus on. For instance, am I a novice at skill X? Well, at this point, I’m better off focusing on learning the rules and imitate what others have done. And so on.
Learning also takes a lot of time – someone has even published a number, about 10.000 hours to become an expert in anything. It’s a lot! It may be discouraging. Luckily, a practice-based approach makes things more rewarding, and time flies when we are enjoying the process.
Past Saturday, AMC aired Halt and Catch Fire season finale. I saw this tv-show grow over 4 seasons and I’m sad it’s over.
HACF resonated with me because it was about the pleasure of making things work and the cost of pursuing your dreams. We need a whole lot more stories about the woes and joys of creation to learn how to navigate that world and to inspire us. We need more builders and dreamers capable of not burning themselves out.
Bonus points for using the evolution of computers as the McGuffin. But, as much as I liked the history of computers being the central plot of a well done period drama, HACF wasn’t about computers. The computers aren’t the thing. They are the thing that get us to the thing.
This was the first book listening experience that I’ve actually finished. Sean Runnette‘s voice was adequate for setting the tone and rhythm – actually, sometimes I felt I was listening to Feinmann himself!
Having read Surely You’re Joking, Mr. Feynman!, What Do You Care What Other People Think? and some other papers/videos, most of the stories in the book I already knew, but it had some new material that made it interesting nonetheless. This is more mathematical/physical intense than the others, probably because it’s mostly focused on the scientific and less in the human Feynman – but also because many chapters are directly transcribed from conferences he gave. It’s also worth noting that, unlike the other two, this book was published without Feynmann intervention: it’s published 10 years after his death.
If I had to choose only a Feynman book I’d choose Surely You’re Joking, Mr. Feynmann! It’s better edited and has more variety. Then, if you are hungry for more, What do you care what other people think? contains new stories. I liked this one, but I doubt it’s a good introduction to Feynmann lifestyle, work, values, and character.
I’ve just finished the book Code Simplicity. It presents a framework for thinking about software development in the form of laws and rules. It’s short but comprehensive. From my experience, the laws and rules hold true. I think the book has value as an overall perspective of what’s important in software development, and there are some chapters that are really spot on: for example, the equation of software design – something that I’ve already included in my glossary and plan to expand.
Code Simplicity doesn’t intend to land the laws and rules to something actionable, though. I’m at a point in my career where I’m focused on consolidating and reflecting upon how to achieve simplicity in software design – that means that I crave for specifics so I can compare them with mine.
As a cross-recommendation, if you are interested in learning about the laws of software development in a manner that is actionable, I’d suggest reading the Beck’s trilogy: Extreme Programming Explained: Embrace Change, Test Driven Development: by example, and Implementation Patterns. Those three books make a great combination of macro-forces (at a project level) and micro-forces (at a coding level) in software design. They were fundamental in consolidating my experiences as a programmer, so I’m highly biased towards them.
Hat tip for the Code Simplicity recommendation: Nikolay.
«Technology is neither good nor bad; nor is it neutral.»
One of the things I was very into a decade ago was studying the intertwine between technology, culture, and society. From those years, I developed a sensitivity about my role as an engineer, or as an enabler of possible worlds.
This is one of the things I wanted to avoid:
If you have ever had a problem grasping the importance of diversity in tech and its impact on society, watch this video pic.twitter.com/ZJ1Je1C4NW
— Chukwuemeka Afigbo (@nke_ise) August 16, 2017
A person isn’t able to clean his hands because the machine sensors are only prepared to detect white hands! That’s a horror story that could make a BlackMirror episode.
This made me think about the mainstream perception of Machine Learning and Artificial Intelligence technology. Lately, some friends of mine are sharing with me clickbait news like Facebook shuts down robots after they invent their own language. They ask me if robots could take over, soon. Well, I can tell you something: at this stage of technology, I am not worried about robots taking over. What I do worry about is how our inability to understand technology creates racists algorithms that reinforce our biases.