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The Japanese world view of wabi-sabi is quite powerful. It allows us to accept imperfections, even better, to be open about imperfections. Whether it be sin, mortality or suffering imperfection is part of this world. Hardly anything man-made is perfect. Interestingly enough, I was browsing through the internet for some good chef’s knives for my wife. I found Japanese knives to be highly rated, but not as attractive as the German ones. May be my naivety, but what struck me immediately is the form and aesthetics that follows function.

I look at my creations, the stuff that I build at my work – mainly software. There are imperfections, and often there is an attempt to wrap those around with nicer elements. Imposing designs, imperfect implementations, and impressive case studies! Contrast it with wabi-sabi – restrained design, simple (and imperfect) implementations, and realistic case studies. That may not make sense to marketing, but it is gratifying (well! for me at least). One of the interesting aspect of wabi-sabi is that, if you make it a movement the essence will be lost. It is to be appreciated, assimilated and practiced, and that’s all.

I have got a new weather app. It is so well-designed that I do not mind checking it daily. One of the features I like is the hourly track of temperature for 36 hours. After getting used to it for a while, I started noticing the daily patterns in temperature.

One of the interesting observations is that the coldest hour comes around 4:00 and 5:00 in the morning. Just before the sun rises. When the night has lost all it’s hope about the day. And then the sun slowly rises, and the cold disappears. The dew slowly melts away and everything is fresh and new.

These days when I get into hopeless situations, I am reminded of the dark and coldest hour. And about the sun, slowly rising in the horizon, about a bright new day, a few hours away.

I always fancied the world of architecture. No, not the software architecture, the real one. While I admire the work of architects, my training as a civil engineer prompts me to look critically into the possibilities and feasibilities of architectural creations.

At the end of my engineering course, I took up an assignment to architect a house, under the guidance of a professor. I derived many valuable lessons from that experience, and one of those lessons is walking through the plans. My guide taught me to mentally walk-through the house after the plan is drafted. I was asked to take a good look at the plan, close my eyes, and enter into the house – constructed in my mind. I should walk through every door, into every room, open every window, lie on every bed, sit on the sofa and feel the finished house. Get the final picture from the plan. Find out what is good and what isn’t.

Strangely, I have not done much in civil engineering after that. But that lesson still helps in my career in information technology. It helps to visualize the final software system right from the architectural details and drawings. Mentally walking through every layer, every module and every screen. Trying to find out what works and what may not.

At times, I think of managing a project as driving a car. The size of the project is the distance to cover, time is time, and effort is the fuel in the tank. The faster we do the work – the faster the fuel burns – the faster we reach the destination. Of course, provided we are traveling in the right direction.

The interesting aspect is that there is usually an optimum speed for driving. If we deviate from that we end up burning more fuel than required.

Sometimes we are forced to drive faster to reach the destination quickly. Sometimes we get into the car, stamp on the pedal and imagine that it is a rocket. Sometimes we put just enough fuel in the car for a long drive. Sometimes we do not put enough!

We live with mathematical models all around us, whether we realize it or not. We apply them at work as if those are deterministic formulae. One such approach is the application of normal distribution which is a common choice for modeling large number of independent or random variables.

Models based on normal distribution is almost considered like a universal truth and we apply it even when the variables are not really random; or even the sample size is clearly small. It may be complexities of work products, productivity figures, appraisal ratings – taken at micro level.

Luckily, I see another normal distribution governing all these. It is the fact that the random errors also follow a normal distribution. So while we apply the such models erroneously, without any obvious bias, the errors align to a normal distribution. And chances of us going grossly wrong, either way, happen to be far less than somehow getting it through. We also use our will, might and a lot of other factors, which were beyond the scope of original model, to get our results established – seemingly validating the model behavior, and passing it on as knowledge.

The world’s knowledge is layered.

There are industries that make metal, industries that make airplanes using that metal, and industries that operate airplanes. The travellers on these airplanes are in turn doing something else – producing and servicing various things across the world. There are people who build operating systems, people who build software tools, people who make applications using those tools and people who use applications and run businesses.

There is hardly anyone who knows all the layers in a single supply-chain stack very well. But it may be a good idea to look into those adjacent layers to understand our own layer well.

The other day I met a software engineer who was struggling with a new software tool. He knew how to operate in the software tool layer, but this turned out to be a little different than the rest. Having no understanding about how tools are built, he kept trying to fit the new tool into his mental image on similar tools. It wouldn’t fit. We then looked a bit closely, just to find that the tool had all the required features. It operates the same way as other tools, from a much lower perspective.

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