Every technical person knows, unlike artists and politicians, that they can be provably wrong; at least to a degree. Math tells the truth. Coupled with this knowledge is an ego which is often entirely based on our output. If our mechanism works, we feel good because we are provably good.
Unfortunately, unlike the robots we build or the simple minds we spin out of code, we are still human at the end of the day. When we feel the sting of being wrong we often respond poorly. Some of us slip into depression, claiming it all and dredging up a few other mistakes from our past along for the ride. Some of us explode into prideful rages, dropping our metaphorical shorts to show that this one fault is no fault at all compared to a history of personal majesty. Others become sullen and inward. Others ignore it all together. Others yet strike out at those around them leaving unpleasant barbs. The variations are endless, but I do think there is an ideal to be reached.
Despite the risk that the nature of the things I’ve learned will reveal exactly what kind of arrogant sod I am, I’ll give it a go anyway. I’ve made many mistakes, and I have many more to make, but these are some of the things I’ve learned. I’ve learned them all in technical fields, so I’m not sure how broadly the advice applies, but luckily this is Hackaday.
You sit there, irritation bubbling deep within as minute forty-five of the meeting ticks past on the clock in the corner of the office. Fight or flight is in a contest with your attention span as you struggle to keep an interested look on your face while they drone on. Real work could be done in this time. Maybe if you go to the bathroom you could sort of… fast forward the meeting. Panicked thinking continues for a bit until your awareness snaps back to the babble of words in the room.
“How long will it take you to do this?” the manager asks.
“A couple of days maybe?” You reply in turn. The manager nods and you take your escape. Little do you know that you have failed.
The project swerves out of control. Two days on the dot the manager is there expecting results. How? How did this happen again? It felt right! Two days is all you’d need to do such a simple project. It ended up taking a week.
The next meeting you say two weeks just to be sure. Everyone nods gravely, upset that something would take so long, but the work must be done. Two days later you sheepishly wander into the manager’s office with a completed project. He looks pleased but confused. The next meeting, he insists that you can do it in half the time. You and your fragile pride bowl ahead only to deliver late. The mystery!
This was my life until I started bugging the more experienced around me. I learned a lot from them and I ended up distilling it down into a few rules.
There Is No Other Unit Than Hours
Promise a Range. Give a Deadline.
Why does someone want a time estimate? What are they going to do with this information? When working on a contract job it often feels like sticking a foot in a trap when a time estimate is given. Are they going to hold me to this? What if it goes wrong? After all, we are not fortune tellers. Unless the manager is extremely bad or you show yourself to be extremely lax in your duties, it is unlikely that a time estimate will be used against you.
Back in the mid 1980’s I worked at a company called Commodore Business Machines, a company that made home computers where our annual Superbowl was the Consumer Electronics Show in Las Vegas the first week in January.
Some time in November a Datsun Z would get parked in the front lot and then not move until whatever snow mounds that got plowed over it melted sometime in early spring. Ultimately I would have it towed leaving behind a sad little pile of rust and nuts and bolts. With a bonus check in hand for finishing the newest computer on time I would go buy another used Z and repeat the cycle.
Climate Change and Rust
These days the old Datsun Z’s; 240Z, 260Z, 280Z, 280ZX, are somewhat rare, probably because they were real rust buckets even when new. After having sacrificed a few myself in search of the next home computer I set out to rescue one for old times’ sake. I really did love the car so I made it my project to restore one. Now I have a total of three Z carcasses, an engine, and a transmission all sitting out back and an almost finished Z in the garage.
Since I had torn the engine down to its bare components I took the opportunity to make some changes: increased the size of the turbocharger, increased bore and stroke of the cylinder/piston, improved the fuel distribution, and improved the flow of air with things like porting the heads and an inter-cooler.
Richard Feynmann noted more than once that complementarity is the central mystery that lies at the heart of quantum theory. Complementarity rules the world of the very small… the quantum world, and surmises that particles and waves are indistinguishable from one other. That they are one and the same. That it is nonsensical to think of something, or even try to visualize that something as an individual “particle” or a “wave.” That the particle/wave/whatever-you-want-to-call-it is in this sort of superposition, where it is neither particle nor wave. It is only the act of trying to measure what it is that disengages the cloaking device and the particle or wave nature is revealed. Look for a particle, and you’ll find a particle. Look for a wave instead, and instead you’ll find a wave.
Complementarity arises from the limits placed on measuring things in the quantum world with classical measuring devices. It turns out that when you try to measure things that are really really really small, some issues come up… some fundamental issues. For instance, you can’t really know exactly where a sub-atomic particle is located in space. You can only know where it is within a certain probability, and this probability is distributed through space in the form of a wave. Understanding uncertainty in measurement is key to avoiding the disbelief that hits you when thinking about complementarity.
This article is a continuation of the one linked above. I shall pick up where I left off, in that everyone agrees that measurement on the quantum scale presents some big problems. However, not everyone agrees what these problems mean. Some, such as Albert Einstein, say that just because something cannot be measured doesn’t mean it’s not there. Others, including most mainstream physicists, say the opposite — that if something cannot be measured, it for all practical purposes is not there. We shall continue on our journey by using modern technology to peer into the murky world of complementarity. But first, a quick review.
Resistor: A passive chunk of material that resists the flow of electrical current. A terminal is connected to each end you’re done. What could be simpler?
It turns out it’s not so simple at all. Temperature, capacitance, inductance and other factors all play a part in making the resistor a rather complex component after all. Even its uses in circuits are many, but here we’ll just focus on the different types of fixed-value resistors, how they’re made, and what makes them desirable for different applications.
Let’s start with a simple one, and one of the oldest.
However you sell your kits online, you’ll have to find a means of shipping them to the customer. For an online operation this unseen part of the offering is more important than any other when it comes to customer satisfaction, yet so many large players get it so wrong.
This is the final article in a series looking on the process of creating and selling a commercial kit from a personal electronic project (read all the posts in this series). We’ve looked at the market, assembling the kit and its instructions, and how to set up an online sales channel. In this part we’ll look at what happens when you’ve made the sale, how to get it safely to the customer and how to keep the customer happy after the sale by offering support for your products. We’ll also give a nod to marketing your site, ensuring a fresh supply of customers.
Evolution is one clever fellow. Next time you’re strolling about outdoors, pick up a pine cone and take a look at the layout of the bract scales. You’ll find an unmistakable geometric structure. In fact, this same structure can be seen in the petals of a rose, the seeds of a sunflower and even the cochlea bone in your inner ear. Look closely enough, and you’ll find this spiraling structure everywhere. It’s based on a series of integers called the Fibonacci sequence. Leonardo Bonacci discovered the sequence while trying to figure out how many rabbits he could make starting with just two. It’s quite simple — add the right most integer to the previous one to get the next one in the sequence. Starting from zero, this would give you 0-1-1-2-3-5-8-13-21 and so on. If one was to look at this sequence in the form of geometric shapes, they can create square tiles whose sides are the length of the value in the sequence. If you connect the diagonal corners of these tiles with an infinite curve, you end up with the spiral that you saw in the pine cone and other natural objects.
So how did mother nature discover this geometric structure? Surely it does not know math. How then can it come up with intricate and sophisticated structures? It turns out that this Fibonacci spiral is the most efficient way of squeezing the most amount of stuff in the least amount of space. And if one takes natural selection seriously, this makes perfect sense. Eons of trial and error to make the most copies of itself has stumbled upon a mathematical principle that permeates life on earth.
The homo sapiens brain is the product of this same evolutionary process, and has been evolving for an estimated 7 million years. It would be foolish to think that this same type of efficiency natural selection has stumbled across would not be present in the current homo sapiens brain. I want to impress upon you this idea of efficiency. Natural selection discovered the Fibonacci sequence solely because it is the most efficient way to do a particular task. If the brain has a task of storing information, it is perfectly reasonable that millions of years of evolution has honed it so that it does this in the most efficient way possible as well. In this article, we shall explore this idea of efficiency in data storage, and leave you to ponder its applications in the computer sciences.