Method to super-boost productivity

In the practical use of our intellect, forgetting is as important a function as remembering. ~ William James

Photo by Matt Ragland on Unsplash

We all have wardrobes full of clothes and still, we spend most of our time deciding what to wear. One of the ways to solve this is to group all the like things together, for instance, group shirts together along with jackets and sweaters together. Now you solve half of the problem, however, how do you still have another half of the problem, what to wear now?

Caching is one of the very fundamental concepts of computer science where we substantially improve the speed of a system using very sophisticated techniques where we kind of anticipate the output as soon as we receive the input.

Memory is expensive, it costs manufacturers a lot of resources to produce modern solid-state hard drives, and still with the level of complexities we have in our computers, engineers and computer scientists came up with very nice techniques to counter these challenges. First, we need hierarchies of memory to store things. For instance, you may need to store your personal expense report, you would do something like this:

Home > Personal > Expense Report > June

This way you can easily go to your expense report for each month. Even after implementing this technique, the computer still works slower. To solve this issue, in 1962, engineers came out with Caching in the first supercomputer called Atlas. The computer uses some clever algorithms to retrieve information faster, one of them called caching.

Caching: In laymen’s terms, caching is pre-processed input when input has not even been called. The advantage of caching is it saves time. When you can predict what task you are going to do next, you can prepare in advance for a particular task.

For instance, let say you go to the gym and you take your post-workout protein after the gym, one of the things you can do in advance is to prepare your meal/shake before or you can buy it from somewhere. You cached your post-workout meal beforehand and it saves time. This simple hack can greatly improve your system ( if you have one) and some estimates say it improves 20–30% of your system’s efficiency.

Now you can imagine you have a system of storing files in a box. What would be the most optimal and efficient way of storing files so it takes you the least amount of time to search a particular book?

One strategy is to order files alphabetically. However, imagine a library with many books, what would be the most efficient algorithm to find the book you are looking for? You may personally not have so many books but research shows fascinating results.

We know according to Pareto distribution, 80% of effects are caused by 20% cause. So at a given moment, only 20% of books will be actively read by people in a given library and the other 80% could just sit for a long time before anyone comes and pick it. So, it may be a better idea to create a place with those frequently used books and surprisingly, the order of stacking books doesn’t produce any significant improvement in finding a particular book. Books that are read frequently would always remain on top because as soon as they hit the stack, someone will again come and read them because they are already in high demand.

For instance, let say this is the current order of task ( or file name in a box ) you have,

A B C D and you take out C, now you won’t pick put it back at 3rd position but at 1st, the task will look now this:

C A B D

and after a while, you see B has been recently used, so remove it from the task.

C A D will be your current task now and so on.

By applying the same principle, you can arrange your cache of different tasks by removing the Least Recently Used item from the list of tasks and focusing on things that you use actively.

So coming to our original question, how should we arrange our clothes. Well, you can still group the like things together but on top of that, you should create your own little cache of clothes you wear most often. You can have two separate collections of clothes, one group-like, and another cache, and remove/add items as per their frequency of use.

Thank you for reading, you may also post your comments in case you want to add something to this essay.

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I write tutorials on Python, JavaScript, React, and Django. I write for InPlainEnglish and TheStartUp .

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Saranjeet Singh

Saranjeet Singh

I write tutorials on Python, JavaScript, React, and Django. I write for InPlainEnglish and TheStartUp .

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