Intuitive Introduction to Big O

The post is going to be an intuitive introduction to Big O notation. I am not going to be rigorous or mathematical in my approach.

Big O indicates how an algorithm behaves when the size of the input changes. The behavior might be either the computation time or the storage required for computation. What Big O tells is how the storage or computation time scales with the change in input.

The above is an intuitive definition of Big O.

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A pivotal point to keep in mind is that Big O talks of change; it does not talk about specifics, i.e., Big O does not tell this algorithm will execute in 1 ms or 1 Mb of space is required for execution.

Let us understand Big O with a non-programmatic example.

There are lots of tennis balls piled up in a court. You have to devise an algorithm to move them to the clubhouse.

One approach is to pick a ball. Take it to the clubhouse and drop it there. Do the same with another ball. Repeat until there are no more balls left.

If there are n balls in the court, you have to go back and forth between the court and the clubhouse n times, O(n) is the Big O of this approach. As the number of balls increases, the work required to clean the court increases. If there are too many balls, with this algorithm, you might end up doing the task the whole day.

If you boil it down, what O(n) tells us is that the task increases linearly with the input, in this case, the number of balls in the court.

Another option is to collect all the balls in a bag in one go1. Take the bag to the clubhouse; this is a constant time algorithm. Since one is not repeatedly going back and forth for each ball; the task does not scale with the number of balls.

When trying to deduce the Big O of an algorithm, figure out the effect of input on storage space or computation.


Why do we need Big O?

Big O has no significance when the input is small. For small input sizes, the efficiency of an algorithm does not make a difference — the effectiveness of an algorithm matters when the input size is large.

Coming back to our example, let us assume we are dim-witted and have institutionalized the back and forth algorithm. We have not bothered to figure out the Big O of the approach. Days pass by; there are at most a couple of balls in the court, and cleaning goes on nicely. One beautiful morning, there are ten thousand balls in the court, and we are in for an unpleasant surprise – we spend hours cleaning the court.

If we had bothered to figure out the Big O of our cleaning algorithm, we could have avoided this surprise.

When someone talks of Big O, they are usually talking about the worst-case complexity.

If the majority of the members of our tennis club are well behaved and do not leave behind balls in the court, then we would not have a tough time cleaning the court. But while calculating the Big O of the algorithm, we assume the worst case, i.e., no one cleans the court after their game, everyone leaves all the balls behind.

While calculating Big O, we do not take into account any of the constant time work done as part of the algorithm. In the first approach of cleaning the balls, if the person takes a break when she is midway through, Big O of the algorithm does not change; it is still O(n).


Reinforcing the critical point, Big O is the relationship between the algorithm and the input; Big O only cares how the efficiency of the algorithm varies with the change in input. The break the person takes does not vary with the input; if there are two balls in the court or a thousand, the person takes a break when she is halfway through. The break time or the number of breaks is not dependent on the number of balls in the court.

While calculating Big O, ignore any constant time work, i.e., work that does not scale with the input.

In recent times, technical interviews have put the spotlight on Big O. Big O is one of the notations to represent the efficiency of an algorithm. There are many others too.

1 The astute reader might observe that the time required to bag the balls increases with the number of balls in the court. We ignore this and assume that it is constant.

Image by Pete Linforth from Pixabay

Thoughts on Product and Feature Development

The post is a listicle on product and feature development in no particular order.

There are three rules for creating a successful product. Unfortunately, no one knows what they are.1

If the success of your product depends on changing a deeply ingrained habit, it is going to be challenging. Your product should be attractive enough for people to overcome the inertia associated with behavior change.

For example, Uber changed a deeply ingrained habit of how one hails a cab. Initially, it was cool; hence, people did it. Now it is convenient. Before launching such a product, list down the motivations for someone to use your product.

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Sometimes, the most crowded markets are ripe for disruption. Example—Dropbox. Dropbox was a late entrant in the file-sharing and storage market, but it worked flawlessly and conquered the market.

Competition may not always be harmful, especially when you are trying to create a new category. Category awareness is crucial, and if a big guy does it for you, you can piggyback on it.

If a startup had launched a smart speaker like Alexa or Google Mini, they would have two challenges. First would be to educate users on what a smart speaker is. The second would be to convince people to buy their product. Competition brings awareness to a category so that you can concentrate on selling the product instead of educating consumers.


Think about product ownership and usage asymmetry. An online payment solution for schools is the perfect example. Parents want this but the want is not strong enough that they use this as criteria for picking schools. Any product that exhibits asymmetry like this needs to have powerful incentives for both sides.

Every time a customer reaches out to you; it is an opportunity for you to make your product better. Product enhancements should stem from customer service requests.

Users will find unique ways to use your product, which you would not have thought. Go with the flow.

More features are not always better. Be ruthless in culling features. New feature addition is a tug of war between simplicity and complexity. Irrespective of how small a feature is, it makes your product more complex and the complexity compounds over the long run. Even though adding a new feature is appealing, think twice before doing this. On the other hand, culling features is counter-intuitive. Be on the lookout for nixing features and simplifying the product.

Do not get attached to a feature based on the amount of effort you put, the technology used, or the uniqueness of the idea. Usage is the only benchmark for a feature’s success.

Customers do not always know what they want. Be careful while actioning on user feedback. Look around your house to see the plethora of unused stuff you brought thinking you need them.

In a consumer study, testers alternatively played French and German music in a supermarket selling French and German wines. Frech music resulted in more French wine sales while German music did the reverse. When quizzed, buyers were clueless about the music influencing their purchase.

Mix your product insight and intuition with customer feedback before acting on them.

You need something, does not mean the entire world is craving for it. There is no sure shot way to assess this but be aware of this.

Another corollary of the above.

You spot a problem does not mean others are looking for a solution to the problem. People are happy to live with minor inconvenience than change their habits.

Do not look at product features from your point of view. You might have a refined sense of UI, but your customers may not. Always assume a customer-centric viewpoint.

Assume no one reads anything. Figure out ways to make instructions implicit in product flows. My car displays a message on the dashboard when the service is due. It does not rely on me keeping a tab on this. If at all, you have to provide instructions, figure out what will make a user read it. The manual that comes with the Dyson vacuum cleaner has infographics familiarising the user with the product.

Treat customers differently based on their lineage. Someone new to the product needs more hand-holding than one who is used to the product.

New features may not pick up on their own. Figure out ways to incentivize a user to try out a new feature.

Do not be drawn to complexity. A simple feature trumps an overly complex one.

Figure out all the metrics to track before launching a feature. If you do this post-launch, it becomes a shifting goalpost where you are trying to prove the success of the feature rather than figure out whether it met the intended goal or not.

1 Quote Source

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Why Work at a Startup?

I have been pondering for a long time – Why is startup experience invaluable? How is it different from working at a big corporation? I have a good vantage point on this as I have been part of many startups, traditional process-driven enterprise corporations, and in-betweens. I knew the benefit of the startup experience. I was finding it tough to put it into words. It clicked when I listened to conversations with David Epstein, author of the book Range.

David Epstein makes a case for generalists. He also talks about Kind and Wicked learning environments.


A kind learning environment is one where all the information needed to make a decision is available. In a kind learning environment, you get timely and accurate feedback too.

A game of chess is a kind learning environment. You have at your disposal the complete information needed to make a move. Once done, you get to know whether the move was right or not.

A wicked learning environment lacks all the information needed to make a decision. The feedback that you get is hazy and inaccurate. Luck plays a role in the outcome.

A game of poker is a wicked learning environment. You do not know the card your opponent has up her sleeves. Even if you play your cards right, you can end up a loser if lady luck frowns on you.

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Working at a startup is more like a game of poker than chess. You are operating in a dynamic resource-constrained environment. You have too many things to do and not enough people to execute. You have to play the role of jack of all trades and master of none or one. You need to step out of your comfort zone. The business strategy is still evolving; you tweak it as you go – nothing is crystal clear or black and white. There is no one to guide you step by step. You need to make decisions with half baked information. Someone said – all startups are train wrecks inside.

Operating in such an environment is a fantastic learning experience. It is like packing a lifetime’s worth of education into a couple of years. It also forces you to look deep into yourself and check your biases and decision-making process.

Herminia Ibarra, an organizational behavioral specialist, says – “First act, and then think.” The reason being – “We learn who we are in practice, not in theory.” Startups give you a platform to do this.

Startups need generalists – people who can move up and down the technology stack as well as carry out many functions as and when required. In a startup, you get to work on the entirety of a product rather than a tiny weeny bit. You get a ringside view into what it takes to build an organization and run the day to day operations.

Being a generalist forces you to adopt the spiral method of learning. You learn enough to get your job done. You go back to it as and when needed and broaden your expertise. As a generalist, you get comfortable with not knowing everything and taking calls with the available information. You become comfortable with being uncomfortable.

Being able to don many roles lets you develop a broad mental model and good judgment – it gives you intellectual range. Also, it engenders curiosity – you want to learn more and more about a variety of subjects. When you are aware of many fields, you can borrow ideas from one and apply it to another. Charlie Munger calls it a latticework of mental models.

Please do not consider this as a case against working at a big corporation. The title of the post is – “Why work at a startup?” not “Why not work at a big corporation?”. All experience is valuable. In the future, I will write a counter post on why working at a big corporation is valuable.

If you like this, you should also read my old post on “What a startup is not?

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