Generalization – The Superpower

I was reading this Twitter thread on Ben Horowitz’s new book on culture. The book’s content is apparent to anyone who has spent time in a corporate setup. I have been listening to the audiobook – “Zen: The Art of Simple Living.” Again, the content is not radically new, something you would already know instinctually. Off late, there has been a spurt of twitter accounts dishing out wisdom. Most of the tweets seem to be a regurgitation of common sense.


Am I trying to say that they do not add any value? No, the opposite. They are doing an excellent service by codifying common sense into pithy one-liners, simple rules, and principles.

All these people are generalizing lessons learned from specific instances into a broader set of rules and guidelines which apply to more expansive areas of life.

What do I mean by the above? Let me explain with an example.

Let us say that a company advertises telling – Deposit your money with us for two years for a guaranteed return of 15%. The bond yield rate is 8%. You see this scheme as attractive and invest. After a year, the company owner goes absconding, taking your money with her.

How do you generalize the lesson from this misadventure?

Be skeptical of any scheme(gold, real estate, etc.) that promises GUARANTEED returns over and above the current bond yield rate.

I believe generalizing lessons learned from specific situations to a much broader arena of life is a superpower which everyone should develop. Get into the habit of doing this for everything. If you followed a particular process that led you to success, try to make this process generic so that you can apply it elsewhere too.

Generalizing wisdom helps in pattern matching as well as molding the way you think. You begin to see patterns in your thoughts and actions where you can apply the principles created out of past experiences.

This habit sharpens decision making. You start seeing patterns in decision-making scenarios and can base your decision on a previously created rule.

You may not be able to create rules out of everything – creating a step by step guide on how to balance a bicycle is next to impossible. Wherever it is possible, do it; train your instincts with the rest and let your intuition take care of it.

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Critique of Critiques of Daily Standups

In HackerNews, I read yet another write-up on daily standups and how they suck. Periodically, a post pops up on daily standup and how it is a nuisance. This entry of mine is an attempt at importing the importance of daily standups and how it adds value. We will also look at some of the familiar oppositions to daily standups and why they hold no water.

All posts on daily standups have a fundamental problem. They shy away from tackling the elephant in the room. I am also guilty of this in my take on daily standups. At some level, meetings are a forced collaboration attempt. In an ideal Utopian world, where everyone excels in collaboration and communication, we would not need meetings. Sadly that world does not exist.

Now that we are done away with addressing the uncomfortable truth, let us go deep into why daily standups are essential.


Timely and efficient communication and collaboration can make or break teams. Humans display a diversity when it comes to communication, some excel at it, and some are bad at it. Sometimes, you might not genuinely know you need to communicate or that you are a blocker to someone’s work.

A primary reason for project failures is unmet dependencies and someone not planning for them. When you get people together and create a platform for them to discuss and collaborate, blockers and dependencies which would have gone unsaid otherwise surface.

How many times has it happened that someone raises a red flag on the day a project is supposed to go live? A daily standup ensures a constant feedback loop wherein this does not come as a last-minute surprise.

Daily standups ensure that everyone in the team knows what their counterparts are working on; this prevents people from becoming islands and ensures everyone knows the big picture.

As an organization, how do you develop this habit?

One of the paradoxes in life is that rules set you free, help build good habits, and reduce cognitive overload. Giants in the field of behavioral psychology – Dan Ariely, Daniel Kahneman, B. F. Skinner; all support this. The general prescription to start a good habit or break bad habits is to create a strict set of rules.

Another trick to aid good habits is to design your environment to support the practice; remove obstacles that prevent you from getting into the said habit. Putting it succinctly, make it easy to start and sustain a habit. Charles Duhigg and James Clear have written books on this.

Scheduling daily standups at a specific time with a well-understood format does both the above; it makes it easy and creates an environment for teams to cultivate the habit of collaboration and communication.

People who rally against daily standups tend to be:

  • Great at communication and pro-actively do it.
  • Individual contributors who excel at their work.

These people operate on individual bits of information and do not see the entire picture. From their narrow perspective, they are correct, but modern workplaces are not only about individual brilliance but more to do with teamwork. An automobile will not function unless all the parts work in tandem; the same goes for a team.

The majority of people do not know how to run efficient meetings; as a result, people have developed an aversion to meetings. This general distaste towards meetings has given the daily standup a lousy reputation.

Paul Graham talks about the maker’s schedule and manager’s schedule and how it is paramount that makers get a long uninterrupted chunk of time to create things. To avoid the context switch, schedule daily standup at the start of the day before everyone gets immersed in their work.

Most workplaces are chaotic. Daily standup gives you the means to bring order to the chaos.

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On Competition

I believe keeping an eye on the competition is a good idea. Keeping track of competition makes you aware of what is the new normal; it helps one to gauge current trends. If your product experience deviates from the prevailing standards, it might be time for a re-think.

When a behemoth does something well regularly, they create an impression that that is the new normal. Customers start expecting the same experience from everyone in the field. For example, Amazon keeps upping the ante in e-commerce. If you are a small boutique e-commerce firm, and if you are not close to the Amazon experience, you might be leaving a lot on the table.


Most of the new age enterprise SAAS tools have user experience on par with consumer applications. Earlier, enterprise tools used to be leaps and bounds behind their consumer counterparts. After using these new-age tools, products from some of the established behemoths look and feel clunky. Using them feels like being teleported to an earlier era. If you are an entrenched behemoth, you can get this wake-up call only if you regularly scan your competition, be it big or small.

Eyeing competition also matters when it comes to feature selection. For example, in Slack, you can edit a message after sending. I have hardly seen anyone modifying messages in Slack post sending. Usually, one sends a new message suffixing an * indicating it is an edit of a previous message. Why? We have been conditioned by popular chat applications not to alter chat messages once we hit the send button. None of the popular consumer chat applications have this feature. If you are bucking the trend – first, you should know of this; second, you should figure out how to educate your users to use the nonintuitive feature.

Incumbents also set standards when it comes to UI patterns. If Facebook shows error messages with a red background, you can be sure that most of the world’s population associates a pop-up with a red background as an error. It makes sense for you too to follow this.

I am not advocating aping the competition blindly.

Keeping track of the competition is essential to:
1. Know what is the new normal.
2. Know what might become the new normal.
3. Gauge how far off you are from the status quo.

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Zen and the Art of Mind Tricks

All of us wish to be in a state of zen. We desire to be cheerful and have a positive frame of mind. We aspire to be clean of bad habits. We want to calm our monkey mind and experience a higher conscious.

Let alone achieving these, we find it tough to start.


Below are mind tricks which help.


Whenever you have an urge to do something you have resolved not to, do not give in instantly. Have a timeout after which you decide.

You have decided to go on a diet. An enticing scoop of ice cream is in front of you. Instead of immediately succumbing to your impulse, count till sixty. After that, decide whether you still want to gulp that scoop. In most cases, the urge would have died by then.


Reframe unpleasant events in an empathetic manner; this nudges us from a feeling of angst to compassion.

We all go through bad experiences in life. Someone cuts us in traffic; a sales representative is rude to us. Whenever you have frustration creeping in due to things outside your control, reframe the situation.

The person who cut us in traffic is in a personal emergency and is trying to get somewhere. The salesperson, who was rude to us, is going through a terrible phase in her life.

Reframing a situation put us in a humane mode which drives away antagonistic thoughts.


Detach yourself from a frame of mind you do not wish to be in and observe your thoughts as a third party.

You want to meditate. You are not able to calm your monkey brain. Do not self-berate. Observe your fleeting thoughts as a third person. Do not curse your inability to control your mind. Watch the rise and ebb of ideas — the simple act of detaching and observing helps to calm the mind.

None of these tricks are my creation; I have summarized them in my own words. I have seen these repeated in various forms by experts in the field of psychology, wellness, and spiritualism.

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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.

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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 for people to change their behavior.

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 influence 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. 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 for 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 which 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 do 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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