Once your non-code prototypes prove that your idea has a pulse, you move to the next stage of writing code for a working prototype.

Here are some key tenets to build your base software development stack that is cutting edge, grows with your needs, and fits your constraints as a small organization.

Quick

The software stack has to support creating very fast and simple prototypes.

A tech idea is a like a bubble. It needs to be preserved and nurtured, all very quickly. …

I am a numbers guy. You can’t improve what you can’t measure. What are your metrics for measuring the success of your life?

You can have a meaningful life by maximizing personal happiness through relationships, achieving your professional purpose, while leading a life of integrity.

One of my gurus Clay Christensen passed away a few days ago. He went away too soon. We will sorely miss him.

Clay was a professor at Harvard Business School and one of the best business thinkers of our time. Clay made a massive dent in the universe with his business and life theories.

Clay wrote all his life about business, innovation, and management theories, including one of his classics, Innovator’s Dilemma, and created the renowned disruption theory. …

ML does not just add another feature as an after-thought but is an entirely new architecture that compels us to completely rethink our relationships with our products and how we conceptualize, design, and develop them.

What makes an ML-first Product Manager (PM)? How do you develop the mindset, the skills, the principles, and the experiences to become an ML-first PM? You can also build the next big ML-first idea by learning the craft, developing intuition, and understanding the levers.

The world around us is changing with Machine Learning. Users’ relationships with products are changing. …

The only thing that matters is getting to product-market fit. — Mark Andreesen, a16z

Corollary: The only thing that matters is getting to founder-product-market fit.

In this article, my goal is to first explain what product-market fit is, why it matters, and tell you some quantitative ways to figure out if you have achieved it. My corollary to the broadly accepted theory of product-market fit is that a founder’s fit to that product and to that market is highly important as well.

After that, I am sharing here my own experiences building vChatter, scaling it to millions of users through explosive growth, my mistakes and going through the whole lifecycle of an idea from 0 to 1 and then back to 0. …

Positive habits are tough to form, especially, after a certain age.

Streaks are powerful.

Notifications help nudge you towards your goals. It also helps to have someone (or something) to keep you on track, continuously.

Like most of you, I started my new year with new resolutions. One of them is to click a photo of myself running 5K every day, and, send it to a select group of my friends/family.

This resolution is inspired by one of my mentors - Rajib Roy.

My initial process was simple. I just started running, like Forrest Gump.

Then, I realized, it is very easy to get started, but, much tougher to keep going. …

“How different would the world look if people spent as much time listening to their conscience as they did to chattering broadcasts? If they could respond to the calls of their convictions as quickly as we answer the dings and rings of technology in our pockets?”
― Ryan Holiday, Stillness is the Key

Is completely quitting Facebook, WhatsApp, YouTube or cable TV the right way to claim back the time that these new platforms stole out of your days?

Smart phones and applications on these phones are great technologies that help us live and connect with our world in unprecedented ways. …

In our previous post we discussed the basics of machine learning. In this post, we talk about what actually happens within a machine learning project.

Machine Learning follows a simple flow like a human brain to continuously enrich what the machine knows and applies it to make better decisions. It is a continuous refinement process since it keeps getting better with every learning iteration. The more data we feed to these iterations the better inferences the machine can make.

In this post we will walk you through the fundamentals of how a typical machine learning framework works.

There are primarily three stages in a machine learning project. First, it is the non-sexy part of machine learning where you bring in and prepare your data. Second, you experiment, train and evaluate your machine learning model (we will learn about the model in upcoming posts), and last but not the least you deploy your trained model to a production network to solve the problem you started with. …