Notes from “Wisdom of Charlie Munger”

Just finished reading this book. I was tweeting a few quotes as I was reading the book. Here is the full list.

Before you read the quotes, please remember the context Charlie was mostly answering questions about being an Investor.

Let us first look at these quotes.

There is no way you can live an adequate life without making many mistakes.

The best thing a human being can do is to help another human being know more.  Being an effective teacher is a high calling.

Those of us who have been very fortunate have a duty to give back. Whether one gives a lot as one goes along as I do, or a little and then a lot (when one dies) as Warren does, is a matter of personal preference.

Spend each day trying to be a little wiser than you were when you woke up. Day by day, and at the end of the day-if you live long enough-like most people, you will get out of life what you deserve.

What are the secret of success? One word answer: rationality.

There’s only one way to the top: hard work. Do what you like and are good at.

Understanding both the power of compound interest and the difficulty of getting it is the heart and soul of understanding a lot of things.

Quickly eliminate the big universe of what not to do, followup with a fluent, multidisciplinary attach on what remains, then act decisively when, and only when, the right circumstances appear.

If you always tell people why, they’ll understand it better, they’ll consider it more important, and they’ll be more likely to comply.

…it never ceases to amaze me to see how much territory can be grasped if one merely masters and consistently uses all.

My habit of committing far more time to learning and thinking than to doing is no accident.

Some people are extraordinarily good at knowing the limits of their knowledge, because they have to be.

So the game is to keep learning, and I don’t think people are going to keep learning who don’t like the learning process. You need to like the learning process.

If you’re capable of understanding the world, you have a moral obligation to become rational.

For me there were many takeaways. There is a note of warning. Whoever edited this book did not do a good job. There are too many repetitions. Please remember it if you buy and read the book. Despite bad editing, the book is full of gems. So it is worth a read.

A few reflections.

  1. The importance of reading. I do read though not anywhere near being “a book with a couple of legs sticking out.”.
  2. When we were programming in early 70s, we spent far more time learning and thinking than doing. Now it is very different. There is a lot more doing. Charlie’s quote “This habit of committing far more time to learning and thinking than to doing” is right on.
  3. Spend each day trying to be a little wiser than you were when you woke up. This can be a powerful micro-habit and a daily goal to shoot for. 

Today Social Media is my Primary News Source

From The New Age of Platforms is a  great read. I particularly resonate with:

Today, social media is my primary news source for many areas I’m  interested in.  That’s not a function of the platforms themselves, but rather of who I’m connected to—experts in particular fields who have ready access to information that would be hard to find anywhere else.

Let me mention three instances on the influence of these people I follow on Twitter, Facebook, LinkedIn and other sources (blogs):

  • A lot of books I bought last year, came from mentions in podcasts I like and blogs I read (Tim Ferriss and Maria Papova for instance)
  • Most of the technical books I read and the webinars I attend come from some of the most influential people I follow on Twitter.
  • A lot of literature I collect and research for Data Science comes from @kdnuggets and other similar sources

I do have a List called News on Twitter and follow most of the popular news sources in India and US. However, I visit it a lot less and prefer to read from my “Daily” list.

The people I choose to follow are my filters. I read their posts more than I do the “other” reading.


Mobile Game Discoverability


The mobile gaming market in North America continues to grow. As of Q3 2014there are 141.9MM NA mobile gamers, up from 111.3MM NA mobile gamersin 2013. These mobile gamers spent an average of $32.65 in the last year,generating $4.63B in NA mobile gaming revenue.
The graph below provides a view of how gamers discover games (click on it to get a larger view)




For a great analysis please see this  free report.

Good Reads: Ways to Acquire Knowledge

14 Ways to Acquire Knowledge: A Timeless Guide from 1936 by  is a great read.

Here are a few fragments from the original post. There is a lot more to read and reflect upon.

On reading:

If you must read in order to acquire knowledge, read critically. Believe nothing till it’s understood, till it’s clearly proven.

On Writing:

To know it — write it! If you’re writing to explain, you’re explaining it to yourself! If you’re writing to inspire, you’re inspiring yourself! If you’re writing to record, you’re recording it on your own memory.

On Learning:

To learn, experiment! Try something new. See what happens. Lindbergh experimented when he flew the Atlantic. Pasteur experimented with bacteria and made cow’s milk safe for the human race. Franklin experimented with a kite and introduced electricity.

On  knowledge:

If you would have knowledge, knowledge sure and sound, teach. Teach your children, teach your associates, teach your friends. In the very act of teaching, you will learn far more than your best pupil.

On Reasoning:

Animals have knowledge. But only men can reason. The better you can reason the farther you separate yourself from animals.


Machine Learning Application: Job Classification at LinkedIn

I am fascinated by Machine Learning (ML) and keep looking for case studies were ML solves real world problems. This Talk – Machine Learning: The Basics by Ron Bekkerman( video), provides a great overview  of machine learning and how it is being used by LinkedIn for Job Analysis. LinkedIn is one of the early companies to jump in to Data Science. With over 200 million subscribers, they have ample data to analyze. The data is very contextual too and that helps build better algorithms (they claim 95% accuracy in prediction in a specific case). At one point in the talk Ron mentions that the ML study helped in building a product that generates about 6 million dollars in revenue for LinkedIn. That is great pay off.


Why is job analysis interesting in general? It provides you with some interesting insights into the direction a specific industry is moving:

  • If you are in the (IT staffing) industry, you may want to know what kinds of jobs are in demand? And which ones are growing and which ones are shrinking?
  • If you are an outsourcing company, you may want to analyze the hiring patterns in different parts of the world
  • What kinds of skills are in demand for startups, medium sized companies and large enterprises? Lots of people from startups to training companies can use this data to build and tailor their offerings.
  • How do training companies and conference organizers meet the need for skills using job analysis?

Ultimately, it is all Market Intelligence of a kind. It is fascinating that, now we have large data to analyze and get some glimpses into the patterns of demand/supply.  So where do you get all this data from? That is a topic for another blog post.


One of our interns is working on an app to do Job Classification and automatic tagging of jobs. We were debating whether we should use some simple techniques or ML. I was going around looking for case studies and stumbled upon this video.

From The Book: Open Data Now

From the book – Open Data Now:

In a series of studies led by Harvey Lewis, a research director in Deloitte’s Insight Team, the firm has identified five Open Data business “archetypes”:

  1. Suppliers publish their data as Open Data that can be easily used. While they don’t charge for the data—if they did, it wouldn’t be Open Data—they increase customer loyalty and enhance their reputations by releasing it.
  2. Aggregators collect Open Data, analyze it, and charge for their insights or make money from the data in other ways.
  3. Developers “design, build, and sell web-based, tablet, or smart-phone applications” using Open Data as a free resource.
  4. Enrichers are “typically large, established businesses” that use Open Data to “enhance their existing products and services,” for example by using demographic data to understand their customers better.
  5. Enablers charge companies to make it easier for them to use Open Data.

You can download a sample chapter free.

Think About Shifting Emphasis on Smart Data

From Key Digital Trends for 2014:

Don’t just focus on Big Data; think about shifting emphasis to “Smart Data”.



So what are the jobs that let us:

  • Find a variety of useful data sources and integrate them?
  • Analyze large volume of (unstructured) data?
  • Intelligence Monitoring?
  • Make sense of it all the “smart data”
  • Gain insight and ask critical business questions?

Something to think about.


Competitive Environment – Pieces of a Puzzle

If run any kind of business, this book  Competitive Intelligence Advantage: How to Minimize Risk, Avoid Surprises, and Grow Your Business in a Changing World is worth a read.

Here is a picture from the book that talks about Competitive Environment (with due acknowledgement) an amazingly clear picture that demystifies Competitive Intelligence.



I will provide a brief summary once I finish the book. Sheena demystifies a lot of confusing terms like Business Intelligence, Competitive Intelligence, Competitor Intelligence, Open Source Intelligence and a whole lot of others.

This book provides  a compelling argument about why every company should pay attention to competitive intelligence. Sheena provokes a lot of reflection about information, intelligence and a context in which you can think about them. .


A Blog Worth Reading

From @sharads tweet, I discovered JP’s blog. I would like to share a couple of snippets from his About This Blog page.

I believe that Moore’s Law and Metcalfe’s Law and Gilder’s Law have created an environment where it is finally possible to demonstrate the value of information technology in simple terms rather than by complex inferences and abstract arguments.

I believe that simplicity and convenience are important, and that we have to learn to respect human time.

I believe we need to discuss these things and find ways of getting them right. And I have a fervent hope that through this blog, I can keep the conversations going and learn from them.

I agree  – we do need to discuss these things and find ways of getting them right.