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Friday, September 13, 2019

Purists, Makers and Managers

this article is inspired by the book Hackers and Painters by Paul Graham


Whenever I hear the word Purists, I imagine John Nash working hard on his dorm room window sill or Ramanujan relentlessly scribbling on his notepad outside his scabby hut in Erode. I study in a pure science branch and the majority of interested folks tend to have a huge admiration towards the intellectual capital. Often academia and pure science seem to be filled with folks who enjoy making interrogative intonation in declarative sentences. (Usually more associated with pride rather than brutish objectivity.)

These self-acclaimed purists are often called upon by the world to solve the most difficult problems of humanity, mostly of the order of magnitude of "Cure Cancer" or "Solve the Energy Crisis" which is extremely hard to accomplish. Even to measure a significant increment of any sort of metric that has been implemented to measure growth, it takes weeks.

This is why all the programs that have been created to cater to purists are in the magnitude of >=4 years or forever. The ambiguity in the career charts is often high and most likely everyone who is purist is pursuing one or other derivative of in-depth problem-solving. The skewed timeline and sheer ambiguity are because of the fact that struggles are internal and thus the conflict is that person's own obtuseness rather than external.


This is the group of people I feel I natively belong to. They are scrappy folks who are able to mess around enough to put things together. And build beautiful things. These people mostly learn by examples (natively known as demos). I have learned how to code from online youtube videos, and from other people's open-source code (this is nothing exclusive, <90% hackers learn like this). A maker/hacker schedule often is measured in days instead of weeks.

They have to be relentlessly resourceful in order to accomplish the tasks that they intend to. A majority of hackers I know want to fit into the narrative of purists, and this is often reflected in how they describe their work. Often at hackathons, I have heard people say that they are working on “AI text to the speech-based neurological transmitter for blind people using haptic interventions” when what they mean is “Navigation Guidance Assistant for the Blind”. It seems there is an outcry to talk about their problem in a much more complex form, (I call this problem project work insecurity). They want to prove that what they are doing is really complex and a difficult task to accomplish when most of the time it is not. Also, any maker/hacker project would make for a really bad research paper/dissertation. There is zero correlation between a good research paper and a useful product (or at least should be so). A much better environment of makers/hackers is startup co-working spaces and hacker houses.


We are all managers in some aspects of our lives. We are managers for the worker that comes to clean our room, the mess worker that serves us food, laundry person that washes our clothes or just anyone that we directly/indirectly hire to do our jobs for us. A purists/hacker hates communication but a manager loves it. Most of the work of a Manager can be divided into hour slots (work gets completed in minutes) and growth trajectory is much more gamified as compared to that of the other two. Managers are basically the power of command. For someone who is purists/maker, to shift to being a manager would often mean that they have ruined their slot of work. This is the reason why some of the best scientists and innovators are absent-minded about many of the regular things that go around them. They do not want to be managers. Any purists or makers trying to manage things often break the continuity streak that is much needed for growth in above and in a world that is filled up with huge amounts of distraction it is very easy to kill off morale. In order to be a better manager, you simply need to manage less and less and do more of any of the above.

Friday, April 19, 2019

Sequoia reveals first cohort list. Analysis.

You can find the startups list over here:-

Azani Sports: Based out of Bangalore, it is sports apparel platform that has pretty amazing fabric/shoe soul technology. They have partnered with multiple sports celebs that help promote their apparel as a marketing strategy. Also, the products are affordable for the middle class section of society. They do seem like a cool startup as innovations in sports apparel are something that has been missing for a long time. Also, they are very smart in their marketing strategies. I am not a consumer of the product yet so cannot say if the claim of an innovative product is present or not.

Industry: Sports Apparel, E-commerce, Athlete Branding.
Note: One of the two founders is Harvard ex-sportsperson himself and other an MBA.
My Rating: 3/5

BoboBox: BoboBox is capsule sized sleeping rooms for people. At first glance, it does not look like you can stand up in these rooms. Also, all the spaces are very compact and there is not much room to move around, not to mention shared bathrooms and toilets. This has been done to save space and thus reduce the rent of the rooms. Why would anyone even think of funding something like this? It seems completely outrageous in the hotel industry space. But I think it makes a lot of sense. All the times I have booked an Airbnb/hotel at some other city it has been for some sort of activity, so I rarely use the hotel room at all. Also, startups that enable social bonding in an offline space are getting big (example ). Seems like a pretty good bet, it can help replace a lot of government/private industrial lodging facilities as it is the cheap and well thought out solution.   I hope they use the OYO model instead of trying to get into the real-estate business which is highly volatile.

Industry: Hotel Space
My Rating: 4.5/5

Tuesday, April 16, 2019

Doing it Right : Building on Organic Ideas

I enjoy interacting with people and try and understand what they think. It is often that I find that some of the smartest of people I know of tend to work in areas that are frighteningly overcrowded (overhyped). There is nothing wrong in "hype", on the contrary hype helps promote a certain field even when it has few results in its' kitty, thus driving innovation faster and faster. Hype becomes a major issue only when people tend to solve problems that don't need to be solved. Or problems that have been solved already. Once you start peeping over your peers and start doing things based on what they are doing, that's when it becomes a problem. Usually, people tend to believe that's the only metric that they have to measure themselves. If someone tends to attain success by doing X, we think that doing X would give us the same success, thus fostering a herd mentality around X. Nothing is graver than this. This may sound something extremely rudimentary but 9 out of 10 times this is the case.

When an apple fell on Newton's head, nobody was working on gravity. Very few people were working on BASIC ALTAIR when Microsoft was set up (the ones that did were called "hobbyists" as no one took electronics seriously in the late 70s). Same goes for so many great ideas and inventions. Are these achievements completely random? If so then how come it is the same people who tend to repeat genius time and again, let it be Galileo Galilei, Newton or Elon Musk. 

So how does one decide which problems to work on? How can we come up with organic ideas to build on? How to think of what are the right questions to ask?

A good way to think of this is as think of your field N years in future and wonder wouldn't it be cool "if this existed". Go ahead and build it. When you think of building things from the future, your inclination and determination to do it increase multiple folds. Usually, great things look extremely fundamental in retrospect and hence living in the future works well enough. Take gravity for an example.

This is a summary of the article by Paul Graham (

Thursday, March 21, 2019

My thoughts on Dystopian Culture and Book Review of The Handmaid's Tale

It is true that today's generation has been crushed under small issues that have been viewed unnecessarily by a microscope, it dreads of political, financial and social instability bundled with mass media addiction. We have the 24*7 access to information which can make us numb any emotion we want while still struggle to remain happy is a constant one. There are so many outlets for the outcry of emotional help yet very few of them can be trusted enough to process it all through.

This book is largely based on a dystopian culture that treats women as baby-producing machines called the handmaids. The repetitive theme in the book is about the life we live today by making all the choices that we do is much more miserable than what it would be if the choices are given to us by society. The book makes a very strong case for why a dystopian culture should exist by reflecting on the failures of people in achieving their desired life goals having a tremendous negative impact on them.

It has been beautifully written and emotions of the lead character are immaculately detailed. If you have not read the book I would definitely recommend it.

We are a generation of information outrage and that is just going to increase in coming years but making that case for a controlled society is a dumb one. I am much more hopeful while making a case for the "millennial generation". It is definitely hard work to improve on self and easy to complain about the "other" but that is what the choice that we as a society have to make.

## Over.

Saturday, December 22, 2018

Building a chatbot using TF-IDF


We want to build a basic chatbot which trains on previous messages and responses. In this tutorial we look at the math that we are using to convert the messages and their associated responses into weights using term frequency and inverse document frequency. (tf-idf).

Once we have the appropriate weights of words present in messages and responses. We write the messages and responses in vector form of the weight present. We then try to find how similar are these vectors using cosine similarity.

We multiply term-frequency and inverse document frequency to obtain the final weight of the word that would be used to construct the vector.  

Cosine Similarity:
This is a measure of orientation and not magnitude. The reason we are not considering magnitude of the vectors is because the magnitude can be more depending on the length of the query or response associated but that does not tell us about how similar is the query and the messages that we have in our training data.

Angle gives us the direction where the vector points towards thus if the query has similar weighted words only 5 times and the message has 500 words but having similar weights then they would point in same direction and be more similar.

The reason for choosing cos(theta) is because it is monotonically decreasing function in [0, pi/2]. We use dot product to calculate the cos(theta) as shown in figure.


In this tutorial we would give a walkthrough of the code. The libraries that have been used are the scikit learn and numpy.

Full code present on github.

First we import the following libraries.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer

Friday, December 21, 2018

(College Education - 1) Data Analytics for Teachers/Students.

Disclaimer: the views presented in this article are personal. 

Initially, I was going to write a rant on how teachers are shit in colleges and continue the age old blame game. In this game, the teachers' think that students are stupid or uninterested and students think that teachers don't know how to teach. It is true (to a certain extent ofc), but the problem is no one ever addresses it. No one thinks of any innovative methods that can be adapted to address what's wrong. Most people (including me) are involved in their own self-interests (includes teachers and students both) and to some extent rightfully so.

Before I propose the solution, I would like you to go through my line of thought.

Teachers of today feel inclined to play entertainers as compared to knowledge imparters. In this information age which we've become privy to, fuelling curiosity is far more important as compared to imparting knowledge. Students need to be introduced to concepts in a way which makes the learning process heuristic. Enabling them to relate these to life, applications around them and have a positive impact using that knowledge.

A lot of students tend to blame the syllabus but I disagree. I think that the syllabus is well defined and in accordance with a given branch of study. The reason most students feel disengaged from the syllabus is that they are unaware of the possibilities that it holds. As students move away from immersive learning and focus only on the parts that are necessary to get them better grades the whole ideology of a model student and a model teacher changes drastically. A model teacher is often one who is able to make sure that knowledge (or the method involved in its dissemination) is transmitted to students in a way that aids them in remembering it for a duration often limited to the exam period. If a teacher can assign tasks to students that lead to good marks then they are a model teacher (and hence they are diligent to their duties) and a model student becomes one who duly completes the tasks assigned to them. The students who are regular, sincere and complete everything on time.

Let us consider the problems that arise because of this.
  • Less than 10% of students/teachers fall into the model student - model teacher zone. 
  • Little accountability and deliverables on teacher's part.  
  • Independent line of thought by the student is not given proper importance. 
  • Fuelling and engaging with the community (online forums) is more important than completing the assigned tasks. 
  • Holistic development is not taken with the same level of sincerity as compared to knowledge importation. 

Solution: Proper Data Analytics for students and teachers. 

1. ) Actionable insights for teachers and students.

Teachers often do not have the time for every student. and students struggle needlessly on things that can be quickly understood. By enabling collection of proper data (for both students and teachers) following actionable insights can be generated.

2.) Regular after class tests instead of end semester / mid-semester examination patterns. 

In order to create real-time data for analysis and actionable insights for teachers and students, it is important to create data points on a short-term basis. This would also allow machine learning techniques such as reinforcement learning come into play and interact with students, thus reducing the workload for teachers. 
Not just that, more data points would result in more answerability on teacher's part. 

3.) Venn Like Diagram for multi-discipline projects and grading on basis of those projects.

I personally think this would be super cool if implemented. The idea is to use a graphical representation shown below to grade projects. 

Here is how it could work.

  • a radius of a circle would be determined by the number of topics covered by the project. 
  • the colour of the circle would be determined by the depth of the topic understood by the person. The darker the shade of circle would imply better understanding. 
  • community comments (feedback) from people who have expertise in that area would be also listed for every project.
  • deep learning model on the employability of these projects based on the above data as input parameters to be measured. 

4.) Awarding in-depth knowledge and understanding in a unique way.

Instead of assignment submissions (which have been reduced to handwriting practice for the majority of students), the assignments should include engaging with the online community (such as StackExchange/medium) on different topics of interest. The idea is to enforce students' interests instead of adding work pressure. By having communications with a community the students would feel more appreciated for their work as opposed to now. 

5.) Incorporating extra-curricular activities (sports) as an important part of a system. 

There is nothing more important than sports. A consistent sport should have some weight-age associated with it in all educational institutes of every field as it teaches teamwork, risk-taking and communication.  

Tuesday, December 4, 2018

Installing Anaconda, Running Jupyter on Google Cloud Remotely

I was just using google collab when I realised it cannot really replace a remote server with a GPU. It is super awesome if you are trying to collaborate on a notebook with multiple authors but it does not really provide you the flexibility of terminal. There is certain extent to which "!" can go. Had google collab provided a virtual instance, it would have been super.
It is already amazing that they are providing GPUs and TPUs completely free of cost. It is too much to ask to give shell access free too, and it would be hard for them to nail down the activities such as mining or torrenting if they did, thus people would be making money on their hardware meant for educational purposes.

This post is about how to setup NGINX along with jupyter notebook.

1.) Let us first install Anaconda by downloading it from here,

Once, you have installed anaconda on your virtual machine, it is time to install and make sure nginx is running.

2.) Start jupyter notebook using the following command. Copy the link

3.) Go to terminal and type the following command.