This is the transcript of my talk at OMSCS Conference 2026

Hello, everyone. I'm Senthil Kumaran, a software developer at Uber. I've been programming for more than 20 years. I completed my OMSCS specialization in 2019 with specializations related to Computer Vision and Computational Systems. I had taken twelve courses in all, and I've stayed close to the program ever since.

This is my first presentation at an academic conference, and I'm grateful to be here.

I chose this topic last year, when we were being bombarded daily with new developments in large language models. The chorus was loud and clear: programming is dead. The industry, or at least parts of it, wants you to believe that.

But if you are a programmer, and especially if you work alongside really good programmers in serious open-source projects or in industry, you know how far that claim is from the truth.

Things have changed. Things have improved. But it is nowhere near what people who don't program think it is.

So, that's the topic of my talk: why large language models make learning to code more important, and not less.

A note on my favorite teacher

Before I get into the substance, I want to say something about the teacher who inspired me to take OMSCS in the first place. I had taken several of his lectures online, and there is a particular quality to his teaching that resonated with me deeply.

He has a real intuition for his subjects, he tries to share that intuition with students, he makes complex ideas approachable, and he understands and communicates the value of hard work.

When you have a favorite teacher and you really pay attention, you recognize that the way they explain hard things just clicks. And the whole point of any of this, the whole point of education, is understanding and intuition.

Programming has never been about coding. Programming is about understanding and intuition, and these are the qualities we should be drawing out in any domain we work in.

Feynman's blackboard

This is a famous photograph of Feynman's blackboard at the time of his death. It's covered in string theory equations, but in the corner is a line that has stayed with me:

What I cannot create, I do not understand.

This is profound. If you want to understand something, you have to create it.

If I want to learn a complex topic, I solve problems in it, I build a model of it, I make a project out of it. Those are the topics I have understood best. Even writing a paper is creating. The act of creating is the act of understanding.

The other line on that blackboard says: Know how to solve every problem that has been solved. We build on the basics. We solve the problems that have already been solved, and we build up from there so that we are ready when new problems arrive.

And the rest is string theory equations — which, as it happens, you can now point an LLM at and have it walk you through, line by line. That itself is part of my point.

The world feels like it's on steroids

Deep neural networks and the applications built on top of them, large language models, have turned our world upside down. Every day there is more news about automating human work, especially programmers. Major labs are talking in numbers that are hard to comprehend.

I want to step back on the money for a moment, because the world tends to translate everything into dollars and miss what's actually happening.

When you read that "Amazon invested a billion dollars in Anthropic," if you read a little deeper, a lot of that is compute. If you run a major lab, you need an enormous amount of compute to train your models. If you are an infrastructure provider, you have compute that's available for rent. Investing compute instead of cash is a win-win, but the public framing collapses it all into a single dollar figure. Even programming-specific tools have absorbed this energy. Cursor, for example, has raised at valuations that climbed from 10 billion to 60 billion with SpaceX deals.

So when some people are saying programming is no longer required, while the industry is pouring this kind of money into LLM-based programming tools, you have to ask: is the claim that, coding is dead, a valid statement?

Given the hype and drumbeat, honestly, when I submitted this proposal a year ago, I was worried that what the industry claims would become true.

Today I am more convinced than before, that particular narrative is not true:

LLMs have not made learning programming obsolete. They have made it more important, and in fact, they have expanded the programmer's toolset.

A wonderful time to learn programming

This is a wonderful and exciting time to learn programming and grow as a software developer. Things that used to be inaccessible are suddenly within reach.

You can study Knuth efficiently now. His work is hard science, and historically required a particular kind of mindset and patience. If you worked as a software developer shipping features for customers, even a strong interest in Knuth would leave the book sitting on your shelf. You'd attempt one or two problems and stop. I have done this.

Now I can solve ten problems where each one used to take five days and now takes one. And I do it by asking the right questions and asking the LLM to handle the mechanical grunt work, then sitting with the result until I actually understand it.

You can do this with any of the great technical books. Algorithms by Sedgewick represents 40 years of effort by its authors. We're not going to absorb 40 years of work in coursework. But we can now absorb it in something on the order of years, understanding their work deeply, and that's an enormous shift.

ref: https://joshmpollock.com/dijkstras-algorithm-article/

And people are doing this. Someone built an interactive visual exploration of the Dijkstra shortest-path algorithm following the Sedgewick book. They understood the model, asked an LLM to help them build the simulation, and ended up with exactly what I'm describing: a one-to-one correspondence between what you read, what you build, and what you understand.

Tools to learn programming

The same shift makes it dramatically easier to build great software, the kind of software that ends up in app stores and on people's phones. The barrier is lower. But to build software, you still have to learn programming. That has not changed.

There is a perception that you can just prompt an LLM and ship an app. Yes, you can produce something. But what you have is an artifact, not software. The moment you need to tweak it, if you don't understand programming, you are stuck. This is why most non-programmers who try this route stall at the first version. They didn't build it. They wished for it. Building requires understanding the basics, and that means learning to program.

So how do you actually learn programming today?

My favorite tool is Exercism, a site that teaches you by solving problems in dozens of languages. Before LLMs, learning a language on Exercism was slow. You'd get stuck, post in the forum, wait two days for a response, finally have it click, and continue. Mastering one or two languages might have taken years.

Exercism is great in that you can have a mentor work with you too. But still as humans, you try to be kind to the mentor, try not to ask "dumb questions" or "lazy" questions.

We don't have to fear that with machines.

Now you can ask your own personal tutor, an AI of your choice, to help you learn — not solve for you, but teach you. The trick is configuring the AI properly.

In my claude.md or agent.md file, I give the AI a specific role:

You are my PhD-level programming languages and computer science teacher. Your
job is not to give me solutions. It is to guide me to write them myself.

Then I give it specific rules:

Never give me a solution. Help me discover it step by step.

Name the concept. When I bring a problem, name the computer science idea and the
language construct at the heart of it. Explain its syntax, semantics, edge
cases, gotchas, and how it compares to similar constructs in other languages I
know.

Tie it to the machine. Connect language constructs to the underlying memory
model: recursion to call stacks, statics to the data segment, pointers to
address spaces, closures to heap-allocated environments. 

Keep this directly tied to what we're working on, not a generic lecture.

Break it down. Give me an ordered list of small sub-tasks to attempt one at a
time. Don't dump all of it at once. Present one step, then wait.

Use tiny, self-contained examples when introducing new syntax in the language.

When I share my code, point out bugs precisely — line number, what's wrong, why.
Ask a leading question to help me fix it. Don't hand me the fix.

If logic is right but style is off, mention it briefly and move on.
Be brutally honest. Attack my assumptions and point out my weak spots.

• Experience Exercism
• Here the CLAUDE.md or AGENT.md file that you can use when you try to learn using Exercism.

With instructions like these, the LLM stops being an answer machine and starts behaving like a teacher. The first thing it now says to me is, "Let's start by making sure you understand the problem before writing any code." This single shift has taught me lessons I would never have arrived at on my own, about heap-allocated storage, about caller expectations of ownership, about the assumptions a test makes about the code it exercises.

It pulls me into corners of the language I would never have looked into.

https://www.youtube.com/watch?v=lgpPDJ2GeUU

Practice, accelerated

Solving problems is essential, but so is everyday practice. I built a small system of programming-language templates filled with comments that ask you to mechanically fill in the next concept. Open one of these in an AI-aware editor and the autocomplete fills in the snippets, you read, you tab, you read, you tab.

This is not "the AI does it for me." It's the opposite. By skipping the mechanical typing, you cross directly into the part that actually matters: the flow and structure of the program.

The latency between curiosity and understanding collapses. You stay in the editor. You don't context-switch into a chat window. The AI sees what you're doing and responds to it.

• Practice Go Project

This is an effective way to learn programming: understanding the toolset, fundamentals, design, and structure of various programming languages.

https://www.youtube.com/watch?v=Hl93y8BarYE

Let's shift a bit into Software Engineering that is more than programming.

Designing, writing and communicating.

The article Don't Let AI Write For You by Alex H Woods brings some of these points in greater detail. In essence, these are

Writing is thinking

Writing is an essential part of programming, and it deserves its own treatment.

In short, writing is thinking. We gain the understanding of the system by writing.

The goal of writing is not to have written. It is to increase your understanding, and the understanding of those around you.

Often with writing we start with an ambiguous state of the world, sort it out, and come out with a structure that is clean and coherent. That is the point of writing. LLMs help, but the understanding of the concept is the human part.

We never want to skip the understanding part with the writing.

Writing is exercising.

I like running. Solving a problem is like exercising. Doing something hard, pushing the boundary, just makes us better.

You are often doing it for that innate feeling that is within you, that you want to improve, you want to cross that boundary. You want to feel good.

Asking an LLM to do your writing for you is like paying somebody to do your workout. It is not your workout.

In fact, people get this. Even middle-school students get this, and serious people do not respect when they see that the real work is outsourced to a machine.

Communicating is also how we build trust.

If I communicate an output of a LLM without a serious understanding, we are building things like the house of cards.

You don't know when it will collapse.

If I send a document that is LLM output, I am often not near the source of truth. This is merely an output that LLM thinks that I and my colleagues want to hear. It misses the point, sometimes, seriously.

And when we realize it, it is usually embarrassing.

Each LLM-generated document is a missed opportunity to think and to build trust.

This matters especially in programs like OMSCS, where students are expected to write their own code and their own analyses. If you understand the work and the LLM just helps you finish faster, great. If you don't, you're trading the actual education for something that is not so valuable.

Effective writing with LLMs

That said, LLMs are extraordinary research partners.

In my own writing, design docs, on-call alert analyses, even Slack questions. I instruct the LLM through claude.md to bring specific depth to specific topics:

• Database and storage engines — discuss transaction model, isolation level, replication protocol, read/write path. Reference Spanner, Bigtable, DynamoDB, Kafka, Zookeeper.

• Consensus protocols — reference the Paxos, Raft, Viewstamped Replication, and pBFT papers, and cite the specific section and theorem.

• Distributed algorithms — vector clocks, CRDTs, consistent hashing, two-phase commit.

• Schedulers and resource managers — reference Borg, Omega, Mesos.

• Networking — BBR, congestion control, RDMA.

I work on compute infrastructure at Uber, large-scale autoscaling, that works extensively with distributed systems, with millions of CPU allocations and GPU allocations for batch, training and inference jobs.

So when an alert fires and I need to understand a system, the LLM hands me back not just the answer but the canonical paper, the relevant section, the theorem. Every alert becomes my reinforcement-learning loop into a fundamental concept.

I love it!

An expert programmer would have built this intuition over decades. With a PhD-level tutor on call for daily affairs, you can build it now.

When AI goes wrong

There's a wonderful article called "The machine didn't take your craft, you gave it up." by David Abram. It captures the failure modes of using AI precisely.

The hardest part of the job was never typing the code. Coding helps us to think deeply and accurately. The code and tests are often the best source of truth, but that is only one part of the stack.

The hard part, is how we build it, how we understand the system as a whole, how we reason with it, and how it works under pressure. How to debug when there is customer at the other end.

How to use the proper tools, how to be yourself, when you are challenged by the system, and you strive to work around it.

These are accomplished only by having a "sound mental model", and intuition for the system, and knowing the details carefully and through hard work.

The hard part has always been understanding, especially with multi-dimensional contexts in our mind.

If we abandon the contexts that we know and accept what the model suggests, we are often giving up on solving the correct problem.

LLMs can help, they can help a lot. But as engineers, we take the decision. It is our work.

Not getting trapped by the news of the day

A line from the creator of Redis I keep coming back to: don't let the daily noise — RAG, MCP, whatever the acronym is this week — consume all your energy.

Some of it is useful. A lot of it is hype from people who can't compete with the AI itself, so they pump the products around it.

The actual product, for the most part, is the neural network.

I'll close with two small notes.

First, an example of AI slop in the wild: I asked NotebookLM to generate a quiz from Attention Is All You Need, and it produced the question, "How did the authors decide to handle the order of the names in the publication?" Who cares?

Second, a few weeks ago, there was an accidental release of source code, which I wanted to study! It was taken down. Just like we have outages with Software Engineering. We're all figuring this out together.

Finally, I'll leave you with a line from Emerson:

To be yourself in a world that is constantly trying to make you something else is the greatest accomplishment.

We have always tried this. I think, it will continue to hold true.

Thank you.

This is a wonderful resource on the History of Modern AI and Deep Learning by Jürgen Schmidhuber.

It opens statement with Machine learning (ML) is the science of credit assignment. It takes the technical term of "Credit Assignment" and overlays it with the human approach of assigning credits to ideas shared by predecessors.

It reminds us that we are all standing on the shoulders of the giants.

I enjoyed reading (listening to Audiobook) Genius Makers by Cade Metz. It was information dense that I wanted to keep a note of all the players in the story to form the idea. Fortunately, the book provided the timeline and the people. Here it is, annotated with links against the people to follow their work. It is easier now to form an idea about the timeline with the story and players in action.

Plus, the paper referenced above is far more approachable with this take.

Timeline and People from the Book Genius Makers by Cade Metz

1960s — The Perceptron Era

• 1960 — Frank Rosenblatt builds the Mark I Perceptron at Cornell Aeronautical Laboratory, the first hardware neural network.
• 1969 — Marvin Minsky and Seymour Papert publish Perceptrons, demonstrating limits of single-layer networks (the XOR problem). The book contributes to the collapse of neural network research.

1970s — First AI Winter

• 1971 — Geoff Hinton begins his PhD at the University of Edinburgh.
• 1973 — The Lighthill Report triggers the first AI winter; funding for neural network research dries up.
• 1978 — Hinton takes a postdoc at UC San Diego, working with David Rumelhart's group.

1980s — Backpropagation and Revival

• 1982 — Carnegie Mellon University hires Hinton.
• 1984 — Hinton meets Yann LeCun at a workshop in France.
• 1986 — Rumelhart, Hinton, and Williams publish the backpropagation paper in Nature, reviving neural networks. LeCun joins Bell Labs and develops LeNet for handwritten digit recognition.
• 1987 — Hinton moves to the University of Toronto.
• 1989 — Dean Pomerleau builds ALVINN at CMU, an autonomous vehicle steered by a neural network.

1990s — Quiet Years

• 1992 — Bengio and LeCun meet at Bell Labs.
• 1993 — University of Montreal hires Yoshua Bengio.
• 1998 — Hinton founds the Gatsby Computational Neuroscience Unit at University College London.
• 1990s–2000s — Second AI winter; neural networks are out of fashion.

2000s — Rebranding and Resurgence

• 2000 — Hinton returns to the University of Toronto.
• 2003 — LeCun joins NYU.
• 2004 — CIFAR begins funding the Neural Computation and Adaptive Perception (NCAP) workshops, organized by Hinton.
• 2007 — Hinton coins the term "deep learning".
• 2008 — Hinton meets Li Deng at the NIPS Whistler workshop.
• 2009 — Hinton visits Microsoft Research; speech recognition collaboration begins.

2010s — The Boom

• 2010 — Mohamed and Dahl visit Microsoft to apply deep learning to speech. DeepMind is founded by Hassabis, Suleyman, and Legg in London. Andrew Ng pitches "Project Marvin" to Larry Page at Google.
• 2011 — Navdeep Jaitly interns at Google. Google Brain is founded. Google deploys deep-learning speech recognition on Android.
• 2012 — The Cat Paper (Le, Ng, Dean) shows unsupervised learning at scale. Ng leaves Google. Hinton interns at Google Brain. AlexNet (Krizhevsky, Sutskever, Hinton) wins ImageNet by a huge margin. DNNresearch is auctioned to Google.
• 2013 — Hinton, Sutskever, and Krizhevsky join Google. FAIR (Facebook AI Research) is founded under LeCun.
• 2014 — Google acquires DeepMind. Goodfellow invents the GAN. Sutskever, Vinyals, and Le publish Seq2Seq.
• 2015 — Hinton spends summer at DeepMind. AlphaGo beats Fan Hui. OpenAI is founded by Musk, Altman, Brockman, Sutskever, and others.
• 2016 — DeepMind Health launches. AlphaGo beats Lee Sedol in Seoul. Qi Lu leaves Microsoft. Google Translate switches to deep learning. Trump elected.
• 2017 — Qi Lu joins Baidu. AlphaGo beats Ke Jie. China announces national AI initiative. Hinton publishes capsule networks. Progressive GANs and deepfakes emerge.
• 2018 — Musk leaves OpenAI's board. Google employees protest Project Maven. BERT released by Google.
• 2019 — Activists protest Amazon's facial recognition. Hinton, LeCun, and Bengio receive the Turing Award. Microsoft invests $1 billion in OpenAI.

2020 — Beyond

• 2020 — Covariant (Pieter Abbeel's startup) deploys a picking robot in a Berlin warehouse, signaling deep learning's arrival in industrial robotics.

The Players

At Google

• Anelia Angelova — computer vision, robotics
• Sergey Brin — Google co-founder
• George Dahl — speech, neural networks
• Jeff Dean — Google Brain co-founder
• Alan Eustace — former SVP of Engineering
• Timnit Gebru — AI ethics
• John Giannandrea — former head of Search and AI
• Ian Goodfellow — inventor of GANs
• Varun Gulshan — diabetic retinopathy AI
• Geoffrey Hinton — deep learning pioneer
• Urs Hölzle — former SVP, Technical Infrastructure
• Alex Krizhevsky — co-creator of AlexNet
• Margaret Mitchell — AI ethics, Model Cards
• Andrew Ng — Google Brain co-founder
• Larry Page — Google co-founder
• Lily Peng — medical imaging AI
• Sundar Pichai — CEO of Alphabet
• Sara Sabour — capsule networks
• Eric Schmidt — former CEO/Chairman
• Ilya Sutskever — Seq2Seq, AlexNet co-author

At DeepMind

• Alex Graves — LSTM, Neural Turing Machines
• Demis Hassabis — co-founder and CEO
• Koray Kavukcuoglu — CTO
• Shane Legg — co-founder, Chief AGI Scientist
• Volodymyr Mnih — Atari DQN
• David Silver — AlphaGo, AlphaZero
• Mustafa Suleyman — co-founder

At Facebook (Meta)

• Lubomir Bourdev — founding member of FAIR
• Yann LeCun — Chief AI Scientist
• Marc'Aurelio Ranzato — early FAIR member
• Mike Schroepfer — former CTO
• Mark Zuckerberg — CEO

At Microsoft

• Chris Brockett — natural language processing
• Li Deng — speech recognition pioneer
• Peter Lee — President, Microsoft Research
• Satya Nadella — CEO

At OpenAI

• Greg Brockman — co-founder
• Elon Musk — co-founder, later departed
• Ilya Sutskever — co-founder, former Chief Scientist
• Wojciech Zaremba — co-founder

At Baidu

• Robin Li — founder, CEO
• Lu Qi — former COO
• Kai Yu — former head of Institute of Deep Learning, founder of Horizon Robotics

At Nvidia

• Clément Farabet — former VP of AI Infrastructure
• Jensen Huang — co-founder and CEO

At Clarifai

• Matthew Zeiler — founder and CEO
• Deborah Raji — interned, exposed bias in NSFW classifiers

In Academia

• Yoshua Bengio — University of Montreal
• Joy Buolamwini — MIT Media Lab, Algorithmic Justice League
• Fei-Fei Li — Stanford, ImageNet creator
• Gary Marcus — NYU, AI critic
• Dean Pomerleau — CMU, ALVINN
• Jürgen Schmidhuber — IDSIA, LSTM
• Terry Sejnowski — Salk Institute, Boltzmann machine

At the Singularity Summit

• Sam Altman — OpenAI CEO
• Peter Thiel — investor, early DeepMind backer
• Eliezer Yudkowsky — AI safety advocate

In the Past (Founding Era)

• Marvin Minsky — MIT, Perceptrons co-author
• Frank Rosenblatt — Cornell, the Perceptron
• David Rumelhart — UCSD, backpropagation
• Alan Turing — pioneer of computing and machine intelligence

Someone compared the nature of our interactions with LLMS, to how we use to ask directions before the era of maps in India.

Strangely this reminds me of exactly how you would navigate in parts of India before the Internet became ubiquitous.

The steps were roughly: Ask a passerby how to get where you want to go. They will usually confidently describe the steps, even if they didn't speak your language. Cheerfully thank them and proceed to follow the directions. After a block or two, ask a new passerby. Follow their directions for a while and repeat. Never follow the instructions fully. This triangulation served to naturally fill out faulty guidance and hucksters.  Never thought that would one day remind me of programming.

I was reading the book, Genius Makers, written by Cade Metz. I was pleasantly surprised to come across the involvement and contributions of Madurai Aravind Eye Hospital to the field of Deep Learning, in year 2015, way before the onset of Large Language Models. The hospital's contributions were huge corpus of the retinal scan images used to identify a condition called diabetic retinopathy and by training the models with these dataset and then research, the models were able to identify the conditions with 90% accuracy.

Here is an excerpt from the chapter of the book.

The Aravind Eye Hospital sits at the southern tip of India, in the middle of a sprawling, crowded, ancient city called Madurai. Each day, more than two thousand people stream into this timeworn building, traveling from across India and sometimes other parts of the world. The hospital offers eyecare to anyone who walks through the front door, with or without an appointment, whether they can pay for care or not. On any given morning, dozens crowd into the waiting rooms on the fourth floor, as dozens more line up in the hallways, all waiting to walk into a tiny office where lab-coated technicians capture images of the backs of their eyes. This is a way of identifying signs of diabetic blindness. In India, nearly 70 million people are diabetic, and all are at risk of blindness. The condition is called diabetic retionopathy, and if detected early enough, it can be treated and stopped. Each year, hospitals like the Aravind scan millions of eyes, and then doctors examine each scan, looking for the tiny lesions, hemorrhages, and subtle discolorations that anticipate blindness. The trouble is that India doesn't produce enough doctors. For every 1 million people, there are only eleven ophthalmologists, and in rural areas, the ratio is even smaller. Most people never receive the screening they need. But in 2015, a Google engineer named Varun Gulshan hoped to change that. Born in India and educated at Oxford before joining a Silicon Valley start-up that was acquired by Google, he officially worked on a virtual-reality gadget called Google Cardboard. But in his "20 percent time," he started exploring diabetic retinopathy. His idea was to build a deep learning system that could automatically screen people for the condition, without help from a doctor, and so identify far more people that needed care than doctors ever could on their own. He soon contacted the Aravind Eye Hospital, and it agreed to share the thousands of digital eye scans that he would need to train his system. Gulshan didn't understand how to read these scans himself. He was a computer scientist, not a doctor. So he and his boss roped in a trained physician and biomedical engineer named Lily Peng, who happened to be working on the Google Search Engine. Others had tried to build systems for automatically reading eye scans in the past, but these efforts had never matched the skills of a trained physician. The difference this time was that Gulshan and Peng were using deep learning. Feeding thousands of retinal scans from the Aravind Eye Hospital into a neural network, they taught it to recognize signs of diabetic blindness. Such was their success, Jeff Dean pulled them into the Google Brain lab, around the same time that DeepMind was tackling the game of Go. The joke among Peng and the rest of her medically minded team was that they were a cancer that metastasized into the Brain. It wasn't a very good joke. But it was not a bad analogy. When Ilya Sutskever published the paper that remade machine translation—known as the Sequence to Sequence paper—he said it was not really about translation. When Jeff Dean and Greg Corrado read it, they agreed. They decided it was an ideal way of analyzing healthcare records. If researchers fed years of old medical records into the same kind of neural network, they decided, it could learn to recognize signs that illness was on the way. "If you line up the medical records data, it looks like a sequence you're trying to predict," Dean says. "Given a patient in this particular stage, how likely are they to develop diabetes in the next 12 months? If I discharge them from the hospital, will they come back in a week?" He and Corrado soon built a team inside Google Brain to explore the idea. It was in this environment that Lily Peng's blindness project took off—to the point where a dedicated healthcare unit was established inside the lab. Peng and her team acquired about one hundred and thirty thousand digital eye scans from the Aravind Eye Hospital and various other sources, and they asked about fifty-five American ophthalmologists to label them—to identify which included those tiny lesions and hemorrhages that indicated diabetic blindness was on the way. After that, they fed these images into a neural network. And then it learned to recognize telltale signs on its own. In the fall of 2016, with a paper in the Journal of the American Medical Association, the team unveiled a system that could identify signs of diabetic blindness as accurately as trained doctors, correctly spotting the condition more than 90 percent of the time, which exceeded the National Institutes of Health's recommended standard of at least 80 percent. Peng and her team acknowledged that the technology would have to clear many regulatory and logistical hurdles in the years to come, but it was ready for clinical trials.

They ran one trial at the Aravind Eye Hospital. In the short term, Google's system could help the hospital deal with the constant stream of patients moving through its doors. But the hope was that Aravind would also deploy the technology across the network of the more than forty "vision centers" it operated in rural areas around the country where few if any eye doctors were available. Aravind was founded in the late 1970s by a man named Govindappa Venkataswamy, an iconic figure known across India as "Dr. V." He envisioned a nationwide network of hospitals and vision centers that operated like McDonald's franchises, systematically reproducing inexpensive forms of eyecare for people across the country. Google's technology could play right into this idea—if they could actually get it in place. Deploying this technology was not like deploying a website or a smartphone app. The task was largely a matter of persuasion, not only in India but in the U.S. and the UK, where many others were exploring similar technology. The widespread concern among healthcare specialists and regulators was that a neural network was a black box. Unlike with past technologies, hospitals wouldn't have the means to explain why a diagnosis was made. Some researchers argued that new technologies could be built to solve this issue. But it was a far from trivial problem. "Don't believe anyone who says that it is," Geoff Hinton told the New Yorker in a sweeping feature story on the rise of deep learning in healthcare. Still, Hinton believed that as Google continued its work with diabetic retinopathy and others explored systems for reading X-rays, MRIs, and other medical scans, deep learning would fundamentally change the industry. "I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon," he said during a lecture at a Toronto hospital. "You're already over the edge of the cliff, but you haven't yet looked down. There's no ground underneath." He argued that neural networks would eclipse the skills of trained doctors because they would continue to improve as researchers fed them more data, and that the black-box problem was something people would learn to live with. The trick was convincing the world it was not a problem, and this would come through testing—proof that even if you could not see inside of them, they did what they were supposed to do. Hinton believed that machines, working alongside doctors, would eventually provide a hitherto impossible level of healthcare. In the near term, he argued, these algorithms would read X-rays, CAT scans, and MRIs. As time went on, they would also make pathological diagnoses, reading Pap smears, identifying heart murmurs, and predicting relapses in psychiatric conditions. "There's much more to learn here," Hinton told a reporter as he let out a small sigh. "Early and accurate diagnosis is not a trivial problem. We can do better. Why not let machines help us?" This was particularly important to him, he said, because his wife had been diagnosed with pancreatic cancer after it advanced beyond the stage where she could be cured.

When we are troubleshooting kubernetes, we often see this in the logs.

E0317 12:34:56.789 leaderelection.go:367] failed to renew lease: context deadline exceeded

What has happened is the Controller has failed to renew the lease and is not able to serve your request. Well that is pretty much obvious from the error message itself. But let us see it in a bit more detail.

Kubernetes Controllers are the manifestation of the control loop mechanism in action for the cluster state. Just like a thermostat in the room, which powers on or off to bring the temperature of your room to the desired level.

The kubernetes controllers bring the state of the cluster to the one defined in your yaml files. You tell Kubernetes "I want 3 replicas of my app." The controller's job is to constantly check: "Are there 3 replicas running right now?" If not, it creates or kills pods until reality matches the wish. This pattern is called reconciliation, closing the gap between the desired state and the actual state. When you update a deployment in Kubernetes, it kills the previously running pods and brings up the number of pods to the required number we want. This is the job of the deployment controller.

Kubernetes Control Plane comes with the default set of controllers that are responsible for the basic things like creating a job, running your deployment, creating the replica set. You can see those control plane controllers if you run your own Kubernetes cluster.

We often tend to extend Kubernetes by running our own controllers too, which work on specific type of jobs. Those can be run on the Control Plane or at the application level on the data plane. The controllers we build are built or extended using the controller-runtime package, which is a wrapper over client-go where the logic to capture the lease and become the leader is encoded. Any controller using controller-runtime / client-go doesn't just run, it first competes for a lease object stored in the etcd via the API server. Only the pod holding the lease runs the reconcile loop. Everything else waits.

Inside each of the Kubernetes Controller there are few processes.

There is an Informer, a long-lived watch connection to the API Server. It maintains a local cache of all the objects the controller cares about. The pods, nodes, all the custom resources. When something changes, it doesn't re-list everything, it receives a stream of events. The informer does not call the reconcile function directly. Instead, it drops the object's key (like the default/my-pod) into a rate limited work queue. This gives the deduplication for free. If the same object changes 10 times a second, the reconciler only runs once because of the queue.

The reconcile function is the main part. It receives the Request containing the object's name / namespace. It reads the current state from the cache (lister), compares it to the desired state, and makes API calls to fix any drift. If it returns an error, the key goes back in the queue with exponential back off.

Leader election is how Kubernetes ensures only one instance of a controller is actively doing work at any time, even if multiple replicas are running. The leader election is the mechanism for the locking in our concurrency scenario. Only one leader writes to ensure consistency of writes. Without single-leader, every reconcile action risks being duplicated.

In the entire leader election process, the pods try to get the lease object and inform the API server that the particular pod is the leader and it will be doing the operations. It constantly renews the lease for its leadership, and after lease expiry another pod of the same controller process can become the leader too.

In a Kubernetes cluster we often run many controllers. They can be seen using

kubectl get pods --all-namespaces -l app.kubernetes.io/component=controller

NAMESPACE       NAME                                        READY   STATUS    RESTARTS      AGE
cert-manager    cert-manager-7d678bfb4f-6hnhp               1/1     Running   1 (15d ago)   101d
ingress-nginx   ingress-nginx-controller-5dc6969574-lgf7h   1/1     Running   0             101d

For instance, I am running cert-manager and the ingress-nginx controller in my cluster. cert-manager provides HTTPS certificates and the ingress controller is responsible for allowing external traffic inside my cluster. It allows the public internet to reach the apps that run inside my cluster, like visiting a website or using an app on my cluster. That's the entry point there. These controllers ensure that adequate resources are present to handle the request.

And you can see the control plane controllers using

kubectl get pods -n kube-system -l component=kube-controller-manager

VPC Resource Controller was one of the controllers that runs on a Kubernetes Control Plane. It is the default in Amazon's Elastic Kubernetes Service offering, but it is open source, and customers can run it in their cluster when they want to utilize Amazon's Security Group feature at the pod level. Running into failed to renew lease: context deadline exceeded was something I had seen quite a few times when working with this controller. There can be multiple reasons for the leader election failure, as it is usually called. There were many reasons for it, like the API server not being reachable due to network partitioning, a slow API server or load on etcd, lease objects deleted by mistake, or the namespace getting deleted. Or the misconfiguration of the Role Based Access Control (permissions thing), which caused the leader election loss too. We had to update the controller with the correct permissions, and it gets running again.

Kubernetes workloads are containers that are running on the machines. The containers are instances of an application created from a container image. Container images are bundled with the application and its dependencies. We build container images with a Dockerfile and its dependencies. The Dockerfile is how we define the container image that runs as the workload.

The lifecycle of the pod from the docker image to a running container goes like this. When you run your application, you communicate with the API Server, which is called the Control Plane, and you ask the API Server to run your application, which is called a Pod on the Worker Node.

When updating the application, very often run into a issue where the pod is struck in the Pending State and hasn't transitioned to running state yet.

This is the lifecycle of pod when the when the deployment is updated to the point when it is run.

The control flows through the API Server to the Scheduler and to the Kubelet. When you update your deployment, the API server accepts the new podspec. PodSpec is a type of resource defined in the Kubernetes objects. When we update our application in the Kubernetes cluster, which is a Deployment, the API Server accepts a new PodSpec, the scheduler picks up a node that meets the criteria for running the application. Updating the deployment usually means it picks the nodes where the pods are already running, and it needs to update the image of the currently running pod.

The Scheduler picks the node and the Kubelet on the node receives the pod assignment via its watch on the API server. The kubelet's syncPod loop kicks in. In the end, the Kubelet's syncPod method calls into the container runtime to ensure that the pods containers are running. Before starting any container, it must ensure that the image of the container is present.

Kubelet does not pull the images itself. It calls another service called Container Runtime Interface (CRI) PullImage Remote Procedure Call.

The imagePullPolicy that we define in the YAML files determines how the Kubelet's image manager will pull the image. The imagePullPolicy matters enormously here. With Always (default for :latest), it always contacts the registry. With IfNotPresent, it checks the local image store first.

The next stage in the pipeline is where containerd resolves and pulls the image layers. containerd, in most cases, and sometimes a program called CRI-O, receives the PullImage RPC from Kubelet, and it resolves the image tag to a digest, and pulls each layer. In the image pull, each layer is downloaded as a compressed blob over HTTPS, then decompressed and unpacked to disk. The key path on the disk is /var/lib/containerd/io.containerd.snapshotter.v1.overlayfs/

There is a process called Snapshotter. The snapshotter creates a directory per layer. When all the layers are ready, it mounts them as a union file system, the lower layers are read-only and the container gets a thin writable layer on the top.

When you update your application and the worker node does not have enough disk capacity to accommodate the uncompressed image, the application fails to update. The previous images and containers will still be running, but the new ones would not have been updated.

And imagine if your application is served by hundreds of pods on the node, and suddenly a few of the containers will try to download and will be stuck in "ErrImagePull".

Your new pods will be in the Pending state and only on close inspection will you see the "reason": "ErrImagePull".

{
  "kind": "Pod",
  "apiVersion": "v1",
  "metadata": {
    "name": "gpu-service-7f8d9c6b4-xk2pn",
    "namespace": "production"
  },
  "status": {
    "phase": "Pending",
    "conditions": [
      {
        "type": "PodScheduled",
        "status": "True"
      }
    ],
    "containerStatuses": [
      {
        "name": "gpu-service",
        "state": {
          "waiting": {
            "reason": "ErrImagePull",
            "message": "rpc error: code = Unknown desc = failed to pull and unpack image: write /var/lib/containerd/io.containerd.snapshotter.v1.overlayfs/snapshots/4192/fs/usr/lib/x86_64-linux-gnu/libcuda.so.535.129.03: no space left on device"
          }
        },
        "image": "your-registry.ecr.us-east-1.amazonaws.com/gpu-service:v2.3.1-ubuntu",
        "imageID": ""
      }
    ]
  }
}

And the kubelet events can give you the cause.

And the events you would see:

{
  "kind": "Event",
  "apiVersion": "v1",
  "metadata": {
    "name": "gpu-service-7f8d9c6b4-xk2pn.17e2a3b4c5d6e7f8"
  },
  "involvedObject": {
    "kind": "Pod",
    "name": "gpu-service-7f8d9c6b4-xk2pn"
  },
  "reason": "Failed",
  "message": "Failed to pull image \"your-registry/gpu-service:v2.3.1-ubuntu\": rpc error: code = Unknown desc = write /var/lib/containerd/...: no space left on device",
  "type": "Warning",
  "count": 3,
  "source": {
    "component": "kubelet",
    "host": "ip-10-0-1-47.ec2.internal"
  }
}

When working with GPU Services, the images often are very large in size. Not ensuring that nodes will have sufficient capacity will often put your workloads in the Pending state with Disk Pressure issues. These are some of the easiest to troubleshoot, but resolution time is not quick here. You will have to go back to source to rebuild the application and ensure that your image is constrained to an acceptable size, rebuild and push to the registry, update your Helm charts or manifests and then apply it again.

This process can be time consuming to resolve. In production critical scenarios, we have resolved to increase the disk size of the worker nodes quickly by attaching additional volume mounts so that worker nodes are given additional capacity.

One of the best approaches I have come across in solving these issues is to have a CI check which can detect if the image size has increased a lot. This can save a lot of pain later down the road.

One of my first experiences with Kubernetes was with using Prow. Prow is a Kubernetes CI/CD system. In the Mesosphere/D2IQ days, I had to transition the CI jobs that we were running in Jenkins on Mesos/Marathon to Prow on Kubernetes. In order to learn Kubernetes, I suggested we set up Prow to run the CI jobs for our Mesosphere/D2IQ frontend. That was the first task that I undertook that would help me understand Kubernetes . It was simple but a critical CI/CD system that handles job execution and GitHub automation through policy enforcement. Anyone who has either contributed to Kubernetes or worked with the testing-sig will have come across the weird-looking Prow robots working with your pull requests. And in the GitHub-based ChatOps, there are multiple /slash-commands that are utilized to interact with the state of the pull request.

The component listeners listen to these webhooks and act on the state and the command. Just like regular CI/CD systems, they trigger a job on the event, and the job usually clones the repository at the pull request HEAD and runs a script that exercises the tests and finally gives a success or failure signal. The intelligence of running the tests is baked into the script that runs the tests. Prow does not do anything fancy that is expected from any continuous integration system. It simply runs the job and returns the results.

The first task before setting up the jobs was setting up the Kubernetes cluster and then deploying Prow.

The various nouns in this diagram are the components of the Prow that we will see.

GitHub sends webhooks to Prow. Prow runs your tests as Kubernetes pods. Prow reports the results back to GitHub. That's it. The core pipeline flows down the center: Hook receives GitHub webhooks, creates a ProwJob custom resource, the Controller Manager picks it up and schedules a pod in the Cluster.

So the setup chain was simple.

I used Terraform to manage the infrastructure state. Terraform was used to create the S3 buckets to store the Terraform state. We used to have a DynamoDB table for locking. It surprises me in hindsight, and perhaps even surprised me back then, but we did use a DynamoDB table just for locking. This is to avoid multiple terraform apply commands acting on the same cluster. The cluster creation was controlled with users with IAM permissions for various operations.

This is how we built the infrastructure for Kubernetes. Any Kubernetes distribution can be used; at that time, I remember we were using a home-built one, which the company, D2IQ was selling. For the worker nodes, I had provisioned very large instances — ones with 16 vCPUs and 64 GB of RAM — plenty of headroom for Prow's microservices and the test pods they'd spawn. Since Prow runs the CI job as a Kubernetes job, it needs those resources to spawn the job and run.

I had the cluster infrastructure stored in Terraform state files; Terraform created the S3 buckets, DynamoDB lock table, and the IAM users. For our Kubernetes cluster, I had a Native K8s cluster on EC2 using a home-grown technology called Konvoy while at Mesosphere/D2IQ. I set up the Prow application on the cluster using the Custom Resource Definitions, and since CI systems are often internal, I had to set up an Internal domain configured to point to Prow's Hook and Deck endpoints, and set up cert-manager for TLS on that internal domain.

Once the Kubernetes cluster was provisioned, the cluster came up cleanly. The nodes were healthy, the API server responding, CoreDNS resolving.

I had to apply the ProwJob Custom Resource Definitions to set up the application. We created two secrets that Prow needs to talk to GitHub: an HMAC (Hash-Based Message Authentication Code, a shared secret) token for validating incoming webhooks, and a GitHub App's private key for authenticating API calls. I deployed this full set of Prow components using the S3-backed starter manifest.

Once the Prow system was set up, the Prow services would be running. The Hook component would be waiting for webhooks, Deck serving the UI, the Prow Controller Manager ready to schedule jobs. Components like Tide watch for mergeable PRs, a component called Horologium handles periodic jobs. Systems like Crier report back to GitHub, and Sinker quietly cleans up after everyone else.

Wiring up the domain

When you architect a CI system for developers, as I did, the primary responsibility is to make the system reliable. Prow needs to be reachable from the public internet — specifically, GitHub needs to send HTTPS webhook requests to Hook's endpoint whenever something happens in the subscribed repositories.

That means in the Kubernetes world, you need a domain, an Ingress, and a valid TLS (Transport Layer Security) certificate.

The internal domain had to be provisioned using Terraform, and with the domain I set up the Ingress that routed /hook traffic to the Hook Service and everything else to Deck. Even these were provisioned in the Terraform state files.

We used cert-manager to handle the TLS. I pointed an internal domain at the cluster, set up an Ingress that routed /hook traffic to the Hook service and everything else to Deck, and used cert-manager to handle TLS. cert-manager would automatically provision and renew certificates, wiring them into the Ingress as a Kubernetes Secret. For cert-manager, we configured a ClusterIssuer, created a Certificate resource for the Prow domain, and let cert-manager do its thing.

All of these were done in Terraform state files, so that when we did a terraform apply, the domain was created, the certificate manager was set up, certificates were gathered, and the system was operational serving traffic over HTTPS. Once the certificate was issued and the Ingress had valid TLS termination, we went to the GitHub App at the org-level, set the webhook URL to point at the Prow domain's /hook path, and plugged in the HMAC secret.

It works. Open a test PR, and within seconds the Hook receives the webhook, the trigger plugin creates the ProwJob, the Controller Manager scheduled a pod, the Crier reported results back to GitHub. The green checkmark appearing on the PR status means all is good, and life is good.

Kubernetes Upgrades

Kubernetes upgrades are talked about as a frequent source of pain points. Even when you use a Managed Kubernetes Provider, when you have made complex changes to your cluster and operate business-critical workloads, teams are seldom confident to upgrade their Kubernetes clusters without a good plan in place. So, in my Kubernetes cluster, as we updated our Kubernetes version and Prow version, we had to upgrade the components too. As I explained above, the internal domain over HTTPS was set up with a separate Terraform plan. And when we upgraded cert-manager, it started to fail. That's when the Hook endpoint stopped receiving webhooks from GitHub.

The Failure Chain

Here's what happened:

The cert-manager upgrade caused the webhook pod to restart. During the upgrade, cert-manager's own internal webhook — which validates cert-manager CRDs — went through a transition period where it was unavailable. This was due to a bug in the version of cert-manager we were upgrading to. When cert-manager's cainjector or controller had issues during the upgrade, the Certificate resource couldn't renew and the Secret wasn't properly updated, as the TLS certificate for Prow's Ingress was tied to cert-manager. During the upgrade, the Ingress lost its valid TLS certificate, and without a valid cert, the HTTPS endpoint that GitHub was hitting for webhooks started returning TLS errors.

GitHub webhook deliveries started failing — this can be seen in the GitHub interface showing that webhook deliveries had failed — and all CI/CD stopped. No webhooks meant no presubmit jobs, no postsubmit jobs, no /retest commands, nothing. Prow was running perfectly fine internally, but it was invisible to the outside world.

Everything inside the cluster looked healthy:

# All pods running?

kubectl get pods -n prow
kubectl get pods -n cert-manager

# ProwJobs being created?  No new ones
kubectl get prowjobs -n prow --sort-by=.metadata.creationTimestamp

# Hook logs showing any incoming webhooks? Nothing
kubectl logs -l app=hook -n prow -f

# Certificate status?
kubectl describe certificate prow-tls -n prow
# This is where I finally saw the problem

The kubectl describe certificate output showed the certificate was in a failed state — cert-manager couldn't issue or renew it because the upgrade had left its internal state inconsistent.

# Check cert-manager's own health
kubectl get pods -n cert-manager
kubectl logs -l app.kubernetes.io/name=webhook -n cert-manager

The webhooks were unhealthy, so I had to delete and reinstall cert-manager cleanly.

I was doing everything using Terraform. Manually mutating state when everything else is maintained in Terraform was going to be tricky. Terraform does not like that. Also, certain versions and dependencies of cert-manager were a problem, so I had to find the right combination of versions and encode it.

# If the webhook is unhealthy, you may need to
# delete and reinstall cert-manager cleanly

kubectl delete -f https://github.com/cert-manager/cert-manager/releases/download/v<OLD>/cert-manager.yaml
kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v<NEW>/cert-manager.yaml

Once everything was stable, I had to wait for the cert-manager rollout to stabilize and force the certificate renewal.

# Wait for everything to stabilize
kubectl -n cert-manager rollout status deployment/cert-manager-webhook

# Force certificate renewal
kubectl delete secret prow-tls-secret -n prow
# cert-manager will recreate it from the Certificate resource

# Verify
kubectl describe certificate prow-tls -n prow
# Should show "Certificate is up to date and has not expired"

Once cert-manager was stable, the GitHub webhook deliveries should be back and hooks succeeding again.

kubectl logs -l app=hook -n prow -f
# Should see incoming webhook events

This downtime affected the productivity of my group for multiple hours. And fixing something when things were down was a challenging one.

Some of the Important Lessons I have learned from this experience are

Cert-manager upgrades are not zero-downtime for your services.

When you upgrade cert-manager, its internal admission webhook restarts. During that window, if any Certificate resources need to be reconciled, things can break. If your Ingress TLS depends on cert-manager and the timing is bad, your endpoints go dark. The best way would be to decouple the TLS from cert-manager for critical webhook endpoints during the upgrade.

The Prow Hook endpoint is the single point of entry.

Everything in Prow depends on Hook receiving webhooks. If Hook is unreachable, whether due to TLS issues, DNS problems, or network configuration, the CI/CD pipeline goes silent. This is an important design consideration for anyone designing an application architecture on Prow.

Having a "break glass" procedure for webhook endpoints.

As in, you can manually create a TLS secret and attach it to your Ingress without cert-manager. When cert-manager is the thing that's broken, you need a way to get your endpoints back up while you fix it:

# Create a self-signed cert to restore connectivity
openssl req -x509 -nodes -days 30 -newkey rsa:2048 \
  -keyout /tmp/tls.key -out /tmp/tls.crt \
  -subj "/CN=prow.your-internal-domain.com"

kubectl create secret tls prow-tls-secret \
  --cert=/tmp/tls.crt --key=/tmp/tls.key \
  -n prow --dry-run=client -o yaml | kubectl apply -f -

This was a firefighting story that helped me realize how infrastructure and complex applications stand on some very simple dependencies.

I built a browser plugin called "Visionary" that overlays meaningful descriptions and context directly onto stunning pictures of the day.

A great photograph has a unique power. Whether it comes from National Geographic, NASA, Wikipedia, or Bing, it captures our attention, sparks curiosity, and triggers the inquisitive neurons in our brain to ask questions and seek answers.

I noticed that existing picture-of-the-day plugins were built over two decades ago and never evolved to harness the capabilities of modern artificial intelligence.

AI can transform the picture viewing experience by distilling complex descriptions into accessible insights and providing references to explore the core concepts in the photo more deeply.

This is exactly the picture of the day extension does.

You can get a sense of how this works by visiting https://picture.learntosolveit.com

See a demo here

The extension for various browser stores like Chrome, Firefox, Brave and Edge is available from the website.

Hitori is a Japanese logic puzzle played on a grid of numbers. The objective is to eliminate numbers so that no row or column contains duplicate numbers, all shaded cells are isolated (not touching horizontally or vertically), and unshaded cells create a single connected group, that is you can you move from one unshaded cell to another unshaded cell following the path vertically or horizontally.

Here’s how a typical Hitori puzzle appears:

How to Play:

• Shade cells to eliminate duplicate numbers from each row and column.
• Shaded (black) cells cannot be adjacent to each other vertically or horizontally.
• All non-shaded (white) cells must be connected.

The source code of this project is here https://gitlab.gnome.org/GNOME/hitori and the architecture is explained here.

Biniax is a very simple, but interesting, vertical scrolling , matching game. You have to move you piece to collapse against the piece of same color. The game ends when you are no longer able to collapse the pieces.

This is how we start.

The game is written in ANSI C and uses SDL. I had to fix the source code for compilation, it is maintained here https://github.com/orsenthil/biniax2/tree/main, and the architecture gives the architectural concepts for developement.

Chromono is a visually appealing and aesthetic circular puzzle game. The game engine and physics is enticing. The rules are simple, you only have to make the circle of the same color overlap/touch with circle with the same patch. The game introduce challenges with bounce, physics and tricky circular structures.

The source code lives here, https://github.com/orsenthil/chromono/tree/main, and I had to fix for the build and runtime issues on Linux for OpenGL.

The game is written in C++11, and uses OpenGL. The architecture and technologies to learn are exciting and modern too.

Atomix is a very simple sliding block tiling puzzle game, in which, the user has to slide the atoms to form the molecule given as a challenge.

The source code is maintained here https://gitlab.gnome.org/GNOME/atomix/-/tree/main

The game is written in C, with the object-oriented capabilities provided by a system called GObject. The windowing toolkit used is GTK. The architecture and learning concepts make it a highly approachable game.

Some of the levels in this game look really challenging.

Achilles is a simulation, not really a game. In Achilles, the world is given an X coordinate, Z coordinate, number of food items and number of organisms. The organisms are not given any objective. But they have functions like they have vision, and learning systems powered by neural networks. Food spawns randomly across the world. Food decays if it is left alone. When the organisms see food in proximity, they eat the food. When the organisms see each other, if they are similar, they mate. If there is enough strength and difference, they can attack each other.

And the world evolves!

Here is a screenshot of the evolution

Here is the attack in progress.

The program is written in C++ and uses OpenGL to display the world. The package that is present in many Linux operating systems does not behave the way we want. I had to check out the source code, and fix the bugs in the program.

The source of the maintained version lives in GitHub here: https://github.com/orsenthil/achilles-1; there is a lifetime worth of learning concepts used by this simple program.

This is a port of 2048 game written in QT framework

The source code here https://github.com/xiaoyong/2048-Qt shows a very simple, and modular approach. The QT game load is written in C++, but the game logic itself is written in qml and javascript. The interface components are in C++, the game logic is in Javascript, which has a native integration with QML, Qt Markup Language; and this whole program is orchestrated via QT framework.

Amoebax is a falling puzzle game, tetris like game. The player has to arrange a group of four.

You have to arrange the falling tiles into groups of four. It has a two player game mechanics too, where in, while you try to ensure that you dont crash to the top, when you remove the paired blocks, you send it to your opponent. You win when your opponent's game is full.

The opponent is usually an AI. The game is written in C++, and uses libraries like SDL, zlib, ogg and vorbis.

The game was written by Jordi Frita. The source code is here https://www.emma-soft.com/games/amoebax/download.html and an emscripten port with the source at https://gitlab.com/perita/amoebax/. The online game can be played at https://peritasoft.com/amoebax/index.html.

When examining the source code, it is written in modular C++ and uses multiple game design concepts like state design pattern to manage different game states, singleton for creating an instance of the game, factory to create ai players, strategy to implement player behaviors, template for customizable steps, observer for communication, and RAII for dealing with sound and image resources.

It has become very easy to learn new things. In these series of blog poss, I will be sharing #100games2025, I will be sharing about 100 different new games I have learned in 2025.

The Zebra variant of AisleRiot / Klondike game is played with two decks. The top row has the two decks to arrange. The player has to arrange the cards with opposite colors. Like Black and Red go alternate on top of each. The bottom is same as solitaire, but in the zebra variant, the top row is arranged alternately. When a slot in the bottom row, called tableau has an empty slot, it is automatically filled in with the latest in the waste pile or from the stock pile.

Source Code of Gnome AisleRiot

https://gitlab.gnome.org/GNOME/aisleriot/

The program is written in Guile Scheme. The games are written declaratively, as in the variant rules and handlers are defined, and AisleRiot Framework handles the rendering, input and the game loop.

What Are Tariffs?

Tariffs are taxes that governments put on products coming from other countries. When a company imports goods, they have to pay this extra tax to bring those goods into their country. For example, when the U.S. puts a 50% tariff on Ayurvedic turmeric from India, an American company that wants to buy that turmeric has to pay 50% more than the original price to import it for U.S. consumers. The Indian company selling the turmeric doesn't pay this tax - it's the American importing company that pays it to the U.S. government.

The key insight is that tariffs are always paid by the importing country's businesses, not the exporting country. This is often misunderstood in public debate, but it's crucial to understand who actually bears the cost.

A Specific Example

Let's say a person in Los Angeles, CA wants to buy a toy at Walmart:

• Without tariffs: A toy made in China costs $10 at Walmart

• With 30% tariff: Walmart pays $3 extra in tariffs to the U.S. government, so the same toy now costs $13 at Walmart ($10 + $3 tariff passed to customer)

• Result: American buyers pay higher prices for the same products, or they might choose to buy fewer toys, or look for American-made alternatives if available

When tariffs are imposed by the U.S. government, any U.S. importing company pays the money to the U.S. government as a tax for importing those goods. For example, Walmart, the largest U.S. retailer (headquartered in Bentonville, Arkansas), imports electronics, toys, and household goods from Chinese manufacturers. When Walmart imports these products from China, Walmart pays the tariff to the U.S. government. Walmart then adjusts the price and profit margins to remain competitive in the U.S. consumer market. According to Walmart CFO John David Rainey, about 34% of Walmart's imports come from China, and the company has had to raise prices on toys, electronics, and household goods due to tariffs.

The theory of tariffs supports the domestic production economy of the U.S. However, if there are no domestic U.S. alternatives available, the importing company pays more taxes to the U.S. government and raises the prices of products for U.S. consumers, with consumers bearing the full cost increase.

How Tariffs Work Step by Step

Companies handle this in three ways:

1. Raise prices for customers (happens most often): Add the full tariff cost to the selling price
2. Keep prices the same: Make less profit to stay competitive
3. Split the difference: Raise prices a little and accept lower profits

The 2025 U.S. Tariff Changes: From Low to High

In 2025, the United States made big changes to its tariff system. The average tariff rate went from 2.5% to 18.6% by August 2025. This was the highest level since 1934. These changes affect over $2.3 trillion worth of products coming into the U.S.

When the Changes Happened

Impact Matrix (US vs India × Producers vs Consumers)

Cross-reference guide for readers:

• US Consumers → see “Consumer Price Effects” (US perspective and product examples)
• US Producers → see “Global Economic Reshaping” (US Domestic Winners/Losers)
• India Producers → see “Indian Economy – Sectoral Impact” and “New Trade Patterns Emerging”
• India Consumers → see “From an Indian Consumer Perspective”

The US-India Trade Relationship - A perspective

Bilateral Trade Volume: US-India total goods trade was estimated at $129.2 billion in 2024. That is US selling about $41.5 billion dollars worth of goods to India, and India selling about $87.3 billion dollars worth of goods to US.

Key Trade Sectors

India's Top Exports to the US:

• Engineering goods, electronic items, and pharmaceuticals account for over 51% of total exports
• Gems & jewelry contribute significantly to Indian exports to the US
• Textiles and garments represent a major labor-intensive export sector
• Chemicals and auto components
• Marine products and agricultural items

US Exports to India:

• Mineral fuels and oils
• Precious stones and gems
• Nuclear reactors and machinery
• Agricultural products (a contentious area due to Indian protections)
• Defense equipment and technology

How Tariffs Worked Before 2025

Before 2025, there was a big difference in how much each country charged for imports:

From US Perspective:

• When Americans bought Indian products, they paid very low tariffs (around 3.3% extra)
• A $100 Indian shirt cost about $103 in American stores
• American farm products faced much higher tariffs when sold to India (39% average)
• A $100 American agricultural product cost $139 for Indian buyers

From Indian Perspective:

• When Indians bought American products, they paid much higher tariffs (17% average for most goods, 39% for farm products)
• A $100 American iPhone cost about $117 in Indian stores due to tariffs
• A $100 American agricultural product cost $139 due to India's high farm tariffs
• This made American products much more expensive for Indian families

India: With the Tariff Rate of 50%

Impact on U.S. Consumers:

• U.S. consumers will pay 50% more on $87 billion worth of Indian products (jewelry, clothing, spices, textiles) - that's 20% of all products Americans buy from India
• India's economy could shrink by 0.6% because when American consumers buy fewer Indian products due to higher prices, Indian companies lose sales and may have to cut jobs.
• $8 billion worth of specific products Americans regularly buy will become much more expensive: gold jewelry at stores like Kay Jewelers, clothing from Indian textile companies, spices and turmeric at grocery stores, and chemicals used in American manufacturing

How India has Responded:

• Energy Diplomacy: India increased its oil purchases from the U.S. by 120% in the last six months, which was one of Trump's primary demands when Modi visited the White House in February 2025. However, this meant Indian consumers paid more for energy as the government shifted from cheaper Russian oil to more expensive American oil, reducing Indians' purchasing power for other goods. The challenge is that Russian oil often costs $10-15 per barrel less than American alternatives, making this a significant fiscal burden.

• Trade Concessions Offered: India offered to remove tariffs on 55% of U.S. imports (worth $23 billion). This would make American products cheaper for Indian consumers - for example, a $500 iPhone might cost $400 instead. However, this could hurt Indian companies that compete with American products, potentially leading to job losses in Indian manufacturing. India also scrapped its 6% digital services tax as a goodwill gesture.

• Protecting Indian Interests: India refused to change rules for farming and dairy, keeping protection for hundreds of millions of farmers and dairy workers from cheaper American agricultural imports. This means Indian consumers continue paying higher prices for milk and farm products, but it saves jobs for rural Indian workers who might not be able to compete with large American farms. The cost of allowing U.S. dairy exports into India is expected to cost India 1.8 lakh crore rupees ($20 billion) alone, according to SBI Research.

• Defense Procurement Pause: India paused purchases of American missiles and vehicles as a retaliation method, according to Reuters reports citing three Indian officials. This includes discussions on purchases of Stryker combat vehicles from General Dynamics and Javelin antitank missiles from Raytheon and Lockheed Martin. This costs U.S. defense companies billions in lost export revenue and could lead to job losses at American defense manufacturers that depend on international sales.

Consumer Price Effects

From a US Consumer Perspective:

American families are experiencing what economists describe as the largest peacetime tax increase in nearly a century. The 2025 tariffs imply an increase in consumer prices of 1.8% in the short-run, equivalent to a $2,400 loss of purchasing power per household on average in 2025 dollars.

The distributional impact reveals the regressive nature of tariffs:

• Poorest families (bottom decile): Additional $1,300 per year in costs
• Middle-class families: Additional $2,200 per year in costs
• Wealthy families (top decile): Additional $5,000 per year in costs

While wealthy families pay more in absolute terms, poor families are hit harder proportionally because they spend a larger percentage of their income on imported goods and have less flexibility to substitute expensive imports with domestic alternatives.

Specific Product Impact Examples:

• Clothing: Indian-made shirts that cost $25 now cost $37.50 in US stores (50% increase)
• Jewelry: A $1,000 gold necklace from India now costs $1,500 at Kay Jewelers
• Spices and Food: Turmeric, cardamom, and other spices from India cost 50% more at grocery stores
• Electronics: Smartphones with Indian components face 37% price increases
• Home Goods: Leather products like shoes and handbags see 39% price increases

Regional Impact Variations: The impact varies significantly by region and demographics:

• Urban areas: Face higher impact due to greater consumption of imported goods
• Rural areas: Somewhat insulated due to higher consumption of domestically-produced food and goods
• Ethnic communities: Indian-American communities face particularly severe impact as cultural products become prohibitively expensive

Real Family Examples: Consider a middle-class family in Ohio that previously spent $200 per month on Indian-made textiles, household goods, and spices. They now spend $300 for the same items, representing $1,200 annually in reduced purchasing power. For families already stretched by inflation in housing and healthcare, this represents a significant reduction in living standards.

Long-term Behavioral Changes: Beyond immediate price increases, families are changing consumption patterns:

• Substituting higher-quality Indian products with lower-quality alternatives from other countries
• Reducing overall consumption of affected product categories
• Shifting spending away from discretionary items toward necessities
• Increasing demand for domestic alternatives where available

From an Indian Consumer Perspective:

Indian consumers face a more complex set of pressures that arise both from their government's policy responses to US tariffs and from broader economic effects of the trade dispute.

Energy Cost Impact: India's decision to increase US oil purchases by 120% means Indian consumers pay more for energy. When the government shifts from cheaper Russian oil (often $10-15 per barrel less expensive) to American oil, this cost difference flows through to gasoline, diesel, and electricity prices. For a typical Indian family spending 15-20% of income on energy, this represents a meaningful reduction in purchasing power.

Currency Effects: The Indian rupee has weakened in offshore markets due to trade tensions, making all imports more expensive. When companies face higher costs due to currency depreciation, they often pass these costs to Indian consumers through higher prices for:

• Imported electronics and technology
• Petroleum products
• Industrial inputs that affect manufactured goods prices
• International travel and education costs

Retaliatory Impact: If India responds with its own tariffs on American products, Indian consumers could face higher prices for:

• iPhones and other American technology products
• American agricultural products (though these are already heavily protected)
• Industrial machinery and equipment
• Entertainment and media services

Domestic Market Protection: However, Indian consumers have significant protection compared to their American counterparts. Over 60% of India's GDP comes from domestic consumption, meaning most Indians buy locally-made products and won't directly feel the tariff impact. A typical family in Mumbai buying:

• Clothes from Indian manufacturers
• Food from domestic producers
• Electronics assembled in India (though components may be imported)
• Services provided locally

This family experiences limited direct impact from US tariffs, though they may face indirect effects through currency depreciation and energy costs.

Income Effects: The broader economic impact of lost export revenues could affect employment and wages in export-oriented sectors, indirectly reducing purchasing power for families connected to these industries.

Global Economic Reshaping

Trade Flow Changes

The US-India tariff dispute is creating clear winners and losers in the global economy, with effects that extend far beyond the two countries involved.

US Domestic Winners:

• American textile manufacturers: May gain customers as Indian clothing becomes too expensive, though this depends on their ability to scale production rapidly
• US steel and aluminum producers: Benefit from 50% tariffs on these materials from all countries, including India
• American jewelry manufacturers: Could capture market share from Indian competitors, though they face higher input costs for raw materials
• US agricultural exporters: May benefit if India retaliates against competitors like Australian beef or Brazilian soybeans

US Domestic Losers:

• American consumers: Face $2,400 average annual cost increases per household
• Import-dependent businesses: Pharmaceutical companies relying on Indian generic drug ingredients, electronics manufacturers using Indian components, and retailers selling Indian-made goods all face margin pressure
• Export industries: US defense contractors lose billions in Indian defense procurement; other exporters may face Indian retaliation

Indian Economy - Sectoral Impact:

• Export sectors: Textiles, jewelry, marine products, and leather face severe pressure with potential job losses affecting millions
• Domestic-focused sectors: IT services, FMCG, banking, and domestic manufacturing remain relatively insulated
• Energy sector: Benefits from increased US oil purchases but faces higher costs
• Defense manufacturing: May benefit from import substitution as India reduces US defense purchases

Third-Country Beneficiaries:

• Vietnam and Bangladesh: Gaining textile and manufacturing orders as US buyers shift away from India
• European Union: Seeing increased Indian export focus as companies diversify away from US markets
• Middle Eastern countries: Benefiting from Indian energy diplomacy and increased trade focus
• China: Indirectly benefits as US-India tensions push India toward alternative partnerships

New Trade Patterns Emerging

India's Strategic Diversification:

India is rapidly accelerating export diversification to reduce dependence on the US market:

• European Focus: Indian exports to EU countries up 9% as companies pivot away from the US market. Industries are fast-tracking the India-EU trade agreement negotiations to formalize this shift.
• Asian Integration: India is planning Modi's first visit to China since 2020 at the Shanghai Cooperation Organisation summit, signaling potential reconciliation with China despite border disputes.
• Middle Eastern Expansion: Leveraging cultural and business ties to expand in Gulf markets, particularly in textiles and jewelry.
• ASEAN Engagement: Deepening integration with Southeast Asian economies as both an export destination and a manufacturing partnership hub.

US Supply Chain Reorganization:

American businesses are implementing "China Plus One" strategies that now need to become "China Plus India Plus One" strategies:

• Mexican Manufacturing: USMCA benefits making Mexico increasingly attractive for US companies seeking to avoid high tariffs
• Vietnamese Electronics: Despite 20% tariffs, Vietnam remains more competitive than India for electronics assembly
• Domestic Reshoring: Some production returning to US, though limited by cost considerations and skilled labor availability

Global South Coordination:

Countries facing high US tariffs are increasingly coordinating responses:

• BRICS Cooperation: India working more closely with Brazil (also facing 50% tariffs), China, and Russia on alternative trade arrangements
• South-South Trade: Accelerating trade relationships between developing countries to reduce dependence on US market access
• Alternative Payment Systems: Exploring non-dollar denominated trade to reduce US financial system leverage

These changes in 2025 has caught everyone by surprise, and this changing the world economic order in ways that will have be dealt with.

Becoming Kareem will always be a special book for me because this is the first book that my Son, Siddhartha, recommended and gifted me to read, after he read it. He had purchased this book from his Montevideo school library sales, and had enjoyed reading it.

At first, I thought it was a chapter book designed for early readers, but man, how wrong I was.

This book is about self-discovery. It is about a journey of a very relatable boy, sharing his story with his friends, his interests in basketball, his thoughts about his school, coaches, and his civics.

I learned so many things about Kareem Abdul-Jabbar, and how he became what he did. I learned about Muhammad Ali, which I hadn't known before, and my respect for him grew a lot more. I learned more about Malcolm X, and I am eager to read his autobiography now.

It is a wonderful book, and it was a joy for me to read this book.

Here is my son's review of the book.

This is a coin flip simulator. It compare theoretical binomial distribution with experimental results

listings/python/coinflip.py (Source)

import random
import math
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter

def Nchoosek(N, k):
    return math.factorial(N) / (math.factorial(k) * math.factorial(N - k))

def binomial(n,k, p):
    return Nchoosek(n, k) * math.pow(p, k) * math.pow(1-p, n-k)


def run_experiment(n_flips, n_experiments, p_heads=0.5):
    """
    Simulate coin flips and count number of heads.

    :param n_flips: Number of coin flips per experiment.
    :param n_experiments: Number of experiments to run.
    :param p_heads: Probability of getting heads (default 0.5 for a fair coin)

    :return: List of head counts for each experiment.
    """
    results = []

    for experiment in range(n_experiments):
        flips = [random.random() < p_heads for _ in range(n_flips)]
        heads_count = sum(flips)
        results.append(heads_count)

    return results


def compare_theoretical_vs_experimental():
    """Compare theoretical binomial distribution with experimental results."""
    # Parameters
    n_flips = 10          # Number of coin flips per experiment
    n_experiments = 1000  # Number of experiments to run
    p_heads = 0.5         # Probability of heads (fair coin)

    # Run the experiment
    print(f"Running {n_experiments} experiments with {n_flips} coin flips each...")
    results = run_experiment(n_flips, n_experiments, p_heads)

    # Count occurrences of each number of heads
    result_counts = Counter(results)

    # Convert to probabilities
    experimental_probs = {k: v/n_experiments for k, v in result_counts.items()}

    # Calculate theoretical probabilities using your binomial function
    theoretical_probs = {k: binomial(n_flips, k, p_heads) for k in range(n_flips + 1)}

    # Prepare data for plotting
    k_values = list(range(n_flips + 1))
    theoretical_values = [theoretical_probs[k] for k in k_values]
    experimental_values = [experimental_probs.get(k, 0) for k in k_values]

    # Plot the results
    plt.figure(figsize=(12, 6))

    # Bar width and positions
    width = 0.35
    x = np.array(k_values)

    # Create bars
    plt.bar(x - width/2, theoretical_values, width, label='Theoretical Probability')
    plt.bar(x + width/2, experimental_values, width, label='Experimental Probability')

    # Add labels and title
    plt.xlabel('Number of Heads')
    plt.ylabel('Probability')
    plt.title(f'Binomial Distribution: {n_flips} coin flips, p(heads)={p_heads}')
    plt.xticks(k_values)
    plt.legend()
    plt.grid(axis='y', linestyle='--', alpha=0.7)

    # Add a text box with statistics
    mean_theoretical = n_flips * p_heads
    var_theoretical = n_flips * p_heads * (1 - p_heads)
    mean_experimental = sum(k * count for k, count in result_counts.items()) / n_experiments

    stats_text = f"Theoretical Mean: {mean_theoretical}\n"
    stats_text += f"Theoretical Variance: {var_theoretical}\n"
    stats_text += f"Experimental Mean: {mean_experimental:.2f}"

    plt.figtext(0.15, 0.8, stats_text, bbox=dict(facecolor='white', alpha=0.8))

    # Show plot
    plt.tight_layout()
    #plt.show()
    plt.savefig('coinflip.png')

    # Print summary
    print("\nSummary:")
    print(f"Theoretical Mean: {mean_theoretical}")
    print(f"Theoretical Variance: {var_theoretical}")
    print(f"Experimental Mean: {mean_experimental:.2f}")

    # Print probabilities table
    print("\nProbabilities Table:")
    print("Heads | Theoretical | Experimental | Difference")
    print("-" * 50)
    for k in k_values:
        theo = theoretical_probs[k]
        exp = experimental_probs.get(k, 0)
        diff = exp - theo
        print(f"{k:5d} | {theo:.6f} | {exp:.6f} | {diff:.6f}")

compare_theoretical_vs_experimental()

A minimalist browser extension that displays a new quote in the browser tab. For example, when you open a new tab, you will see quotes like this:

"And this is aviation; I give it to the world." - Louis Mouillard, French Inventor/Aeronaut (1834-1897)

"We were on the point of abandoning our work when the book of Mouillard fell into our hands, and we continued with the results you know." - Wilbur Wright, American Inventor/Aviator (1867-1921)

This extension is named after popular Unix Program Fortune_(Unix) fortune is a program that displays a pseudorandom message from a database of quotations. , a command line utility which displays quotes in the shell. The browser with tabs is a modern shell interface of the computer. And this is the fortune program for the browser.

Screenshot

Video Demo

Teamcity is my favorite CI/CD tool. I like the intuitive architecture, and the ability to customize the process. The hosted version of both the servers and agents provide a lot of flexibility, and provides a transparent window into the build process that I appreciate.

Configuring teamcity server and agent will require a bit of work. So, I created this repository with the scripts to setup the server and agent.

https://github.com/orsenthil/teamcity

I was looking for a simple and full featured django react app as starter template that I can use to fork and build new apps. I ended up creating one which is very simple, but still demonstrates a full Django app, with django backend and flask frontend with moden design and layout.

It provides an out-of-box functionality for

• Social Login

• Encrypted Data

• Support for AI with client-side API key storage.

Apart from the code, which is open source, the app is a private journaling app that users can use to keep track of their thoughts; the encrypted data storage should provide a feeling of safety to the users. when configured with AI, the system will analyze the most recent thoughts use the principles of REBT and share it's analysis to the user.

Try the hosted application of this app to know more https://introspect.learntosolveit.com

https://family.thinnal.org

Using Gramps software, I was able to create a family tree for the Sourashtra Community. It is great software that helps preserve connections and family history. While Gramps was originally desktop software, a new web-based version called Grampsweb greatly enhances the accessibility and usability of the software.

It makes the family tree available to others and allows shared editing.

Whenever I edit my family tree, it always takes me down memory lane and into my childhood, recollecting my time with my grandparents, uncles, and others who are often no longer with me.

Even if I don't use this software often, it is like opening a valuable album, looking at the pictures, and traveling back in time.

Writing a simple text editor is a great starter project. Even after learning the language and libraries, it is still a challenging task. And as project it is usable and relable one for any programmer, as we always deal with the text.

https://github.com/orsenthil/first-texteditor is a very simple text editor that I wrote with Python, Tkinter.

It is usable, and is a great start to write any other text editor with a different language.

This book is a collection of interviews by Ray Bradbury Ray Douglas Bradbury (US: BRAD-berr-ee; 22 August 1920 – 5 June 2012) was an American author and screenwriter. One of the most celebrated 20th-century American writers, he worked in a variety of genres, including fantasy, science fiction, horror, mystery, and realistic fiction. 's biographer, Sam Weller.

Sam presents a glimpse into the final years of Ray Bradbury's life, which in itself is moving. It took a moment for me to realize the depravity of life during the old age, even as we constantly associate Ray Bradbury with his work on Fahrenheit 451 Fahrenheit 451 is a 1953 dystopian novel by American writer Ray Bradbury. It presents a future American society where books have been outlawed and "firemen" burn any that are found. The novel follows in the viewpoint of Guy Montag, a fireman who becomes disillusioned with his role of censoring literature and destroying knowledge, eventually quitting his job and committing himself to the preservation of literary and cultural writings. , which he had written multiple years ago. This was the poignant part of the entire book for me, and it shook me a bit.

But despite this, the book is hip and is full of love. It is about the love that Ray Bradbury shared for books, authors and his craft. He encouraged everyone to follow their path of love and to true to themselves.

I got a glimpse into why Ray Bradbury wrote Fahrenheit 451, and how did he shape his characters. I haven't read Fahrenheit 451, but I had watched the movie and listened to the short version called blink of that book. It seems funny in retrospect that I had approached Fahrenheit 451 in these media, and not read it. But I will certainly read it and appreciate it even more now.

This medical journal paper Prevention of perioperative stroke in patients undergoing non-cardiac surgery states that stroke is preventable.

I was motivated to read this as the topic is close to my heart. I approached and understood this paper using tools like Fermat's Library, histre for annotation and ChatGPT to understand medical terms.

Stroke was attributed to hypertension and psychological stress when patients with hypertension were subjected to hospital care for the first time. Prevention is possible by improving blood viscosity and addressing factors contributing to hypertension.

Bram is the creator of Vim (text editor) Vim ( ; short for vi improved) is a free and open-source text editor. Vim provides both a terminal screen user interface as well as a graphical user interface (called gvim). software used by developers throughout the world. He ":wq" from this world on August 3rd, 2023 in his inimitable, quiet way.

Bram influenced me. The splash screen that encouraged donation to a charity caught my attention like nothing else. Inspired by that splash screen, I started volunteering my time and donated money to many causes that I cared about.

I once had a brief interaction with Bram. He clearly said, he didn't need any money and encouraged all donations to causes he cared about. In one case, I had a brief email interaction with him to facilitate a donation, and he directed it to the Kilbale Children's Center, he volunteered with.

Thank you, Bram. Your influence will live on through the software you created and the people you inspired.

This book is about Amazon. I read this quickly to see the phrases used like "working backwards" and press release ahead of the design which are used even now.

This book is about American Farming. I couldn't complete reading it as it is a lengthy one, but the premise was America needs to take care of farming as a personal profession which people can care about. The mechanization has led to "unsettling of America" is the point of the book. I took this upon recommendation from Mr.Sridhar Venbu in a tweet.

A beginners book on investing and finance. It is an easy read, and enlists why one should participate and invest in stock markets. It was recommended to me by friend, Krishnaram.

A philosophy book. It is about keeping an open mind.

I liked reading examples of talented people mentioned in this book.

Book to read after reading Learn to Earn. Easy and accessible. Recommends everyone create a personal portfolio and keep evaluating it.

I was captured by the narrative in the early chapters. A meta book about learning.

I got influenced, and started having a budget after listening to this audio book. It is a wonderful book, and YNAB app is a great tool. It shaped my life a bit with my approach to money and planning.

I revisit Albert Ellis every now and then. I read this to reinforce the principles of Rational Emotive Behavior taught by Albert Ellis and practioners. I find these helpful.

A fun book about kitchen science.

Listened only to the first Chapter of Ada Lovelace. The depth and details were amazing.

This is a science fiction book that helped me understand racism in america. It gave me bone chills. It is a very powerful book. Illustration and story telling captures the reader.

A very serious book on world economics, and presented graphically as a comic. What else do you need? An excellent book to understand how Economy works and plays a part in our every day life.

Measuring something for measurement's sake, a pointless activity as we don't know or define why, is the tyranny of metric. The premise of this book is how obssession with quantitive metrics is misleading.

A very good book about Bitcoin, and users of bitcoin in a social context.

“Understanding what chimpanzees are like has made me realize that we humans are
not so different from other animals as we used to think. What makes us most
different is that we are far more clever than even the cleverest chimp, and we
have words. We have a spoken language. We can tell stories about what happened a
week or a year or a decade ago. We can plan for the future, and we can discuss
things - one person's idea can grow and change as other people contribute their
ideas. Great ideas become greater, problems are solved.”

- Jane Goodall, My Life with the Chimpanzees

Jane Goodall shares her story for the rest of us. Her love for animals and nature comes out through every word of this book. I was absolutely thrilled to read this book. It is a wonderful book by a wonderful person.

Zen Shorts by Jon J. Muth

My rating: 5 of 5 stars

Book that beautifully shares the essence of Zen. The art and story reaches the reader.

package main

import "fmt"

func main() {
     fmt.Println("ꢥꢪ꣄ꢱ꣄ꢒꢵꢬ꣄, ꢨꢸꢳꣂꢔꢸ !")
}

The program string is in Sourashtra.

The Computer From Pascal to Von Neumann is a computer history book by Herman H. Goldstine. It surveys the history from the laws of thought by inventors across ages. It goes from earliest philosophers like Pascal, to mathematicians like George Boole, to implementors like Von Neumann.

The author reveals how these inventors built their theories on top of others. Almost everyone involved in this exercise had a shared objective for computers.

These inventors wanted to "free" mankind from the repetitive but mundane tasks.

And these inventors lived in different eras like Leibniz lived in 1600s, Charles Babbage in 1800s and Dijskstra (1930-2002).

When introducing Charles Babbage, author directly goes the motivation that drove the inventor.

The theme of Leibniz— to free men from slavery by the automation of dull but simple - tasks was next taken up by one of the most unusual figures in modern intellectual history, Charles Babbage

And here is how Dijskstra explains how and why Computers will exceed human reasoning.

In the long run I expect computing science to transcend its parent disciplines, mathematics and logic, by effectively realizing a significant part of Leibniz’s Dream of providing symbolic calculation as an alternative to human reasoning. - Dijskstra

(Please note the difference between "mimicking" and "providing an alternative to": alternatives are allowed to be better.)

Author also associated United States Military and Government to various advancements in Computers. The final chapters gave references to when other parts of the world got their first computer. I noted that India's first computers were in 1960s with Tata Institute of Fundamental Research. Here are some interesting historical photos from this book.

12788555 Essays In Humanism by Albert Einstein

My rating: 4 of 5 stars

There we Einstein's thoughts on various topics. He desired a World-Government, powerful and in control of Nuclear Weapons. Had a balanced view of both Socialism and Capitalism. Readily allowed his critics to address their rebuttal and answered that.

He writes In memoriam for fellow scientists and many other great leaders of the world. He feels guilty for being associated with development of Atom Bomb and urges Nations to work towards peace.

He shares ample stories about the difficulties Jews have through, supports the Uprising of Warsaw ghetto.

The books reveals social and personal side of Albert Einstein.

A Crack in Creation: Gene Editing and Unthinkable power to control evolution by Jennifer Doudna and Samuel Steinberg is a book on gene-editing and a technology called CRISPR.

The book is a personal narration of Jennifer Doudna as she explains the development of CRISPR and it's discovery for use in gene editing. Rather than a review, this are notes while reading this book. CRISPR is molecular structure found in Bacteria, but now more popular term, commonly associated with a gene editing technique.

Given the technical nature of this article, I must have used the text from the sources only with slight modification for explanation. References should give the materials I consulted to write this post. In you notice technical inaccuracy, I aplogize, please point out, and I will correct it.

Terms

As I reader, I found reviewing biological terms helped me understand the material better.

Human Body is made of cells, in-fact trillions of cells. Each of these cells contain something called DNA. DNA is like recipe, just like a food recipe, but for building and maintaining living organisms.

Cells use DNA to make proteins. Proteins are the workhorses of the body, they do all the stuff we need to do to survive, from digesting food to making other proteins. Proteins are molecules made up of cells.

DNA is made up of a long combination of some very basic organic components called Adenine, Thymine, Guanine and Cytosine. Human DNA consists of about 3 billion of these. The sequence of these determines the information available for building and maintaining an organism, similar to the way in which letters of the alphabet appear in a certain order to form words and sentences.

In the nucleus of each cell, the DNA molecule is packaged into thread-like structure called chromosomes. A Chromosome is a DNA containing structure.

RNA are like cousins of DNA, which has an oxygen atom with it. One type called messager RNA, mRNA, act as carrier of information to different cells, carrying information from DNA to those cells to produce proteins.

So far, in above definitions, we didn't emphasize on heredity , that is, sending information from parent to child yet. As soon as we start talking about heredity, we use the term, Genes.

A gene is the basic physical and functional unit of heredity. Each chromosome of human body has many genes.

If we take a single cell from human body, and find out the entire set of genetic information in the chromosomes of that cell, we call that a Genome. A Genome, from Gen e and Chromos ome, is the entire set of genetic instructions found inside a cell.

CRISPR in bacteria

Single celled organisms like Bacteria were using a technique to fight off some diseases. The term CRISPR was given to an identified characteristic in Bacterial DNA sequence, which was used to produce a protein called CAS-9, which in turn, helped to kill the enemy virus.

CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats and is a family of DNA Sequences found in genomes of bacteria. CAS9 stands for CRISPR associated protein 9.

The bacteria were found to capture snippets of DNA from invading viruses and use them to create DNA segments known as CRISPR arrays. The CRISPR arrays allow the bacteria to "remember" the viruses. If the viruses attack again, the bacteria produce RNA segments from the CRISPR arrays to target the viruses' DNA. The bacteria then use Cas9 to cut the DNA apart and kill the virus.

CRISPR Shaping Human Genome

The CRISPR-Cas9 system works similarly in the lab. Researchers create a small piece of RNA with a short "guide" sequence that attaches (binds) to a specific target sequence of DNA in a genome. The RNA also binds to the Cas9 enzyme. As in bacteria, the modified RNA is used to recognize the DNA sequence, and the Cas9 enzyme cuts the DNA at the targeted location. Although Cas9 is the enzyme that is used most often, other enzymes (for example Cpf1) can also be used. Once the DNA is cut, researchers use the cell's own DNA repair machinery to add or delete pieces of genetic material, or to make changes to the DNA by replacing an existing segment with a customized DNA sequence

When CRISPR was determind that it could be used in lab on living organisms, the potential for shaping the genome unfolded.

First time ever, in over 100,000 years, we have ability to shape the Homo Sapien evolution by mechanisms other than random mutation and natural selection.

In humans, CRISPR can be used to do a precise repair and produce a normal protein from a non-functional gene.

CRISPR enables scientists to edit and fix single incorrect letters of DNA from 3.2 billion letters that comprise the human genome. It can also be used to perform even more complicated edits to Human DNA.

A relatively straightforward DNA editing has transformed every genetic disease, at-least the diseases for which we know the underlying mutation(s) into a potentially treatable disease.

CRISPR on Animals

CRISPR has been used to create gene edited mouse wherein the genome of the embroyo was edited and introduced back into womb to have an offspring with the desirable characteristics embedded at time of birth.

And we have used gene editing to create animals desirable characteristics

This is currently used in practice. Like Recombinetics uses gene editing for dehorning cattle, a safer method than physical dehorning using hot iron-rods.

Pigs as Bio Reactors

An important field of bio technology is regenerative medicine, desired by human society who are fighting of some disease eithe naturally or have lost some ability due an accident.

Many scientists see the pig itself as a source of medicine. It is seen that we might be using pigs as bioreactors to produce valuable drugs like therapeutic human proteins, which are too complex to synthesize from scratch and can only be produced in living cells.

Scientists have already been looking to other transgenic animals to produce these biopharmaceutical drugs, or farmaceuticals, as they’re colloquially called.

Revivicor is a company that is using CRISPR to produce regenerative medicine, following the process exactly outlined above. A workflow from their website gives the details on how Pigs are used as Bio Reactors for regenerative medicine.

Malaria Resistant Mosquitos

The deadliest animal on earth, Mosquito can also be killed using CRISPR. The idea seems to create malaria resistant mosquitoes using gene editing so that the entire family is disabled from being a carriers of malaria.

CRISPR for Therapeutics

CRISPR can be utilized to edit the germ cells outside the body. The edited germ cells can be planted inside for beneficiary aspects.

For targeted drug delivery, like fixing the lung or particular muscle instead of injecting the drug into blood stream.

Adult Homo sapiens are among the last animals to be treated with CRISPR, human cell: have been subjected to more CRISPR gene editing than those of any other organism.

Scientists have applied CRISPR in lung cells to correct the genetic mutation that causes cystic fibrosis, in blood cells to correct the mutations that cause sickle cell disease and beta-thalassemia, and in muscle cells to correct the mutations that cause Duchenne muscular dystrophy.

Scientists have used CRISPR to edit and repair mutations in stem cells, which can then be coaxed to transform into virtually any cell or tissue type in the body.

Even as CRISPR continues to be useful, it's power as a technology and it's potential misuse is a concern for everyone.

Whether we'll ever have the intellectual and moral capacity to guide our own genetic destiny is an open question - one that has been in my mind since I began to realize what CRISPR is capable of. - Jennifer Doudna

And Jennifer Doudna shares her stance as she says, that the nature will still be our supreme master.

Any mutations that CRISPR might make—intentional or not—would almost certainly pale in comparison to the genetic storm that rages inside each of us from birth to death. As one writer put it, “Genetic editing would be a droplet in the maelstrom of naturally churning genomes.” If CRISPR could eliminate a disease-causing mutation in the embryo with high certainty and only a slight risk of introducing a second off-target mutation elsewhere, the potential payoffs might well outweigh the dangers. - Jennifer Doudna

We might have heard about Albert Einstein Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist best known for developing the theory of relativity. Einstein also made important contributions to quantum theory. His mass–energy equivalence formula E = mc2, which arises from special relativity, has been called "the world's most famous equation". He received the 1921 Nobel Prize in Physics for "his services to theoretical physics, and especially for his discovery of the law of the photoelectric effect". 's Famous quote on Mahatma Gandhi Mohandas Karamchand Gandhi (; GAHN-dee; 2 October 1869 – 30 January 1948) was an Indian lawyer, anti-colonial nationalist and political ethicist who employed nonviolent resistance to lead the successful campaign for India's independence from British rule, and to later inspire movements for civil rights and freedom across the world. The honorific Mahātmā (Sanskrit: "great-souled", "venerable"), first applied to him in 1914 in South Africa, is now used throughout the world. . Few years ago, I discovered that the quote is from a book by Albert Einstein with Title " Essays in Humanism Humanism is a philosophical stance that emphasizes the individual and social potential, and agency of human beings, whom it considers the starting point for serious moral and philosophical inquiry. "

Mahatma Gandhi

A LEADER OF his people, unsupported by any outward authority: a politician whose success rests not upon craft nor the mastery of technical devices, but simply on the convincing power of his personality; a victorious fighter who has always scorned the use of force; a man of wisdom and humility, armed with resolve and inflexible consistency, who has devoted all his strength to the up lifting of his people and the betterment of their lot; a man who has confronted the brutality of Europe Europe is a continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere. It is bordered by the Arctic Ocean to the north, the Atlantic Ocean to the west, the Mediterranean Sea to the south, and Asia to the east. Europe shares the landmass of Eurasia with Asia, and of Afro-Eurasia with both Africa and Asia. Europe is commonly considered to be separated from Asia by the watershed of the Ural Mountains, the Ural River, the Caspian Sea, the Greater Caucasus, the Black Sea, and the Turkish straits. with the dignity of the simple human being, and thus at all times risen superior.

Generations to come, it may be, will scarce believe that such a one as this ever in flesh and blood walked upon this earth.

Ref: Essays in humanism - Einstein, Albert, 1879-1955

Six-months into pandemic there is a lot of settled knowledge about Coronavirus now. This medium article - The Most Likely Way You’ll Get Infected With Covid-19 covered it very well.

What we know

1. Surfaces don’t seem to matter as much as originally thought.

   1. SARS-CoV-2 does not spread through surfaces.

   2. No need to disinfect food, parcels, etc.

2. Close range droplets are the new leading theory and Aerosol transmission has gradually gained acceptance

   1. Coronavirus Spreads from infected person exhaling and non-infected inhaling.

   2. A tiny droplets of virus spread in air in trajectory less than 6 feet.

   3. Best way to prevent this - "Wear Mask".

Six months in, "Wearing Masks" is the most effective way to protect ourselves. Both to avoid the spread and also to protect ourselves from getting infected. That's it.

Incidentally, the first strong support for wearing mask as reducing infections came via statistical analysis by Jeremy Howard in the article from May 15 - Masks help stop the spread of coronavirus

Best way for us to fight the virus - "Wearing Mask".

If your child, between age kindergarten to 3rd grade is interested in Learning Minecraft, we have a new club called "Occamy Minecraft Club". The classes are taught by my Son, Siddhartha, and kids learning from other kids usually well. I am there to make sure all children learn well from each other during the class.

It is USD 5/- per class, limited to only 5 students per class. Age range kindergarten to 3rd grade.

A Concise History of Modern India by Barbara D. Metcalf

My rating: 5 of 5 stars

I found this book totally captivating. I have followed Indian history through books, and have been witnessed incidents taking shape, like Babri Masjid Demolition, Killing of Rajiv Gandhi, Curfew, Coalition Government, Gujarat Violence and burning of the train, the rise of Cyberabad, etc. The details provided in this book still captured my attention. The authors do a wonderful job of providing a "concise history of modern India", and trying to present the facts as they are. If we come across any review that either accuses the book of having some prejudice by labeling it with terms like "British authors", "leftist" or "does not capture greatness" or "congress" etc, we can safely assume that the review-writer was standing in front of the mirror rather than in front of text and words. Reading history, I often realize that reality can be stranger than Fiction. This book share ample anecdotes along those fronts. The first thing I realized was - British East India Company had a much difficult time establishing trade relations in the subcontinent than French or Portuguese who had arrived earlier because India under Mughal had some resistance going on. They established pure trade relations, incurring a loss, buying cows from India, and facing criticism from Britain. The company did not want to give up on the business opportunity with India and incurred losses for decades. Then we notice how Britain captured the whole of India. Robert Clive and Mir Jafir, a name that has become eponymous with a traitor start the conquer from South to Nawabs of Bengal. The loot and wealth of India were too tempting for the British to give up or lose control to locals. I came to know through this book that for administering India, British setup "Indian Civil Services", the highest administrative body in India, which trains qualified candidates in both Britain and India through rigorous exams. The motivation was for that administrative body to report directly to the British state. The "Indian Civil Services" served the system very well, continued after Independence, and reporting structure replaced to the democratically elected official instead of the British state. The book is a whirlwind, each capture captures multiple events in a century or decades. I came to know that Mohammed Ali Jinnah, had requested Pakistan to be consisting of multiple divergent states in India which had Muslim Majority (like Hyderabad, Kashmir, parts of Punjab, Bengal, etc). He very well knew that having a separate country with interspersed states was never going to be practical, he wanted to use that demand as negotiation tactic. However, Nehru and congress never gave into it. They receded two states of Pakistan on religious identity. Nehru declared during Independence that India was not built on religious identity and is not a religious state, thus keeping the plan for India with all the states in order. It seems like India had decided that after independence they will have some money for nation-building purposes. Since India got partition, I came to know that, it was Gandhi who, in his just tendencies, requested for 40 million pounds to be given to Pakistan as it's share. I had known about the factors leading to Independence a little, but I didn't realize that the British were spending roughly 1000 million pounds per year on India after the war (like supporting Indian soldiers, infrastructure), which had proven economically very costly to hold on. The book also deals with more recent events, and particularly things that struck me was a. Keezhaldi massacre and how no one was ever brought to justice. b. No one was brought to justice for the Gujarath train massacre. The chief minister, Modi was let go by the then government in power, BJP under Atal Bihari Vajpayee. It is often that case that being in political majority determined the outcome of the punishable act, and humanitarian qualities like justice didn't have a say against power. As an aside, I could relate to the above statement even in the 2020 Delhi Riots. BJP government did not bring anyone to justice. The same argument holds worldwide too wherein, in the USA, cronyism is so prevalent in republican led administration, and bringing someone to justice for the wrongdoing seems also non-applicable if the person has power.

Bill Gates has an article on What you need to know about the COVID-19 vaccine The entire article is impressive in terms of the approach to solve this problem. The particular highlight for me was Bill Gate's emphasis on making sure everyone in this world, yes 7 billion people in this world get the vaccine as soon as it is available.

This is an impressive thinking for a individual. Considering the solution for the entire humanrace, because no human is different and is equally susceptible to this virus.

I completed the leetcode 30 day challenge for April 2020.

Masterminds of Programming by Federico Biancuzzi

My rating: 5 of 5 stars

This was a lengthy book. The interviewers did a fantastic job with their questions and covered a range of expert programmers and language designers across the spectrum. The important thing I gained from reading this book was my "enthusiasm" again for different programming languages and styles. Each different language creator had some "opinions" about the state of affairs and went about in a personal way to do something about it, create a language, create a community, solve the problem, and usually building on top of what the person had learned from others. The language creation is both a scientific process (standing upon the shoulders of giants), as well as the unique taste that each language designer brought to the table. I was excited to learn more about AWK, Haskell, Perl, and Eiffel as languages after reading this book.

I came across this article on Brian Lewy, who was once the CEO of RadioShack, but after 30+ years in business, decided to pursue his interest in Medicine, and became a clinician and helping with fight against COVID-19.

Source : Heroes of the pandemic: Former CEO of RadioShack now an ER doctor on frontlines of COVID-19 fight

I noticed today morning that CVS has come up with Rapid Testing via drive through, and it is free of cost.

https://www.cvs.com/minuteclinic/covid-19-testing

At the moment it is available for Georgia and Rhode Island, but I certainly see this sprouting in all other states of US.

Tesla has a ventilator in making using the car parts. Even if this only a technical demo, I appreciate it, and the language used in the demo was appropriate and careful for the purpose.

Bill and Melinda Gates Foundation is funding the therapeutics accelerator to find the vaccine for covid-19. They are funding almost 8 companies working in parallel and joined by other life sciences companies too.

Companies participating in the collaboration include

• Bayer

• BD

• bioMérieux

• Boehringer Ingelheim

• Bristol-Myers Squibb

• Eisai

• Eli Lilly

• Gilead

• GSK

• Johnson & Johnson,

• Merck (known as MSD outside the U.S. and Canada)

• Merck KGaA

• Novartis

• Pfizer

• Sanofi.

Salesforce CEO, Marc Benioff took a 8 step pledge, and with few pledges being, No significant layoff for next 90 days and encouraging employees to keep their own hourly workers like gardeners, home cleaners employed with pay. I really appreciate the social responsiblity shown by Marc.

The Plague by Albert Camus by Albert Camus

My rating: 5 of 5 stars

The essence of this book is about sympathy. The story is presented through the eyes of a few friends as they witness the plague in the city of Oran. The author, Abert Camus's writing, and the philosophy of the principal character, Dr. Rieux stands out in this Novel. It will probably etch in the mind of the reader forever. It is about sympathy, the qualities of truth, understanding, and comprehension are at the core of it.

It is the year 2021, and I logged into one of my servers using IPv6 address. So far I had struggled with using IPv6 address for anything useful. Today, I was able login to a server directly from my home using an IPv6 address.

vultr.com provides servers for USD 2.5 per month, with a restriction that it will only have IPv6 address. But if you can ssh to it, why not get those cheap boxes?

`bash $ ssh -6 root@2001:19f0:5401:129f:5400:02ff:fea8:1fde root@meet:~# `

I setup Folding@Home and started contributing computing resources to Covid-19 project.

For Covid-19, the Folding@Home project is trying to find out the structure of covid-19 virus as it comes in contact with human bodies through ACE2 receptor Angiotensin-converting enzyme 2 (ACE2) is an enzyme that can be found either attached to the membrane of cells (mACE2) in the intestines, kidney, testis, gallbladder, and heart or in a soluble form (sACE2). Both membrane bound and soluble ACE2 are integral parts of the renin–angiotensin–aldosterone system (RAAS) that exists to keep the body's blood pressure in check. mACE2 is cleaved by the enzyme ADAM17 in a process regulated by substrate presentation. ADAM17 cleavage releases the extracellular domain creating soluble ACE2 (sACE2). ACE2 enzyme activity opposes the classical arm of the RAAS by lowering blood pressure through catalyzing the hydrolysis of angiotensin II (a vasoconstrictor peptide which raises blood pressure) into angiotensin (1–7) (a vasodilator). Angiotensin (1-7) in turns binds to MasR receptors creating localized vasodilation and hence decreasing blood pressure. This decrease in blood pressure makes the entire process a promising drug target for treating cardiovascular diseases. , and how an understanding of this can help build therapeutic antibiotics or molecules that might disrupt the viral interaction.

Stats: https://stats.foldingathome.org/donor/Senthil.Kumaran

Folding@Home hit a milestone Exaflop Exascale computing refers to computing systems capable of calculating at least 1018 IEEE 754 double precision (64-bit) operations (multiplications and/or additions) per second (exaFLOPS)"; it is a measure of supercomputer performance. computing speed, which is a billion billion floating point operations per second.

Let's not leave any stone unturned.

Bill Gates followed up with a Live Question and Answer session with TED's Chris Anderson.

• Testing is the still "key part". We need a way to find to detect who has-virus and who does not. This is done using swabs. All of us, will have to be tested before we resume social interaction.

• He is still very much concerned about developing countries, like India, Pakistan, and countries in the southern hemisphere, as he perceives, will have more difficulty than developed countries like the US to get this through.

• Economic Impact can be reversed, but if a life is lost, it cannot be brought back.

• The idea of building herd-immunity against this virus, in reality, doesn't look like a choice. The numbers don't add up.

Please watch the entire video conversation here:

https://www.facebook.com/TED/videos/1071228706574778/

Bill Gates did a Ask Me Anything on Covid-19 on Reddit. The entire AMA is educational, read it in reddit or in Gates Notes AMA

The following question and answers caught my attention in particular.

This is a short text available for reading in project gutenberg, titled "The Discovery of Radium by Marie Curie"

Some interesting quotes from this book

3Blue1Brown did an excellent video explaining exponential growth. He took the opportunity to explain Covid-19, growth, trajectory and more importantly pressing question on when this growth will stop using mathematical equations. I was highly impressed. It helped to make sense of the situation, and help with dealing with the situation.

Please watch this video

The video deals with the questions explained in the slide.

Exponential growth means as you go from one day to the next, it involves multiplying by a constant.

In terms of an Equation the exponential growth is represented mathematically like this.

3Blue1Brown says that this data fits in a linear regression model.

Linear regression calculates an equation that minimizes the distance between the fitted line and all of the data points. And if want to find the equation that minimizes the distance between a line and the data point, the most common way is to find "least square regression" .

We will notice that the least square regression ends up with an equation of line \(y = m \dot x + b\) Where m is same as the multiplying factor the exponential growth.

So he uses the data point to track the growth for linear regression.

And finds out \(R^2\) which is a coefficient of determination, a statistical measure of how close the data are to the fitted regression line. We notice that \(R^2\) is close to 1.0 which is a straight line, increasing by a factor, \(m\) each day.

He is making a point with logistic regression that, when a country has 100 times lower cases than another country, the country is not 100 times better, but just 30 days behind!

And following the numbers, if we extrapolate, we will hit a 1 billion cases in 81 days. But that's hardly desirable, and everyone wants to know when we do stop growing linearly and hit a plateau. He notes that "Linear Growth" is a theoretical phenomenon, not a realistic one. Just like, if you were not married last year, and you got married this yea, it does not mean, will you marry every year from now. (Explain XKCD)

But unlike marriage, we are seeing an exponential growth here. We will have to find out the factors that reduce the exponential growth.

So, in reality what we are expecting is a logistic curve where the exponential growth includes damping factor contributed by people who are not infected.

Our whole idea, then becomes to increase this damping factor, that is not get infected by this virus.

What makes the growth factor go down other than maxing out at the total population?

• Decrease the number of people getting exposed - aka social distancing

• Decrease the probability of exposure - aka wash your hands completely, maintain hygiene amongst other things.

And important point to remember in this damping factor is, the output decreases exponentially too

For instance with the growth factor of 1.15, we have the first number and reduces significantly, only by 10% decrease in the damping factor.

3Blue1Brown has tried to quantify our worry factor, and give us hope that by social distancing, good hygiene we can reduce the damping factor and control the growth. However, "If we are not worried, that's only thing to worry about".

Related Articles / Simulations

• Why outbreaks like coronavirus spread exponentially, and how to flatten the curve by Harry Stevens

Happy Pi Day Pi Day is an annual celebration of the mathematical constant π (pi) with events mostly in the United States. Pi Day is observed on March 14 (the 3rd month) since 3, 1, and 4 are the first three significant figures of π and was founded in 1988 by Larry Shaw, an employee of the Exploratorium, a science museum in San Francisco.

I celebrated the Pi day during Covid-19 The global COVID-19 pandemic (also known as the coronavirus pandemic), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began with an outbreak in Wuhan, China, in December 2019. It spread to other parts of Asia and then worldwide in early 2020. The World Health Organization (WHO) declared the outbreak a public health emergency of international concern (PHEIC) on 30 January 2020 and assessed it as having become a pandemic on 11 March. The WHO declared that the public health emergency caused by COVID-19 had ended in May 2023. times with these activities.

• Learning AWS ML Modelling

• Solving an Easy Brilliant.org Brilliant.org is an American for-profit company and associated community that features 90+ guided courses in three categories: maths, computer science and science. It operates via a freemium business model. Brilliant was founded in 2012. At the Launch Festival in March 2013, CEO and co-founder Sue Khim presented the idea of Brilliant, attracting funding from venture capitalist Chamath Palihapitiya. In August 2013, TechCrunch reported that Brilliant.org had secured funding from Palihapitiya's Social+Capital Partnership, as well as from 500 Startups, Kapor Capital, Learn Capital, and Hyde Park Angels. The website boasted over 100,000 users at that time. By April 2019, it had achieved a valuation of $50 million. quiz but getting it wrong.

• Reading a little about Thales Theorem In geometry, Thales's theorem states that if A, B, and C are distinct points on a circle where the line AC is a diameter, the angle ∠ ABC is a right angle. Thales's theorem is a special case of the inscribed angle theorem and is mentioned and proved as part of the 31st proposition in the third book of Euclid's Elements. It is generally attributed to Thales of Miletus, but it is sometimes attributed to Pythagoras. from the Timeline of Mathematics at Mathigon.

• I also did a leetcode weekly contest

Blown to Bits by Hal Abelson

My rating: 5 of 5 stars

Bits are Bit. Even if they are transmitted through electrons in copper wires or light in fiber optical cables or via modulation in radio waves. All of these carry bits and run the world. This book covers the intersection between technology, society, the justice system, policies, and politics. It is highly approachable and helps you appreciate technology, not just from a technical standpoint but from a societal standpoint. I really appreciate the concern for fellow human beings put forefront in this book. It opens the reader's mind to care a lot about the political process and be aware that any technology we design is going to have a wide societal impact.

The entire book is available free online: http://www.bitsbook.com/excerpts/

This is from Masterminds of programming. Person sharing the story and the perspective is Thomas_E._Kurtz Thomas Eugene Kurtz (February 22, 1928 – November 12, 2024) was an American computer scientist and educator. A Dartmouth professor of mathematics, he and colleague John G. Kemeny are best known for co-developing the BASIC programming language and the Dartmouth Time-Sharing System in 1963 and 1964. These innovations made computing more accessible by simplifying programming for non-experts and allowing multiple users to share a single computer, transforming how computers were used in education and research.

I cleared the CKAD exam.

I took 3 attempts to clear this exam. In my first attemp, I had forgotten the concepts. I was not very confident during 2nd, and prepared well for the 3rd attempt to clear it.

I read "Kubernetes from Ground Up" again. Practised and "understood" the concepts better. With all these, the tests were actually very enjoyable.

I had setup a raspberry pi cluster, and suddenly the master became non-operational. The cluster was able to see itself, but if I login to master node, I noticed it was not able communicate externally.

The DNS Resolution for any site from master was failing.

Then I figured that Ubuntu had made some changes to resolve.conf protocol

Ubuntu requested users not to edit /etc/resolv.conf, and it's content on my cluster was not something that I wanted.

I had setup pihole, and my devices had started to see the internet through this. I noticed that Ubuntu since 18.04 had not set my router as the first nameserver and thus after pi-hole experiment, my master node lost its resolution capability

Fixing the Ubuntu DNS resolution was easy. I followed this post from datawookie

1. resolvconf package was already installed.

2. Edit /etc/resolvconf/resolv.conf.d/head

nameserver <pi-hole-server>
nameserver 8.8.4.4
nameserver 8.8.8.8

3. Restart the resolvconf server

sudo service resolvconf restart

I had supported this project called "Modeling Is Key to Understand Processes, Structures and Scales!" by Mrs. Sexton of Montevideo Elementary School for the fifth graders. They had this project going on for the family science night in the winter of 2019.

On my birthday this year, I received a ton of thank you notes for the supporting this project. It felt amazing to see so many handwritten cards!

Thanks to students, and the teacher for this gesture. I will support your science efforts anytime! :)

Siddhartha and I built a Modern House in Minecraft.

We got the idea and followed this Youtube video https://www.youtube.com/watch?v=-LcdpQ2LLK0 for building the house.

It was a fun project!

I setup a Kubernetes Kubernetes (), also known as K8s, is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by a worldwide community of contributors, and the trademark is held by the Cloud Native Computing Foundation. cluster using Raspberry Pi Raspberry Pi ( PY) is a series of small single-board computers (SBCs) originally developed in the United Kingdom by the Raspberry Pi Foundation in collaboration with Broadcom. To commercialize the product and support its growing demand, the Foundation established a commercial entity, now known as Raspberry Pi Holdings. . It was much easier than I had imagined.

I used a Raspberry 4 for the master, and an agent. And I used two Raspberry B for two agents.

I setup using mhausenblas tutorial, which uses k3s for setting up of kubernetes on raspberry pis. I used k3sup tool for installation of packages, which worked well.

My overall goal ran into some challenges.

• I was not able to setup the kubernetes dashboard via helm Helm is a package manager for Kubernetes. It uses 'charts' as its package format, which is based on YAML. It provides support for defining templates using the Go template syntax to generate Kubernetes resource manifests dynamically. package, and I had to use instructions from github.

• I was not able to setup Minecraft Minecraft is a sandbox game developed and published by the Swedish company Mojang Studios. Following its initial public alpha release as an early access title in 2009, it was formally released in November 2011 for personal computers. The game has since been ported to numerous platforms, including mobile devices and various video game consoles. using the available helm package: https://github.com/itzg/docker-minecraft-server/issues/433

• I used k3sup to install packages, but I do not know how to uninstall the package using k3sup https://github.com/alexellis/k3sup/issues/179

Givem all these, I still have the my local kubernetes cluster up and running and I am excited about the possiblities of using kubernetes on raspberry pis.

The Manga Guide to Calculus by Hiroyuki Kojima

My rating: 2 of 5 stars

I tried to pick up some high-level intuition on the basics of calculus with this book, but I failed. Unlike, other Manga guides on electricity, and linear algebra, I found this book not very strong on the plot and I felt it did not present the story in a cohesive manner. The concepts were introduced randomly, and I could not see how one built upon the previously introduced concept. On the positive note, it did introduce very basics of calculus well, and I am eager to pick up other books on calculus with that foundation. View all my reviews

Journey to the Center of the Earth by Jules Verne

My rating: 5 of 5 stars

This is a science-fiction adventure book. If you are a science-fiction fan like me, then you are in for a treat reading this book.

I really loved this book and the narration. It started as a simple story but quickly turned into an adventure plot, and before I realized it, excellent science was mixed into every element of this fictitious story. It has references to Fourier and Poisson, calculations for measuring distances based on sound lag, adventures of an Icelandic hiker, a cave explorer, a climber, and even biologists encountering a trilobite! Yes, a trilobite! You will also find all the pre-historic animals and references to famous explorers, biologists, and taxonomists—all present in a single story.

When I chose the book and read the plot, I wondered, "How is this possible?" But when I read it, I was taken on a real adventure by the author.

I read this book in the "Kindle-In-Motion" format, and the illustrator, Killian Eng, is in a class of his own. He did excellent work and did justice to Jules Verne's timeless work.

Here are some of my notes and highlights from this book:

Here is something about Fourier:

Was it not always believed until Fourier that the temperature of the interplanetary spaces decreased perpetually?

About Poisson:

“Well, I will tell you that true savants, among them Poisson, have demonstrated that if a heat of 360,000 degrees existed in the interior of the globe.”

A Trilobite Trilobites (; meaning "three-lobed entities") are extinct marine arthropods that form the class Trilobita. One of the earliest groups of arthropods to appear in the fossil record, trilobites were among the most successful of all early animals, existing in oceans for almost 270 million years, with over 22,000 species having been described. Because trilobites had wide diversity and an easily fossilized mineralised exoskeleton made of calcite, they left an extensive fossil record. The study of their fossils has facilitated important contributions to biostratigraphy, paleontology, evolutionary biology, and plate tectonics. Trilobites are placed within the clade Artiopoda, which includes many organisms that are morphologically similar to trilobites, but are largely unmineralised. The relationship of Artiopoda to other arthropods is uncertain. !

"Very well," said he quietly, "it is the shell of a crustacean, of an extinct species called a trilobite. Nothing more."

All these pre-historic animals:

leptotheria, mericotheria, lophiodia, anoplotheria, megatheria, mastodons, protopitheci, pterodactyls, and all sorts of extinct monsters here assembled together for his special satisfaction.

And the adventurers:

"Mr. Milne-Edwards! Ah! Mr. de Quatrefages, how I wish you were standing here at the side of Otto Liedenbrock!"

This book was a thorough adventure, with so many references to explore further!

Sita's Ramayana by Samhita Arni

My rating: 5 of 5 stars

I really enjoyed reading Samhita Arni's take on Ramayana, Sita's Ramayana, the story of Ramayana entirely narrated by Sita. It gives a new perspective to the entire story, while not-at-all deviating from the plot and details of the story. As a reader who is familiar with the story, I picked up this book with a notion of what I can expect. However, I was still surprised and thoroughly enjoyed reading this story presented by an excellent writer (Samhita) and a fantastic illustration (Moyna Chitrakar). Following this book, I became eager to check out other works of this author, and I found that her writings bring a freshness to the Indian mythology genre, and I am highly eager to read her next work "The Prince". View all my reviews

Hackers & Painters: Big Ideas from the Computer Age by Paul Graham

My rating: 4 of 5 stars

Enjoyed reading Paul Graham's essays. It is quite dated, for example, with lots of references to the Web in the 1990s and 2000s, a time when Yahoo was a prominent .com company. Some of his opinions on wealth creation are still applicable in 2020. However, we have to read his essays with plenty of skepticism. He sings praise for Lisp and instills some desire in the reader to study Lisp and start a startup.

View all my reviews

There There by Tommy Orange by Tommy Orange

My rating: 4 of 5 stars

This book is one of it's kind. I had never come across a topic like this one. It reads like a monologue but dwells a lot into History and societal norms. It dwells in Native American history because the author is a native American, and explains his life story and story of his friends in a compelling manner. The incidents take place in Oakland, ca, and the reader familiar with the region can relate to many incidents narrated in the story. The story is an eye-opener for many to perceive the native American culture a little broadly and in a practical manner. View all my reviews

I came across a translated work of Rabindranath Tagore's Poem: Dialogue Between Karna and Kunti The poem, even the translation captures the emotions between Karna and Kunti very well. We all know about this episode of Mahabharata. In every critical interaction, Karna comes out as a highly ethical character.

I graduated from OMSCS, the Online Master's in Computer Science Program at Georgia Tech. I achieved my personal goal of earning a Master's degree in Computer Science from a prestigious university. I was admitted to this program in 2014 and continued to take one class per 15-week term. I decided to go beyond the requirements by taking three additional classes and graduated with specializations in "Computational Perception and Robotics" and "Software Systems."

My Journey

Pursuing a master's degree in computer science has been one of my long-standing aspirations.

I completed my schooling and college in India but missed the opportunity to attend a prestigious institution. Determined to pursue a Master's in Computer Science, I made my first attempt in 2002 but failed. I started working and earning money while continuing to take courses. I tried again in 2003 and failed, and once more in 2004 with the same result. Despite these setbacks, my passion for learning Computer Science kept me motivated to continue taking courses and improving my understanding of the subjects.

I enrolled in Continuing Education Proficiency Courses at IISc, which were offered on Wednesday evenings. These courses eventually paved the way for me to contribute to CPython development through the Google Summer of Code program.

However, I still had not achieved my ultimate goal. In 2007, I decided to try again.

With work experience, four patents from my job, and part-time courses under my belt, I applied to US universities in 2007. I applied to 10 universities but was rejected by all of them.

In 2008, I applied to 5 universities and faced rejection again. I repeated the process in 2009, applying to another 5 universities, but was unsuccessful.

In 2010, I received one admission offer through a referral, but I decided not to pursue it as it was not a good fit for me.

In 2012, I almost enrolled in a part-time master's program, but I chose not to continue because it was not in Computer Science.

In 2014, the OMSCS program was launched, and it looked very promising. I applied and was admitted in its second offering. I started my classes in January 2015, and after taking 13 courses, I completed the program in December 2019 with a GPA of 3.21.

One of my most important lessons during this course was my failure, getting grade C, in Computational Photography due to a mistake I made. I could have been removed from the program, but I became more careful, corrected myself, and retook the course to earn a B grade.

The experience has shaped me well as a person. The journey was fun and eventful. My family, especially my wife, Shalini, supported me a lot. She would regularly check in with my plans and adjust our social outings accordingly.

We celebrated our first child, Siddhartha's first birthday, when I started this program. We had our second child, Saharsha, while I was taking the courses. Keeping them entertained while pursuing a master's degree was challenging. I used to put them to sleep and then return to my computer to complete assignments.

In terms of work, I lost a job, got a new one, and then changed jobs again during this timeframe. I struggled a bit to balance work and study but managed to focus on work without letting one interfere with the other. I also got promoted twice in the same organization during this period. Overcoming this long-pending desire and mental block was a significant achievement for me.

Finally, we celebrated my graduation with a family trip to Atlanta, and my wife Shalini organized a party with our friends. She also requested me to give a short speech. Here is the speech that I shared with my friends.

We can celebrate anything. My wife, Shalini, is organizing this party as she wanted to celebrate my graduation.

I feel a little awkward and embarrassed to be honest, but I am supporting her just as she helped me throughout this journey. And I want to thank my friends for coming.

For sharing things that I have learned, I want to talk about the quality of honesty because I think about it a lot.

It is challenging to keep up with the changes; it is challenging when our desires fail; it becomes challenging to lose opportunities every day. Given all these, I still think that our time and the journey is enjoyable, if on any task we dedicate our time to, we do so with honesty. There is a lot of value in doing things with our bent of mind. Learning, understanding, and then doing things that we like to do. Failing is okay, and the number of attempts overtime does not matter. The result might be enjoyable, just as I realized that with my graduation.

The Great Scientists in Bite-sized Chunks by Meredith MacArdle

My rating: 5 of 5 stars

A simple book gives an overview of many scientists and explains their achievements within a page. The book is divided into categories such as Astronomy, Physics, Biology, Environment, etc, and goes from the earliest changemaker in this field to the latest. It is very easy to see how each of the scientists stood on top of the research done by the previous one, sometimes in a different field. The personal characteristics and the challenges faced by these scientists in their respective eras were entertaining to read. In terms of personal characteristics, Roentgen, after discovering X-rays, had refused to benefit financially from his discovery believing that it should be freely available to all. I wanted to note down Rudolf Virchow's view, wherein he believes: "Medicine is a social science, and politics is nothing else but medicine on a large scale".

https://www.deeplearningillustrated.com/ - This book helped me get into Deep Learning. This is a high-level overview that any software engineer will desire as we try to understand “this new world” (even as of 2019). The terms in new-world are used in multiple Medium articles, and github.io pages and github.com READMEs, and we wonder what any of these have to do with solving the problem, how does it fit in, etc. This book took a top-down approach to explain it all and help me made sense of every article that I read about Deep Learning, Convolutional Neural Networks, and Artificial Intelligence as of 2019. I like to thank the Authors for their invaluable and accessible contribution to this field of AI and Neural Networks.

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Docker Up and Running by Karl Matthias, Sean P. Kane by Karl Matthias

My rating: 3 of 5 stars

I picked this book to gain some structured knowledge of docker. I had done a costly mistake with docker, and I realized, it was because my knowledge of docker was mostly by trial and error, reading the docs and trying it out. The book focusses on operational aspects of docker. This is perhaps aimed at the beginners of docker who want to pick and learn the commands. Experienced users might find that this book goes more into breadth than depth.

I am a CNCF certified Kubernetes administrator now.

I did not clear the exam in the first attempt, came close to clearing in the second attempt, did a horrible docker mistake in my third attempt. In my third attempt I ran a sleep 3600 when with -it flag flag on for docker run, and I could not exit or kill the shell.

Finally, cleared the exam in my fourth attempt. It is fun a exam for someone interested in Kubernetes to take.

Kubernetes Up and Running by Kelsey Hightower

My rating: 4 of 5 stars

This book is from the pioneers of the Kubernetes world. The authors explain the design elements of Kubernetes pretty well. It was enjoyable to read this book.

Kubernetes Cookbook: Building Cloud Native Applications by Sebastien Goasguen, Michael Hausenblas by Sebastien Goasguen

My rating: 3 of 5 stars

This is a useful book, aimed towards the beginners of Kubernetes. The cookbook recipes could help reinforce the concepts that we learn in Kubernetes in a practical manner.

Kubernetes in Action by Marco Luksa by Marko Luksa

My rating: 4 of 5 stars

This book explained the concepts behind Kubernetes well. It just did keep itself at the level of a book for an operator of Kubernetes and went into explaining what is happening underneath quite well.

Kubernetes: Kubernetes Kubernetes (), also known as K8s, is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by a worldwide community of contributors, and the trademark is held by the Cloud Native Computing Foundation.

They Called Us Enemy by George Takei by George Takei

My rating: 5 of 5 stars

This book is a straightforward narration by Georgia Takei of a Japanese Internment by the US Government during World War II. George and his family had suffered this cruelty. He highlights how opportunistic senators took chances to rise to power by promoting racial discrimination. I had not known about this part of US history. I also did not know how ACLU helped the Japenese in the US then. US had detained 250,000 Japanese families living in the US, many of them US citizens, into camps during WWII. Having lived through this horror, a lot of these Japanese families restarted and rebuilt their lives again. George Takei turned to activism to share his story with the masses, and I think he has been very effective. This book has come the right time when similar mistreatment is being carried out by certain opportunistic politicians.

20657408 The Mahabharata by R.K. Narayan

My rating: 5 of 5 stars

The Mahabharata is one of the most complex stories I have ever read. I read this book following The Ramayana by R. K. Narayan. The ethical dilemmas presented in this story is at a whole different level than Ramayana. While in Ramayana, there are good guys and bad guys in Mahabharata there is nothing like that. No one is entirely good or no one is entirely bad. The Pandavas and Kauravas are brothers, just competing against each other, but their quarrel reaches to a level for war. Coming to the war, which is an important topic of this story, the book has 3 chapters dedicated to the avoidance of war and only one chapter for the war. In spite of suffering all the atrocities presented by Duryodhana, Pandavas realize that war is not good for anyone, and try for multiple attempts to prevent the war. Duryodhana plays politics and chooses to listen, and highlight points only which could give a chance for war. And when it comes to war, Pandavas understands that a deterministic outcome is achieved by eliminating everyone in the enemy camp, even babies. So, after trying to avoid war, when the war becomes inevitable, Krisha proposes and leads a _genocide_ against the Kaurava clan. To me, the most complex characters in this story were that of Karna and Krishna. Karna has chances to prevent war, he understands his background well but still chooses to side with Duryodhana. Krishna is one who leads the Pandavas to war and gives guidance "on duties" in ambiguous times. There is no good or a bad person in this entire story.

India has a Online Digital Library for everyone: https://ndl.iitkgp.ac.in

When I created an account, it asked me about my Education Level, and gave me a choice for "Life Long Learner"

Appreciate it!

I ordered Pizza using terraform today. I was learning about terraform and how provider can be anything, even Software as a Service, which is basically an API.

So, it made possible to develop software like terraform-provider-dominos using which you can order Pizza. I tried it. The terraform apply crashed for me, but I knew that terraform might have attempted the purchase operation and had probably crashed due to slow response from the API. It was the case. I saw my credit card charged in my account, an order confirmation email, and in 30 minutes, I got my pizza.

I tried this codeforces problem contest/1189/problem/A. Trying a contest problem after many years and I thought, it was a problem either with Dynamic Programming or with Recursion.

Turned out be a much much simpler one, that if a person reads the problem carefully and understands the scope of the solution, the solution can be written in < 2 minutes.

I spent 30 minutes unsuccessfully. Lack of experience shows up if you don't practice.

The Compass of Pleasure by David J Linden by David J. Linden

My rating: 5 of 5 stars

This was a very good book. It made me rethink about the pleasures of life in terms of chemicals, and its properties on the human brain. Everything we 'enjoy' is like a positive signal to a particular area in the brain called VTA where Neuron's release dopamine to make us feel the pleasure. Various experiments that humans have done, such as chemicals we eat (the taste factor in food), drink, inhale, and physiological activities such as running, exercise, sex, and brain activities like deep-thinking, understanding, associative thinking, are all related and trigger those VTA neurons to give pleasure. The process of pleasures are understandable, and once we understand this, we have better control over things around it, like society norms, and optimizing for our desired outcome. The book also touched upon the topic of addiction, which is commonly associated with pleasure, but in reality, it is not. The perspective shared in the book about addiction, was it should be treated as a disease, like having a fever/cold/cough, and instead of feeling sorry or ashamed, actions should involve just as we take actions to come out of fever/cold and cough.

After watching Infinity War at a friends home, I became immensely curious and interested in watching all the related movies. Thor taking the power of the dying star etched in my mind from the Infinity War. I looked up the order to watch the entire series and before watching the Avengers: End Game, my family and I decided to watch the entire 21 movies in the Marvel Cinematic Universe. We finished our MCU movie watching adventure finally watching with EndGame on 21st May, 2019.

It was a treat to imagination, and a pleasure follow this saga along!

Here is a Marvel Cinematic Universe theme shared in a reddit comment.

If you end up watching the entire series try out this MCU Quiz (The answer key is here)

I was reading an old issue of Chandamama and I stumbled upon a short story called "Fault in Others" which I felt applicable even now.

39306535 Origin Story by David Christian

My rating: <a href="https://www.goodreads.com/review/show/2604151046">5 of 5 stars</a><br /><br />

This is a highly accessible account to the Origin of Human Civilization, from BigBang, the birth of stars, universe, dinosaurs, humans, governments, the current social system to the possibilities of what lies ahead. <br /><br />Reducing the origin of the universe from 13.6 billion years to 13-day scale was very interesting to get a sense of the scale. Discussion of the progress of human societies and adding adequate details as supported facts was a good approach too. The author takes us to the origin of governments, international bodies, and touches about the topics of origin of democracy supported by nationalism. <br /><br />I like to dwell on the topics of Big History / Origin Story, and this was a worthy book. <br/> <br/>

A central theme of this book is humans are engaged in some sort of a "collective-learning", and that forms the central theme "our origin history".

This is a shared origin history for everyone in the universe, and there is course https://www.bighistoryproject.com based on this book. These are some important highlights I had noted in this book.

Newton's View:

Isaac Newton saw God as the "first cause" of everything and argued that He was present in all of space.

About Goldilocks Condition:

We don't know what Goldilocks conditions allowed a universe to emerge, and we still can't explain it any better than novelist Terry Pratchett did when he wrote, "The current state of knowledge can be summarized thus: In the beginning, there was nothing, which exploded."

Indian Veda's view on creation of universe:

Like the "neither non-existence nor existence" of the Indian Vedas, this tension seems to have bootstrapped our universe.

Attempt to recreate the conditions of origin of universe:

Remarkably, we humans have managed to re-create such energies briefly, in the Large Hadron Collider outside Geneva. And, yes, particles do start popping out of that boiling ocean of energy.

Charles Darwin's recognition of Emotions:

Charles Darwin understood that the emotions are decision-makers that have evolved through natural selection to help organisms survive.

Humans interbreeding with Neanderthal Neanderthals ( nee-AN-də(r)-TAHL, nay-, -⁠THAHL; Homo neanderthalensis or sometimes Homo sapiens neanderthalensis) are an extinct group of archaic humans who inhabited Europe and Western and Central Asia during the Middle to Late Pleistocene. Neanderthal extinction occurred roughly 40,000 years ago with the immigration of modern humans (Cro-Magnons), but Neanderthals in Gibraltar may have persisted for thousands of years longer. :

At sites such as the caves of Skhul and Qafzeh in modern Israel, they may have encountered and occasionally interbred with Neanderthals. (We know this because today, most humans who live outside Africa have some Neanderthal genes.)

Neanderthals: Neanderthal Neanderthals ( nee-AN-də(r)-TAHL, nay-, -⁠THAHL; Homo neanderthalensis or sometimes Homo sapiens neanderthalensis) are an extinct group of archaic humans who inhabited Europe and Western and Central Asia during the Middle to Late Pleistocene. Neanderthal extinction occurred roughly 40,000 years ago with the immigration of modern humans (Cro-Magnons), but Neanderthals in Gibraltar may have persisted for thousands of years longer.

On genetic changes as a result of choice of vocation.

Humans have changed genetically as a result of farming. For example, if you're descended from people who once herded cattle and consumed cow's or mare's milk, you will probably be able to digest their milk even as an adult because you can keep producing lactase, the enzyme that digests lactose (milk sugar). Hunter-gatherers consumed only breast milk till about four years of age, and after childhood, they no longer needed to produce lactase. But where cow's or mare's milk became a major food source, humans began to produce lactase into adulthood—a genetic mutation had occurred.

Rise of a new religion, Islam, in 8th century

Perhaps most astonishing of all was the rise of new political systems associated with a new world religion, Islam, in the eighth century CE.

On wealth distribution in early 1900s

The French economist Thomas Piketty has estimated that in most European countries as late as 1900, 1 percent of the population owned about 50 percent of national wealth, and 10 percent of the population accounted for 90 percent of national wealth. The other 90 percent of the population made do with just 10 percent of national wealth.

On Capitalism

Like the appearance of the first oxygen atmosphere or the sudden death of the dinosaurs, this was an example of what the Austrian economist Joseph Schumpeter termed creative destruction—the constant, often violent replacement of the old by the new, which Schumpeter saw as the very heart of modern capitalism.

First Global Currency

Mule trains carried silver to the Mexican port of Acapulco, where it was minted into silver pesos, the world's first global currency.

Relation between Government and Commerce

Rulers protected and supported commerce, and in return they got the right to tax and profit from commercial wealth. This was the earliest and crudest form of capitalism, a system admired by European economists from Adam Smith to Karl Marx.

New way to think about "wealth"

Many early economists understood perfectly well that the wealth traded by and generated by capitalists really represented control over compressed sunlight, over energy flows through the biosphere.

Cheap energy encouraged experimentation and investment in many new technologies. One of the most important was electricity. In the 1820s, Michael Faraday realized that you could generate an electric current by moving a metal coil inside an electric field.

Conquest on Silk Road

The Nemesis, the first iron-hulled steam-powered gunship, with its seventeen cannons and its ability to sail fast in shallow waters, helped England win control of China

Indian Railroads

Even the building of India's major railroads benefited Britain more than India.

Most of the track and rolling stock was manufactured in Britain, and the huge Indian rail network was designed primarily to move British troops quickly and cheaply, to export cheap Indian raw materials, and to import English manufactured goods

Europe view on civilizing the rest of the world

Europe's economic, political, and military conquests encouraged a sense of European or Western superiority, and many Europeans began to see their conquests as part of a European or Western mission to civilize and modernize the rest of the world.

Increasing Productivity

Prokaryotes had solved the problem billions of years ago, but Haber and Bosch were the first multicellular organisms to successfully fix atmospheric nitrogen. The Haber-Bosch process uses huge amounts of energy to overcome nitrogen's reluctance to combine chemically, so it was viable only in a world of fossil fuels. But artificial nitrogen-based fertilizers transformed agriculture, raised the productivity of arable land throughout the world, and made it possible to feed several billion more humans. It turned fossil-fuel energy into food.

Rise of Nationalism

The governments of revolutionary France and the United States began to mobilize the loyalty of their subjects through democratization, which brought more of the population into the work of government, and through nationalism, which appealed to people's sense of a shared national community.

Some governments, such as the Communist regimes of the Soviet Union and China, attempted to micromanage the entire national economy.

What it might be like in future

Eventually, as economic growth ceases to become the primary goal of governments, individuals will begin to value quality of life and leisure over increased income.

A Mind for Numbers by Barbara Oakley by Barbara Oakley

My rating: 5 of 5 stars

Like most readers, I stumbled upon this book after taking Barbara Oakley's Learning How to Learn Course in Coursera. This book reinforces the concepts taught in that course. The idea of relaxed learning, giving ourselves plenty of time while learning difficult concepts, avoiding procrastination, and tools that help us avoid the procrastination. The idea of repetition for remembering different concepts is presented and stressed well this book. The importance of sleep for learning stayed with me and changed me when I took the course based on this book. I never discount sleep for important exams or deadlines now. The crux of learning is expressed well by this quote from Santiago Ramón y Cajal introduced to us in this book What a wonderful stimulant it would be for the beginner if his instructor, instead of amazing and dismaying him with the sublimity of great past achievements, would reveal instead the origin of each scientific discovery, the series of errors and missteps that preceded it - information that, from a human perspective, is essential to an accurate explanation of discovery. - Santiago Ramón y Cajal

The Ramayana by R.K. Narayan

My rating: 5 of 5 stars

This is Narayan's narration of the famous Indian Epic, Ramayana. It follows Valmiki's script, instead of the Kamban, that I thought, I would expect from the South Indian author. The introduction gives the details of the Ramanand Sagar's Television series and how India stood still when Ramayan was telecast on weekends. The story is well know, but the best part of this book are in the details. I liked the chapter on Vali and Sugreeva, wherein the author does not mince words and shares about the ethical lapse of Rama. That was a difficult chapter in Ramayana. Rama tries to help Sugreeva to fight against his brother Vali. He kills Vali by hiding. Rama being a warrior, is supposed to fight straight, and being righteous person, who is supposed to not harm anyone unnecessarily, forgoes both in this episode. Vali questions him about this. And Rama rationalizes that Vali is not a sub-human, Monkey, but a higher form since he possesses the judgement skills of Right vs Wrong, and since Vali choose the Wrong approaches, when knowing what was Right, he had to meet with this fate. Also, by having killed by Rama, Vali is elevated to higher form in his death. Other mistakes of Rama are explained as a consequence of Rama forgetting his inherent divinity, making mistakes as a human, and needed constant reminders from gods. This book is action packed, has good stories, morals, stories of ethical dilemmas and is entertaining. The author does a very good job of maintaining balance of religious piety and story telling in this book.

The Smart Girl's Guide to Privacy: Practical Tips for Staying Safe Online by Violet Blue

My rating: 3 of 5 stars

This is a checklist book for the various steps that a person can take to stay secure and manage their privacy online. This is written for general populace, and does a good work to highlights of perils of getting hacked online, problems associated with online privacy. Author provides references to tools and mechanisms that can help person stay safe online.

Autonomy: The Quest to Build the Driverless Car—And How It Will Reshape Our World by Lawrence D. Burns, Christopher Shulgan

36039329 Autonomy by Lawrence D. Burns

My rating: 5 of 5 stars

This book reminded me of "The Road Ahead" by Bill Gates. This is an impressive account on Self Driving Technology, that is about to come and consume us in the near future. The book is very well written. Initially, I had plenty of doubts on the author, Lawrence D Burn's style, thinking that he was one of the pure management type guys, looking at things in a disconnected way, trying to associate himself with changes brought about by others. I was proven wrong. I started appreciating his insights, his outlook towards this project, his commitments, and really understood where he was coming from when the author provided more context into his own up bringing and background. He provided the view from Detroit, that many following the self-driving space will miss, and it an important viewpoint to consider. The book starts with the DARPA race, narrates the events, and stories of people who are shaping this story. GM, and Google play a very important role in the story. The book shines in presenting, well researching personal accounts from various actors like Red Whitaker, Chris Urmson, Sebastian Thrun, Larry Page, Antony Levandowski, Travis Kalanick, as well as many people from the top management in established car companies. It was good to get a first hand account on how people running established businesses think, and make decisions. It also shares the grit, and adventures of engineers who work to push the envelop of the possibilities. The book indirectly highlights the value/policy stances taken by companies such as Waymo, Tesla, and Uber pursuing self driving technology after giving the backround on the limitations of the technology, which were known to everyone developing it. It should be noted that as of 2019, Tesla and Uber have both been responsible for loss of lives with their pursuit of this adventure, and both have escaped consequences for their mistakes. With all the events, book lays the solid ground for what is to come and expected in the next few years or decades for Autonomy. I will count this book as one of the good business books that I have read in recent years.

Most of us were attracted towards maths, science and programming because there was certain level of rigor, factual information that can be verified by repeating the experiment, equality based on understanding of these facts, and desire to elevate ourselves to the equal position of someone who had a better understanding of the facts that can obtained by ourselves repeating the experiment, understanding the concepts and thereby understanding the greater sum.

Soon, as adults, we run into the opinion territory. What is considered an opinion vs fact is a topic in itself. However, let us assume that opinion is different from mathematical fact.

When we run into opinion territory, the way the world works is, if a person has power over another, the person exercises that power. We often find this irrational, argue in the opinion territory and try to find out if facts can be established so that equality order can be restored.

Some scientist identify the opinion territory from science and choose to devote more of their energies to science, aligned with what brought them to this interest in the first place.

I have known "by heart" that total number of the subsets of a set of n number is \(2^n\) I was struggling to find an intuitive explanation, and two answers helped me to understand it.

For each element, you have two choices: either you put it in your subset, or you don't; and these choices are all independent.

Citation:

Bruno Joyal (https://math.stackexchange.com/users/12507/bruno-joyal), What is the proof that the total number of subsets of a set is $2^n$?, URL (version: 2013-10-31): https://math.stackexchange.com/q/546417

Another was this table that represented the sets as binary, to denote 0 as an absence of the element and 1 as presence of the element.

That can help us understand that the total number of subsets of set of n is \(2^n\)

In 2018, I read 19 books that I have kept track via GoodReads.

These were some notable books that I had read.

• Lauren Ipsum - A Story about Computer Science and Other Improbable Things by Carlos Bueno

• Apache Mesos Essentials by Darmesh Kakadia

• Beyond the Door by Philip K Dick

• Manga Guide to Linear Algebra by Shin Takahashi

• Guns, Germs and Steel by Jared Diamond

• Chaos: Making of a new science by James Gleick

• Yummy Sourashtra Recipe Book by my parents

• Astrophysics for people in a hurry by Neil deGrasse Tyson

• The Mysterious affairs at Styles by Agatha Christie

• The Hackers Diet by John Walker

• Code: The Hidden Language of Computer Hardware and Software by Charles Petzold

• The Best we could do by Thi Bui

• I am Home, the portraits of immigrant teenagers by Erica McConnell, Rachel Neumann and Thi Bui

• The Myth of a strong leader: Political leadership in the modern age by Archie Brown

• Stranger in a strange land by Robert Heinlein

Stranger in a Strange Land Stranger in a Strange Land is a 1961 science fiction novel by the American author Robert A. Heinlein. It tells the story of Valentine Michael Smith, a human who comes to Earth in early adulthood after being born on the planet Mars and raised by Martians, and explores his interaction with and eventual transformation of Terran culture. . Wow, what a book. It goes into multiple topics like being human, politics, religion, love, sex, community, spirituality, entrepreneurship, money, influence, negotiation, strategy and "understanding" of it all.

This gave a new word Grok Grok () is a neologism coined by the American writer Robert A. Heinlein in his 1961 science fiction novel Stranger in a Strange Land. The Oxford English Dictionary summarizes the meaning of grok as "to understand intuitively or by empathy, to establish rapport with", and "to empathize or communicate sympathetically (with); also, to experience enjoyment". However, Heinlein's original concept, of a human who comes to Earth in early adulthood after being born on the planet Mars, is far more nuanced. to English speaking people, which roughly means to "understand thoroughly" and becoming one with the concept. I have used this phrase in Tamil "கரைச்சி குடிச்சிட்டான்" which means someone thoroughly understood to subject at hand.

One interesting thing about this science fiction is, it combined the real world concepts of business, stocks, politics, and ownership with an imaginary situation where humans have already traveled to Mars and there is a human raised by Martians among us. The details covering the rise of Mike to the status of a godman, and formation of his communion was a fun read.

Just imagine how close is this quote "Thou art god, I am god. All that groks is god.” by Valentine Michael Smith to the Sanskrit Verse Tat Tvam Asi The Mahāvākyas (sing.: mahāvākyam, महावाक्यम्; plural: mahāvākyāni, महावाक्यानि) are the 'Great Sayings' of the Upanishads, with mahā meaning 'great' and vākya ''sentence'. The Mahāvākyas are traditionally considered to be four in number, though actually five are prominent in the post-Vedic literature: .

The character Jubal Harshaw attributes that one can explain away everything by holding on to Solipsism Solipsism ( SOLL-ip-siz-əm; from Latin sōlus 'alone' and ipse 'self') is the philosophical idea that only one's mind is sure to exist. As an epistemological position, solipsism holds that knowledge of anything outside one's own mind is unsure; the external world and other minds cannot be known and might not exist outside the mind. and Pantheism Pantheism is a diverse family of philosophical and religious beliefs, that equate reality with divinity. Pantheistic concepts date back thousands of years, and pantheistic elements have been identified in diverse religious traditions, such as Christianity. Most notably, pantheism refers to the belief that the totality of being—called by various names Nature, universe, cosmos—is a self-organizing unity that needs no distinct creator, and can be met with the same sense of reveration and awe as theists attribute to their gods. .

I was impressed with Robert Heinlein Robert Anson Heinlein ( HYNE-lyne; July 7, 1907 – May 8, 1988) was an American science fiction author, engineer, and naval officer. Sometimes called the "dean of science fiction writers", he was among the first to emphasize scientific accuracy in his fiction and was thus a pioneer of the subgenre of hard science fiction. His published works, both fiction and non-fiction, express admiration for competence and emphasize the value of critical thinking. His plots often presented provocative situations which challenged conventional social mores. His work continues to have an influence on the science fiction genre and on modern culture more generally. covering so many topics. I really enjoyed it.

The Myth of the Strong Leader - Political Leadership in the Modern Age by Archie Brown

My rating: 5 of 5 stars

This book covers a great deal on political leadership and helps the reader to understand the political landscape of multiple nations. The crux of this, it is not a strong leader that brings a net positive change, but a leader who is flexible, who understands the situation well, goes into the details, establishes that the processes, and follows it through that will bring the net positive change. The way, the different leaders have accomplished is quite varied, and depends on the situation and the time. This book introduces some amazing real life characters like, Adolfo Suárez of spain who transformed the country to Democracy from the previously established monarchy in a peaceful manner. It also shares how Deng Xioping is the person behind the economic advancement of China from 1970 onward. It shares the perspective on how Fidel Castro overthrew the dictatorship in his country, and how lived by his principles, and did not accumulate any wealth for himself. One thing that struck with me, when this book was written, Obama was the president of USA, and even then, the system in place in USA was such that, no one particular person was ultimately responsible everything in the government. It has been understood that answer for "who controls the whitehouse of USA", seems " no one knows". This is not a snarky comment, but a statement that workings of the government in USA is very complex, and there are multiple forces in effect at any time, including political parties, supporters, lobbyists, and corporations with interests, and it will be too much to give credit and put focus on a single person. This book covers lot. It covers Britain, USA, Europe, South America, South Africa, China, Russia, touches upon New-Zealand, India and other nations. The author explores how various leaders in these countries have shaped the political landscapes of these places for better.

I am Home portraits of immigrant teenagers by Erica McConnell, Rachel Neumann and Thi Bui

My rating: 5 of 5 stars

Memorable book. It covers the stories of teenagers various different parts of the world like Keren, Eritrea, San Salvador, Huehuetenango, and lots of places around the world, who are now studying in Oakland International High School, and share what they think, when they think of "Home". Book does a very good job of portraying the diversity that exists in OIHS.

The Best We Could Do by Thi Bui

My rating: 5 of 5 stars

This is a personal memoir of the author, Thi Bui, as she tries to recollect her own story of coming to the US as a refugee from Vietnam on a boat. Fast forward many years, the author is well educated, accomplished in life, settled in society, and has a family of her own. When she has her first child, she tries to associate herself with her mother and her times in Vietnam and their family migration to the US. It covers the political history of Vietnam well, and how the ordinary people suffered, without any exception, both at the hands of external oppressors and from local oppressors alike. There was no way out, except to Run away. And, run they did, in search for stabler societies so they and their children at-least can have a chance to live. It is a heart-wrenching story as she understands the story of her own mother, father, and the reason they become the way they are, the hardships and life that they have seen in other parts of the world, the risks they took, and ultimately, the best they could do for their children. It is a family story, and it helps us understand our own parents better.

Quotes that I liked from the book

“This - not any particular piece of Vietnamese culture - is my inheritance: the inexplicable need and extraordinary ability to run when the shit hits the fan. My refugee reflex.”

― Thi Bui, The Best We Could Do

...

“To understand how my father became the way he was, I had to learn what happened to him as a little boy. It took a long time to learn the right questions to ask.”

― Thi Bui, The Best We Could Do

Code by Charles Petzold

My rating: 5 of 5 stars

This is a highly influential book in the subject of how Computers work from ground up. They author deals with the subject and introduces computers as if they were a natural evolution of inventions that had already taken effect and had proved useful to the society. After introducing the various needs for communication, the author explains how it can achieved using bulbs, and then chips, and then how it is stored and done at the higher scale using Computers. He goes both in the Hardware as well as Software section of how computers work, and why we need operating systems and programming languages. The section on float point arithmetic caught my attention and imagination completely. This was a lucid explanation on storing decimal valued numbers using binary digits. I throughly enjoyed this, and is already helping me appreciate some of the basic concepts used in Computers and Computer Science.

The Hacker's Diet by John Walker

My rating: 5 of 5 stars

This is the first health book that I could not put down. This is the book that is helping me to manage my weight and my health. So, it is a life-saving book for me. I was worried about my weight gain, and my lack of time to do exercises. I have 2 kids now and have an hour commute to my work. Both of these leave me with very less time to do exercises regularly, and I was constantly feeling worried about weight gain. This book helped to break the problem into two. a) Managing Weight. b) Doing Exercises. For a), I completely understood how this can be controlled by paying attention to the Calorie intake. That' it. I adjusted my calories and I went back to -3 KG within 3 weeks. I have goal to go -13 kgs further so that I can maintain my weight at a desirable number. I am into managing of my weight now. Once that is done, I know the need and utility value for exercises and I will able to make time for it to live for longer so that I could continue doing whatever I like. Rarely, self-help books have proved valuable to me. This the 2 nd self-help book that I can say that, it has proved valuable to me and the concepts taught will remain with me for the rest of my life. Thank you, John Walker.

I have three projects that are focused on technical writing, note taking and documentation of computer science concepts.

• http://www.learntosolveit.com/

• http://www.coursedocs.org/

• And my blog http://xtoinfinity.com/

I feel like these projects could be consolidated, merged into a single domain. I am thinking of merging

• http://www.learntosolveit.com/

• http://www.coursedocs.org/

and calling it learntosolveit.com

And I am thinking of hosting http://xtoinfinity.com/ at senthil.learntosolveit.com setup the appropriate domain redirections.

Since these are writing projects, keeping them consolidated seem to make sense to me. Still wondering what the dis-advantages of this will be.

I tried this simple project to write a hash table in C - https://github.com/orsenthil/ht using this tutorial (https://github.com/jamesroutley/write-a-hash-table) - The tutorial was really good, simple and helped appreciate how a simple hash table can be implemented from ground-up.

To prepare for my next course with Introduction to Operating Systems I had to brush up my knowledge of C and C++.

I studied and worked through

• Essential C

• Pointers and Memory

I found a video series on Pointers in C, by mycodeschool that helped clarify a lot of concepts with Pointers in C. It started making me feel comfortable again. It is amazing to have resources like these when we are learning difficult concepts today.

39716791 The Mysterious Affair At Styles by Agatha Christie

My rating: 4 of 5 stars

Agatha Christie won the bet my with my reading of the book too. I could not figure out the murderer or figure out the plot. It may be hard or impossible for the reader to find the plot as the information that is shared with is limited and more information is revealed as the plot is disclosed. It was an enjoyable book with the variety of characters and it took me a to mid-century England for the hours I was reading this book.

Astrophysics for People in a Hurry by Neil deGrasse Tyson

My rating: 4 of 5 stars

I loved this book. This is a very short book, to the point, and explains the universe in simple terms. What I didn't expect and was a surprise to me was the Author's humorous anecdotes, completely relevant to the present day reader inserted between the concepts in the pages. For e. g. It was funny to know that behind US Govt which consumes Helium for military and research, the next second highest consumer of "Helium" was Macy's for it's Macy's day parade. It was startling to know that life as we understand in any form, the single-celled organism has started in existence only the last 2% of the history of the universe. The universe is very very old, very very big. Often there is a question on "Is there life elsewhere in the universe? " - The more I read these kinds of books, the more I realize that "There is life elsewhere in the universe. Highly likely. It is inevitable that either Earthians discover the other life forms, and they discover the Earthians. Also, the timeline of advancement, the calamities that are possible in next thousand years, humankind will, of course, take the ship, explore, and start settling in various other parts of the universe. Thinking like this doesn't sound like fiction to me. It was a great book. I will start looking out for Neil De Grasse Tyson from now.