A Column, A Tweet and a Substack

There’s been a fair bit of controversy recently in my corner of the internet. What does my corner mean, you ask? Well, my corner of the internet is where I focus on figuring out how to help people (young folks in particular) learn better. Anything related to this topic is my specific area of interest, and anybody who writes on this topic is my tribe.

And as it so happened, a recent column, a recently deleted tweet and an old Substack coalesced in my mind recently.

The recent column was written by Nitin Pai. The title of the column is “The Misleading Outrage over 18-Hour Work Days“. As always, it is an interesting, thought-provoking read. If the Livemint article is behind a paywall, Nitin has a version up on his own website, available here.

Earlier this month, the chief executive officer (CEO) of Bombay Shaving Company was forced to apologize for advising people in their early twenties to put in 18-hour work days for 4-5 years, and work hard. The social media backlash accusing him of promoting a toxic work culture was so strong that he quit that platform entirely.


I have not read the original post, and if the person who came up with the post has now deleted his entire profile, let alone the specific post, I suppose digging it up will be quite difficult. If you’ll permit me the indulgence, I won’t go around looking for archives of the post.

Nitin’s next paragraph is where things get truly interesting:

The man would be wrong if he had demanded that his company’s employees work such long hours and judged them solely on that basis. But to the extent that he was counselling young people on the attitude you need to adopt early in your career, he was speaking in your interest.
Remember, all those people on social media stoning the folk devil of the day don’t care about your interests. I think you have a better chance of success heeding his advice than of those who criticized him for what he said. For those of us who started from humble beginnings, hard work is the surest ticket to upward mobility.


And I’m sure somebody on Twitter will tell me why I’m wrong to agree with these two paragraphs, but… I honestly do not see why what Nitin has said here is even remotely controversial. The attitude that Nitin is speaking about is, in my opinion, indispensable. There’s no alternative to hard work, and you can pick any number you like – 18 a day or 10,000 in total, or any other. But it’s not so much about the number as it is about the attitude – and the attitude is about being willing to put in the work.

And because there is no such thing as a free lunch, putting in the work might mean having to cut some things out of your life.

Now, you might well decide that 18 hours of working is far too much. Or you might decide that you might want to put in 18 hours five days a week. But don’t get hung up on the specific number – put in the hours that you need to put in, and you need to figure out what those hours look like given what you know about yourself and your own life.

That’s it.

And if this is deemed controversial, so be it. This was good advice to me when I started on my career, and this is advice that I give to young folks today.

But there was another line in Nitin’s column that caught my eye.

“Your family and well- wishers would have advised you to sacrifice some of your leisure so that you can improve your life prospects.”

A person I follow on Twitter recently came up with a somewhat similar tweet. He’s since deleted it, and I’m afraid I cannot quote it in its entirety from memory, but here’s what I remember of it:

The tweet advised one to have more serious pursuits/hobbies at a young age. Sports might come later, as might dance, for example. If you’re seeking out leisure, try and develop hobbies of a more serious nature.

Again, let me be very clear. I’m telling you what I remember of the tweet, and I may well be not remembering it entirely accurately. But while in Nitin’s case I found myself in complete agreement with his column, I find myself in slight disagreement with this tweet.

Disagreements are fine! Please feel free to disagree with my post, and please feel free to tell me, whether on social media or by way of commenting on this post, or by email. I hope we can get into a lovely little argument about why we disagree, and figure out for both our selves about what is the correct way to think about this issue. Not, note, who is right and who is wrong between the two of us. But rather, what is the right way to think about it. That is the point of an argument – figuring out what is the correct answer. Not winning it.

I should note over here that this is easy for me to write, but difficult for me to practice. I struggle with it every time I get into an argument, but this is one of those things that is worth the struggle.

But let me now tell you why I disagree with the spirit of the tweet. Because the tweet was telling me what to do to get the best out of life, not how to go about it.

The YouTube channel MKBHD produces some really good videos, as does 3Blue1Brown, and they both work extremely hard at both creating fantastic videos, and also at doing so regularly. You might say that one is frivolous and the other is not, but trust me, both require a lot of extremely hard work.

In my opinion, it is not for me to tell you what you should be doing at any age. That answer can only come from within. The answer (for you) may well be making Instagram reels, or TikTok videos. Or it could be composing a saga in Sanksrit. Or it could be becoming the next best thing in Indian cricket. Or something else altogether.

But once you’ve decided what that something is, Nitin’s point becomes applicable. Boss, you want to be the best at this thing you’ve chosen to do? Start working!

Which brings me to the Substack post. So you should work hard, no matter what you chose to do. But what should you choose to do?

Well, as I said, not my place to tell you – that choice must come from you, and you alone. Not me, not Nitin, not the person who wrote the deleted tweet, not your best friend, not your parents, not your significant other. You, and you alone.

But I’m happy to tell you that I think I can give you advice in this regard. If you’ve chosen to do something, do you enjoy the process of working at it? Do you enjoy the hours you’re going to have to spend on working at it, day in and day out? Do you, in fact, enjoy this so much that this – the journey – ends up becoming its own reward? So much so, in fact, that the outcome doesn’t matter that much in comparison?

If yes, then congratulations, for now you’re not working 18 hours a day. For it’s no longer work, is it? It is something that you truly enjoy doing for its own sake. And a positive outcome, however defined (a prize, first place in class, a great career, awesome pay packages) is a bonus, but not the point. The point is the process.

So, your motivation should not be primarily based on the goal/outcome/results. Success is best achieved by those who find motivation in the process, rather than the outcome.


So what should you do in life? You know best! But don’t choose to do something for the sake of the expected outcome, choose to do something in which you enjoy the process.

“For example”, I hear you ask? Well, I enjoy the process of trying to write (and then post) everyday. Do I manage to do so every single day? Alas, no. Life sometimes gets in the way. But do I enjoy the process of reading stuff, and asking myself if what I’ve read will help me help young folks learn better, and then writing about it?

I love it. I adore it, I am besotted with it, and I never want to stop.

Try it. And by it, I mean optimizing for the enjoyment of the process, rather than the potential outcome.

And if it clicks, I envy you the happiness you’re about to feel. 🙂

The Crypto Trilemma, by The Conversable Economist

I’ve said it before, and I’ll say it again, please subscribe to his blog.

This will be a very short post, for two reasons. One, the more I read about cryptocurrencies and related topics, the more confused I get. Two, given my very limited understanding, there isn’t much more to add to Timothy Taylor‘s post.

But the reason I wanted to write a post is because I found the idea of the crypto trilemma really useful (and hopefully you will too), and writing about it helps make the idea clearer in my head.


My most important takeaway from his post was this diagram. The point is that the crypto trilemma means that any currency can be any two things at any given point of time, but all three at the same time are simply not going to be possible.

  1. Traditional currencies, such as the rupee notes in your pocket are secure and scalable, which means that the rupee system can’t be ‘hacked’ and the system can grow large in terms of transactions per second without any difficulty. But it is not, of course, decentralized.
  2. Bitcoin and Ethereum (and possibly others, but I think these two are by far and away the biggest cryptocurrencies) are secure, and by definition decentralized, but they simply can’t scale. Navin Kabra, who is my guru in these matters (and many others!), tells me that Bitcoin can support 7-10 transactions per second, while Ethereum can do about 30. Even if they implement ‘sharding‘, Navin says they’ll go up to 30 transactions per second. Both the link I’ve given here and Timothy Taylor’s blog explain more about sharding, if you’re interested.
    Visa, on the other hand, can do twenty-four thousand transactions per second.
    TL;DR? Not scalable.
    As with everything in life, it’s more complicated than that. Here are three additional links shared by Navin: Visa’s claims | Bitcoin’s Refutation | An academic paper on the topic (bonus: a helpful table)
  3. And as we’re all discovering over the past few days, the newer cryptocurrencies ain’t quite that secure.

As I said, a useful framework to keep in mind when thinking about crypticurrencies.

AI/ML: Some Thoughts

This is a true story, but I’ll (of course) anonymize the name of the educational institute and the student concerned:

One of the semester end examinations conducted during the pandemic at an educational institute had an error. Students asked about the error, and since the professor who had designed the paper was not available, another professor was asked what could be done. Said professor copied the text of the question and searched for it online, in the hope that the question (or a variant thereof) had been sourced online.

Alas, that didn’t work, but a related discovery was made. A student writing that same question paper had copied the question, and put it up for folks online to solve. It hadn’t been solved yet, but the fact that all of this could happen so quickly was mind-boggling.

The kicker? The student in question had not bothered to remain anonymous. Their name had been appended with the question.

Welcome to learning and examinations in the time of Coviid-19.

I have often joked in my classes in this past decade that it is only a matter of time before professors outsource the design of the question paper to freelance websites online – and students outsource the writing of the submission online. And who knows, it may end up being the same freelancer doing both of these “projects”.

All of which is a very roundabout way to get to thinking about Elicit, videos about which I had put up yesterday.

But let’s begin at the beginning: what is Elicit?

Elicit is a GPT-3 powered research assistant. Elicit helps you classify datasets, brainstorm research questions, and search through publications.


Which of course begs a follow-up question: what is GPT-3? And if you haven’t discovered GPT-3 yet, well, buckle up for the ride:

GPT-3 belongs to a category of deep learning known as a large language model, a complex neural net that has been trained on a titanic data set of text: in GPT-3’s case, roughly 700 gigabytes of data drawn from across the web, including Wikipedia, supplemented with a large collection of text from digitized books. GPT-3 is the most celebrated of the large language models, and the most publicly available, but Google, Meta (formerly known as Facebook) and DeepMind have all developed their own L.L.M.s in recent years. Advances in computational power — and new mathematical techniques — have enabled L.L.M.s of GPT-3’s vintage to ingest far larger data sets than their predecessors, and employ much deeper layers of artificial neurons for their training.
Chances are you have already interacted with a large language model if you’ve ever used an application — like Gmail — that includes an autocomplete feature, gently prompting you with the word ‘‘attend’’ after you type the sentence ‘‘Sadly I won’t be able to….’’ But autocomplete is only the most rudimentary expression of what software like GPT-3 is capable of. It turns out that with enough training data and sufficiently deep neural nets, large language models can display remarkable skill if you ask them not just to fill in the missing word, but also to continue on writing whole paragraphs in the style of the initial prompt.


It’s wild, there’s no other way to put it:

So, OK, cool tech. But cool tech without the ability to apply it is less than half of the story. So what might be some applications of GPT-3?

A few months after GPT-3 went online, the OpenAI team discovered that the neural net had developed surprisingly effective skills at writing computer software, even though the training data had not deliberately included examples of code. It turned out that the web is filled with countless pages that include examples of computer programming, accompanied by descriptions of what the code is designed to do; from those elemental clues, GPT-3 effectively taught itself how to program. (OpenAI refined those embryonic coding skills with more targeted training, and now offers an interface called Codex that generates structured code in a dozen programming languages in response to natural-language instructions.)


For example:

(Before we proceed, assuming it is not behind a paywall, please read the entire article from the NYT.)

But about a week ago or so, I first heard about Elicit.org:

Watch the video, play around with the tool once you register (it’s free) and if you are at all involved with academia, reflect on how much has changed, and how much more is likely to change in the time to come.

But there are things to worry about, of course. An excellent place to begin is with this essay by Emily M. Blender, on Medium. It’s a great essay, and deserves to be read in full. Here’s one relevant extract:

There is a talk I’ve given a couple of times now (first at the University of Edinburgh in August 2021) titled “Meaning making with artificial interlocutors and risks of language technology”. I end that talk by reminding the audience to not be too impressed, and to remember:
Just because that text seems coherent doesn’t mean the model behind it has understood anything or is trustworthy
Just because that answer was correct doesn’t mean the next one will be
When a computer seems to “speak our language”, we’re actually the ones doing all of the work


I haven’t seen the talk at the University of Edinburgh referred to in the extract, but it’s on my to-watch list. Here is the link, if you’re interested.

And here’s a Twitter thread by Emily M. Blender about Elicit.org specifically:

In response to this critique and other feedback, Elicit.org have come up with an explainer of sorts about how to use Elicit.org responsibly:


Before we proceed, I hope aficionados of statistics have noted the null hypothesis problem (which error would you rather avoid) in the last sentence of pt. 1 in that clipping above!

So all that being said, what do I think about GPT3 in general and elicit.org in particular?

I’m a sucker for trying out new things, especially from the world of tech. Innocent until proven guilty is a good maxim for approaching many things in life, and to me, so also with new tech. I’m gobsmacked to see tools like GPT3 and DallE2, and their applications to new tasks is amazing to see.

But that being said, there is a lot to think about, be wary of and guard against. I’m happy to keep an open mind and try these amazing technologies out, while keeping a close eye on what thoughtful critics have to say.

Which is exactly what I plan to do!

And for a person with a plan such as mine, what a time to be alive, no?

Blockchains Are A Bad Idea

Via Krish Ashok, via Navin Kabra

Games and Microsoft Excel

Via Navin Kabra on Twitter:

Correlation, Causation and Thinking Things Through

Us teaching type folks love to say that correlation isn’t causation. As with most things in life, the trouble starts when you try to decipher what this means, exactly. Wikipedia has an entire article devoted to the phrase, and it has occupied space in some of the most brilliant minds that have ever been around.

Simply put, here’s a way to think about it: not everything that is correlated is necessarily going to imply causation.

For example, this one chart from this magnificent website (and please, do take a look at all the charts):


But if there is causation involved, there will definitely be correlation. In academic speak, if x and y are correlated, we cannot necessarily say that x causes y. But if x does indeed cause y, x and y will definitely be correlated.

OK, you might be saying right now. So what?

Well, how about using this to figure out what ingredients were being used to make nuclear bombs? Say the government would like to keep the recipe (and the ingredients) for the nuclear bomb a secret. But what if you decide to take a look at the stock market data? What if you try to see if there is an increase in the stock price of firms that make the ingredients likely to be used in a nuclear bomb?

If the stuff that your firm produces (call this x) is in high demand, your firm’s stock price will go up (call this y). If y has gone up, it (almost certainly) will be because of x going up. So if I can check if y has gone up, I can assume that x will be up, and hey, I can figure out the ingredients for a nuclear bomb.

Sounds outlandish? Try this on for size:

Realizing that positive developments in the testing and mass production of the two-stage thermonuclear (hydrogen) bomb would boost future cash flows and thus market capitalizations of the relevant companies, Alchian used stock prices of publicly traded industrial corporations to infer the secret fuel component in the device in a paper titled “The Stock Market Speaks.” Alchian (2000) relates the story in an interview:
We knew they were developing this H-bomb, but we wanted to know, what’s in it? What’s the fissile material? Well there’s thorium, thallium, beryllium, and something else, and we asked Herman Kahn and he said, ‘Can’t tell you’… I said, ‘I’ll find out’, so I went down to the RAND library and had them get for me the US Government’s Dept. of Commerce Yearbook which has items on every industry by product, so I went through and looked up thorium, who makes it, looked up beryllium, who makes it, looked them all up, took me about 10 minutes to do it, and got them. There were about five companies, five of these things, and then I called Dean Witter… they had the names of the companies also making these things, ‘Look up for me the price of these companies…’ and here were these four or five stocks going like this, and then about, I think it was September, this was now around October, one of them started to go like that, from $2 to around $10, the rest were going like this, so I thought ‘Well, that’s interesting’… I wrote it up and distributed it around the social science group the next day. I got a phone call from the head of RAND calling me in, nice guy, knew him well, he said ‘Armen, we’ve got to suppress this’… I said ‘Yes, sir’, and I took it and put it away, and that was the first event study. Anyway, it made my reputation among a lot of the engineers at RAND.


I learnt about this while reading Navin Kabra’s Twitter round-up from yesterday. Navin also mentions the discovery of Neptune using the same underlying principle, and then asks this question:

Do you know other, more recent examples of people deducing important information by guessing from correlated data?


… and I was reminded of this tweet:

Whether it is Neptune, the nuclear bomb or the under-reporting of Covid deaths, the lesson for you as a student of economics is this: when you marry the ability to connect the dots with the ability to understand and apply statistics, truly remarkable things can happen.

Of course, the reverse is equally true, and perhaps even more important. When you marry the ability to connect the dots with a misplaced ability to understand and apply statistics, truly horrific things can happen.

Tread carefully when it comes to statistics!

Navin Kabra on the Power of Networking

Besides putting out super-awesome threads on Twitter, Navin Kabra also writes a newsletter. (He also runs a firm, and makes time for being interviewed for podcasts, and much else besides, but thinking about that will only depress the rest of us, so let’s stop)

So he sent out a post yesterday on that newsletter, which I found fascinating:

There are 3 kinds of power in an organization and most people focus on the wrong ones.
Jacob Kaplan-Moss has a great article about The Three Kinds of Organizational Power: role power, expertise power, and power through relationships. Most people focus on the less important ones. Understanding what these powers are and how to use them is key to becoming effective at your work.


First, if you haven’t already, please subscribe to his newsletter. It’s not just free, it ends up being worth more than the time you spend reading it, and if that is not a bargain, I don’t know what is. Second, maybe I’m guilty of over-fitting, but it was fascinating to me how role power is LinkedIn, expertise power is Coursera and networking power is Starbucks:

College is a bundle: education | credentialing | peer networks


If I were to write that blog post again today, I would remove the word peer. That part, I really do think that role power is about signaling, expertise power is about learning, and relationship power is about networks (the last one is obviously true, it is the others that make me think I might be over-reaching).

Food for thought, as they say.

Navin’s article speaks about the last bit, relationship power, as the most powerful/useful one. And anybody who is in any part of the higher education supply chain would likely agree: it is networks that get things done.

Now, as a student, what should you take away from this?

You need to consciously spend some time in developing your networks. And that means putting yourself out there as often as possible. Write blogposts. Make videos. Start podcasts. Make TikTok or Takatak (or whatever else we’re calling it these days) clips.

And once you do all of that, as often as possible, start sending those links to folks. Ask them for feedback, and ask them specifically for areas of improvement. Ask them for learning recommendations. The magic of the internet will mean that conversations, debates and opportunities will crop up on their own.

But networking does not mean sending people requests on LinkedIn. That just means you’re added to a person’s network. Networking matters, not the network itself. It is a garden that needs regular tending to. The bad news is that it is hard work, the good news is that there are surprisingly large payoffs, and over surprisingly large periods of time.

Make connections with your peers, your professors and your potential mentors. Use this network to share your thoughts, and put those thoughts out for public consumption. Optimize for quantity, and quality will be the eventual outcome. Respond to other people’s publicly available output.

Most importantly, do this for its own sake.

Job opportunities is one of the benefits of doing all this. It is not the only goal, and it is not the end-goal.

For you will change your job eventually, but your network will either shrivel or grow. Please, learn how to nurture it, and keep at it every day.

Navin promises towards the end of his post that he will share his own tips about networking. I’ll link to that post whenever it comes out, of course. But in the meantime, start learning, and help others learn, and build out your network.

Why would you want to not acquire a superpower, eh?

About Ergodicity

Anything that Zeynep Tufekci writes is worth reading, and people like Navin Kabra make Twitter a place of learning and knowledge. Therefore this tweet is worth the price of admission twice over.

But it gets better!

Because the replies took me to this excellent essay on ergodicity:

In an ergodic scenario, the average outcome of the group is the same as the average outcome of the individual over time. An example of an ergodic systems would be the outcomes of a coin toss (heads/tails). If 100 people flip a coin once or 1 person flips a coin 100 times, you get the same outcome. (Though the consequences of those outcomes (e.g. win/lose money) are typically not ergodic)!
In a non-ergodic system, the individual, over time, does not get the average outcome of the group.


… And therefore this set of essays and this newsletter.

As the kids say these days: sorted.

Learn Macro by Reading the Paper

Macro, and I’ve said this before, is hard.

But a useful way to start understanding it, at least in an Indian context, is by:

  • carefully reading a well written article
  • understanding and noting for oneself key concepts within that article
  • recreating the charts from that article
    • That includes figuring out the source of the data…
    • … as well as acquiring the ability to build out these charts
  • And most important of all, creating a piece of your own (could be a YouTube video/short, a blog, an Instagram story, a Twitter thread) that helps simplify the article you’ve read.1

Now, Arvind Subramanian and Josh Felman have generously obliged us by writing a well written article. I’ll oblige you by carefully reading it and annotating it, including pointing out key concepts, sources for data and recommendations for building out the charts.

That just leaves the last point for you, dear reader. We’ll call that homework.

Now, the well written article:

For more than a decade, India’s fiscal problem has been on the back-burner, acknowledged as a concern, but excluded from the ranks of pressing issues. Now, however, the problem is back with a vengeance. COVID has upended the fiscal position, and fixing it will require considerable time and effort, even if the economy recovers. This worrisome prospect has prompted calls for the Fiscal Responsibility and Budget Management Act (FRBM) to be dusted off, reintroduced, and implemented — this time, strictly and faithfully. But before we heed them, we need to understand why the previous FRBM strategy failed and how to prevent a repeat. We argue below that the new strategy will look nothing like the current FRBM.


First things first, what is FRBM?

The Fiscal Responsibility and Budget Management Act, 2003 (FRBMA) is an Act of the Parliament of India to institutionalize financial discipline, reduce India’s fiscal deficit, improve macroeconomic management and the overall management of the public funds by moving towards a balanced budget and strengthen fiscal prudence. The main purpose was to eliminate revenue deficit of the country (building revenue surplus thereafter) and bring down the fiscal deficit to a manageable 3% of the GDP by March 2008.


Think of it as a one-person Alcoholic’s Anonymous club. It is of the government, for the government and by the government, and the idea is to wean the government off a dangerous addiction that it is hopelessly affixed to: debt.

By the way, there are many reasons this is a good essay, not the least of which is how well structured it is. The first three sentences in the very first paragraph, excerpted above, point out the problem that is going to be addressed, without using any difficult words or jargon. Then they point out the tool that will be used to address the problem. Then they point out the tool itself has problems. Finally, the explain that the essay is about fixing those problems. And then the essay follows. You might want to keep this in mind when writing your own essays (or indeed creating your own podcasts/videos etc.)

Now, back to the essay:

  1. What is general government debt? Where can I access the data?
    Note the second hyperlink above: I’ve linked to the Fred St Louis page about India’s debt, which itself gets the data from the IMF. Here is the page from the Ministry of Finance’s own website titled Public Finance Statistics. It has not been updated since September 2015. Here is a Motilal Oswal report on the subject that pegs general government debt at INR 157,227 billion. (Exhibit 1 in the report). If you read footnote 3 of that exhibit, two things happen. The first thing that happens is that you realize that tracking down general government debt might take a while. The second thing that happens is you feel a rather large twinge of sympathy for the folks who have tried to do this exercise.
    Figure 1 in the well-written article that we are analyzing in today’s post doesn’t mention a source, unfortunately. So recreating that chart will involve a rather large part of our day – but I would strongly recommend that you do the exercise. If you want to analyze Indian macroeconomic data for a living, this will be a good initiation. And indeed, a write-up about this exercise alone is a worthy addition to your CV!
  2. Second r-g: what is r, and what is g?
    1. “r” is the policy rate, which in our case will be the repo rate. This is available on the homepage of the RBI, top-left, under current rates.
    2. Time series data? Available on the DBIE page, under key rates.
    3. “g” is the nominal growth rate of the economy, and can be found at MOSPI.
    4. A useful thing to do as a student is to try and recreate the chart in the well-written article.
    5. Pts 1 and 2 here will help you get most of the data, and try and use either Microsoft Excel or Datawrapper to recreate the chart.2
  3. Next, what is primary balance?3 Where does one get that data in India?4
  4. Next, this sentence from the article: “Simple fiscal arithmetic shows that debt does not explode when the former (primary balance) is greater than the latter (interest-growth differential)”. What is this “simple fiscal arithmetic”? They’ve explained it in equations 1 and 2 in this paper.5
  5. The next three paragraphs after Figure 1 in the article point out how precarious India’s situation is when it comes to government debt, and why. It is one thing to read about the equation in a textbook, it is quite another to “run” the numbers in practice. Give it a shot, please, and see if it makes sense.
  6. Next, this paragraph from the article:
    “First, India should abandon multiple fiscal criteria for guiding fiscal policy. The current FRBM sets targets for the overall deficit, the revenue deficit and debt. This proliferation of targets impedes the objective of ensuring sustainability, since the targets can conflict with each other, creating confusion about which one to follow and thereby obfuscating accountability.”
    This paragraph is a good way to understand the importance of reading In The Service of the Republic, by Kelkar and Shah (and also to read up about the Tinbergen Rule).
  7. The next three paragraphs after that are a good way to understand what Goodhart’s Law means in practice.
  8. And finally, see if you can explain to yourself why targeting the primary balance is better than other options. Personally, I agree that it is a better target, and I agree that rather than setting down a concrete number to reach, averaging out half a percentage point worth of reduction is better. In essence, what they’re saying is that you shouldn’t try to reach x kilos of weight on a diet, but lose x% body weight every month. As our ex-captain might have put it, process over results. One of our gods advocates this too, as Navin Kabra points out.
    My reservation comes from the fact that sticking to a diet is hard, and that is true whether you’re targeting a process or a target. In other words, it is the ongoing implementation of the plan that is the challenge, not it’s design!
  9. One last point: without creating something that you are willing to put up for public consumption, and highlighting on your CV as an exercise you have done – you haven’t really learnt. Reading either that article or this blog is the easy part – explaining it somebody else is the much more difficult (and causally speaking, therefore meaningful) bit.
  10. Please, do it!
  1. Skipping this last point is missing the point altogether, rascalla![]
  2. Document your learnings as you go along.[]
  3. Read the whole article, please. It’s a good way to clear your understanding of this topic, and it is free[]
  4. The Excel link under Deficit Statistics was down when I tried to access the data. Your mileage may vary.[]
  5. Page 3[]

Pre and Post Nuclear Bomb Steel

I had referred to Patrick Collison’s “Yes, and” rather than “No, But” approach to Twitter earlier this week.

In a world in which there was a “Yes, and” Society, and a Pune chapter for this hypothetical (but much needed) society, I’d have voted for Navin Kabra as Lifetime President. Today’s twitter thread of choice is one of many reasons why:

Recommended pairing: The Life and Times of the Thunderbolt Kid, by Bill Bryson. The whole book is a delightful read, but I have the pages where he speaks about America’s fascination with the atomic bomb in mind.