Tag: health

CGM Notes

At about 5:30 pm last Wednesday, I chanced upon a box of Sandesh crumbs lying in the office. A colleague had brought the sweets to share the previous day, and people had devoured it; but left aside the crumbs. I picked up the box and proceeded to demolish it as I reviewed a teammate’s work.

Soon the box was in the dustbin. I chanced upon a cookie box that another colleague had got. And started to demolish the cookies. This was highly atypical behaviour for me, since I’m trying to follow a low-carb diet. At the moment, I assumed it was because I was stressed that day.

Presently, I took out my phone to log this “meal” in the Ultrahuman app. There the reason for my binge was clearly visible – my blood sugar had gone down to 68 mg / dL, pretty much my lowest low in the 2 weeks I wore the last sensor.

This, I realised, was a consequence of the day’s lunch, at Sodabottleopenerwala. Maybe it was the batter (or more likely, the sauce) of the fried chicken wings. Or the batter of the onion pakoda. Something I had eaten that afternoon had spiked my blood sugar high enough to trigger a massive insulin response. And that insulin, having acted upon my lunch, had acted upon the rest of the sugars in my blood. Sending it really low. To a point where I was gorging on whatever sweets I could find.

About a year (or maybe two?) back, I had read Jason Fung’s The Obesity Code, which had talked about insulin being the hormone responsible for weight gain. High levels of insulin in the blood means you feel hungrier and you gorge more, or something like that the argument went. The answer was to not keep triggering insulin release in the blood – for that would make the body “insulin resistant” (so you need more insulin than usual to take care of a particular amount of blood sugar). Which can lead to Type 2 diabetes, high triglycerides, weight gain, etc.

And so Fung’s recommendations (paraphrasing – you should see my full blogpost based on the book ) included fasting, and eating fewer carbs. Here I was, two years later, finding evidence of the concepts in my CGM data.

I have worn a CGM a couple of times before. Those were primarily to figure out my body’s response to different kinds of foods, and find out what I should eat to maintain a healthy blood sugar level. The insights had been fairly clear. However, since it had been ten months since I last wore a CGM, I had forgotten some of the insights. I was “cheating” (eating what I wasn’t supposed to eat) too much. And my blood sugar had started going up to scary levels.

The objective of this round of the CGM was to find out “high ROI foods”. Foods that gave me a lot of “satisfaction” while not triggering much of a blood glucose response. The specific hypothesis I was trying to test was that sweets and traditional south indian lunch trigger my blood sugar in the same manner, so I might as well have dessert instead of traditional south indian lunches!

Two weeks of this CGM and I rejected this hypothesis. I had sweets enough number of times (kalakand, sandesh, corner house cake fudge, etc) to notice that the glucose response was not scary at all. The problem, each time, however, occurred later – maybe the “density of sugars” in the sweets triggered off too much of an insulin response, leading to a glucose crash (and low glucose levels at the end of it).

Traditional south indian lunch (I would start with the vegetables before I moved on to rice with sambar and then rice with curd) was something I tested multiple times. And it’s not funny how much the response varied – a couple of times, my blood glucose went up very high (160 etc.). A couple of times there was a minimal impact on my blood glucose. It was all over the place. That said, given the ease of preparation, it is something I’m not cutting out.

What I’m cutting out is pretty much anything that involves “pulverised grains”. Those just don’t work for me. Two times I had dosa – once it sent my blood sugar beyond 200, once beyond 180. One idli with vade sent my blood sugar from 80 to 140 (on the other hand, khara bath (uppit) with vaDe only sent it to 120). Paneer paratha (on the streets of Gurgaon) sent my sugar up to 200.

That some flours work for me I had established in previous iterations wearing the CGM – rice rotti hadn’t worked, jowar rotti hadn’t worked, ragi mudde had been especially bad. But that dose and idli and paratha also don’t work for me was an interesting observation this time. I guess I’ll be eating much less of these.

What did work for me was what has sort of become my usual meals when going out of late – avoiding carbs. One Wednesday, I got my team to order me an entire Paneer Butter Masala for lunch (Gurgaon again). Minimal change in glucose levels. That Friday, I had butter chicken (only; no bread or rice with it). Minimal change yet again! Omelettes simply don’t register on my blood sugar levels (even with generous amounts of cheese).

To summarise,

  • Sweets may not send my sugar very high, but in due course they send it very low (due to high insulin response). The only time this crash doesn’t happen is if I’ve had the sweets at the end of a meal. Basically, avoid.
  • Any kinds of pulverised grains (dosa, idli, rotti, paratha) is not good for me. Avoid again
  • The same food can have very different response at different times. This could be due to the pre-existing levels of insulin in the body. So any data analysis (I plan to do it) needs to be done very carefully
  • On a couple of occasions I found artificial sweeteners (like those in my whey protein) causing a glucose crash – maybe they get the body to release insulin despite not having sugars. Avoid again.
  • Again last week I met a friend for dinner and we had humongous amounts of seafood. I didn’t eat carbs with it. Minimal spike.
  • Some foods cause an immediate spike. Some cause a delayed spike. Some cause a crash.
  • Crashes in glucose levels (usually 1-2 hours after a massively insulin-triggering meal) were massively correlated with me feeling low and jittery and unable to focus. It didn’t matter how recently I had taken the last dose of my ADHD medication – glucose crash meant I was unable to focus.
  • Milk is not as good for me as I thought. It does produce a spike (and crash), especially when I’m drinking on an empty stomach
  • Speaking of drinking, minimal impact from alcohols such as whiskey or wine. I didn’t test beer (I know it’s not good)
  • Biryani (Nagarjuna) wasn’t so bad – again it was important I ate very little rice and lots of chicken (ordered sides)
  • Just omelette is great. Omelette with a slice of toast not so.

All these notes are for myself. Any benefit you get from this is only a bonus.

Alcohol, dinner time and sleep

A couple of months back, I presented what I now realise is a piece of bad data analysis. At the outset, there is nothing special about this – I present bad data analysis all the time at work. In fact, I may even argue that as a head of Data Science and BI, I’m entitled to do this. Anyway, this is not about work.

In that piece, I had looked at some of the data I’ve been diligently collecting about myself for over a year, correlated it with the data collected through my Apple Watch, and found a correlation that on days I drank alcohol, my sleeping heart rate average was higher.

And so I had concluded that alcohol is bad for me. Then again, I’m an experimenter so I didn’t let that stop me from having alcohol altogether. In fact, if I look at my data, the frequency of having alcohol actually went up after my previous blog post, though for a very different reason.

However, having written this blog post, every time I drank, I would check my sleeping heart rate the next day. Most days it seemed “normal”. No spike due to the alcohol. I decided it merited more investigation – which I finished yesterday.

First, the anecdotal evidence – what kind of alcohol I have matters. Wine and scotch have very little impact on my sleep or heart rate (last year with my Ultrahuman patch I’d figured that they had very little impact on blood sugar as well). Beer, on the other hand, has a significant (negative) impact on heart rate (I normally don’t drink anything else).

Unfortunately this data point (what kind of alcohol I drank or how much I drank) I don’t capture in my daily log. So it is impossible to analyse it scientifically.

Anecdotally I started noticing another thing – all the big spikes I had reported in my previous blogpost on the topic were on days when I kept drinking (usually with others) and then had dinner very late. Could late dinner be the cause of my elevated heart rate? Again, in the days after my previous blogpost, I would notice that late dinners would lead to elevated sleeping heart rates  (even if I hadn’t had alcohol that day). Looking at my nightly heart rate graph, I could see that the heart rate on these days would be elevated in the early part of my sleep.

The good news is this (dinner time) is a data point I regularly capture. So when I finally got down to revisiting the analysis yesterday, I had a LOT of data to work with. I won’t go into the intricacies of the analysis (and all the negative results) here. But here are the key insights.

If I regress my resting heart rate against the binary of whether I had alcohol the previous day, I get a significant regression, with a R^2 of 6.1% (i.e. whether I had alcohol the previous day or not explains 6.1% of the variance in my sleeping heart rate). If I have had alcohol the previous day, my sleeping heart rate is higher by about 2 beats per minute on average.

Call:
lm(formula = HR ~ Alcohol, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.6523 -2.6349 -0.3849  2.0314 17.5477 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  69.4849     0.3843 180.793  < 2e-16 ***
AlcoholYes    2.1674     0.6234   3.477 0.000645 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.957 on 169 degrees of freedom
Multiple R-squared:  0.06676,   Adjusted R-squared:  0.06123 
F-statistic: 12.09 on 1 and 169 DF,  p-value: 0.000645

Then I regressed my resting heart rate on dinner time (expressed in hours) alone. Again a significant regression but with a much higher R^2 of 9.7%. So what time I have dinner explains a lot more of the variance in my resting heart rate than whether I’ve had alcohol. And each hour later I have my dinner, my sleeping heart rate that night goes up by 0.8 bpm.

Call:
lm(formula = HR ~ Dinner, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6047 -2.4551 -0.0042  2.0453 16.7891 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  54.7719     3.5540  15.411  < 2e-16 ***
Dinner        0.8018     0.1828   4.387 2.02e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.881 on 169 degrees of freedom
Multiple R-squared:  0.1022,    Adjusted R-squared:  0.09693 
F-statistic: 19.25 on 1 and 169 DF,  p-value: 2.017e-05

Finally, for the sake of completeness, I regressed with both. The interesting thing is the adjusted R^2 pretty much added up – giving me > 16% now (so effectively the two (dinner time and alcohol) are uncorrelated). The coefficients are pretty much the same once again.

Call:
lm(formula = HR ~ Dinner, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6047 -2.4551 -0.0042  2.0453 16.7891 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  54.7719     3.5540  15.411  < 2e-16 ***
Dinner        0.8018     0.1828   4.387 2.02e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.881 on 169 degrees of freedom
Multiple R-squared:  0.1022,    Adjusted R-squared:  0.09693 
F-statistic: 19.25 on 1 and 169 DF,  p-value: 2.017e-05

So the takeaway is simple – alcohol might be okay, but have dinner at my regular time (~ 6pm). Also – if I’m going out drinking, I better finish my dinner and go. And no – having beer won’t work – it is going to be another dinner in itself. So stick to wine or scotch.

I must mention things I analysed against and didn’t find significant – whether I have coffee, what time I sleep, the time gap between dinner time and sleep time – all of these have no impact on my resting heart rate. All that matters is alcohol and when I have dinner.

And the last one is something I should never compromise on.

 

 

 

Diabetes, sugar and insulin

Last weekend I finished off Jason Fung’s The Complete Guide to Fasting. Like his earlier book that I read (The Obesity Code), this book makes a very compelling case to fast as a means of reversing type 2 diabetes, lose weight and generally have a much better life.

I’m compelled enough by the book to have put its message into practice immediately. Apart from days when I go to the gym early in the morning, I’ve been making it a point to not eat breakfast (this isn’t the first time I’m trying this, I must mention). And while the weighing scales haven’t moved yet, I’m pretty happy.

In any case, in both his books, one thing that Fung rails against is the conventional medical practice of telling people suffering from Type 2 Diabetes to “eat 6 meals a day”, while most medical research shows that this leads to higher insulin resistance (and thus worse diabetes), and that what is better is to eat a smaller number of meals in a day.

So a few days ago, I came across this tweetstorm by this guy who installed a continuous glucose monitor in his blood. The tweetstorm is very instructive.

And this helped explain to me why despite research showing the contrary, eating “several meals a day” has been part of the treatment manual for diabetes, even if in reality (as per Fung’s book), it hasn’t helped.

This graph from the tweetstorm is instructive:

Blood glucose spike after a meal

Look at how his blood glucose spiked immediately after a meal that he ate after a longish fast. The conventional medical wisdom has been that if a diabetic eats infrequently, every meal will spike his blood glucose, which then leads to a spike in insulin, and that is not good for the person.

Instead, the wisdom goes (I’m guessing here) that if you have several small meals, then you don’t have a single big jump in glucose levels like this. And so you don’t have single big jumps in insulin levels.

Moreover, the big risk with Type 2 diabetes is hypoglycemia – where your blood sugar drops to such a low level that you start sweating rapidly and come under the risk of a heart attack. And when you don’t eat frequently, your blood sugar can drop like crazy. And so several small meals works.

Logical right? I guess that’s what most doctors have been thinking over time.

The little problem, of course, is that if you eat too many meals (and small meals at that), your blood glucose doesn’t spike by a lot at any one point in time. However, that you haven’t given sufficient gap in your meals means that your insulin levels never drop below a point. And that means that your body becomes resistant to insulin. Which means your diabetes becomes worse.

So what do you do? How do you let your insulin level drop to an extent where your body is not resistant to it, while also making the spike in insulin when you finally eat not so much? Again, I’m NOT a medical professional, but seems like what you eat matters – fat spikes insulin much less than carbs or protein.

Maybe I should change the nature of my lunch on days I don’t eat breakfast.

PS: This entire blogpost is entirely my conjecture, and none of it is to be taken as any kind of medical opinion.

 

Ending on a high

Now that I have a “proper job” I don’t get that much of an opportunity to post during the week. So I might dump “ideas gathered during the week” each weekend. Hopefully quality won’t suffer. Also, I should add that all opinions here are my own and don’t reflect that of any organisation(s) I’m associated with. 

My lifting had suffered massively during the lockdown. In the first week of March, just before the lockdown had hit, I had managed all-time personal bests in front squat, back squat and bench press. And then the gym shut for six months.

Both physical and mental health suffered. Physical because I wasn’t lifting, and so wasn’t burning as much calories as I used to, and so I lost muscle, and put on fat, and triglycerides and other things.

Mental because I wasn’t lifting, so I wasn’t sure any more what or how much I could eat. I would be anxious about every little thing I ate, or didn’t eat (after considering eating). All the mental models I had built up over time of what is good or bad for me went for a toss, meaning I had to make decisions on the fly. And that wasn’t easy at all.

So when the gym reopened in the middle of August, I was among the first to get back. Yes, the risk of catching the disease of 2020 was there, but that got counterbalanced by the prospect of vastly improved physical and mental health.

I restarted slowly, at about half the weights I had left off at in March. I had expected it to take a year to reach my previous highs. The guy who runs the gym thought it will take a couple of months. He had the better prediction – in the beginning of November I managed to deadlift twice my body weight (I had done that once before, in September 2019, but for post-lockdown, this was a massive high).

And then things went for a toss. Maybe I started going to the gym too often. Maybe I started sleeping too little. Maybe the diet I went on (after the elevated levels of triglycerides in my blood got confirmed due to a blood test) ended up reducing my strength.

The following week, I attempted 5 kg above twice my body weight. Failure. A week later (I do “normal deadlifts” once a week, and “sumo deadlifts” once a week), I tried 2X my body weight again. Failure again. And yet again. And three continuous weeks of failure was a bit too much to take. And it didn’t help that in my usual program, the deadlift is the last exercise before I wind up. Irrespective of how much I had lifted before, ending the workout with a failure wasn’t a great thing to do.

A T-shirt I bought recently

And so this week, I decided to reverse course. I still continued with the deadlift as my last lift of the day, but gave myself enough time for it (by changing my workout schedule) that there was time to “end on a high”.

So on Tuesday, I tried 2X my body weight once again. Failure. However, my schedule meant that I had time left over. I removed 5 kg, and tried again. Failure again. I wasn’t going to be done. I took off another 10kg and attempted again, and managed to complete 3 reps. I was done for the day.

It happened once again with the sumo deadlift yesterday, and with the overhead press three days back – giving myself more time meant that I had the time to scale back upon the end of my unsuccessful lift, and finish the day on a high, even if it is a lower high than what I wanted to end on before I started my workout.

Oh, and I should mention that in the last week, I’ve managed to hit all time personal bests (including pre-lockdown) in front squat, sumo deadlift and bench press. I think the “ending on a high” philosophy, combined with giving myself more time, have something to do with it.

PS: Ending on a failure, apart from ruining the rest of that day, also makes you more apprehensive the next time you want to lift, and might lead you to lift less than your potential next time.

Covid-19 Prevalence in Karnataka

Finally, many months after other Indian states had conducted a similar exercise, Karnataka released the results of its first “covid-19 sero survey” earlier this week. The headline number being put out is that about 27% of the state has already suffered from the infection, and has antibodies to show for it. From the press release:

Out of 7.07 crore estimated populationin Karnataka, the study estimates that 1.93 crore (27.3%) of the people are either currently infected or already had the infection in the past, as of 16 September 2020.

To put that number in context, as of 16th September, there were a total of 485,000 confirmed cases in Karnataka (official statistics via covid19india.org), and 7536 people had died of the disease in the state.

It had long been estimated that official numbers of covid-19 cases are off by a factor of 10 or 20 – that the actual number of people who have got the disease is actually 10 to 20 times the official number. The serosurvey, assuming it has been done properly, suggests that the factor (as of September) is 40!

If the ratio has continued to hold (and the survey accurate), nearly one in two people in Karnataka have already got the disease! (as of today, there are 839,000 known cases in Karnataka)

Of course, there are regional variations, though I should mention that the smaller the region you take, the less accurate the survey will be (smaller sample size and all that). In Bangalore Urban, for example, the survey estimates that 30% of the population had been infected by mid-September. If the ratio holds, we see that nearly 60% of the population in the city has already got the disease.

The official statistics (separate from the survey) also suggest that the disease has peaked in Karnataka. In fact, it seems to have peaked right around the time the survey was being conducted, in September. In September, it was common to see 7000-1000 new cases confirmed in Karnataka each day. That number has come down to about 3000 per day now.

Now, there are a few questions we need to answer. Firstly – is this factor of 40 (actual cases to known cases) feasible? Based on this data point, it makes sense:

In May, when Karnataka had a very small number of “native cases” and was aggressively testing everyone who had returned to the state from elsewhere, a staggering 93% of currently active cases were asymptomatic. In other words, only 1 in 14 people who was affected was showing any sign of symptoms.

Then, as I might have remarked on Twitter a few times, compulsory quarantining or hospitalisation (which was in force until July IIRC) has been a strong disincentive to people from seeking medical help or getting tested. This has meant that people get themselves tested only when the symptoms are really clear, or when they need attention. The downside of this, of course, has been that many people have got themselves tested too late for help. One statistic I remember is that about 33% of people who died of covid-19 in hospitals died within 24 hours of hospitalisation.

So if only one in 14 show any symptoms, and only those with relatively serious symptoms (or with close relatives who have serious symptoms) get themselves tested, this undercount by a factor of 40 can make sense.

Then – does the survey makes sense? Is 15000 samples big enough for a state of 70 million? For starters, the population of the state doesn’t matter. Rudimentary statistics (I always go to this presentation by Rajeeva Karandikar of CMI)  tells us that the size of the population doesn’t matter. As long as the sample has been chosen randomly, all that matters for the accuracy of the survey is the size of the sample. And for a binary decision (infected / not), 15000 is good enough as long as the sample has been random.

And that is where the survey raises questions – the survey has used an equal number of low risk, high risk and medium risk samples. “High risk” have been defined as people with comorbidities. Moderate risk are people who interact a lot with a lot of people (shopkeepers, healthcare workers, etc.). Both seem fine. It’s the “low risk” that seems suspect, where they have included pregnant women and attendants of outpatient patients in hospitals.

I have a few concerns – are the “low risk” low risk enough? Doesn’t the fact that you have accompanied someone to hospital, or  gone to hospital yourself (because you are pregnant), make you higher than average risk? And then – there are an equal number of low risk, medium risk and high risk people in the sample and there doesn’t seem to be any re-weighting. This suggests to me that the medium and high risk people have been overrepresented in the sample.

Finally, the press release says:

We excluded those already diagnosed with SARS-CoV2 infection, unwilling to provide a sample for the test, or did not agree to provide informed consent

I wonder if this sort of exclusion doesn’t result in a bias in itself.

Putting all this together – that there are qual samples of low, medium and high risk, that the “low risk” sample itself contains people of higher than normal risk, and that people who have refused to participate in the survey have been excluded – I sense that the total prevalence of covid-19 in Karnataka is likely to be overstated. By what factor, it is impossible to say. Maybe our original guess that the incidence of the disease is about 20 times the number of known cases is still valid? We will never know.

Nevertheless, we can be confident that a large section of the state (may not be 50%, but maybe 40%?) has already been infected with covid-19 and unless the ongoing festive season plays havoc, the number of cases is likely to continue dipping.

However, this is no reason to be complacent. I think Nitin Pai is  bang on here.

And I know a lot of people who have been aggressively social distancing (not even meeting people who have domestic help coming home, etc.). It is important that when they do relax, they do so in a graded manner.

Wear masks. Avoid crowded closed places. If you are going to get covid-19 anyway (and many of us have already got it, whether we know it or not), it is significantly better for you that you get a small viral load of it.

Anxiety and computer viruses

I think, and hope, that I’ve been cured of anxiety, which I was probably suffering from for over six years. It was a case of Murphy’s Law taken to its extreme. If anything can go wrong, it will, states the law, and in those six or seven years, I would subconsciously search for things that could possibly go wrong, and then worry about them. And worry about them so much that I would get paranoid.

Let me give you an example. Back in 2008, after a four-month spell of unemployment, I had signed up with a startup. Two days after I signed, which was three weeks before I was going to start work, I started worrying about the health of the startup founder, and what would happen to my career in case he happened to croak between then and my joining the company! It had been a major effort on my part to try and get back to finance, and that job was extremely important to me from a career signaling standpoint (it played a major role in my joining Goldman Sachs, subsequently, I think). So I started getting worried that if for some reason the founder died before I joined, that signaling wouldn’t happen! I worried about it for three days and broke my head about it, until sanity reigned.

This wasn’t a one-off. I would take ages to reply to emails because I would be paranoid that I had said something inappropriate. When I landed in Venice on vacation last year, my office blackberry didn’t get connected for an hour or so, and I thought that was because they had fired me while I was on vacation. It would be similar when I would look at my blackberry first thing in the morning after I woke up, and found no mails. I needed no real reason to worry about something. It was crazy.

When a virus attacks your computer, one of the ways in which it slows down the computer is by running “background processes”. These processes run in the background, independent of what you intend to do, but nevertheless take up so much of your computing power that it becomes extremely hard to function. Anxiety works pretty much the same way. Because there is always so much going on in your mind (most of it unintended, of course), a lot of your brain’s “computing power” is taken up in processing those unwanted thoughts (the brain, unfortunately, has no way of figuring out that those thoughts are unintended). And that leaves you with so much lesser mindspace to do what you want to do.

So you stop functioning. You stop being able to do as much as you were able to. Initially you don’t recognize this, until you bite of more than you could possibly chew a number of times in succession. And then, having failed to deliver on so many occasions, you lose confidence. And lesser confidence means more worry. Which means more background process. And means diminished mental ability. Things can spiral out of hand way too quickly.

I’ve been on anxiety medication for over seven months now, and the only times when I realize how bad things were are when I happen to miss a dose or two, and there is relapse. And having been through it, trust me, it is quite bad.

On the positive side, the impact a well-guided medication process (administered by an expert psychiatrist) can have on anxiety is also tremendous. For the six years I suffered, I had no clue that I was under a cloud of a clinically treatable condition. I didn’t know that it was only a virus that had attacked my CPU, which could be got rid off with sustained dosage of anti-virus, and I had instead thought my CPU itself was slowing down, maybe rusting (at the ripe old age of late twenties). After I started responding to my medication, I was delirious with happiness, with the realization that I hadn’t become dumb, after all.

It was sometime in March or April, I think, when I realized that my medication had come into effect, thus freeing up so much mind space, and I started feeling smart again. When I met the psychiatrist next, I told her, “I feel exactly the way I felt back in 2005 once again!”.

Working for money

One of these days during lunch at office, we had a fairly heated discussion about why people work. One guy and I were of the opinion that the primary reason people work is for money, and everything else is secondary. The third guy, who among the three of us perhaps works the hardest, argued that “people who make a difference” never work for money, and that it is only “ordinary people”, who have no desire to “make a difference” that work for money. He took the examples of people like Steve Jobs and a few famous scientists to make his point.

Now, while I agree that money is the primary reason I work, and which is what I argued that day during lunch, I disagree that the end-of-month salary credit tells the whole story. The way I see it, you need to take a longer-term view of things. So while the short-term money you make is important, and affects important decisions such as quality of short-term life, a more important thing is sustainable returns. While you do your work and get that end-of-month salary credit to bolster your bank account, an important thing is about how much the work you’re doing now will contribute to your income later on in life.

Digression 1: I keep oscillating between wanting to retire at forty and wanting to retire at sixty. And I must admit I haven’t frankly decided which one is more suitable for me. This analysis is more relevant with the retirement at sixty model (which is what I think I’ll end up following, health etc permitting). End of Digression 1.

Digression 2: Not so long ago, some people in my firm wanted to recruit “software engineers from IIT with two to three years of work experience”. Being one of the “CS guys” around, I interviewed quite a few people for that role. Their CVs indicated that had we “caught them” on campus, they would have been sure hires. But two years at a software services shop, I figured in all cases, had made them “rusty”. Spending all their time in mind-numbing activities (like building UIs), they had failed to build on the skills that would have been useful for the higher-up-the-value-chain job I was recruiting for (finally that team went to IITs and got a bunch of campus hires. They gave up on lateral hiring altogether). End of Digression 2.

Those two digressions weren’t particularly meaningless. I guess you know where this post is headed now. So, the thing with a job is that along with the short-term benefits it provides, it should also help you build on those skills that you think you can monetize later on in life. Every job (most jobs, really) teach you something. There is constant learning everywhere. But what matters is if the learning that the job offers is aligned with the kind of learning that you think you are geared for, which you think you can monetize at a later point of time in life.

I still claim that I work for money, but just that I take a longer-term view of it. And I strive to learn those things on a job which I think will be helpful for me in terms of monetization at a later point of time in my life.

 

Bilateral Crib Arrangements and Correlation

People say that cribbing is in general good for health, and I heartily agree. I love to crib. Occasionally I bore the hell out of my listener with my cribbing. And I’m sure the readers of this blog have also been on the receiving end of this on more than one occasion. There have been occasions when I’ve been specifically asked not to crib, and others when people have tried to subtly indicate to me that they are not comfortable with my cribbing.

In order to prevent the latter problem (of boring someone with my cribs and them not being able to directly tell me to shut up), over the last few years, I’ve entered into several informal “Bilateral crib arrangements”. Ok – I’ve never used that term before – in fact, I invented that term only some two or three days back. But that doesn’t take anything away from the nature of the arrangements.

So a bilateral crib arrangement is an informal arrangement you get into where you agree to listen to someone’s cribs and lend a friendly shoulder wiht the implicit agreement that they return the favour. The terms of the arrangement are never really described in that many words but that is essentially what it is. It usually has a component where one party says “ok let’s change the subject now” or something to that effect, and the counterparty replies “no no it’s ok you can crib on”.

Occasionally I’ve also gotten into one-way arrangements – where I either only put or receive cribs, but dont’ do the opposite action. Basically this happens when one of the two parties is more comfortable with the ohter than the opposite relationship, or if one of the parties alreeady has enough crib-receivers and doesn’t need one more, but is happy to receive cribs. Though some of them have lasted, occasionally I’ve felt uncomfortable in those – assymetric relationships create mental obligations.

So coming to bilateral crib arrangements – the biggest threat to these arrangements that I’ve observed is what I call as correlation. For a bilateral crib arrangement to work effectively, it is useful if one party is in the position to receive cribs while the other wants to crib. The situation when both don’t need to crib is also good. The problem occurs if both parties want to crib and want to crib to each other.

I’ve been through this several times and it hasn’t really been pleasant. On a number of occasions, I’ve had to back down and somehow bring my cribs under control while lending a friendly shoulder to my crib-partner. On others, I’ve visibly noticed crib-partners putting up with my cribs just so as to not create conflict. Such situations are suboptimal for both parties involved, and need to be avoided.

In this regard, it is important to choose a crib partner whose correlation with you is low. That way, the chances that both of you will want to crib at the same time to each other is low, and the awkward situation of competitive cribbing or backing out can be avoided. I don’t really know how you can choose people with low correlation with you, but I supopse you’ll have to take a few data points and extrapolate. Also avoid people whose correlation with you is obviously high – such as collagues.

Another effective tool in cribpartner management is to be diversified. You need not have several bilateral crib arrangements, but with a judicious combination of unidirectional and bidirectional crib arrangements, keeping in mind various time zones, you can ensure that there is a receiver to listen to you whenever you want to crib.