Discover more from The Long Game by Mehdi Yacoubi
The Long Game 90: Improving HRV, Moving in Sync, Wanting, Early-Stage Lessons
🧬 Software in Life Sciences, Russia/Ukraine, Carbon Tax, Online Privacy, South Korea, and Much More!
📣 We are hiring at Vital, help us build the “Strava for Health.” We are now looking for:
Senior Frontend Engineer
We offer $5,000 in Bitcoin if you refer to us a candidate we hire.
In this episode, we explore:
Moving in sync with others
Lessons for early-stage founders
Let’s dive in!
🫀 More Notes on HRV
I’m currently experimenting with ways to improve my heart rate variability (HRV). I already mentioned this in TLG 84 and thought it was the moment to revisit this topic as Peter Attia recently published an AMA on the subject (🔒).
Here are a few notes:
You can do this with most wearables, but some are more precise than others. On top of the list would be the Oura Ring.
Typical smartwatches also work (Apple Health, Garmin), but you need an app to get better measurements (HRV4Training, for example.)
Why HRV is important:
This study shows an association between lower HRV and all-cause mortality, but it’s hard to draw a causal relationship from it.
This meta-analysis of 8 studies and 22,000 participants shows a low HRV (10th percentile) was associated with a 50% increased risk [1.50 (95% CI 1.22, 1.83)] of a first cardiovascular event in people without CVD compared to those in the 50th percentile.
There’s a non-significant trend toward a decreased risk with these higher HRV values compared to the median or compared to the, say, the 50th percentile.
HRV and athletic performance
“HRV can be a metric for measuring how ready your body is to adapt, and ultimately perform, in sports.”
“High HRV means you’re getting both strong “on” and “off” outputs, and your body is highly responsive to your environment. It can quickly shift its energy from “fight or flight” to “rest and digest” to easily match its surroundings.”
“It’s like this nice balance struggle, almost back and forth between the sympathetic and the parasympathetic systems.”
Factors that can lower HRV
Alcohol is on top of the list. I stopped drinking on January 1st and saw a swift increase in my HRV (this is anecdotal, but many people noticed the same.) Looking forward to testing this hypothesis with the community on Vital.
Being anxious about something
Lack of physical activity
Too much physical activity
High blood pressure/High triglycerides
Things that can raise HRV
Being aware of your stress and practicing breathing techniques
Genetics and heritability may account for 25—50% of the interindividual variation in HRV.
Don’t compare your baseline levels to others: what matters most is the trend. Is it going up or down?
Wondering why you like dancing or group sports? Part of the answer might be because moving in sync with other people creates social bonds.
Although not everyone experiences the effects of synchrony with equal force, the experience of moving in rhythm with others or of harmonizing voices appears to play an important role in human societies. That is likely why we see synchrony all over, in large symphony concerts, in dance parties and in village ceremonial performances. When we are in sync, our hormones and our brain activity help to smooth societal wrinkles, keeping us together. Joining a marching band may not be the path to world peace, but behavior like it may help make us more tolerant and better able to see the greater good in wider communities.
Remember this scene from Avatar?
🧠 Better Thinking
After seeing the book everywhere, I picked up Wanting, a book about… you guessed it: mimetic desire! It’s a short, clear, and very good explanation of these ideas that are harder to understand in their original format in the books of René Girard.
Here are some notes on the book:
Girard developed the theory of mimetic desire. The theory states that our desires aren’t intrinsic; instead, we learn what to want, just like we learn how to speak a language.
Mimesis dictates everything we desire. We always think that our desires are spontaneous, that we just want something, but that’s a delusion. In reality, our desires are always mediated by models – people or things that cause us to want something.
What’s the problem with mimesis? It drives us to be the same, and it also causes intense rivalry. The more similar we become, the more different we strive to be – and this can be dangerous.
Models are everywhere, and we diminish their power by naming them.
Often, businesses take advantage of mimesis to sell us things: think about Instagram.
One great tip for identifying some of your models? Think about the people you least want to see succeed. You’re probably engaged in a mimetic rivalry with them.
Models affect us differently depending on whether they’re inside or outside our social world. You probably care more about your co-worker than about Jeff Bezos. The author created the concept of Freshmanistan, it’s where most of us live, and we’re subject to a wide range of distortions in it.
Mimesis can be a positive cycle, as long as one person eventually ends it.
The pharmakos served as a scapegoat for the ancient Greeks – a person chosen to absorb the blame and violence in a given situation. People could release their anger through this ritual, and society could return to harmony. According to René Girard, this – the scapegoating mechanism – is the only way a true mimetic crisis can end. Mimetic crises end only when a scapegoat is named and punished.
The other danger of mimetic desire is that it binds us to artificial, unfulfilling goals. Are you sure you really want what you’re working to achieve, or are you just trying to keep up with the Jones?
Empathy disrupts mimetic crises and kickstarts positive cycles of desire. One great way to practice it is to listen to other’s people stories.
The last part might be the most important: We must shift away from engineering desire and move toward transforming desire. Western culture is growing more and more mimetic, and tensions are rising globally while innovation stagnates.
We monetize videos of people reacting to other videos instead of incentivizing people to build tools that will better serve humanity. Much of this is a result of the big four tech companies – Google, Facebook, Amazon, and Apple – engineering our desires. By mining and using our data, these companies create a system that tells us what to want.
The big tech companies focused on advertising created a machine to tell us what to want because it generates tremendous profit.
As a personal experiment, I deleted Instagram a month ago and noticed significantly less desire to consume stuff and even less desire to travel. I’ve just been more satisfied with where I’m at and with what I have.
⚡️ Startup Stuff
📖 Lessons for Early Stage Founders
I greatly enjoyed this piece about common errors early-stage founders make. It’s a short article, but here’s the TL;DR:
“A great set of goals answers the question: "what would have to be true in order for us to feel good about our progress at the end of the month?"“
Send investor updates
“With each investor, include the goals you're working towards, as well as the asks for them. I think monthly is about the right cadence for this in the early days, moving to quarterly around Series A/B time when you start partnering more with a few board members.”
Launch (bonus: self-service)
“Lesson learned: the people you happen to be talking to now are probably not the people who have the biggest problem in your space. Do everything you can to reach the folks with the biggest problem, and then, reduce any barriers they might encounter.”
Consider working in public
Unless you are working in a space that heavily depends on IP, you should probably be publishing more content about what you are doing. This could be open source, it could be a weekly newsletter, it could be a changelog.
Know when to hire
“I get it. Hiring isn't the most fun, especially for an introvert. It's a lot of interviewing and feeling like there's a lot of rejection. Rejecting people sucks. Losing candidates sucks. For many of our roles at Segment, we've had to talk with 50-100 different people to make an eventual hire.”
Optimize for learning
“The startups who most rapidly improve are the ones who are able to take outside information, and then incorporate it into their product, their strategy, and their worldview. That's why, you should always be optimizing for one metric in addition to revenue and user growth: learning.”
Pick the partner, not the firm
“The people you have in your board meetings matter far more than the email domain they use.”
“As a startup, the ability to focus is your best advantage against a big incumbent. Any one of your bigger competitors will have many more resources (both money and people) than you do. Your competitive edge is the ability to tackle a narrower market or use-case, and grow your business from that narrow wedge.”
📚 What I Read
A phenomenal article that I often revisit on how TikTok became so successful.
The problem with approximating an interest graph with a social graph is that social graphs have negative network effects that kick in at scale. Take a social network like Twitter: the one-way follow graph structure is well-suited to interest graph construction, but the problem is that you’re rarely interested in everything from any single person you follow. You may enjoy Gruber’s thoughts on Apple but not his Yankees tweets. Or my tweets on tech but not on film. And so on. You can try to use Twitter Lists, or mute or block certain people or topics, but it’s all a big hassle that few have the energy or will to tackle.
Think of what happened to Facebook when it’s users went from having their classmates as friends to hundreds and often thousands of people as friends, including coworkers, parents, and that random person you met at the open bar at a wedding reception and felt obligated to accept a friend request from even though their jokes didn’t seem as funny the next morning in the cold light of sobriety. Some have termed it context collapse, but by any name, it’s an annoyance everyone understands. It manifests itself in the declining visit and posting frequency on Facebook across many cohorts.
Bari Weiss compiled a list of thoughts on what some key people changed their minds about in 2021.
For some time, I’ve been increasingly concerned that instead of marching into a future of freedom we were heading toward a time of tyranny. I still think something like this may take place in China’s sphere of influence. Arguably, it already is. But as the events of this year have unfolded, it appears that we are not on track for a dictatorship of either the right- or left-wing variety, but for something more like American anarchy.
Squint past today’s half-ignored, TSA-like Covid regulations and you see a half-ignored, TSA-like Covid regulator—namely, a failing state that people can half-ignore, and arguably must half-ignore, because the USA itself is now the TSA, and the TSA, we know, is safety theater.
In the territory governed by this inept bureaucracy, you see power outages, supply chain shortages, rampant flooding, and uncontrolled fires. You see riots, arsons, shootings, stabbings, robberies, and murders. You see digital mobs that become physical mobs. You see a complete loss of trust in institutions from the state to the media. You see anti-capitalism and anti-vaxxism. You see states breaking away from the U.S. federal government, at home and abroad. And you see the End of Power, the Revolt of the Public, the defeat of the military, the inflation of the dollar, and—looming ahead—an American anarchy.
Another testimony that things are constantly moving and being redefined. The countries considered prosperous & developed today might be very different from those in a few decades.
It was headline news when the Japan Center for Economic Research predicted that Korea would surpass Japan in nominal GDP per capita in 2027 and Taiwan would do so in 2027. However, according to the World Bank, in real terms, Korea already surpassed Japan in 2018 and, with its better performance in the COVID era, its lead is growing. The IMF projects that, in 2023, Japan’s GDP (total, not per capita) will be only 0.2% above its pre-Covid level in 2019 whereas Korea’s will be up 6%.
(The real measure—called Purchasing Power Parity with a 2017 base year—provides a better portrait of living standards than the nominal measure. That’s because the gyrations of a country’s currency can abruptly send a dollar-based comparison of nominal GDP up or down by several percent of GDP. The PPP measure avoids this statistical illusion.)
The main reason Korea has passed Japan in per capita GDP is that its productivity—i.e., GDP per work-hour—has been growing much faster than Japan’s during the latter’s “lost decades.” Until the mid-1990s, Japan was rapidly catching up to the US in productivity, rising to a peak of 71% of the US level in 1997. Since then, it has fallen back to just 63%. Japan has also fallen back relative to Europe. By contrast, Korea’s catchup has continued and it now just a percentage points below Japan. If current trends continue, its only a matter of several years before Korea surpasses Japan by this measure as well.
🎙 Podcast Episodes of the Week
This week in podcasts:
This was an excellent conversation exploring the problems of the modern dating market. It explores some of these questions:
Why More Men are Single than Women
Impact of Eugenics in Dating
The reality of the Gender Wage-gap
The Disney-fication of Relationships
Should We Take Marriage More Seriously?
I’ve been very open to the idea that mindset can have a tremendous impact on your health. Still, it’s great to finally see these ideas being rigorously tested and validated by science. Some of the fascinating learnings include:
Mindsets Change Our Biological Responses to Food
Beliefs About Our Food Matter
Mindset (Dramatically) Impacts the Effects of Exercise
🍭 Brain Food
Elliot Hershberg recently wrote a great article explaining why it’s crucial for life sciences to invest more in software.
To set the stage, here are some facts you might not know:
Genomics is projected to require up to 110 petabytes (PB) of storage a day within the next decade—for reference, if you were storing all of that data on 1TB hard drives, you’d need 110,000 of them per day. This would make genomics one of the largest data generating endeavors on the planet, topping other contenders such as Youtube (3-5 PB/day), and Astronomy (3 PB/day). Genomes are not the only “-ome” being measured at breakneck speed, as multi-omic studies are now generating enormous catalogs of different cellular measurements. High-resolution microscopes routinely generate terabytes (1012 bytes) of rich spatial data. In the modern life sciences, quality analytical software is utterly essential for making sense of this enormous amount of new data. Biology is changing.
The reason software innovations in life sciences are lagging comes from the initial reliance on open source projects:
Kent and many other incredibly talented scientists created a large body of tools that enabled genomics to continue to grow. Inspired by the free software movement, the vast majority of these tools were made freely available along with their source code. While this innovative work propelled science forward, it made it possible for funding agencies to maintain their status quo of largely ignoring software. The computational biologist Adam Siepel summarizes this saying: “Institutions have not been forced to pay professional programmers competitive salaries; grant agencies have not been compelled to set aside appropriate funds for a software infrastructure; and the line items for professional software engineering have not made it into budget models. Thus, genomics has become accustomed to, even addicted to, abundant free software. In a sense, in our idealistic, anti-establishment zeal, we free software warriors have locked computational genomics into an unsustainable financial model.”
The short-term panacea of abundant free software in the life sciences has allowed funding agencies to carry on with the existing investigator-based grant model, without ever rigorously evaluating how to fund software development directly.
Finally, the way forward seems once again to bet on more innovative ways to fund science and scientists:
We are not limited exclusively to our existing academic model or the traditional VC model. By studying the history of our field, we can identify the random contingencies that have shaped the institutions that we inhabit today, and envision new systems for the future. In 2021, we have seen a Cambrian explosion of new models for funding and organizing science—such as Arcadia Institute, Arc Institute, and New Science–where you are reading this post. New funding models such as Focused Research Organizations (FROs) and Private ARPAs (PARPAs) represent new ways to address gaps present in our current research ecosystem. These new institutions and funding structures have the potential to have an enormous impact on biological software.
To start, we could dramatically reduce the friction associated with establishing teams of professional research software engineers (RSEs), designers, and product managers to build systems far outside the scope of what a single graduate student could build when developing a standalone method.
Pair with: Elliott’s excellent newsletter, The Century of Biology
🎥 What I’m Watching
🇷🇺🇺🇦 What a Russian Assault on Ukraine Would Look Like
If you’ve been following the recent news between Ukraine and Russia and you’re wondering what might come next, here’s a video exploring a possible scenario.
🌍 The Truth About Carbon Taxes
One thing seems to be a recurring theme: seemingly good ideas that are, in fact, not great. This is why it’s essential to have quick feedback loops and change what isn’t working, and above all, not equate your beliefs to your identity.
🔧 The Tool of the Week
If you’re looking to step up your online security and privacy, this website will help you. You can also try Jumbo for privacy and control over your data on your phone.
Pair with: 16 Practical Privacy Tips for Your iPhone
🪐 Quote I’m Pondering
“If you want to be successful, you must respect one rule: never lie to yourself.”
— Paulo Coelho
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