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Why Bigger Animals Live Longer: The Relationship between Size, Energy, and Longevity Summary: The larger an animal is, the more efficient it becomes in terms of energy consumption. This is because the self-similar fractal structure of larger animals allows them to save energy. Bigger animals require less energy proportionally to run their bodies due to the massive amount of tissue per gram or per cell. As a result, bigger animals experience less wear and tear and live longer than smaller animals. The reason for less wear and tear is that bigger animals use less energy and create less damage, reducing entropy. This principle can also be observed in machines, where those subjected to less stress and driven at lower revs per minute tend to last longer. Transcript: Speaker 2 So that's why we don't need to double our metabolism when we double our weight. It's that fractal like self similarity that allows us to get these essentially efficient savings in the amount of energy we need. So it's better to be bigger, isn't it? Because you don't need as much energy proportionally to run yourself. Correct. Speaker 1 So you need massive tissue per gram of tissue or per cell. You need less energy, the bigger you are. And by the way, this has huge consequences throughout all aspects of biology and life. And maybe one just to tie it back to the beginning of this discussion where we started out by talking about aging and mortality. This means that the bigger you are, the less hard your cell is working. The bigger you are, there's less wear and tear the longer you live systematically. So this is the origin of why bigger things live longer than smaller things. Speaker 2 And why is there less wear and tear if you're bigger? Speaker 1 You're using less energy and creating less entropy. That is you're creating less damage the bigger you are because simply you're using much less energy if you have an engine, an automobile and you insist on racing it at 10,000 revs per Minute every time you drive it, I can assure you that car will not live as long as a car that's driven by a little old lady or a little old man like me who keeps the revs at about two or three Thousand revs per minute. So you know, cars and machines last much longer, the less stress you put on them.

Scaling 2 — You and I Are Fractals

Simplifying Complexity

Level's Company Onboarding Process Summary: The company has a well-guided onboarding checklist for all employees, which spans over a full month. Each new employee is guided to take onboarding seriously, and not expected to start producing for the first month. There is emphasis on reading specific documentation that outlines the company's culture, which is highlighted as significantly different from past experiences. The company eases new employees into the transparency of operations and has a unique practice of requiring employees to update the onboarding process at the end of the month, reflecting the value that 'everything's written in pencil' and is subject to change. Transcript: Speaker 1 And we have an onboarding checklist in notion. We have a template. We copy it for each new person that joins and they have a set of tasks that they do each day. It's pretty well guided. I can share the template with you if you're curious. That'd be amazing. Speaker 2 I would love that. Is this for all employees or EA specifically? All employees. All employees. Okay. Speaker 1 And there is a video of me at the start of each week. It's a loom where I specifically say, Hey, at this point, people usually want to skip onboarding and start jumping into their tasks. Don't do that. It's always a mistake. Really take onboarding seriously. Our onboarding process is a full month. And we don't expect people to start producing for a month. It really does take that long for a lot of people to get fully up to speed. And we help guide them in more slowly. Read these books. Read this documentation that we have about how we built our culture, especially for our case, because the way that we operate is very different than a lot of people's previous experiences. And so it's pretty jarring when you see a lot of the transparency of when your first one on one gets published to the rest of the company, it's pretty jarring. Speaker 2 And so we try to ease people into these things. You know, it's also going to be jarring is if you become a public company, yeah, totally. Things will have to change a bit. Probably. But yeah, continues. All right. That's a job. Speaker 1 That's true. And over time, people get used to it over the course of about a month. I think the biggest thing is the cultural assimilation. In our case, has been the biggest hurdle over the course of onboarding is getting people reading the memos, practicing some of the things. One of the cultural values that we have is everything's written in pencil. But also you can change things here. And one of the things that we do is at the end of onboarding, everybody is required to update the onboarding process for something that was out of date, and then post to a channel confirming What they changed and just giving a list of what they changed. And it's pretty weird for people, especially those who come from larger companies, like when they've had, you know, the same onboarding process that the company's had for 20 years, And then they go in the actual files and edit it themselves. I'm a new employee.

#694 — Sam Corcos, Co-Founder of Levels — The Ultimate Guide to Virtual Assistants, 10x Delegation, and Winning Freedom by Letting Go

The Tim Ferriss Show

While Algorithmic Decision-Making Does Suffer From Bias, It Offers the Potential for Unparalleled Transparency In the Decision-Making Process Summary: Algorithms offer a transparent and accountable way for decision making. They can detect bias and perpetuated patterns, but must be transparent, independently audited, and not proprietary or snake oil. Transcript: Speaker 1 And then the response comes back saying yes but if you're basing it on historical data then you're feeding in biases of the past which you're going to propagate into the future there Is a kind of new attitude about all this which is kind of orthogonal to these two axes which I personally find pretty compelling and it's come up in from a couple of different places independently I could drop a few names but let me just say that the attitude is that algorithms at their best offer a new way for decision making to be transparent and accountable that's at their best So you know if an algorithm is something that everyone understands how it works everyone understands why we are chose to use this algorithm how it was trained and it's something which Can be independently audited it's even something which could be tinkered with to see if it could be made more fair and more accurate that kind of algorithm could raise the standard of Decision making in many areas and let us detect bias where it crops up and also help us detect where historical patterns are being perpetuated and what we might do to fix that but the big But is they have to be transparent they have to be independently audited they can't be proprietary and opaque and hidden behind veils of intellectual property and they also can't just Be snake oil right so there is a lot of snake oil out there there's a lot of products being put out to market which have not in any sense been independently verified or validated and where Their users and customers frankly don't really know whether their results ought to be interpreted the way they ought to be interpreted and so there needs to be a lot more critical thinking Aimed at these

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

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