📚 Quinn's Highlights

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A batch of the best highlights from what Quinn's read, .

Create a User Manual of Yourself for Others Summary: Creating a user manual for yourself, including strengths, weaknesses, triggers, blind spots, and insights for working effectively with you, enables others to understand and collaborate with you more easily. By soliciting feedback from colleagues and using their input to enhance self-awareness, you can provide new team members with valuable insights about working with you, facilitating quicker and more effective collaboration. Transcript: Speaker 1 He said, you know, when i buy a new car, it comes with an owner's manual, so i know how to operate it. But when i work with a new person, whose way more complex than a car, i don't get anything. And so i'm kind of starting from square one, when fact, they have all these experiences that could teach me something from their past about how to work with them better in the present And the future. And so what he did, same as orschol nick, he sat down and he wrote up one pager on how to work with him effectively. What are his strength what are his weaknesses, what are the triggers that bring out the worst in him? What are the the moments that bring out in the best in him? And then he didn't stop therehe asked his team to write their user manual for him, so that he could gauge his own self awareness. And of course, he found the team's os is much more ecihtful and accurate than his own, because of the blind spot factor in part. But now every new person who works with him gets that one pager and gets to immediately start as if they'd known him for a month or two, and say, ok, you know, here are the things i might want To adapt if i want to be really affective with this nager. And so i've gone, i've gone and done that. I asked a bunch of people who worke with me to write my user manual. Andit is very simple. The questions are, what are my strengths? What brings those out? What are my weaknesses? What brings those out? What are my blind spots? And what do you know now about working with me that you wish you had known when we first started working?

#399 — Adam Grant — The Man Who Does Everything

The Tim Ferriss Show

The need for transparent and democratic decision-making: Human bullshit and algorithmic bullshit are two sides of the same coin Summary: Data and algorithms are not inherently bad, but they should be used in a transparent and democratic way that empowers everyone. Instead of arguing about whether computer or human decision-making is better, we should focus on accountable and transparent decision-making. This means avoiding human biases and stereotypes as well as naive machine learning without considering its real-world implications. Transcript: Speaker 1 So the point is that it's not that data and algorithms are bad it's that they need to be applied in a way which is transparent and which is democratic and which empowers all of us to carry On these debates rather than simply being tools which accurately or inaccurately are being used to buy the powerful to control the rest of us it's silly to argue about which is better You know computer decision making or human decision making that's really not the point I mean the point is we should have accountable transparent decision making instead of bs there's Human bs which comes in the form of stereotypes in ideology and there's algorithmic bs which comes in the form of naive machine learning without thinking enough about its applications

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

COMPLEXITY: Physics of Life

Left unchecked, your team members move from one task to the next, doing the easiest things, the things someone asked them to do, or simply the things right in front of them. Especially as stress increases, prioritization effectiveness declines. In one study of 43,000 encounters of doctors and patients, researchers found that when the workload was heaviest, physicians prioritized their easiest cases, leaving the most severe cases to wait the longest – a tendency known as “completion bias” (Gino and Staats 2016). Among all professions, it can be easy to get sucked into an endless stream of activities that feel like progress but that leave tomorrow looking much like yesterday.

The Leader Lab

Tania Luna and LeeAnn Renninger

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