The Power of Full Engagement: The Four Energy Management Principles That Drive Performance
> The ultimate measure of our lives is not how much time we spend on the planet, but rather how much energy we invest in the time that we have.

Four principles of energy management:

## Full engagement requires drawing on four separate but related sources of energy: physical, emotional, mental and spiritual.

## Because energy capacity diminishes both with overuse and with underuse, we must balance energy expenditure with intermittent energy renewal.

> We rarely consider how much energy we are spending because we take it for granted that the energy available to us is limitless. … The richest, happiest and most productive lives are characterized by the ability to fully engage in the challenge at hand, but also to disengage periodically and seek renewal. Instead, many of us live our lives as if we are running in an endless marathon, pushing ourselves far beyond healthy levels of exertion. … We, too, must learn to live our own lives as a series of sprints— fully engaging for periods of time, and then fully disengaging and seeking renewal before jumping back into the fray to face whatever challenges confront us.

## To build capacity, we must push beyond our normal limits, training in the same systematic way that elite athletes do.

## Positive energy rituals—highly specific routines for managing energy— are the key to full engagement and sustained high performance.

> Look at any part of your life in which you are consistently effective and you will find that certain habits help make that possible. If you manage others effectively, you likely have a style of giving feedback that leaves people feeling challenged rather than threatened. If you are closely connected to your spouse and your children, you probably have rituals around spending time with them. If you sustain high positive energy despite an extremely demanding job, you almost certainly have predictable ways of ensuring that you get intermittent recovery. Creating positive rituals is the most powerful means we have found to effectively manage energy in the service of full engagement.
critical-thinking  cognition 
13 days ago
Avoiding Stupidity is Easier than Seeking Brilliance
"Wesco continues to try more to profit from always remembering the obvious than from grasping the esoteric. … It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent. There must be some wisdom in the folk saying, `It’s the strong swimmers who drown.’"
cognition  critical-thinking 
13 days ago
Learning How to Think: The Skill No One Taught You
"Multitasking, in short, is not only not thinking, it impairs your ability to think. Thinking means concentrating on one thing long enough to develop an idea about it. Not learning other people’s ideas, or memorizing a body of information, however much those may sometimes be useful. Developing your own ideas."

"It’s only by concentrating, sticking to the question, being patient, letting all the parts of my mind come into play, that I arrive at an original idea. By giving my brain a chance to make associations, draw connections, take me by surprise. And often even that idea doesn’t turn out to be very good. I need time to think about it, too, to make mistakes and recognize them, to make false starts and correct them, to outlast my impulses, to defeat my desire to declare the job done and move on to the next thing."

"So it is with any other form of thought. You do your best thinking by slowing down and concentrating."
critical-thinking  creativity  cognition 
13 days ago
Ways to think about machine learning — Benedict Evans
So, this is a good grounding way to think about ML today - it’s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine.

It's all very well to say 'this lets you ask these new kinds of questions', but it isn't always very obvious what questions. You can do impressive demos of voice recognition and image recognition, but again, what would a normal company do with that? As a team at a major US media company said to me a while ago: 'well, we know we can use ML to index ten years of video of our talent interviewing athletes - but what do we look for?'

What, then, are the washing machines of machine learning, for real companies? I think there are two sets of tools for thinking about this. The first is to think in terms of a procession of types of data and types of question:

- Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google's open-source tools Keras and Tensorflow).
- Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,
- Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.

That is, machine learning doesn't have to match experts or decades of experience or judgement. We’re not automating experts. Rather, we’re asking ‘listen to all the phone calls and find the angry ones’. ‘Read all the emails and find the anxious ones’. ‘Look at a hundred thousand photos and find the cool (or at least weird) people’.

But after we’ve talked about wrinkles in fabric or sentiment analysis in the call center, these companies tend to sit back and ask, ‘well, what else?’ What are the other things that this will enable, and what are the unknown unknowns that it will find? We’ve probably got ten to fifteen years before that starts getting boring.
ml  business_strategy  business_ideas 
17 days ago
Sam Hinkie – Data, Decisions, and Basketball - [Invest Like the Best, EP.88]
Amazing episode with Sam Hinkie on investing in high-variance outcomes, taking the long view on investments and life. Also lots of basketball.
business_strategy  basketball  investment 
4 weeks ago
Internet Trends: Smartphones and Internet Usage, Tech’s Takeover, The Small Business Revolution, Tech’s Deflationary Effect – Stratechery by Ben Thompson
In my estimation the single most important regulatory issue today is not privacy or big tech companies; it is zoning in urban areas. The fallout of those R&D numbers and the massive returns that come from it is that more and more wealth is being generated in areas that are increasingly inaccessible to the poor. The single best thing that could be done for inequality in the United States would be making it easier for those gains to be distributed broadly through increased economic activity in urban areas; that, though, requires sufficient housing.
business  stratechery  business_strategy 
5 weeks ago
Microsoft to Buy GitHub, A Win for GitHub, Facebook’s Data-Sharing Deals with Device Makers – Stratechery by Ben Thompson
Microsoft will uniquely enable the company to do both: of course the enterprise offering will fold nicely into Microsoft’s other businesses, but again, the real win for Microsoft is not incremental peanuts in enterprise revenue but winning hearts and minds with developers broadly. In other words, not only should skeptics not be worried about Microsoft unduly favoring their own platforms, they should also be excited that, more than any other potential acquirer, Microsoft is likely to push the individual and community aspects that make GitHub so unique.
business  stratechery  business_strategy 
6 weeks ago
Sports Gambling Defederalized, Amazon Channels – Stratechery by Ben Thompson
Consider this idea in the context of Amazon Channels: Amazon is acting as a platform/aggregator (still working on this!) selling 3rd party content to Prime customers to view in the Prime Video app; why, though, is this such an effective channel? It’s not simply the case that Amazon has a ton of Prime customers (although that is obviously important). It is also the fact that Prime customers are very likely to have the Prime Video app installed thanks to the money Amazon invested in buying Prime Video content!
business  stratechery  business_strategy 
6 weeks ago
The Moat Map Follow-up; Uber, YouTube, and Spotify; The Public Cloud and Scale – Stratechery by Ben Thompson
Ben explains how network effects can be internalized or externalized:

“Because Facebook has completely internalized the network effect that drives its business, it can treat its suppliers like pure commodities; because the network effect that mattered to Microsoft was not internal to Microsoft’s product but rather sat on top as a two-sided network between developers and users, the optimal strategy for Microsoft was very different. Here again Google is closer to Facebook, and Apple to Microsoft.”

He also adds Spotify:

“Spotify: I regret not using Spotify in place of the carriers: on one hand, Spotify has lots of internalized network effects, particularly in terms of data, but also user-generated content in the form of playlists. That puts the company on the left side of the map. The problem, though, is that Spotify has effectively three suppliers, which are highly differentiated; that leaves the company well outside of moat territory.”
stratechery  business  business_strategy 
7 weeks ago
The Moat Map – Stratechery by Ben Thompson
The Moat Map describes how tech giants build their moats along two axes: network effects and supplier differentiation. Interesting theory, although I’m not fully sold on the axes.

“This relationship between the differentiation of the supplier base and the degree of externalization of the network effect forms a map of effective moats; to again take these six companies in order.”

Ben’s reminder on network effects suggests Spotify also experiences network effects through improvement of personalization algorithms.

“Another way to consider this spectrum is in terms of user-related network effects. The idea of a network effect is that an additional user increases the value of a good or service, and indeed all of these companies depend on network effects.”
stratechery  business  business_strategy 
7 weeks ago
Open, Closed, and Privacy – Stratechery by Ben Thompson
Ben on how open and closed systems affect access to privacy and encryption options. Ironically, Google (who lauded Android’s “openness”) has actually shared none of its acquired data with partners. This makes it incredibly unlikely that a competitor appears.
privacy  business_strategy  business  stratechery 
8 weeks ago
ZTE and Trump, Dropbox Earnings, Bloomberg’s Paywall – Stratechery by Ben Thompson
On investing in oneself to see significant returns:

This, though, is a reminder that profit comes not from right but from worth: the value of Bloomberg’s journalism is whatever one is willing to pay, and I, for one, was more than happy to pony up the $39.99 month necessary to get full access to Bloomberg’s reporting. To put it another way, the ongoing shift to subscriptions is an example of the adage “the best way to get rich is to be rich.” In this case, the breadth of Bloomberg’s reporting, paid for by the terminal business, makes it an easy call to subscribe as an individual, much as the the coverage and reputation of the New York Times or Wall Street Journal or Financial Times, built off the print advertising business model, give each respective publication a far easier path to a subscriber-supported future. That the rich getting richer may not be the optimal outcome for society as a whole remains one of the most pressing challenges facing the industry.
stratechery  investment  business_strategy  business 
9 weeks ago
Netflix’s Earnings and the Video Value Chain, The Conditions of Aggregation, Comcast and Netflix and the New Bundle – Stratechery by Ben Thompson
Thus the point I made above: Netflix is so valuable because it leveraged aggregation into integration. That, though, leads to a further point about the conditions that make Aggregation Theory possible: the more modularized that supply is, the easier it is for an Aggregator to take over the market.
aggregation_theory  stratechery  business_strategy 
10 weeks ago
Machine Learning Yearning
Shared by Sam at iHeartRadio. Great discussion of error analysis and work prioritization.
error_analysis  ml  model_evaluation 
11 weeks ago
DataFrames, RDDs, and Datasets: A Tale of Three Apache Spark APIs
Using the Dataset API to write strongly-typed Spark jobs. Catches more errors at compilation.
11 weeks ago
Excellent episode on socializing and keeping friends as a man in America.
11 weeks ago
Zillow, Aggregation, and Integration – Stratechery by Ben Thompson
Ben explains why lead generators/media companies are NOT aggregators:

"This is the original piece of Aggregation Theory that was missing from last year’s Defining Aggregators: it is one thing to sit on top of an existing industry and, well, be a media company/lead generation tool. There have been a whole host of businesses that did exactly that, and while there is plenty of money to be made, without some sort of integration into the value chain of the industry itself they simply aren’t transformative. To put it another way, aggregation doesn’t transform value chains; integration does."
business_strategy  business_ideas  stratechery 
11 weeks ago
Stress in Research. Part II: Research Worth and Self-Worth – The Exclusive Or – On academic, expatriate, and other unusual forms of life.
The mental tricks that work a bit for me, may not work for you. So I’ll list a few different mental strategies, strategies for self-talk — those who know me will know that I am a big fan of talking to myself — that you can experiment with.

One strategy is to focus on the fun, for example, by treating a tenure-track faculty job like a seven-year postdoc. Richard Feynman has a wonderful story about recapturing fun in his research. One always wonders if Feynman’s stories are exaggerated, but the principle of countering stress by fun is surely sound.

Another good strategy is to have backup sources of self-worth. Academic jobs are naturally set up this way. Even if no one reads my papers the minute after I die, or frankly, the minute after they’re written, I’ve still taught hundreds of students about fundamental knowledge that has helped them, if only a little. That’s another source of professional self-worth. Or of course, one can seek self-worth from being a good child, parent, spouse, sibling, and friend. Both of these — teaching and family — are instances of a more general point. We should find worth in our relationships with other people.
research_advice  career_advice 
12 weeks ago
The mythical 10x programmer - <antirez>
The number of hours spent writing code is irrelevant without looking at the quality of the time. Lack of focus can be generated by internal and external factors. Internal factors are procrastination, lack of interest in the project at hand (you can’t be good doing things you do not love), lack of exercise / well-being, poor or little sleeping. External factors are frequent meetings, work environments without actual offices, coworkers interrupting often and so forth. It seems natural that trying to improve focus and to reduce interruptions is going to have a non marginal effect on the programming productivity. Sometimes in order to gain focus, extreme measures are needed. For instance I only read emails from time to time and do not reply to most of them.

I believe that the two main drivers of complexity are the unwillingness to perform design sacrifices, and the accumulation of errors in the design activity.
12 weeks ago
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