“I've recently been inspired by Deep Work as well, will have to check out Cal Newport's other book you recommended. His "Digital Minimalism" was good. He recommends his friends book "Ultralearning" either in his book or blog. Been reading that, really good. Love your Dad's two principals as well!”

“I read deep work by Cal Newport, the key of the book is "start to do things and stop bullshitting" So.. that's it The hard part is find "the thing" to do IMO, but start with something and try to stay there. Makes sense? I hope so (Sorry for my English, it's no my first language) ((PS I read Digital Minimalism by Cal Newport too, just to say to you))”

“The research method he appears to be using (identifying successful people and then trying to identify patterns) isn't sound. It's basically data mining + selection bias. I'm not aware of any academic research on his work particularly, but you can read a good [critique of Jim Collinss "Good to Great"](http://www.business.unr.edu/faculty/simmonsb/badm720/ampgoodtogreat2.pdf) which runs on similar lines. > The use of data mining is a fundamental research flaw in GTG. Data mining is the process of collecting and searching for patterns in data and then once patterns are identified, formulating explanations that are treated as underlying causes or principles. The problem is that since the patterns may depend completely on the specific time period and dataset gathered, data mining provides no legitimate evidence of applicability outside the sample firms or time period. This problem with data mining (also called data dredging, data grubbing, or fishing) is aptly described by Hand (1998, p. 112): > > By definition, data that are not simply uniform have differences which can be interpreted as patterns. The trouble is that many of these “patterns” will simply be a product of random fluctuations, and will not represent any underlying structure. The object of data analysis is not to model the fleeting random patterns of the moment, but to model the underlying structures which give rise to consistent and replicable patterns. To statisticians, then, the term data mining conveys the sense of naı¨ve hope vainly struggling against the cold realities of chance. > Thus, according to Hand, researchers using data mining may well draw conclusions that are based either on purely random patterns or on patterns that exist only in the sample firms or time period studied. > Collins’ method of identifying his five principles in GTG is data mining. By Collins’ description, he started with a list of companies that appeared in the Fortune 500 rankings and screened them four times to arrive at the 11 GTG companies. He then studied the 11 GTG companies relative to 11 comparison companies to “discover the essential and distinguishing factors at work” (p. 3). This is data mining. As Collins put it: “It is important to understand that we developed all of the concepts in this book by making empirical deductions directly from the data. We did not begin this project with a theory to test or prove. We sought to build a theory from the ground up, derived directly from the evidence” (p. 10). Given the data mining methodology in GTG, Collins provided no evidence that his five principles are anything other than five “fleeting random patterns.” In other words, if Collins is correct, his statistics suggest that it’s just luck. As Walker (2006) noted, this “creates a neat little circle of inference: Great firms do these things, so do these things and your firm will be great” (p. 120). I can't speak as to any of the specific claims Newport makes. They may well be correct, but his method of discovery was flawed.”

“I'll bite. This'll be long so strap in I've read the book twice and have thought deeply on this. Its about 2 things: having rare and valuable skills that employers actually want, and having unfakable signals that indicate how good you actually are at these skills.The second test is were most people get tripped up. First, you need to think like a layperson or exec when evaluating career capital. Data engineering itself is a fairly rare and valuable skill. Every software tech company needs data engineers. But its not a particularly sexy role like frontend or AI engineering, thus there are far less DEs. Data engineering is a necessary expense, therefore youre already valuable from an employers perspective. If you weren't they wouldn't pay you so much. But Im assuming you want to become MORE rare and MORE valuable. This is where having strong signaling for your skills comes into play. Can you convince employers you are one of the best data engineers in the industry? This is about branding: coke isnt inherently better than your local cola brand: they have a stronger message and presence. Coke is part of the ethos of our very culture. Its cool to drink coke. Everyone agrees coke is the best because its has constructed a legend surrounding itself. Like coke you need to build this story around yourself: one that is unignorable. Make no mistake though, this is HARD. Extremely so. In software and data engineering think about the AI scientists/engineers who received 9 figure offers to work for metas super AI team. Why were they chosen? Spoiler: It isnt because they were inherently the best qualified in the world. Its because they attained legendary status in the AI world. Most of them did this by having been employed by a legendary company in the space with known and extremely rigorous hiring standards (most of them were poached from openAI, antropic, and google brain). To work for any of these companies you needed to have a phd from a top 4 cs school, harvard, or have done exceptional research at a top ai research lab. If youre a regular engineer, you basically need to have worked for a FAANG level company beforehand. Even if you have all that you still need to be a fantastic interviewer to work at any of these companies. Most companies use signals like these to guess how good you are. At a certain skill and signal strength level, it becomes impossible to ignore certain people. I can pass over a random data engineer from a no name startup. I cant pass up the MIT grad, ex Google, Staff Data Engineer with an award winning blog. The latter is a talent top companies clamor over. They are, by definition, so good they cant be ignored. Of course, theres a lot of nuance here. Please ask questions and I can clarify a lot of points”

“I suppose it is bad advice *if* those employees then couldn’t find a new job and somehow had to change careers and were unhappy. Alternatively, they moved to a new company who appreciated their experience and maybe it worked out overall..? It is hard to extrapolate from some of one person’s experiences. I would say that a person who is seriously experienced and enjoys their field is likely to land on their feet even if a company treats them badly, and that Cal Newport’s advice is *general* not specific to any field.”