Just wanted to say I've enjoyed this HN rabbithole gold, where we've gone from talking about increasing house costs to discussing the role of bakers in modern day industrial bakeries :)
If our perception of events are in relation to our past experiences, that could also explain this phenomenon. For example let's say you think back to that carefree, fun summer when you were five years old and marvel at how long that seemed to last, compared to the summer at age thirty that you barely remember. Well that 3-month summer made up 3 / (5 x 12) or 5% of your life at age five but only 3 / ( 30 x 12) or 0.8% of your life at age thirty, so the memory from age five would be 6x more salient (ignoring other factors like uniqueness of experience, how "present" you are, visual information processing, etc). Or maybe you were just on a real bender that summer you turned 30.
edit: just realized emreb has a similar comment after reading further down the thread!
Thanks for sharing Troy! I imagine it's not easy to build in public and to write in public when you don't necessarily have it all figured out. Respect your willingness to put yourself out there as you figure it out, and looking forward to learning alongside you on your journey.
I recently found this site with a bunch of detailed information on interview questions and process at a lot of top tech companies. Wondering what people on hacker news think about the ethics of using this, and if hiring managers at these companies look out for people using resources like this.
I've been looking for a book that balances technical rigor with real-world application, and more importantly, is fun to read. This looks promising - adding it to my reading list!
Thanks, really enjoyed that foreword and it really pushed High Output Management up on my reading list. Plus as a bonus I finally learned the name of "The Peter Principle" (the idea that people in a hierarchy tend to rise to their level of incompetence).
Good point, though the author does address that a bit in the epilogue:
Almost none of this applies to small companies. They are so small that the founders and the CEO actually do have a chance of fully understanding problems, which means they don't yet need to delegate decisions.
This was a stimulating read and I'll be adding "High Output Management" to my reading list.
I've worked in organizations where the company strategy was poorly defined and this led to confusion and misalignment at the lower levels. I remember talking to some of my coworkers, wondering whether we had an issue with our executive team. It's interesting to think that it's not the role of the exec team to set strategy, and the issue may have been with the management layer below the exec team. Then again, maybe the problem was with the culture/value of diffuse ownership set by the exec team...
Hi all, author here. Wanted to give some more background on why I made this sheet, and why I think it would be cool if other people used and added to this sheet. I've been working to teach myself machine learning, software engineering, and stats for years now. The internet is full of helpful articles, curated book collections, and well-answered Stack Overflow questions, as well as gobs of less useful junk. My two biggest problems have been: 1.) Separating the good stuff from the bad stuff and 2.) Finding the right type of material for different stages in the learning process.
Unfortunately, Google has let me down. So this is my attempt at compiling and tagging the most helpful resources, so other people (and I) can speed up our learning process. The sheet is completely open, so please feel free to modify or feel free to comment if you have any suggestions. Thanks Internet!
Agree, this is a gem and I wish more talks like this made it to the front page. As a long-time HN lurker, this post finally got me to create an account and start commenting.
I really appreciate the practical perspective of the talk and focus on what's happening in industry. I've seen a lot of data science/ML posts on the latest research and how this can lead to automating X in Y years. While it's great the research is advancing, there's not as much focus on how the industry is evolving to capture the value of that research.
I love the range of topics Paco covers in his talk, from explaining how we got to today's data science environment to what's differentiating big players from the aspirers to making predictions on future M&A activity. The deck is also full of useful links and resources - I especially enjoyed Cassie Kozyrkov's talk on "The Missing Piece".