There's an abrupt life transition almost everyone goes through: school to job. You might go from middle school to manual labour, or PhD to teacher. But regardless of details, a more gradual shift would be better. Most entry-level jobs could be apprenticeships that mix education and work. Why mix them? Trying to do a job well gives
Startups have high variance: the top few % reap most of the rewards. The same is true for artists of all kind, and to a lesser extent, software engineers: some Waymo engineers earn 100x the salary of the lowest-paid programmers. Risk is usually taken to be bad. But it might not be if you can influence the outcome. If you
Who needs an office these days? It’s a needless expense. I currently work alone, and when I meet others I do it via video call or on a walk outside. Plus, private offices are expensive. A desk in a co-working space is 150€ — and not an improvement over my working-from-home situation — and private room rents tend
I ran my first design sprint last week. According [https://www.gv.com/sprint/] to the creators: > The sprint is a five-day process for answering critical business questions through design, prototyping, and testing ideas with customers. The starting point of a sprint could be a problem like “website visitors don’t understand our product.” Monday is spent mapping
I recently reflected on my productivity and discovered, to my horror, that I’m doing pretty badly! I used to be very organized and do lots of creative, important work consistently every day for months on end. Somehow, I had lost that in the past 3-5 years. Doing what comes naturally is a lousy method for doing open-ended,
Every classification task is linearly separable in the right feature space. This statement is a clue to building ML solutions that scale to many different use cases. Here’s a visual explanation. Say you have data points in a given two-dimensional vector space. If you can use a straight line to separate the two classes, the dataset (and prediction
“Founders at Work [https://www.goodreads.com/book/show/98233.Founders_at_Work]” is a unique book by Jessica Livingston, one of the founders of YCombinator. It contains un-narrated, almost unedited interviews with founders of successful startups like Paypal, Hotmail, Apple, and 27 others. I’m only through about 10% of the book, and it’s clearly one of
Lately, Twitter has been full of the 2020 US election, which has displaced everything interesting. That means it’s a good time to blog. I’ve recently been reading James C. Scott’s Seeing Like a State, which manages to combine forestry, agriculture, and land/city planning into a history of top-down planning. One thing that stuck with me
A few years ago at Starship, I contributed to the Data is the Specification [http://dataisspec.github.io/] manifesto. The core idea is that it's better to solve problems directly against a collection of examples, as opposed to trying to generalise the problem first and then solving the general problem. Typically you would run an employee engagement survey,
