I like the idea of power as a function of performance, credentials, and relationships. From your experience in the tech industry, how would you prioritize these 3 dimensions?

Different types of orgs are defined by how they are weighted.

Security startups, pure performance and relationships, with credentials a distant third. Historically anyway, it's changing.

Since we're talking power, not rewards, relationships and credentials appear to trump raw performance at some FAANGs as their business models professionalize.

Impression is venture funding goes to whoever fits the profile, so ivy credentials with relationships have the advantage over performance.

Different stages weight accordingly. The best businesses require the least competence to operate and maintain, so maturity converges on incompetent, nepotistic snake pits. :)

Thanks for sharing this! I was looking for a resource on CSS animations for a couple web apps I'm working on.

I have a mixed view on this. I don't have a traditional CS background, so when I started focusing on datastructures and algorithms a couple years ago, I found that it helped my programming. Recently, I was in a position where I had a chance to create an interview process for a smaller firm. I included some algorithm questions, but it was roughly 25% of what mattered. We looked more at communication skills and past work. Judging someone on a random algorithm question seems a bit too harsh for me. Even the best developers probably have some holes in their algorithm knowledge and would miss a few leetcode "easy" questions!

I’m currently on week 8 of Andrew Ng’s original course on machine learning. All the exercises are in Octave/Matlab. I was wondering if Octave is still widely used. I’ve been programming in Python almost exclusively for the past few years. I appreciate the math explanations in this course. I don’t have a formal math background but it is really helping me understand what is going on. Looking forward to finishing this course then moving forward to a course specifically on deep learning.

Octave isn't widely used in industry for ML. Python is.

Andrew says in one of the video that before using python, it is recommended to get the model right first using matlab or octave.

I like his idea of choosing a startup idea that is in an industry poised for growth and "riding the elevator."

That’s sort of like saying that “buy low and sell high” is clever investment advice.

If I thought I could pick an industry poised for growth I wouldn't be mucking about with index funds.

The trouble often isn't picking the industry but figuring how to make money out of it. Like AI and self driving cars are obviously poised for growth but Google/Alphabet and other large companies are likely to dominate. And a lot of that is already priced into the stocks.

I remember Altman saying an AI for X startup would be a good idea though. I guess Cruise Automation played things well.

Actually neither of those things are obvious to me, AI seems over hyped and the head of Waymo has stated that ubiquitous self driving cars are 30 years away. Although perhaps I'm just getting a bit cynical in my old age.

Any source on the 30 year thing? They already have test cars driving around with no one in the front seat (https://www.youtube.com/watch?v=jPfC9Yfsjd8&t=2m40s) and it seems there will be both intense competition to grab market share and also a push from regulators to reduce the present 1.25 million road deaths per year.

Ta for the link though that talk was over two years ago and things have moved on. Personally I'd be surprised if it took that long.

These allegations are more severe than those in a typical sexual harassment lawsuit. I'm interested to read their defense.

Thanks for sharing. I agree with his advice about keeping the flashcards short. It is easy to get into a flow while answering the cards, but when one of them is too long, it disrupts the flow. Try breaking down larger cards into smaller bits of information.

Working through some of Strang's problems has also helped me. 3blue1brown is a great introduction to give you intuition, but you cannot commit the skills to long-term memory without struggling through problems.

I'm a Python developer who wanted to learn more about the CPython internals. I watched these videos a couple months ago and found them helpful.

Would you say it's worth going through with a rudimentary knowledge of C or will I be wasting my time?

I think it's worth it. I have a rudimentary understanding of C, but I still gained from it. The first few videos are about the project structure and the main Python loop. You get to disassemble some Python code into bytecode and see what's really going on. Don't let the lack of C expertise stop you.

I started working on resilience about a month ago after reading a post on HN regarding stoicism. I didn't know about stoicism prior to reading the post. In a short time it has had a positive impact on my life. Certain things just don't seem so stressful anymore.

Pragmatism, stoicism, resilience. morality.

It's the mental "gym" for living a healthy, happy life.