A list of reflective questions to ask yourself Antifragile Availability bias Black Swan Bottlenecks Cause and Effect Compound Interest Confirmation Bias Create momentum to make progress Emergence Environment Design Every field has some core principles and ideas upon which all other derive Evolution Excellence is mundane Fail Fast So You Can Fix Early Feedback Loop First instinct fallacy Force multiplier Free Rider Problem Friction Gall's Law Hanlon's Razor Haskell Hickam's Dictum Hindsight Bias How to Acquire Mental Models Increase your leverage to become more productive Innovation Multiplier Inversion Leverage Local maxima vs. Global maxima Mario Marketing Mental Models Metcalfe's Law Minimize time getting into position, just go Mise en place Network Effects Not making time for yourself first leads to burnout Occam's Razor Opportunity Cost Overton windows Perverse Incentive Positive Sum Post Hoc Ergo Propter Hoc Fallacy PP 1 - Introduction to Haskell PP 2 - Types and classes PP 3 - Functions and lists PP 4 - Recursion PP 5 - Higher-order functions PP 6 - Declaring types and type classes Premature Optimization Reciprocity Regression to the Mean Sharpen the saw Sherlock Holmes and Mental Models Signal vs. Noise Skin in the Game Stop being ungrateful of having so little life, and be grateful that you were given any at all Survivorship bias Take on the role before you can assume the role The Barbell Strategy The Diderot Effect The Dunning-Kruger Effect The Feature-Positive Effect The Ivy Lee Method for daily planning The Law of Inertia The Lindy Effect The Map Is Not The Territory The Mere-Exposure Effect The Pareto Principle The Pauli Exclusion Principle The Pratfall Effect The QEC method The Red Queen Effect Theory of Constraints To catch a second wind, you have to stay in the game long enough Using notes to advance in life Via Negativa Wittgenstein's Ruler Working on vs. working in Your second brain should actively grow and compound Zero Sum

Local maxima vs. Global maxima

Don’t stay at a local maxima. Find the global maxima. This takes guts and effort.

This is also called Hill Climbing, or Greedy ascent.

Beginners mind: abandon your ego

Naval Ravikant mentions in his Joe Rogan interview that too many people are afraid to start the climb over (listen to it again, it’s great), and so they’ll be stuck on top of the “wrong” mountain. They could have gone higher if they were willing to abandon their ego.

Naval also mentions that this is an ability that great artists and creators have. Take Elon Musk for example. He has started over many times; disrupted various (very) different fields.

Being able to take a beginners mind and start over seems to be a mark of a top performer.

Don’t focus on local maxima

Nat Eliason makes a great point that we shouldn’t focus on local maxima. Then we end up focusing on suboptimal results. It’s the Availability bias—something being readily & easily available does not make it the best option. He also makes the point that we are competing for local maxima opportunities—we compete for unremarkable things, just because we’ll be the best in our local community. But to anyone external, it’s quite unremarkable. Just another student with a high GPA. I’ve been saying this for years. Why should I try to compete against the 150 others in my class, when I can compete against myself and learn from the best online? The former is a suckers game. The latter is The Great Online Game.

Why wouldn’t you chase the best there is?

I’d call this “raising your ceiling.” If you only compete against low-skill people, you might be the best between them, but you’re still low-skill.
If you compete against high-skill people, even if you’re low-skill currently, you’ll have a much higher potential.

The learning environment is poor: quality of teaching might not be up to par
You’ll learn poor habits

Define new global maxima

This also relates to trying to become the best in a very broad field. When you start skill stacking (How to Fail at Almost Everything and Still Win Big), you’re defining a new global maxima. Being good (even great, but not the best) in an area doesn’t make you stand out.

After all, it isn’t hard to become a ‘top performer’: Most participants in any given activity aren’t that good, or, how to become a top performer in any skill.

Naval Ravikant, in How to Get Rich (without getting lucky), also defines this. He says ‘arm yourself with Specific Knowledge’, that which you cannot be trained for. If others can be trained for it, you’re competing against the masses: another local maxima.

Don’t chase local maxima. Define a new global maxima. Look at what everyone else is doing. Don’t do that. Don’t just do it better. Do it better, differently.

That’s what Peter Thiel is talking about when he says competition is for losers.

Your content should say something new or different. But obviously, if you’re trying to do something differently, you’ll probably fail a lot. So it’s important you learn from the data you collect. Iterate and improve.

==Argument:== Why is competition against the masses bad? Does it make sense to strive for something everyone else is also striving for? Or is it just that, you can strive for the same/similar things, but must take your own approach—because going the same way will lead to suboptimal (or worse) results?

Resources

The best article I’ve read on this general concept is Climbing the wrong hill by Chris Dixon. I read it after first learning of Hill Climbing, so I had thought the things through a few times. But Dixon articulated it very well.

==META==
In this note, I’ve related the concept to quite a few things. When you revisit, try to refactor the note and separate corollaries into new, separate, self-contained notes.