Cedric Chin writes Commoncog on business operations, tacit knowledge, learning, data-driven management, and how companies build advantage.
Commenters recommend its series on becoming data driven and Deming, plus its cash flow and mental models posts, and note it overlaps with Farnam Street.
"4" is not something you find in life right away. Finding a passion should never be the first goal you think of, some may never find a true passion worth following. That's okay, because not everyone is this way.
A good example of a "4" is elon musk. His motivation stems from reading Hitchhikers guide to the galaxy at any early age. He stuck to the same quotes that motivated him today. That is true passion.
I have a friend who is very good at "4". Knowing "4" as a deep level, helps you identify similar "4"s. People will remember you as that person with a strong "4" and drive inspiration from you.
My "4" is pretty generic. Its to give back to all the technology that has defined me growing up. This is the internet, TV shows, video games, smartphones, etc I have taken for granted in this generation. And by definition, the hardware and software to enable those things to be. That's my reasoning. Anything that benefits the persuit of my "4" is fun to me. I like building tools to empower others, my guiding philsophy is similar to Douglas_Engelbart, inventor of computer mouse. "4" is the small things in life
https://en.wikipedia.org/wiki/Douglas_Engelbart. > guiding philosophies
https://www.youtube.com/watch?v=IPYeCltXpxw. > how, why what
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The "how" of "4" is driven by 2 different things:
- Automation
- Documentation
Growing up, I have always been obsessed with automation. This is because I started working at…
Books do work if you know how to be a good reader (I'm taking a course right now on "How to Read a Book" based on the Mortimer Adler book).
The key to learning from a book is to interact with it kinesthetically and through metacognition. Skimming, outlining, writing notes in the margins, questioning, are all part of analytical reading. Furthermore, there's syntopical reading which tries to fit things into a larger tree and seeing how things interrelate through comparative reading. It's all really hard work, but the goal is deep comprehension (the kind that enables synthesis of new ideas).
Memory is not the goal, though having a good memory helps when one is diving into a new book or journal paper. A memory aid like Anki can help one get up to speed quickly when there are lots of new vocabulary and concepts. [1] But memory alone will not lead to comprehension -- one needs to converse with the text as if one were picking the brain of someone who is our better. (if the author is not our better, best to just read the book cursorily, extract a few points and discard -- no need to do a deep reading)
That said, having Anki as a memory aid can lower the activation needed to read the literature say of a new unfamiliar field.
Another way to lower the activation to reading the literature is the "land and expand strategy" [2] where you read stories about thing you're trying to read rather than starting with the thing itself.
[1] http://augmentingcognition.com/ltm.html
[2]…
There's been many studies around the concept "how do you get expertise? In what field can you actually develop expertise?" such as Naturalistic Decision Making [1] [2]. The wisdom from there can be applied to the question of automation.
If your task is for example, to pilot a preset route with stable condition and very low surprise, you will fall for the "getting too comfortable" trap and we tend to start to get lazy(or efficient) and offload the mental effort and skills atrophy. A common workaround to this is to have regular training(deliberate practice) that introduce the "tricky" situation to keep the skill up. Problem ofc arises when people don't keep this up.
This can be seen in the diagnostic performance differences between junior and senior doctors, not always in the favor to the senior [3]. If you add a layer of automation but the insight gathered by working on that layer is great (and falls off) then deliberate practice start to become a requirement
[1] https://commoncog.com/putting-mental-models-to-practice/
[2] https://youtu.be/5eW6Eagr9XA?si=Y9exacaW-F4PDOKF
[3] https://pubmed.ncbi.nlm.nih.gov/26375267/
There's a great book called the Procrastination Equation that addresses this, and offers a different equation than Fogg.
Motivation = (Expectancy + Value) / (Impulsiveness + Delay)
It's useful to know what each of those terms means.
Your motivation is for a specific task or goal.
Expectancy is your confidence that you can complete a task or reach the goal.
Value is the importance you assign to the goal.
Impulsiveness is how distractable you are, or your susceptibility to other interesting things that you might do instead of working toward the goal (like writing comments on HN! ;).
Delay is the amount of time you have until the task should be completed.
The more distractable you are, and the more time until you hit your deadline, the less motivation you have.
The more confidence you have in your ability to complete the task, and the more importance you assign to it (eg this is life or death), the more motivated you are.
Cedric Chin has a nice overview here:
https://commoncog.com/blog/a-user-review-of-the-procrastination-equation/
This is the problem with both SMART goals and OKRs. They purport to be meaningful by measuring results, but results are outside of one's control and subject to the inputs of lady Fortune!
What we want to incentivise is not success, but the kind of behaviour that leads to success. But that, on the other hand, suffers even worse from Goodhart's law.
Cedric Chin dug deeply into this dilemma[1] about a year ago. His suggestion is to frequently follow up on both behaviour metrics and result metrics to build a tacit understanding of how one is affected by another. This then allows you to focus improvements on behaviour metrics, which are mostly in your control.
I have yet to try putting it to practise, but I like the idea.
[1]: https://commoncog.com/goodharts-law-not-useful/
This is a tech-focused selection from my personal list, with a few more business-y sources thrown in:
- https://erikbern.com
- https://lethain.com
- https://commoncog.com
- https://scattered-thoughts.net
- https://danluu.com
- https://ideolalia.com
- https://staysaasy.com
- https://collabfund.com
- https://blog.nelhage.com
- https://jacobian.org
- https://equalventures.substack.com
- https://defmacro.org
- https://spakhm.com
- https://apenwarr.ca
- https://scarletink.com
- https://benkuhn.net
- https://cpojer.net
- https://brandur.org
- https://zeptonaut.com
Let me know if you want some reading recommendations for any of these!