on 06-Sep-2022 (Tue)

Reality is largely negotiable. If you stress-test the boundaries and experiment with the “impossibles,” you’ll quickly discover that most limitations are a fragile collection of socially reinforced rules you can choose to break at any time.

#1 What if I did the opposite for 48 hours?

...one day I realized something: all of the sales guys made their sales calls between 9 a.m. and 5 p.m. Obvious, right? But that’s part one. Part two: I realized that all of the gatekeepers who kept me from the decision makers—CEOs and CTOs—also worked from 9 to 5. What if I did the opposite of all the other sales guys, just for 48 hours? I decided to take a Thursday and Friday and make sales calls only from 7 to 8:30 a.m. and 6 to 7:30 p.m. For the rest of the day, I focused on cold emails. It worked like gangbusters. The big boss often picked up the phone directly, and I began doing more experiments with “What if I did the opposite?”: What if I only asked questions instead of pitching? What if I studied technical material so I sounded like an engineer instead of a sales guy? What if I ended my emails with “I totally understand if you’re too busy to reply, and thank you for reading this far,” instead of the usual “I look forward to your reply and speaking soon” presumptuous BS?

Typically native speakers know 15,000 to 20,000 word families - or lemmas - in their first language.

Word family/lemma is a root word and all its inflections, for example: run, running, ran; blue, bluer, bluest, blueish, etc.

So does someone who can hold a decent conversation in a second language know 15,000 to 20,000 words? Is this a realistic goal for our listener to aim for? Unlikely.

Prof Webb found that people who have been studying languages in a traditional setting - say French in Britain or English in Japan - often struggle to learn more than 2,000 to 3,000 words, even after years of study.

In fact, a study in Taiwan showed that after nine years of learning a foreign language half of the students failed to learn the most frequently-used 1,000 words.

So which words should we learn? Prof Webb says the most effective way to be able to speak a language quickly is to pick the 800 to 1,000 lemmas which appear most frequently in a language, and learn those.

If you learn only 800 of the most frequently-used lemmas in English, you'll be able to understand 75% of the language as it is spoken in normal life.

Eight hundred lemmas will help you speak a language in a day-to-day setting, but to understand dialogue in film or TV you'll need to know the 3,000 most common lemmas.

And if you want to get your head around the written word - so novels, newspapers, excellently-written BBC articles - you need to learn 8,000 to 9,000 lemmas.

This was from a post of someone talking about a Japanese text they read (so take Japan with a grain of salt):

There's often talk of how many x thousand words is enough to cover x% of any average text. A book I was reading had statistics for a few languages.

English

1000 words covers 80.5%

2000 words covers 86.6%

5000 words covers 93.5%

French

1000 words covers 83.5%

2000 words covers 89.4%

5000 words covers 96.0%

Spanish

1000 words covers 81.0%

2000 words covers 86.6%

5000 words covers 92.5%

Chinese (Mandarin)

1000 words covers 73.0%

2000 words covers 82.2%

5000 words covers 91.64%

Japanese

1000 words covers 60.5%

2000 words covers 70.0%

5000 words covers 81.7%

To be a successful creator you don’t need millions. You don’t need millions of dollars or millions of customers, millions of clients or millions of fans. To make a living as a craftsperson, photographer, musician, designer, author, animator, app maker, entrepreneur, or inventor you need only thousands of true fans.

A true fan is defined as a fan that will buy anything you produce. These diehard fans will drive 200 miles to see you sing; they will buy the hardback and paperback and audible versions of your book; they will purchase your next figurine sight unseen; they will pay for the “best-of” DVD version of your free youtube channel; they will come to your chef’s table once a month. If you have roughly a thousand of true fans like this (also known as super fans), you can make a living — if you are content to make a living but not a fortune.

Here’s how the math works. You need to meet two criteria. First, you have to create enough each year that you can earn, on average, $100 profit from each true fan. That is easier to do in some arts and businesses than others, but it is a good creative challenge in every area because it is always easier and better to give your existing customers more, than it is to find new fans.

Second, you must have a direct relationship with your fans. That is, they must pay you directly. You get to keep all of their support, unlike the small percent of their fees you might get from a music label, publisher, studio, retailer, or other intermediate. If you keep the full $100 of each true fan, then you need only 1,000 of them to earn $100,000 per year. That’s a living for most folks.

Fans, customers, patrons have been around forever. What’s new here? A couple of things. While direct relationship with customers was the default mode in old times, the benefits of modern retailing meant that most creators in the last century did not have direct contact with consumers. ... For previous creators these intermediates (and there was often more than one) meant you need much larger audiences to have a success. With the advent of ubiquitous peer-to-peer communication and payment systems — also known as the web today — everyone has access to excellent tools that allow anyone to sell directly to anyone else in the world. ... This new technology permits creators to maintain relationships, so that the customer can become a fan, and so that the creator keeps the total amount of payment, which reduces the number of fans needed.

One of the limiting factors in acquiring new knowledge is the barrier of understanding. Building knowledge in your brain is like assembling a jigsaw puzzle. Some pieces cannot be placed in the puzzle before the others. Some pieces capitalize on others. There is no point in memorizing facts about Higgs boson before you learn what the standard model is and that, in turn, should follow the general understanding of particle physics which itself requires some ABC of physics. In incremental reading, if you encounter a text related to Higgs boson you can manually delay it until the time you hope your Physics ABC will provide the ground for understanding the boson.

Dendritic progress

Instead of focusing on a single subject of study, the student will review dozens of subject areas in a single day. Instead of monopolizing her knowledge with a single area of expertise, she will harmoniously deepen all facets of her knowledge in proportion to needs and/or interests.

Creativity

The key to creativity is an association of remote ideas. By studying multiple subjects in unpredictable order, you will increase your power to associate ideas. This will immensely improve your creativity. Incremental reading may be compared to brainstorming with yourself.

Problem solving

Mankind's progress is based on problem solving. Interestingly, we solve problems but seem to have a pretty poor understanding of the mechanics of problem solving. The main problem of schooling in that respect is that it models the brain as a perfectly deductive and a perfectly imitative computer. It seems to be blind to the fact that our brain is a concept network whose great power resides in taking imperfect heuristic shortcuts in achieving goals.

What do I spend a silly amount of money on? How might I scratch my own itch?

To explore business opportunities, I didn’t do in-depth market research; I started with my credit card statement and asked myself, “What do I spend a silly amount of money on?” Where did I spend a disproportionate amount of my income? Where was I price insensitive? The answer was sports supplements. At the time, I was making less than $40K a year and spending $500 or more per month on supplements. It was insane, but dozens of my male friends were equally overboard. I already knew which ads got me to buy, which stores and websites I used to purchase goods, which bulletin boards I frequented, and all the rest. Could I create a product that would scratch my own itch? What was I currently cobbling together (I had enough science background to be dangerous) that I couldn’t conveniently find at retail? The result was a cognitive enhancer called BrainQUICKEN.

“What would I want to do, have, and be if I had $10 million in the bank?” and “What’s my real target monthly income (TMI)?” For the latter, in other words: How much does my dream life—the stuff I’m deferring for “retirement”—really cost if I pay on a monthly basis? (See fourhourworkweek.com/tmi.) After running the numbers, most of my fantasies were far more affordable than I’d expected. Perhaps I didn’t need to keep grinding and building? Perhaps I needed more time and mobility, not more income? This made me think that maybe, just maybe, I could afford to be happy and not just “successful.” I decided to take a long overseas trip.

Data Analysis with Python and PySpark

What are the worst things that could happen? Could I get back here?

After reading The E-Myth Revisited by Michael Gerber and The 80/20 Principle by Richard Koch, I decided that extreme questions were the forcing function I needed. The question I found most helpful was, “If I could only work two hours per week on my business, what would I do?”

Adding to the complexity of ML projects are two other critical elements that are not shared by (most) traditional software development projects: a frequent lack of detail in project expectations and the relative industry immaturity in tooling. Both aspects are no different from the state of software engineering in the early 1990s. Businesses then were unsure of how to best leverage new aspects of technological capability, tooling was woefully underdeveloped, and many projects failed to meet the expectations of those who were commissioning engineering teams to build them. ML work is (from my biased view of working with only so many companies) at the same place now in the second decade of the 21st century that software engineering was 30 years ago.

Honestly speaking, it was more like, “Yes, I know it’s impossible, but if I had a gun to my head or contracted some horrible disease, and I had to limit work to two hours per week, what would I do to keep things afloat?” The 80/20 principle, also known as Pareto’s law, is the primary tool in this case. It dictates that 80% (or more) of your desired outcomes are the result of 20% (or less) of your activities and inputs.

Nothing is more demoralizing than building an ML solution that solves the wrong problem