• Decide what habit you want to have and record why you want this habit
• Start with the smallest amount of effort possible
• Increase the amount of effort regularly until you reach the desired amount of regular effort
• Track your habit in a habit recording system such as Loop Habit Tracker
• Review on a regular basis (monthly) whether you want to keep this habit or not
  • If you decide not to keep the habit, record why you decided to drop the habit such that if you decide to pick it up again, you can determine whether you might end up dropping it again.
• It's not a problem if you miss doing your habit, just make sure to do it when you're supposed to

My habits

Daily

• Daily planning
• Daily review
• Eat/Drink
• Shower
• Wash dishes
• Cook
• Exercise
• Read
• Watch TV shows
• Floss
• Brush teeth

Weekly

• Weekly planning
• Weekly review
• Wash clothes
• Clean apartment
• Buy groceries

Monthly

• Monthly planning
• Monthly review
• Pay bills

Yearly

• Yearly planning
• Year review

My prompts

• Daily planning at 10 am
• Weekly planning on Mondays
• Monthly planning on the first of the month
• Pay credit card on the first of the month

My prompt sources

• Google Keep
• Google Calendar
• Google Mail
• Slack
• Daylio
• Notion

• List all the features you would like to develop
• Define an appetite for the task (day, week, month, quarter)
• Identify the features where the appetite differs between individuals and discuss them to reach consensus
• Define the roles necessary to complete the task
• Identify dependencies between features
• Categorize the dependencies
  • Soft: somewhat depends on this other feature but isn't blocked by its absence from the codebase
  • Hard: depends on this other feature and is blocked by its absence from the codebase
• Prioritize the features
  • Use the RICE framework
  • Priority can also be skipped if using ROI as prioritization metric
• Estimate the value of a features in dollars
• Calculate a ROI (return on investment) as the estimated value of the feature divided by the defined appetite
• Order tasks according to dependencies and ROI

• Do you understand what is at stake?
• Do you know what already exists?
• Can you take an informed decision?
• What did you consider during your decision?
• What are your unknowns (things you'd need to know before your can make a decision)?

• Do you have experience with the problem which needs a decision?

• Indicate who has the most expertise to make the decision

• You should be allowed to cast a vote if the decision will impact you. If it doesn't (i.e., you're not a stakeholder), your vote will not be counted towards any option

Let's start by saying I'm not suggesting you read a full book per day. What I'm suggesting is to read at least a few pages of a book per day, reading a variety of books over the course of a week.

For a while I used to start book and finish them before starting another one. I'd allow myself to read a fiction book and a technical book at the same time, but not more than that. The idea was that by reading more than one of each my brain would have trouble with context and information retention.

I've recently decided to switch this approach. The main reason was that I found myself spending too much time reading articles online that I thought didn't bring me much value over time. I always thought books were more valuable, but their biggest problem was that it required a good amount of time involvement for the value to kick in.

Just like there are two strategies in learning systems, exploration and exploitation, I decided that leaning more on the exploration side might provide useful. Instead of spending hours on the same book over a short period of time (1-3 months), I would instead read bits of many books at once.

Here are the benefits I've observed through this approach:

It's easier to identify similar sources. I would read a few books on a similar topic, and of course they would all cite the same sources. The difference between processing all those books in parallel instead of sequentially is that you notice the pattern of reuse more clearly. When reading the books sequentially, what happens is that this type of information decays over time. We start to forget what the last book was referring to, so that the next book appears to have new references.

Similar ideas can be identified and speed up reading. As you identify the same ideas in different books, instead of reading the arguments careful in each book, the best argument is read thoroughly and the others quickly scanned for additional information.

You are exposed to more variety. Some people get topic fatigue, which is that you get bored of reading on the same topic. Reading on different topics avoids this issue while also stimulating you to think about many topics. This is a great way to sometimes make connections between unrelated topics.

Overall I've been very satisfied with this experiment and I've been doing it for over 4 months now. I highly recommend it if you have a large list of books you haven't started yet. See my article How to prioritize which book to read to help you organize your reading.

Tesseract (an open source OCR engine) supports a TSV format as output. I looked online for some documentation about the columns but couldn't find anything, so I looked at the source code.

Here is a summary description of each column, what they represent, and the range of valid values they can have.

• level: hierarchical layout (a word is in a line, which is in a paragraph, which is in a block, which is in a page), a value from 1 to 5
  • 1: page
  • 2: block
  • 3: paragraph
  • 4: line
  • 5: word
• page_num: when provided with a list of images, indicates the number of the file, when provided with a multi-pages document, indicates the page number, starting from 1
• block_num: block number within the page, starting from 0
• par_num: paragraph number within the block, starting from 0
• line_num: line number within the paragraph, starting from 0
• word_num: word number within the line, starting from 0
• left: x coordinate in pixels of the text bounding box top left corner, starting from the left of the image
• top: y coordinate in pixels of the text bounding box top left corner, starting from the top of the image
• width: width of the text bounding box in pixels
• height: height of the text bounding box in pixels
• conf: confidence value, from 0 (no confidence) to 100 (maximum confidence), -1 for all level except 5
• text: detected text, empty for all levels except 5

Here is an example of the TSV format output, for reference.

References

• https://tesseract-ocr.github.io/tessdoc/Command-Line-Usage.html