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Fast processing Simple Marketing for Smart People

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How I Make Reading Books 7x More Effective 📚💡

Do you find yourself forgetting most of what you read?
I used to struggle with this too, but then I discovered a system that supports knowledge retrieval and skyrockets my retention by 7-9 times.

Here’s how I applied it to the book Simple Marketing for Smart People
(by Billy Broas Broad and Tiago Forte).
→Capture: I read the book on Kindle, highlighting key points as I go. This took about 6-8 hours.

→Organize & Distill: I exported the highlights to Obsidian and
spent 20 minutes bolding the crucial points and highlighting essential insights.

→Distill: I created a PDF of my refined notes and fed it into Gemini.
Gemini helped summarize and format the notes, focusing on the bold and highlighted sections.

→Express: Using my distilled notes, I completed tasks for the 5 Lightbulbs course and worked on my argumentation for QuintSmart, adding new insights and examples along the way.

💡This semi-automated process allows me to retain 70-90% of what I read,
compared to just 10% from reading alone.

Main components used:
→Kindle for capturing and highlighting
→Obsidian for organizing notes using PARA and Zettelkasten principles
→Gemini for summarizing and formatting notes

By investing just 25% more effort than simply reading,
I transform information into actionable knowledge.

Isn’t this a much better leverage of your reading time?
What’s your approach to retaining knowledge?

👉Share your thoughts or ask me how to get started!

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Content preparation

(details for Belief-Claim-Proof) Belief: I can be much faster in processing a book when I leverage CODE and AI Claim: With an optimized book processing pipeline I safe time and ensure knowledge retention Proof: Comparison of times needed and outcomes (logos) Format: Text + Image

I’m much faster in working with books leveraging my C.O.D.E stack (combined with 7-9 times higher knowledge retention)

Do you also forget most of what you read? I struggled with that too but then I implemented a system that supports knowledge retrieval.

Here is how it works (slides provide you some visual impressions):

  • I read the book on Kindle with taking highlights while reading (estimated reading time 6-8h, Capture)
  • I went through the highlights exported from Kindle to Obsidian and did a Second Step - bolding important aspects and yellow highlights the essential insights (took about 20 min, distill)
  • Export as Pdf and feed that to Gemini. Work with Gemini on summarizing my highlight. Especially taking bold and yellow highlights into consideration. (distill)
  • I aggregate these insights in my distilled literature note in parallel to working with Gemini on further drilling down on aspects. (took about 1h) (distill)
  • I started using my distilled note already today to do my homework for the 5 Lightbulbs course and to work on my argumentation for QuintSmart (express)
  • While working with my distilled literature note I add more examples, extend the note with insights and add questions. (capture)

My approach builds on this Capture-Organize-Distill-Express stack:

  • Reading and Highlighting in Kindle
  • Obsidian for note taking and connection to Kindle via Obisidian plugin
  • Organizing principle PARA blended with Zettelkasten - book highlights become a Literature note in an area
  • Export highlight note as Pdf to import in Gemini 1.5 Advanced. Gemini has a larger context window and can process the full Pdf
  • Leveraging Gemini-AI also for text formatting (e.g. as bulletpoints)

This semi-automated book processing approach enables me to manifest learnings from books with just about 25% more effort than just reading it. Compared to the rate of forgetting from simply reading that is about 90% (so I retain just about 10%). I can increase my retention to 70%-90% though experiencing and doing the “real thing”.

Isn’t that a much better leverage? What do you think?

Looking forward to your comments about your current approach and/or what you would like to try next.

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Posted on LINKEDIN on 2024-06-11_Tue


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