Serg

The Best of Charlie Munger pic.twitter.com/rEfYK5CYbn

— Dividend Growth Investor (@DividendGrowth) September 28, 2023

*MTP: Massive Transformative Purpose

IDEAS (internal)

Interfaces

• Example Uber, ChatGPT, Coinbase
• OG example: Mosiac
• Started as APIs
• Automate SCALE elements
• Platforms are Interfaces

Dashboard

• Componenets of dashboard
• Engagement Metrics
• OKRs

Components of experimentation

• Lean startup thinking
• Culture of risk taking
• A learning orgnization
• Example, Amazon
• If we fail a lot, learn a lot
  • To do both is to experiment a lot

Componenets of Autonomy

• Decentralized decision making
• Autonomous teams
• DAO
• Think: bee hive

Components of social tech (Team and Community)

• WhatsApp, Asana, Canva, Slack
• Peer to peer collabration
• Community features (team members included)
• Virtual Reality: Apple Vision Pro (soon)

SCALE (External)

Leverage one or more of these 5 externalities, will enable you to SCALE with a very small resource footprint

Staff on demand (SoD)

• Hire independent contractors or SoD platforms
• Example: Uber, Task Rabbit, Fiverr, Gigwalk

Community and Crowd

• Community: Large group of individuals who are passionate about your MTP and are directly involved in the main functions of your organizaion
• Example: TED, XPrize, Uber, MI, Wikipedia

AI and algorithms

• Computation has been doubling (power)
• Global data doubling every two years (training data)
• Cost to train Ai is decreasing
• Capital: investments

Leverage

• Fractional ownership, open data sets, peer-2-peer, Cars, co-working
• Leverage asset, create platform
• Other example: Waze, Getaround, Amazon web services, Lab Central

Engagement (confluence)

• Incentive prizes
• Gameification
• Token economics
• Example: Duolingo, Veefriends, TikTok, Reddit

I need to revisit these videos post-graduation! The first video is from FastAi.com: https://course.fast.ai/Lessons/lesson1.html

Data Analytics for Beginners

The BIG 5 Projects – in Data Science

How to learn Data Science Smartly

Here’s are some of the most commonly used list methods in Python:

1. append(item): Adds an item to the end of the list.
2. extend(iterable): Adds all the elements of an iterable (like list, tuple, string etc.) to the end of the list.
3. insert(index, item): Inserts an item at a specific position in the list.
4. remove(item): Removes the first occurrence of the item from the list.
5. pop(index): Removes and returns the item at the given index. If no index is specified, it removes and returns the last item in the list.
6. index(item): Returns the index of the first occurrence of the item in the list.
7. count(item): Returns the number of times the item appears in the list.
8. sort(): Sorts the items in the list in ascending order.
9. reverse(): Reverses the order of the items in the list.
10. clear(): Removes all items from the list.

Here’s an example of how you might use some of these methods:

# Create a list
numbers = [1, 2, 3, 4, 5]

# Append a number to the list
numbers.append(6)

# Extend the list with another list
numbers.extend([7, 8, 9])

# Insert a number at a specific position
numbers.insert(0, 0)

# Remove the first occurrence of a number
numbers.remove(0)

# Pop the last number from the list
last_number = numbers.pop()

# Get the index of a number
index_of_five = numbers.index(5)

# Count the occurrences of a number
count_of_five = numbers.count(5)

# Sort the numbers
numbers.sort()

# Reverse the numbers
numbers.reverse()

# Clear the list
numbers.clear()