A simple #TechnoFunda screen using @mystockedge -

Relative Performance + Relative Strength + CFO

#MANORG - R.Damani has 2.17%

Now from here you get to digging in more
https://t.co/KhIxJ38GvL

https://t.co/SgZ6Y28qe4

@vivbajaj

In 1937, a trucker got frustrated waiting for his cargo to be unloaded. He later sold his company for $1.1 billion.

Here’s the story of how Malcom McLean made a billion dollars and which startup could be following in his footsteps. (Inspired by our episode with @laurabehrenswu)

In the early 1950s, the shipping industry was a dying business.

New York’s docks were handling half as much domestic cargo in the early 1950s as they had been in the depressed 1930s.

In thirty years, no one had invested significant money in coastal shipping.

Why?

As McLean found out, loading ships was difficult and slow.

A truck or train would deliver items to the port. Each item was unloaded separately, recorded, and carried to storage.

When a ship was ready, each item was taken from storage, counted again, and hauled on-board.

The cost of this labor was significant. As one expert at the time explained:

“A four-thousand-mile voyage for a shipment might consume 50 percent of its costs in covering just the two ten-mile movements through two ports.”

Yet, despite the industry’s issues, it felt little pressure to change.

Foreigners were barred from operating domestically. Cartels ran international routes. And gov. subsidies eased the pain of labor costs.

Reshaping the industry needed an outsider.

Enter Malcolm McLean.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

1) Core attributes of a promising founder worth following:
- Progress/trajectory over time
- How well they lead a team &
- Make the vision happen
- Most impressed when someone is way ahead of where you thought they would be when you check up/see them a few months later.

2) Indie Makers have such a benefit of being small, fast, nimble workers in comparison to big companies.

We can easily pivot and vastly change a project of ours. We can change as we learn more about our users.

Big companies can take months/years for even the smallest of pivots.

3) Before you find a product market you can pivot your project as many times as necessary until you find something that works.

Then you only have one thing to do: scale, scale, scale.

4) Be obsessed with the little details.
- listen to every single person who uses your product
- reply to every single person who emails you about your product
- ask them what they like and do not like
- iterate your product, make changes, pivot if necessary

5) Paul Graham's (@paulg) name came up with a feeling of great admiration! Naturally! It was Paul who said:

In the early stages of a company just;
- write code
- talk/sell to customers to stress test your idea

Nothing else.