Frameworks are useless...

Unless you use them to solve specific problems.

Here are 10 frameworks that'll solve problems for you:

How to choose your next project:

If a project is successful, it should make the rest of your career look like a footnote.

@sama

How to write:

Your creativity is a backed-up pipe of waste water.

And the waste water must be emptied before you get to clear water.

The path to clear water (or great writing) is through bad first drafts.

@julian

How to build a network:

• ATTRACT interesting people to you with your content

• SEARCH for interesting people with your DMs

• STRENGTHEN relationships with Zoom

@chrishlad

How to send the perfect cold email:

• SHORT = keep it under 5 lines

• SPECIFIC to the person + their work

• SPACING = no one reads big blocks of text

@chrishlad

This is Karma. Karma is not a machine learning classifier 🐕‍🦺

Karma is a real dog trained to detect drugs. However, he would fail the simplest tests we apply in ML...

Let me take you through this story from the eyes of an ML engineer.

https://t.co/WAXRUlTvSI

Thread 🧵

Story TLDR 🔖

The story is about police dogs trained to sniff drugs. The problem is that the dogs often signal drugs even if there are none. Then innocent people land in jail for days.

The cops even joke about the “probable cause on four legs”.

Let's see why is that 👇

1. Sampling Bias 🤏

Drugs were found in 64% of the cars Karma identified, which was praised by the police as very good. In the end, most people don't carry drugs in their cars, so 64% seems solid.

There was a sampling problem though... 👇

The cars were not sampled at random! The police only did the sniff test if there was a serious suspicion that something is wrong.

The chance there are drugs in the car is much higher in this case!

2. Evaluation Metrics 🔍

The police referred to a 2014 study from Poland measuring the efficacy of sniffer dogs. The problem was that every test actually contained drugs!

This means there was no chance to measure false positives from the dogs! Only recall, not precision 🤦‍♂️