Here is a formula for growth.

(Not the only one, but one that works.)

People follow you when they are afraid of missing out on what you have to say.

People get afraid of missing out when they meet you and have a chance to realize that they want more of you.

🧵👇 Image
Then, we can conclude that there are two steps:

1. You need to get in front of people by posting "shareable" content.

2. Once people meet you, you need to hook them so they follow you instead of moving on.

👇
In Twitter, shareable content means content that people want to engage with:

▫️They like it
▫️They leave a comment
▫️They retweet it

There are multiple ways to create shareable content that spreads quickly.

👇
Any of the following posts is a good candidate to get a lot of engagement:

▫️Controversial content
▫️Shitposting
▫️Reply-bait content ("only wrong answers")
▫️Comedy
▫️Lists ("Top 10 something")
▫️Quizzes

Basically, anything that causes a reaction in people.

👇
Despite having many different formulas to get a lot of engagement, keep in mind that not all of those will make people want to keep you close.

(That's the reason why that 20,000-likes viral tweet got you only 100 new followers.)

👇
You have to think:

What's the type of content that will make yourself follow others?

Not like, not retweet, not comment, but actually *follow* people?

There's a high probability that a lot of people act just like you. What works for you, may also work for them.

👇
If you got to this point, you realize that most of what I've said translates into "Content. Content. Content."

That's the key.

To make people want to stick around, you also want to optimize your profile. This is not as important as your content, but it's not irrelevant.

👇
Experiment with your avatar, background image, and most importantly, your bio.

I've done a lot of experiments. The bio I have right now converts 50-60% more followers than the last version I had.

Try different things and measure the results.

👇
Finally, a couple of approaches you could follow to improve the content you post:

1. If you had to pick only 3 people to follow, who would they be? Why is that?

Look at what they do. Imitate them (don't just copy them, but try to understand their formula.)

👇
2. The second strategy is to tell people your story. What are you doing every day? How are you doing it? What challenges are you facing? What did you learn?

People love to hear about what others are going through.

Hope this helps!

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More from @svpino

11 Oct
This is ridiculous.

I've been coding in Python 🐍 since 2014. Somehow I've always resisted embracing one of their most important principles.

This is a short thread 🧵about idiomatic Python.

👇 Image
Python is all about readability.

One of its core principles is around writing explicit code.

Explicitness is about making your intentions clear.

Take a look at the attached code. It's one of those classic examples showing bad versus good.

See the difference?

👇 Image
There are more subtle ways in which Python encourages explicitness.

This example shows a function that checks whether two keys exist in a dictionary and adds them up if they do.

If one of the keys doesn't exist, the function returns None.

Nothing wrong here, right?

👇 Image
Read 9 tweets
10 Oct
I had a breakthrough that turned a Deep Learning problem on its head!

Here is the story.
Here is the lesson I learned.

🧵👇 Image
No, I did not cure cancer.

This story is about a classification problem —specifically, computer vision.

I get images, and I need to determine the objects represented by them.

I have a ton of training data. I'm doing Deep Learning.

Life is good so far.

👇
I'm using transfer learning.

In this context, transfer learning consists of taking a model that was trained to identify other types of objects and leverage everything that it learned to make my problem easier.

This way I don't have to teach a model from scratch!

👇
Read 11 tweets
8 Oct
A real-life Machine Learning solution that works.

Here is a breakdown of every piece and how they work together.

🧵👇
There's a website.

Users upload a group of pictures of an item and select the category it belongs to.

The system returns how much money the item is worth.

👇
Before:

▫️A group of people reviewed the pictures submitted by the user and decided how much the item was worth.

Today:

▫️The system quotes some of the items automatically (some still have to go through humans for a quote.)

👇
Read 11 tweets
7 Oct
Want to hear a secret?

Regardless of your experience, here is an area of Machine Learning where you can have a huge impact:

▫️ Feature Engineering ▫️

It sounds fancy because people love to complicate things, but let's make it simple: 🧵👇
In Machine Learning we deal with a lot of data.

Let's assume we are working with the information of the passengers of the Titanic.

Look at the picture here. That's what our data looks like.

The goal is to create a model that determines whether a passenger survived.

👇
Each one of the columns of our dataset is a "feature."

A Machine Learning algorithm will use these "features" to produce results.

"Feature engineering" is the process that decides which of these features are useful, comes up with new features, or changes the existing ones.

👇
Read 14 tweets
6 Oct
I don't have proof, but I have empirical evidence that this is true:

▫️The outcome of a pair programming session is directly proportional to each developer's capacity to challenge each other.

Let me explain: 🧵👇
If you pair 2 developers with very different seniority levels, the session will become more of a training opportunity for the least senior person.

The short-term impact on the project will be negligible. Most of the ideas and progress will come from the senior person.

👇
If you pair two developers with a similar experience, their contributions multiply, giving you a much larger short-term impact.

You aren't getting ideas from one or the other anymore. You are getting a polished version that's better than any idea individually.

👇
Read 4 tweets
6 Oct
There are different categories of Machine Learning problems:

▫️Supervised Learning
▫️Unsupervised Learning
▫️Semi-supervised Learning
▫️Reinforcement Learning

This is a quick introduction to each one of them: 🧵👇
1⃣ Supervised Learning

🔹We train an algorithm using labeled data. This means that we give it the "questions" and the correct "answers."

The goal is for the algorithm to learn the concepts, so it can later answer similar questions.

👇
An example of Supervised Learning:

Given a dataset with pictures of different dogs and their breed, we can use a classification algorithm to determine the breed of new pictures of dogs.

Noticed how here we are getting labeled data (picture + breed.)

👇
Read 14 tweets

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