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.

👇
Again, not proof that this is the case. This has been my observation over 20 years of using pair programming.

I'd love to hear what some of the people that have studied this before think:

@macerub
@dmokafa
@mcsee1
@MattCodeJourney
@KentBeck
@unclebobmartin
@Grady_Booch

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More from @svpino

8 Oct
A real-life Machine Learning solution that works.

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

🧵👇 Image
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: 🧵👇 Image
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.

👇 Image
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
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
5 Oct
Python 3.9 🐍 is out! 🥳

Here are the 5 new features you care about.

🧵👇
1⃣ Merging dictionaries

There's a new operator "|" that can be used to merge two dictionaries.

See PEP 584 for more information: python.org/dev/peps/pep-0…

👇 Image
2⃣ Updating dictionaries

Another new operator "|=" will let you update dictionaries.

See PEP 584 for more information: python.org/dev/peps/pep-0…

👇 Image
Read 7 tweets
5 Oct
Some applications of Machine Learning:

▫️Ranking
▫️Recommendation
▫️Classification
▫️Regression
▫️Clustering
▫️Anomaly Detection

Here is a 3-second description of each one them: 🧵👇
1⃣ Ranking

🔹 Help your users find the most relevant items they are looking for.

For example, Google's algorithm to rank search results when a user searches for something combines multiple signals to offer the best results: your location, interests, past searches, etc.

👇
2⃣ Recommendation

🔹 Give your users the items they may be most interested in.

For example, Netflix's recommendation system to suggest what to watch based on your preferences, genre watch time, ratings, location, etc.

👇
Read 8 tweets
4 Oct
My recommendations for your first 30 days of Python 🐍.

🧵👇
I get many messages, and the most frequent question by far is, "How do I start with Python?"

There are multiple ways. Every one as valid as the one before.

Here is my way. These are my recommendations.

👇
1⃣Before anything else, remember that you need to make a commitment and be consistent.

Dedicate time to learn every day. It doesn't matter how much. Find a time that works for you.

The hashtag #100DaysOfCode is a great way to share your progress and stay accountable.

👇
Read 11 tweets

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