A few weeks ago there was a lot of the internet attention around the recently opened for beta project called GPT-3. Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI, a for-profit San Francisco-based artificial intelligence research laboratory. 
I spent a few minutes using the playground they offered and generating text output. One of the early ideas I had was to create Tweets from historic Tweets. I ended up writing a short program to fetch Tweets from a series of accounts. This lesson will be less structured and more of a blog post on how to play with GPT-3. I am going to show you how to fetch Twitter data and get new tweets using the GPT-3 playground. I am assuming you are in the beta program with OpenAI, you have a Twitter developer application, and Python installed on your computer.
The output on the terminal will be a little unclear. It more tells you the state and progress. After it completes, you will check the tweets folder. For this example, I will have two new files: tweets/wealth_theory.json and tweets/wealth_theory.txt. We will put tweets/wealth_theory.txt into the GPT-3 playground. The nice aspect of this script is it grabs 5 pages worth of Tweets. If you want more, you can just run it again. The script will use the historic data as a starting point and continue from there to work backwards. You can also add to the number of pages.
In the top navigation bar, you will see Playground. Click it and open the playground page.
Next, open the tweets output as text, and copy it into the playground.
Hit submit. You may see a content warning like below.
You should see the new tweets using the existing data.
With the Wealth Theory tweets, it creates these three potential tweets:
If you are not using leverage, you are missing out on the wealth creation.
A financial plan is a good idea for anyone.
Cash flow is the foundation of a strong financial plan.
The ability to convert cash flow into other forms of wealth is the key to moving up on the wealth
You can find more details about GPT-3 here in the introduction docs. Example: davinci is the model we picked. Since I started playing around with GPT-3 in July, they've added these sliders to the side of the playground. If you hover over them, they provide you with more information.
Thanks for reading! This was fairly high level and to show you a really simple example. As mentioned in the introduction, I just wanted to show how to fetch the Twitter data and how to plug into into the playground.