Using openAI to create content for social media

Hello Hello 👋👋👋

Today my post is a bit different from the others as I was having some fun with AI last weekend. I realised I was spending too much time thinking about the content, adding some fun emojis and adding the hashtags for posts on social media.

Then I spent last Saturday evening creating this tool with OpenAI 🤖 quite basic now, but I am planning to have small increments as we go.

It's been a fun couple of hours doing research, coding, and testing, but it's here!  This AI tool can increase efficiency and optimize the content creation. 💪    

This paragraph was generated with the tool

If you are curious about the code and you want to run locally, this is the repo:

You will need to create a .env file and add your OpenAI API Key, which is generated on their website after you create your account there (It is FREE).

If you just want to try it out, open the link and add the text explaining what your content should be about, maybe even add some personality like (friendly, assertive, etc) to help AI to find out what is more close to what you want. It will generate a post with emojis, hashtags and a picture related to what you wrote. The picture is definitely not the brightest feature as you saw above, but maybe you will have some fun like me and generate a picture of dogs without faces 😂

PS: you might get some 502 errors (they are random and unfortunately is because I am using a free trial and OpenAI api is returning the query with some timeouts) 😔

This really represents me after I deployed my code !

Benefits of AI in Test Automation

Photo by Kindel Media on

There are several interesting web app automation scenarios that we can improve using AI:

  • Reduce the execution time: Nowadays you have the feature target function already even without an AI test automation project, but with AI you can add this feature without having cucumber in place or even the need to tag the scenarios or features. The AI should be able to identify the features related to the change automatically.
  • Converted manual test cases to automation: you can use Natural Language Processing (NLP) to automatically translate manual test cases into automated test cases. I have seen this done with cucumber not AI yet, but totally possible as AI models work on datasets.
  • Creating different data combinations by training the AI to identify the possible combinations based on a dataset is possible. This would increase the data coverage and bring more confidence to the automation project.
  • Visual validations: Many tools perform this functionality already. I personally tried one tool ages ago called Percy, but you can also try some other popular tools like Applitools and Telerik
  • Test execution stability or self-healing automation: AI can automatically locate web elements when the primary locators fail. You can see this feature in some cutting-edge automation tools like Mabl and Xray and Functionize. Self-healing employs data analytics to identify objects in a script even after they have changed. When your script fails due to being unable to find the object it expected, the self-healing mechanism provides a fuller understanding and analysis of options. Rather than shutting down the process, it examines objects holistically, evaluates the attributes and properties of all available objects, and uses a weighted scoring system to select the one most similar to the one previously used.

Becoming a Domain Model Expert

Creating a model for your test automation requires a domain expert, therefore is critical to have a test automation specialist that also knows the business so the AI can bring the desired innovation. With such extensive use cases, AI systems will need different parameters from domain experts.

Machine Learning Algorithms In Layman's Terms, Part 1 | by Audrey  Lorberfeld | Towards Data Science

Be careful to not run more automated tests than you actually need it. A stage of supervision when the AI is learning the patterns is definitely needed it.