When a bunch of people get together to explore, discuss and master a topic of interest, something is getting better. Passion for knowledge, continuous learning and common interactions encourage the Python MeetUp held in our Headquarters led by Rami Gallo, Product & Engineering Manager of Eureka Labs, who emphasized:

We capitalize on the opportunity of listening to science data professionals about two powerful topics: the trendy ChatGPT daily use and testing that could improve our daily performances.

Andrés Rios and Matias David Lee were keynote speakers about current and relevant themes in the Python universe:

• "Increase your efficiency by programming in Python through AI" by Andrés Rios.
• "Testing: modularize, mock, use the right tools, and win" by Matias David Lee.

Mati started with simple but key advice: testing should be a habit and a learning process to improve results. Following that premise, he considers that a nice code has to have: significant names, specific and short functions, simple methods, short files, and low coupling, high cohesion codes.

We know that testing refers to an ongoing verification of acts and efficiency. After explaining some Test Theories, Matías shared an entire example of testing creating an API to manage products’ prices and explaining the design, database, connector, product repository, cloud service, business module that complete the API in a well-organized system. When talking about implementation, Mati used FASTApi, PonyORM and SQLite. Then, he considered each phase of the process explaining how to work on endpoints, the test of Python fixtures, TDD, Unit Testing, Stubbing of a third party module, IPDB to develop and write codes, and frameworks.

Mati was clear when showing that Python’s fixtures and its magic need organization, continuous checking and improvements, taking some risks and trusting the repetition of codes, automatization and strictness to overcome any situation. Finally, he added that Pytest configures tools and helps to optimize database errors.

Getting into the world of artificial intelligence, Andrés Rios works in data science and gave some guidance on how to be efficient when programming with Python using AI. He started the talk by asking what is the future and what is present in current times. After introducing the trending issue of artificial intelligence, he specified that in the programming industry it could be used to facilitate processes, simplify work while we focus on something else. For example, AI helps to improve code docs, translate technical specs to other codes, audiobooks, crips with corrections to do thanks to Notion or ChatGPT.

Some interesting assistant tools to discover are GitHubCopilot, AmazonCodeWhisperer, Tabnine, Replit, Sourcegraph cody, AskCodi, Codiga and Bugasura. These tools are useful for those tasks that anyone knows how to do, in his case, how to raise a static in Kubernetes and save some time on documentation to read. Also artificial intelligence could help in the following: resolving issues becomes easier due to recommendations of what to change, explaining the code and giving it to you; contextualizing so that you can analyze whether or not the problem is solved; asking business questions to databases and translating into SQL.

Andrés recommend:

• Check how to build Docker with agility
• Read the code you already have or are used in other projects to use patterns
• Learn software fundamentals, design patrons and how to add value to clients.
• Try and experiment with different AI tools.
• Without trusting %100.
• Remember: anyone can use AI to program but not everyone can program with AI because you need the bases.

The Python MeetUp was organized with Python Argentina and the complete event is upload to its YouTube channel you can explore here:

Last but not least, Rami says that “every person who came to the event manifested the relevance of sharing ideas. And the most valuable takeaway is the opportunity to exchange stories, experiences and laugh with people who enrich you”.