I’m currently in the midst of coding my latest technical project: A backup program only for Linux distros that performs either a full backup or a partial backup of the user’s Home Directory. So far I’ve made great progress with the code, in part because I’m writing code with Google AI. I know there is a big debate whether AI chatbots help or hinder developers, especially Junior Developers. However, my experience so far has been positive, and I’m using this post to explain why.
Writing Code With Google AI: It’s Better Than Using Search For Long (Or Complex) Questions
The main reason I’m making great progress with my project because Google AI, especially Gemini, understand my longer questions better about Python modules and features better than Google Search.
I still do think that Google Search has its place for quick questions, but Gemini is so much better when it comes to asking longer questions about coding issues.
For example: I wanted to know if it was possible in Python to validate a path to a directory provided by the end user through user input. Gemini was able to formulate an answer to that question within seconds, and provide example code I could use in my project! Oh, and the chatbot explained what each line of code did! Thus, I got a learning lesson and code I could use.
I find myself learning more about the Python programming language and its built-in functions and/or modules I can use through Gemini than reading through the official Python documentation. Not to say I don’t read the documentation because I do. Especially when I want to use a specific flag or option. However, I find myself turning to Google AI more and more often to provide a different, and sometimes simpler, explanation on how to use certain modules or functions.
Writing Code With Google AI: It Understands My Code And Provides Good Suggestions For Improvement
Writing code with Google AI is like having access to a Senior Developer who not only understands my code and what you’re trying to accomplish, but also provides improvements and explains why I should use them.
My backup program is going to run the rsync command to run the backup. In my original design I use the os.system module to run the command. I provided this snippet of code to Gemini for help for some reason. Unfortunately, I’ve forgotten now. The chatbot came back with the answer to my question, but then suggested I use the subprocess.run module instead because it offered between security and performance.
I took this information and did my own research because I wanted to learn more about what the subprocess module was in Python, and how it worked. And in my research I discovered Gemini was correct.
That’s why I’m excited for this technology: It is helping people do more than ever before faster!