AI Vibe Coding
As a seasoned veteran of software development with 50 years of experience spanning numerous platforms, database structures and more than 10 programming languages, I have to tip my baseball cap to the AI implementations for Vibe Coding.
Back in the 1980’s, I participated in some of the first neural networking testing using Lotus 123. These were the building blocks of what we see here today.
My initial thoughts about resolving a manufacturing problem had me spending several months learning Python and struggling through the lack of experience with coding, technical and environmental nuances.
After reading through several articles about Vibe Coding, I thought it would be worth a try. Over the years I have been an astute problem solver as well as being able to visualize solutions for business opportunities relative to data capture and knowledge creation. However, coding has always been somewhat of an art form for me to get what I needed without all of the accepted coding techniques.
I opened a tutorial on Vibe Coding and selected Cursor as the Ai repository to attempt my first project using the toolset. What a surprise! Intuitively, I knew that asking questions or explaining requirements were going to make or break the interaction with AI.
Given the fact that I spent many years using floppy disks to download drivers, I inherently understand that setting up the environment would require effort. For my project, I wanted the code base in Python, but required connectivity to Microsoft Access and Excel as well.
Providing detailed descriptions with specific technical attributes facilitates the Vibe Coding interactions. From a traditional perspective, one enters the technical specifications derived from user requirements starting with the creation of a working environment including database structures, communications pathing, user interface and output results. Then, logical processes and data usages are entered to establish process flow.
Hit enter and the magic begins. First, the code module is created. It contains relevant comments. When completed, documentation about the functions is provided. When issues are encountered, troubleshooting methods are used to resolve them. AI is intuitive enough to understand what appropriate tools are necessary for problem resolution up to a certain point. For example, if there are issues within the code, checkpoints are added to aid with problem resolution. Suggestions that I had based on my experience were used to change the code. If successful, a positive response was given to recognize the effort.
The next step was to create a Web interface for development and analytical activities. This is where the process showed its immaturity. While the code generation and explanations are excellent, the problem-solving paths are limited and require the user to direct the software when necessary. My particular issue occurred with trying to set up a development Django implementation on a Google Drive file structure. One of the issues was that the superusers created for the python project for the Web did not have permissions on Google Drive. The AI engine kept trying to rebuild the environment and only provided instructions when I pointed out that the permissions were missing. I expect these scenarios will eventually be recognized at the tool matures.
Therefore, the current user should have some problem-solving technical skills in order to direct the AI process when needed. I will say that this iterative testing process results in much faster testing turn-around. It took me time to understand how to better communicate with the AI processor. Another caution: When setting up test environments, one has to direct the AI processor to clean up resources as environments change. This is an important step. It it also imperative that the developer/user understands some of the nuances that cause errors and have the ability to read code. One example is the default script to create superusers uses the same User name and password. These can be changed in the script before execution for the expected result.
Even though I’ve just begun to use AI coding, I definitely see the potential for faster project implementations. I’m especially confident of generating components that would have taken much more time to develop – in my case – the Web components. I am excited to continue on this learning experience.