As access to AI models has become more widely available and more and more people are using these tools on a regular basis, many organizations are interested in providing AI tools to their employees and customers. A key problem with the publicly available APIs is that they have varying levels of security and privacy that don’t always meet the needs …
This blog post serves as a thought experiment, delving into potential solutions for a pattern I have noticed on projects throughout my career. As a consultant, I work with many companies, each with unique ways of organizing and handling software development. However, throughout my career at Keyhole and elsewhere, I’ve noticed something that seems to be consistent across all dev teams: the existence of legacy code.
Legacy code can be frustrating and time-consuming to work with, so I used AI to create a solution to mitigate the hassle. While other solutions may already exist (and some may be more efficient), I found the process of creating this tool expanded my understanding; it really helped me grow as an engineer.
So, I’m using this blog post to share my process with you! Let’s dive into how AI can assist in improving application design (specifically legacy code) through automation.
Lately, the buzz about AI has been inescapable among my peers – especially around OpenAI’s GPT-4 and its implementations: Chat GPT, VideoGPT, and DALL·E. Tools like these are rapidly changing how we interact with and develop on the internet. They are defining our future. Web 5.0 is here, believe it or not, and AI is a big part of that.
Using Open AI’s GPT-4 doesn’t come without some controversy, but the implementation of CodeGPT within VS Code should not cause concern. Time-traveling, unstoppable intelligent robots are not coming after you… at least not just yet.
In this post, I’ll briefly cover why using CodeGPT is helpful, how to use it, and how to set it up. Let’s get started!
