When we first landed code The environment feels like stepping into the passenger seat and encoding. Codex aims to take over many of the regular or overwhelming parts of software engineering, such as understanding large-scale code bases, drafting PRs, and finding errors, and helping us focus on higher-level thinking. In this guided setup, we explore how to connect to the GitHub repository, configure a smart environment, and use Codex to start useful engineering tasks.
When we start, we start with this blank workspace. At this point, we have not linked any code or given any instructions to the assistant, so it is patiently waiting for our first step to define. It feels clean, open, and prepares us for directions to develop our development efforts.
We then go on to select the GITHUB organization and repository, which Codex will use. In this case, we selected the “TeamMMTP” organization and linked it to the private AI-ScribeStore’s Repo. Codex cleverly filters the repositories we can access to to ensure we don’t accidentally link the wrong repository. We also asked if we would like to allow agents to use the internet. We chose to delete it now, which means Codex will rely solely on local dependencies and scripts. This setting is ideal when we want to maintain a safe and completely certain environment.
Now, we are introduced to Codex as a software engineering agent for actual power. It outlines four main features: drafting GitHub pull requests, automatically browsing our code base to identify errors and suggesting improvements, running lint and testing to ensure code quality, and powered by miniature models specially designed to understand large repositories. At this point, we can also access the GitHub Push menu by clicking the drop-down list, which allows you to choose between creating PRS, copying patch code, or applying GIT commands. This interface makes our workflow seamless and gives us great control over how we want to ship our code.
With our repository and features, Codex recommends an initial set of tasks to get us started. We chose to recommend, including explaining the overall code structure, identifying and fixing bugs, and reviewing small issues such as typos or breaking tests. What’s great here is that Codex helps break the ice for us even if we’re not familiar with the project. These cards are bite-sized onboarding challenges, allowing us to quickly understand and improve the code base while seeing Codex. We checked these three to show that we were ready for the assistant to start analyzing and working with us.
In this task dashboard, we were asked, “What are we going to code next?”, a gentle push to control what we are now focusing on. We can create a fully customized task or choose from one of three predefined options. We noticed that Codex also enables “Best-N”, which generates multiple implementation suggestions for tasks, allowing us to choose our favorite task. We have linked the proxy to the “main” branch of the repository and configured the task to run as 1x container. It’s like telling a teammate: “This is a branch, this is a task, go to work.”
Now the code begins to mine into the code base. We see a command running in the terminal, which is grepper in `vite.config.ts`ts” react’react’react. This step demonstrates that code is not only a blind assumption. It can actively search through our files, identify references to libraries and components, and build images of the tools our project is using. Watching this in real time makes the experience vibrant, like having an assistant is not only smart, but also curious and methodical in its methodology.
Finally, Codex provides a detailed breakdown of the code base and some well-thought-out suggestions for improvement. We learned that the project was built using Vite, React, TypeScript, Tailwind CSS and ShadCN-UI. It identifies our routing, style configuration and toast logic. It also tells us what is missing, such as automatic testing and actual data acquisition. These insights go beyond basic code reading; they help us prioritize important tasks and create a roadmap for evolving projects. Codex also uses specific file names and components in its reports, proving that it does understand our structure, not only on the surface but also on the functional basis.
In short, we have connected a GITHUB repository and unlocked the AI-powered engineering assistant who reads our code, explains its design and proactively proposes improved methods. We went through the transition from passive assistants to active co-developers, providing guidance, running commands and generating summary, just like skilled teammates. Whether we are improving tests, recording logic, or cleaning up structures, Codex will usually require clarity and motivation when diving into unfamiliar code. With this setup, we are now ready to build faster, smarter debugging faster and more efficiently with AI as our coding partners.
Sana Hassan, a consulting intern at Marktechpost and a dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. He is very interested in solving practical problems, and he brings a new perspective to the intersection of AI and real-life solutions.
