Got Access to Codex, Here’s What Surprised Me
Earlier today, I got access to OpenAI Codex, a new agentic tool designed for software engineering tasks. Shout out to my brother Anuj Saharan, who works at OpenAI. Super proud of him.
Naturally, I gave it a spin.
My first test was simple: I pointed Codex at my this site's codebase and asked a few questions. It responded with solid, accurate answers. Of course, it’s an AI, mistakes can happen but overall, the results were impressive. It understood structure, usage, and dependencies with very little effort from my end.
Then I went bigger.
I connected a 6 million-line codebase based on Golang. Historically, most AI tools I’ve tried either hallucinate heavily, choke on scale, or forget context after the first few interactions. Codex didn’t. It indexed the repository, ran commands, wrote meaningful test cases, and located answers buried deep in the code, far better than anything else I’ve used.
To compare, I asked GitHub Copilot the same question on the same repo. Copilot either took too long to respond, gave a vague suggestion, or was outright wrong. It often feels like it should understand the context, but it doesn’t, at least not across large codebases. Codex, on the other hand, took about 2 minutes but returned a 100% accurate, context-aware answer. It was clear that Codex had better visibility into the full codebase and could reason about it more effectively.
It didn’t stop at just reading code. Codex executed tasks inside dedicated Docker containers, unique to each chat session. These containers came preconfigured with setup instructions, secrets, and environments tailored to the repo. That’s a significant UX win, it abstracts a lot of operational noise while preserving repeatability.
Now to be clear: I haven’t used it long enough to give a comprehensive verdict. But from what I’ve tested so far, Codex actually performs the coding tasks that most tools promise but rarely deliver on, searching, fixing, testing, and even proposing PR-quality changes.
This isn’t a paid post or an endorsement, just sharing what stood out. Me and my team were genuinely excited to try it out. If Codex evolves further in this direction, it’s going to fundamentally shift how devs offload focused work without losing context.
Looking forward to seeing how this matures.
FAQs
What is OpenAI Codex, and how does it differ from tools like GitHub Copilot?
OpenAI Codex is an agentic software engineering tool designed to handle large-scale codebases with deep contextual awareness. Unlike Copilot, which often struggles with context in large repositories, Codex demonstrated accurate understanding, indexing, and reasoning across millions of lines of code.
How well does Codex handle large codebases?
Codex successfully processed a 6 million-line Golang codebase, indexing it, answering deep questions, generating tests, and navigating dependencies without losing context, a task that typically breaks or degrades other tools.
What surprised me most about Codex’s capabilities?
Codex ran tasks inside Docker containers customized per chat session, with preloaded setup instructions and environment variables. This seamless operational integration reduced friction and improved task reliability, making it stand out in terms of developer experience.
How does Codex perform compared to Copilot in real-world testing?
In side-by-side tests, Codex returned accurate and context-aware answers even on large repositories, while Copilot often returned vague or incorrect suggestions and struggled with response times and scale.
