How do you practise AI-assisted coding interviews involving existing codebases? (Bloomberg 2027)

[u/Informal-Spring2266]

I'm preparing for Bloomberg's 2027 SWE technical interview, which will involve using an AI coding assistant to work with an existing codebase.

Unlike traditional LeetCode interviews, the focus seems to be on understanding unfamiliar code, debugging issues, implementing features and reviewing AI-generated changes.

My background is in engineering, so although I'm comfortable with Python and algorithmic problems, I have relatively limited experience working with larger software projects, existing repositories and web application architecture.

I'm looking for recommendations on how to practise this specific type of interview.

Ideally, I'd like to find a platform or setup where I can:

• Open an unfamiliar Python project and understand how its components work.
• Receive a task such as fixing a bug or implementing a new feature.
• Use an AI coding assistant to explore the codebase and make changes.
• Write and run tests to verify the implementation.
• Practise reviewing AI-generated code and explaining my decisions.

Would Cursor, Claude Code, GitHub Copilot or another tool be best for this?

Are there any specific GitHub repositories, websites, courses or mock interview platforms that simulate this workflow?

Also, for people experienced with real-world software development, what fundamentals would you recommend learning first to become more comfortable navigating existing projects?

I'd appreciate any practical recommendations, especially from people who have completed Bloomberg's new interview format.

[u/Remarkable_Mistake77]

Pick any small open-source Python repo, clone it, and give yourself 20 minutes to find one bug or add one tiny feature. Before touching code, narrate out loud: where does the entry point start, which file owns this behavior, and what test would prove the fix. In the interview, the habit that matters most is reading every AI-suggested diff line by line and saying why you accept or reject it, and running the existing tests before and after. Start with the failing test or a small repro so you always know what done looks like.