About 18 months ago, I interviewed with a YC startup and the first round was actually a code review. It included an API which included some SQL commands as well as a SQL schema. I pointed out missing validations, missing error handling, lack of indices, alternate data types, possible XSS vectors, etc.
It was a really great format and I think one that creates better separation between good candidates and great candidates because it is more open-ended and collaborative. One of my favorite technical interviews I've engaged in.
I think that now, with AI coding assistants becoming even more integral than those early days 18 months ago, this approach is more relevant than ever since it gives insight into how efficiently a candidate can quickly review AI generated code for correctness, defects, performance issues, and other gaps in quality.
(The interviewer had to create a private GH repo and the repo itself didn't support commenting inline; I took my notes in a text file and reviewed it interactively with the interviewer).
It was a really great format and I think one that creates better separation between good candidates and great candidates because it is more open-ended and collaborative. One of my favorite technical interviews I've engaged in.
I think that now, with AI coding assistants becoming even more integral than those early days 18 months ago, this approach is more relevant than ever since it gives insight into how efficiently a candidate can quickly review AI generated code for correctness, defects, performance issues, and other gaps in quality.
I liked this format so much that I ended up creating a small open-source tool for it to make it easier to manage this process: https://coderev.app (https://github.com/CharlieDigital/coderev)
(The interviewer had to create a private GH repo and the repo itself didn't support commenting inline; I took my notes in a text file and reviewed it interactively with the interviewer).