The experience of organizing a frontend hackathon makes me reflect on how coding interviews have changed in the last few years. Tools like Cursor, Copilot, and large language models have become central to how developers approach writing code. In the past, an interview would often test the ability to recall syntax, implement algorithms from scratch, and debug without external help. Today, the process is different because the default assumption is that these tools exist and can be used. This changes both the expectations of what a candidate can demonstrate and the meaning of practical coding skills.
The reliance on AI assistance shifts the focus from memorization to orchestration. A developer is now less about typing every line correctly and more about structuring the problem, guiding the tool toward a solution, and knowing when the output makes sense or fails. This is a useful shift because in real work environments, nobody writes code in a vacuum. At the same time, it raises questions about what is being measured in interviews. If the goal is to assess the depth of understanding, then giving space for debugging sessions or architectural discussions may be more revealing than timed implementation exercises where AI fills the gaps.
Debugging remains the skill that separates surface-level competence from real problem-solving. Even with LLMs generating code, the ability to trace why something is failing, how dependencies interact, and where the logic breaks cannot be outsourced fully. A candidate who only knows how to prompt tools without verifying or correcting results will struggle when systems behave unexpectedly. This is why hackathons often expose more about how someone thinks under pressure than about their ability to deliver a polished product. The code may be partially AI-generated, but the process of integrating, fixing, and deploying shows whether the person understands what is happening.
Another effect of this shift is that interviews emphasizing data structure puzzles or abstract algorithms feel less relevant. They were never a perfect proxy for practical software development, but now they are even further removed from reality. The interview formats that align more closely with actual workflows—building small features, improving existing code, or designing components—seem better suited for evaluating ability. This does not eliminate the need for theoretical grounding, but it acknowledges that knowing how to apply it in an environment rich with automation is what matters most.
Looking ahead, the question is not whether these tools will stay but how hiring processes adapt. A fair interview in today’s context should test how someone uses AI responsibly, how they debug when AI is wrong, and how they design for maintainability. Conducting the frontend hackathon reminded me that the measure of a developer is shifting from rote execution toward judgment, clarity of thought, and the capacity to make sense of complexity. Coding interviews will have to reflect that reality if they want to remain meaningful.