  Teaching students how to write code that is elegant, reusable, and
comprehensible is a fundamental part of CS1 education. However, providing this
"style feedback" in a timely manner has proven difficult to scale. In this
paper, we present our experience deploying a novel, real-time style feedback
tool in Code in Place, a large-scale online CS1 course. Our tool is based on
the latest breakthroughs in large-language models (LLMs) and was carefully
designed to be safe and helpful for students. We used our Real-Time Style
Feedback tool (RTSF) in a class with over 8,000 diverse students from across
the globe and ran a randomized control trial to understand its benefits. We
show that students who received style feedback in real-time were five times
more likely to view and engage with their feedback compared to students who
received delayed feedback. Moreover, those who viewed feedback were more likely
to make significant style-related edits to their code, with over 79% of these
edits directly incorporating their feedback. We also discuss the practicality
and dangers of LLM-based tools for feedback, investigating the quality of the
feedback generated, LLM limitations, and techniques for consistency,
standardization, and safeguarding against demographic bias, all of which are
crucial for a tool utilized by students.
