Keeping up with the latest research is a critical part of the job for most data scientists. Faced with this challenge myself, I often struggled to maintain a consistent habit of reading academic papers and wondered if I could design a system that would lower the barrier to exploring new research making it easier to engage with developments in my field without the need for extensive time commitments. Given my long commute to work and an innate lack of motivation to perform weekend chores, an audio playlist that I could listen to while doing both sounded like the obvious option.

This led me to build Scholcast, a simple Python package that creates detailed audio summaries of academic papers. While I had previously built versions using language models, the recent advancements in expanded context lengths for Transformers and improved vocalization finally aligned with all my requirements.