Leonie’s work powers some of the key machine learning features at Pieces, such as text recognition and search. She discovered her interest in working with data when she was analyzing local politicians' tweeting behavior during elections. She then went on to study machine learning and causal inference in-depth, pursuing research on generative models, deep learning, and causal discovery. At Pieces, she is especially interested in making local models perform better, enabling users to find their content at the right time, and making Pieces work seamlessly with existing tech stacks.
Posts by this author
Exploring text segmentation in retrieval-augmented generation (RAG)
Uncover how effective text segmentation can enhance information retrieval and boost the performance of your generation models. Discover techniques and best practices to transform your approach to RAG.
Question-Answering on Source Code Repositories by Combining Local and Cloud Processing
Learn how we combined local and cloud processing to understand personal source code repositories with our Pieces Copilot.
Enhancing AI Code Review Efficiency with Pieces' qGPT-powered RAG Feature
Learn how we created a copilot built on retrieval-augmented generation that enables AI code review on any code repository.
How We Made Our Optical Character Recognition (OCR) Code More Accurate
We've spent months developing an OCR code engine that's fine-tuned to code through a combination of pre- and post-processing.
