The field of relational programming and database querying is witnessing significant advancements, driven by innovations in programming languages, query optimization, and data modeling. Researchers are exploring new ways to improve the expressiveness and efficiency of relational programming languages, such as incorporating rich types, on-demand laziness, and structured traces. Additionally, there is a growing interest in developing more accurate and efficient methods for cardinality estimation, which is crucial for optimizing query execution plans. Novel approaches, such as learning-based estimators and ambidextrous degree sequence bounds, are being proposed to address the challenges of pessimistic cardinality estimation. Furthermore, the development of new data models, such as property graphs, and query languages, such as SQL/PGQ, is expanding the capabilities of relational databases. Noteworthy papers in this area include: Designing Walrus, which presents a functional relational programming language with rich types and on-demand laziness. Is it Bigger than a Breadbox, which proposes an efficient cardinality estimation method using simple regressors learned online. Ambidextrous Degree Sequence Bounds for Pessimistic Cardinality Estimation, which introduces a new bound for pessimistic cardinality estimation that is provably not looser and empirically tighter than existing bounds.