The field of communication complexity is witnessing significant developments, with a focus on understanding the limitations and potential of various communication models. Researchers are exploring the relationship between communication complexity and other fundamental concepts, such as NP-hardness and statistical learning. Notably, recent studies have made progress in establishing sharp computational-statistical tradeoffs, which have far-reaching implications for our understanding of the interplay between computational efficiency and statistical optimality. Furthermore, new results are shedding light on the derandomization of communication protocols and the learnability of graph structures. Overall, the field is moving towards a deeper understanding of the intrinsic limitations and possibilities of communication and learning. Noteworthy papers in this area include: Computational-Statistical Tradeoffs from NP-hardness, which establishes sharp computational-statistical tradeoffs based on NP-hardness. Equality is Far Weaker than Constant-Cost Communication, which improves on several recent results and provides a significantly simpler proof of the main result of Fang, G"o"os, Harms, and Hatami.