While networks have evolved in profound ways, the tools and practices used to measure them have not always kept pace with their evolution. To tackle this problem, we examine the Extended Berkeley Packet Filter (eBPF) as a new way to improve the practice of passive network measurements. eBPF is designed to provide a safe, efficient, and powerful mechanism for in-kernel programming, going far beyond the packet filtering-specific nature of the earlier Berkeley Packet Filter (BPF). In this repository, we present eBPFlow and gradually increase the number of utilized CPU cores to perform lossless capture at 10 Gb/s on 5 cores without special hardware acceleration or storage configuration.