We present λλ1, a programming language for silicon photonics. λλ uses a linear type system to encode the physical constraints of optics, rejecting unrealizable programs at compile time. The compiler lowers well-typed programs to a graph-based intermediate representation, then solves a constrained embedding problem to map these graphs onto arbitrary silicon photonic switch targets while minimizing signal loss. We validate λλ on a commercial photonic switch, demonstrating correct operation for circuit switching, time-varying rotor switching and analog in-network computation. Across various hardware targets and programs, the λλ compiler scales to silicon photonic switches with over 100,000 programmable elements and handles switch programs with 128 input-output pairs. Finally, we develop a synthesizer to automatically generate λλ programs from high-level specifications, allowing users to program photonic hardware without reasoning about optical primitives.

Wide-area network operators are pushing optical channels toward their theoretical capacity limits, i.e., Shannon's limit, by dynamically adjusting data rates as signal quality fluctuates. This rate-adaptive approach promises to maximize the utilization of expensive fiber infrastructure, yet it introduces a key challenge: each adjustment in the data rate risks temporary wavelength failures that compromise network reliability. Through a six-month empirical study of a production ISP network in the United States, we quantify the fundamental trade-off between capacity efficiency and operational reliability in rate-adaptive optical networks. Our analysis highlights two contributors to this trade-off. First, wider spectral channels, while offering higher peak capacity, can experience up to 4x more frequent capacity fluctuations than narrower channels despite identical signal quality. Second, the precise signal-to-noise ratio (SNR) at which wavelengths operate creates distinct reliability profiles, with certain SNR values located in stability ''sweet spots'' while others could trigger frequent and disruptive rate adaptations. Our findings highlight how operators can strategically approach Shannon's limit by jointly considering the allocation of optical spectral widths, SNR distributions of wavelengths, and transponder capabilities.