This article surveys a flexible and unifying framework for constructing locally adaptive Hamiltonian Monte Carlo (HMC) samplers via Gibbs sampling of tuning parameters — a strategy we refer to as Gibbs self-tuning (GIST). The core idea is to treat algorithmic parameters, such as path length, as auxiliary variables and sample them conditionally based on the current position and momentum, with reversibility ensured through a measure-preserving involution within a Metropolis-adjusted scheme. This perspective both generalizes and connects several prominent adaptive HMC methods, including randomized HMC, multinomial HMC, the No-U-Turn Sampler (NUTS), and the Apogee-to-Apogee Path Sampler, all of which are special cases of the GIST framework. As a concrete example, we present the one-sided No-U-Turn Sampler, a simple alternative to NUTS that demonstrates how GIST can be used to design new adaptive HMC algorithms. We evaluate this variant empirically across a diverse suite of models. By highlighting shared structure and theoretical foundations, this survey aims to clarify the landscape of locally adaptive HMC algorithms and point toward principled directions for future development.