The field of physics-informed neural networks (PINNs) is experiencing significant developments, with a focus on improving precision and stability. Researchers are exploring new architectures and methods to enhance the accuracy and reliability of PINNs, particularly in solving partial differential equations (PDEs) and hyperbolic conservation laws. Notable advancements include the introduction of novel layers and techniques that can effectively handle complex PDEs and improve the conditioning of the PDE loss. These innovations have led to substantial improvements in performance, with some approaches achieving near-machine-precision accuracy and outperforming existing state-of-the-art methods. Noteworthy papers include: BWLer, which introduces a Barycentric Weight Layer to improve precision and expose a tradeoff between accuracy and conditioning, achieving up to 30x improvement in RMSE on benchmark PDEs. Neural Entropy-stable conservative flux form neural networks, which propose a data-driven approach to learn hyperbolic conservation laws and their associated entropy functions, ensuring conservation and entropy dissipation for long-term stability.