Online handwritten signature verification plays a crucial role in digital security applications, particularly in scenarios requiring high authenticity and fraud prevention. Path Signature, serving as a robust temporal modeling method, has demonstrated its effectiveness in recent years for its time reparametrization invariance. However, its practical application is limited by inherent challenges, such as the curse of dimensionality. Thanks to Path Development and its trainable variant Path Development layer (Dev), which retain the virtue in Path Signature while overcoming its limitations, we structurally weave Path Development into Online Signature Verification and propose DevInSight (with extra ‘ht’ for legibility). Our approach incorporates several modifications for the hierarchically stacked Dev, including Windowing Strategy, Grouping Strategy and Absolute-Information-Augmentation. Additionally, Local Focus Module (LFM) and Global Fusion Module (GFM) are designed and integrated into the model to improve feature extraction and expand perceptual scope. For verification decision making, we utilize a Dynamic Time Warping (DTW) based verifier. Experiments on two large-scale online signature datasets in different languages show that DevInSight outperforms existing methods on MSDS-ChS (Chinese), especially in challenging scenarios like single-template and cross-session verification, and achieves competitive results on DeepSignDB (Latin). Furthermore, we extend DevInSight to online digital string verification, showcasing its promising generalization capability for other handwritten content. The code will be made publicly available.