The field of error-correcting codes is witnessing significant developments, driven by emerging applications such as DNA storage and high-rate regimes. Researchers are exploring new code constructions, decoding algorithms, and theoretical bounds to improve the efficiency and reliability of data transmission. Notably, advancements in linear codes for insertion-deletion errors and near-maximum distance separable (NMDS) codes are enhancing the capabilities of error-correcting codes. Furthermore, innovative decoding architectures, such as hybrid normalized min-sum decoders and parallel guessing random additive noise decoders, are being proposed to achieve near-maximum likelihood performance while maintaining advantages in throughput, latency, and complexity. Some noteworthy papers in this area include: Capacity-Achieving Codes for Noisy Insertion Channels, which determines the coding capacity of a new noisy insertion channel and constructs asymptotically optimal error-correcting codes. Improved Constructions of Linear Codes for Insertions and Deletions, which improves upon previous results by constructing explicit codes with rates close to the half-Singleton bound. Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes, which introduces a hybrid decoding architecture that achieves near-maximum likelihood performance for short linear block codes.