The field of web data extraction and front-end engineering is witnessing significant developments, with a focus on improving the accuracy and efficiency of data extraction methods. Researchers are working on creating standardized evaluation frameworks and benchmarks to compare the performance of different approaches, including traditional algorithmic techniques and Large Language Model (LLM)-based methods. The use of multimodal models is also being explored to improve the front-end engineering pipeline, including webpage design, perception, and code generation. Furthermore, there is a growing interest in automated methods for generating machine learning leaderboards and mitigating bias in machine learning models. Overall, these advancements aim to enhance the reliability and fairness of web data extraction and front-end engineering techniques. Noteworthy papers include: NEXT-EVAL, which introduces a concrete evaluation framework for web data record extraction methods, and FullFront, which presents a benchmark for evaluating Multimodal Large Language Models across the full front-end development pipeline.