Large language models are increasingly deployed as interactive coding agents for end-to-end web development, yet existing benchmarks evaluate narrow slices of this workflow—typically text-conditioned generation with static correctness metrics—overlooking visual fidelity, interactivity, and codebase-level reasoning. We introduce WebCompass, a benchmark that covers the full web development lifecycle by spanning three input modalities (text, image, video) and three task types (generation, editing, repair). To match the diverse nature of these tasks, we pair checklist-guided LLM-as-a-Judge for patch-based evaluation with a novel Agent-as-a-Judge protocol that executes generated websites in a real browser and synthesizes targeted test cases. Experiments on ten models reveal a 25-point gap between the best closed- and open-source models, persistent weaknesses in visual aesthetics even among frontier systems, and divergent performance profiles across task types that challenge the notion of a single "best" web coding agent.

Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous training primarily relies on optimizing the InfoNCE loss by comparing positive natural language (NL)-code pairs with in-batch negatives. However, due to the sparse nature of code contexts, training solely by comparing the major differences between positive and negative pairs may fail to capture deeper semantic nuances. To address this issue, we propose a novel order-augmented strategy for improved code search (OASIS). It leverages order-based similarity labels to train models to capture subtle differences in similarity among negative pairs. Extensive benchmark evaluations demonstrate that our OASIS model significantly outperforms previous state-of-the-art models focusing solely on major positive-negative differences. It underscores the value of exploiting subtle differences among negative pairs with order labels for effective code embedding training.