With the advent of powerful large language models (LLMs), research in automated software engineering has increasingly focused on leveraging these models to achieve a deeper semantic understanding of code or to engineer sophisticated agent-based processes. The predominant goal of these efforts is to enhance developer productivity through automated assistance. However, this research trajectory has largely overlooked a critical factor: the developers themselves. Programming is a deeply human and individualized activity; developers exhibit significant variation in their coding styles, tool-chain preferences, domain-specific expertise, and problem-solving strategies. Consequently, the current paradigm of one-size-fits-all code intelligence systems struggles to accommodate the unique characteristics and needs of individual developers. To address this gap, we introduce VirtualME, a novel IDE-embedded data infrastructure designed to model the developer by continuously capturing and interpreting their dynamic programming behaviors and preferences. VirtualME contains three components. (1) Log-level Behavior Extraction: it captures and extracts developers’ log-level behaviors (edits, navigations, etc.) from IDE. (2) Task-level Behavior Recognition: it aggregates log-level behaviors into task-level behaviors (“skimming API docs”, “iterative debugging”, etc.) via a multi-agent pipeline. (3) Developer-personality Measurement: it builds a rule engine to distill a four-dimensional developer persona: technology stack, ability, behavioral habits, and learning style. On top of VirtualME, we propose a solution for personalized repository-level knowledge Q&A by integrating the developer persona into a Chain-of-Thought (CoT) guided agent. We evaluated VirtualME by building a multi-repository benchmark with real-world developer trajectories, balancing correctness and personalization. Experimental results show that VirtualME-enhanced answers outperform generic baselines on five dimensions: correctness, cognitive-level fit, technology-stack relevance, behavioral-pattern alignment, and stylistic preference, yielding an average 33.80% improvement. Our results demonstrate that abundant, continuous developer-behavior data can unlockPersonalized Code Intelligence. By integrating this personalized understanding into the code intelligence loop, our approach paves the new way for adaptive and personalized code intelligence.