Personalization Requires Data (www.uzpg.me)

In 2025, AI advancements have significantly improved the ability to process vast amounts of information, particularly in coding environments with tools like Claude Code enhancing programmer workflows. However, the personalization of interactions with AI models remains limited, primarily due to a lack of sufficient data and effective evaluation methods. Currently, models often rely on basic system prompts without truly understanding users' contexts, intentions, or preferences, leading to suboptimal personalization outcomes. The challenge lies in the subjective nature of personalization, which complicates the creation of standardized evaluation rubrics. To address this, a new tool called Whorl has been introduced, enabling users to curate and share their personal data—such as journal entries and notes—with AI systems. This local server allows models to access detailed, personalized context, significantly enriching interactions. By leveraging this curated data, Whorl can prompt AI to deliver more tailored responses and recommendations that align more closely with individual user experiences. The initiative underscores the critical need for consumers to actively participate in data sharing to foster deeper personalization in AI, ultimately enhancing the technology’s ability to empower users in achieving their goals.