Fine-Tuning Without Fine-Tuning: How Fints Reinvents Personalization at Inference Time
Opening — Why this matters now Personalization has long been the Achilles’ heel of large language models (LLMs). Despite their impressive fluency, they often behave like charming strangers—articulate, but impersonal. As AI assistants, tutors, and agents move toward the mainstream, the inability to instantly adapt to user preferences isn’t just inconvenient—it’s commercially limiting. Retraining is costly; prompt-tweaking is shallow. The question is: can a model become personal without being retrained? ...