Simulating an online community at scale calls for language models that can stand in for many distinct users at once, not collapse them into a single generic voice. Prior work shows that a hypernetwork-driven language model can efficiently rebuild known users by storing a small per-user profile vector rather than separate fine-tuned models, one per user. This study explores whether those profile vectors support a harder task: generating behaviorally consistent new users who were never directly observed. Our frozen LLM uses adapter offsets emitted by a hypernetwork. We sample profiles within and beyond the real-user range, then score the generated replies on behavioral measures to determine whether synthetic users match the cohort patterns their profiles imply. We show that synthetic users reproduce behavioral patterns similar to those of real users in the training set: in short threads they stay close to their target profile, and across a longer multi-turn forum simulation they stay largely coherent and do not drift from it. Performance declines as profiles move farther from the distribution of users seen in the original data, suggesting that the method works best for synthetic users that recombine observed patterns rather than extrapolate beyond them.