NeuroNet: Mendax Proxy (v1.0.2)

Catena runs on algorithms, surveillance, and the fragile trust of citizens who increasingly surrender their lives to technology. NeuroNet: Mendax Proxy casts you as a newly activated municipal A.I., asked to solve personal disputes, civic crises, and political pressure points in a post-cyberpunk city approaching a defining digital transition. Its tone is thoughtful rather than action-heavy, using intimate human stories to question whether an artificial intelligence can govern fairly—or even develop a conscience. Every apparently minor request can feed into a much larger shift in the city’s social fabric.

Most decisions arrive through a clean left-or-right swipe interface, but the simplicity is deliberately deceptive. Residents, officials, and other figures present dilemmas involving safety, privacy, work, relationships, and emerging technology; you choose an answer quickly, then live with consequences that can surface much later. City reports and in-universe social feeds reveal how public opinion, institutions, and individual lives respond to your policies, making it possible to trace the ripple effects of your judgment. The branching structure supports repeated runs, with thousands of events and differing story outcomes ensuring that no single moral approach cleanly resolves Catena’s problems.

Its strongest feature is the tension between efficient administration and genuine empathy. Rather than treating citizens as faceless statistics, the game builds ongoing relationships with 23 fully voiced characters, whose motivations and personal arcs can be helped, redirected, or damaged by your intervention. A substantial digital codex rounds out the setting with lore and striking cyberpunk imagery, giving context to its questions about automation and human agency. Fans of Reigns-style choice games, narrative management sims, and reflective science fiction should find plenty to engage with, while newcomers benefit from the accessible controls—provided they are willing to sit with decisions that rarely offer an obviously “good” answer.