During October 2025, Ztyharbin focused on backend improvements for the BerriAI/litellm repository, addressing a critical configuration issue in Fireworks AI model selection. By refining the model prefixing logic in Python, Ztyharbin ensured that only models requiring the accounts/fireworks/models prefix received it, while others were handled appropriately. This adjustment reduced the risk of misconfiguration and deployment failures, supporting more reliable experimentation with AI models. The work demonstrated a solid understanding of AI development and backend systems, resulting in a more maintainable configuration flow and enabling faster feature delivery by streamlining model selection across diverse deployment scenarios.