June 2025: Kilocode (hacksider) monthly summary focused on strengthening AI cost controls and resource allocation through the Requesty API. Key feature delivered: refined thinking budget controls and model effort checks to better manage Gemini and Claude budgets, while accounting for reasoning effort across OpenAI and Gemini models. Commit reference: e2516ebe0298b247700c24fbadfa1a19deddf7cb (Control Gemini thinking budget via Requesty (#4486)).

May 2025 monthly performance summary focused on reliability, telemetry, and cross-repo traceability across JanCode and cline. Delivered critical feature work and stability fixes that directly impact business value through improved reliability, cost visibility, and better developer experience. The work emphasizes OpenAI SDK alignment, robust error handling, and enhanced usage metadata for clearer analytics and budgeting.

In April 2025, two key Requesty enhancements were delivered across the cline/cline and JanCode repositories, focusing on cost control, secure access, and on-demand model loading. The work aligns with business goals to improve cost visibility, performance, and security while enabling more precise model usage.

Month: 2025-03 – Focused on delivering cost-aware enhancements to the Requesty API Provider Model within menloresearch/JanCode. Key features included image and computer-use flags for provider capabilities, per-provider cost accounting, and cache read/write pricing. The work also introduced OpenAI-style cost calculation to standardize pricing across providers and ensured accurate per-request cost calculations. These changes improve billing transparency, provider-level cost visibility, and caching efficiency, driving better pricing decisions and platform profitability.

February 2025 monthly summary for cline/cline: Focused on strengthening the Requesty API provider with dynamic model support, cost visibility, and caching, while improving cost tracking and stability for streaming responses. Delivered measurable business value through cost transparency, better model selection UX, and increased API reliability for high-traffic scenarios.