
Worked on the justlovemaki/AIClient-2-API repository to enhance token economy and billing accuracy for Claude model variants. Developed a dynamic per-model context window mapping in JavaScript, replacing a hardcoded constant with a maintainable function to align input token calculations with each model’s context size. Addressed an underbilling risk by updating the backend logic to support Kiro’s expanded 1M-token context window, ensuring reliable pricing and reducing revenue leakage. Validated token estimation across multiple model families and improved maintainability through clear commit practices and targeted tests. Focused on scalable API integration and robust backend development to support future model-specific tuning.
March 2026 monthly summary for justlovemaki/AIClient-2-API focused on token economy, billing accuracy, and model-aware context management. Delivered two core improvements to align input_tokens with model-specific context windows and ensure reliable pricing across Claude model variants. Implemented a dynamic per-model context window mapping, replacing a single hardcoded constant with a maintainable function, and fixed an underbilling risk by updating TOTAL_CONTEXT_TOKENS to reflect the 1M-token Kiro context window. These changes improve token estimation accuracy, reduce revenue leakage, and support scalable model-specific tuning. Achieved robust traceability through clear commits and targeted tests, with a focus on business value and technical maintainability.
March 2026 monthly summary for justlovemaki/AIClient-2-API focused on token economy, billing accuracy, and model-aware context management. Delivered two core improvements to align input_tokens with model-specific context windows and ensure reliable pricing across Claude model variants. Implemented a dynamic per-model context window mapping, replacing a single hardcoded constant with a maintainable function, and fixed an underbilling risk by updating TOTAL_CONTEXT_TOKENS to reflect the 1M-token Kiro context window. These changes improve token estimation accuracy, reduce revenue leakage, and support scalable model-specific tuning. Achieved robust traceability through clear commits and targeted tests, with a focus on business value and technical maintainability.

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