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Sharing a practical stack to reduce Claude Code token usage by 30-40%, focusing on real-world efficiency gains for AI coding.
A study finds that 58% of tokens consumed in failed AI agent runs occur after the model had a clear signal to stop, highlighting a missed opportunity for mid-run cost savings.
A developer used Codex 5.5 as an orchestrator and Deepseek v4 pro as an executor to generate a 240M token fine-tuning dataset, burning 359M tokens at a cost of only $78.