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The post demonstrates that in multi-agent fanout pipelines, context assembly before the LLM call — not the LLM itself — can become the dominant latency and cost driver, and that passing only compact summary structs rather than full subagent outputs resolves both problems simultaneously.
Developers building long-horizon agentic pipelines can now launch Kimi K2.6's multi-agent system directly from Ollama, while MLX users benefit from faster sampling and tokenization without any configuration changes.
Developer Atlas Whoff shares three real-world performance wins using Claude for code optimization — including an 83% API speedup, 96% render reduction, and a 14x faster Python script — by feeding Claude profiler data instead of guessing at bottlenecks.