Build Hour: Valuemaxxing with GPT-5.6
The session provides a deep dive into 'Value Maxxing' with GPT-5.6, shifting focus from merely maximizing token usage to optimizing AI agents for concrete business outcomes and cost efficiency. Key strategies include selecting the optimal model (Sol, Terra, Luna) based on workload needs, implementing advanced API features like programmatic tool calling and prompt caching, and structuring agent workflows to minimize redundant context processing.
Key takeaways
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Shift from Token Maxxing to Value Maxxing
2:30
Progress should be measured by the value generated (e.g., time saved, quality improved), not just the number of tokens consumed. This requires defining clear outcomes and measurable 'good' for AI agents (evals).
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Model Selection Strategy
4:09
The GPT-3.5 family includes Sol (flagship/complex tasks), Terra (balanced intelligence/cost/latency), and Luna (high-volume, cost/latency sensitive) to optimize for specific workloads.
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Optimizing Agent Workflows with Codex
5:28
For day-to-day coding, starting with `GPT-5.6 Soul` on medium reasoning is often sufficient. Developers can also trade tokens for time using Fast mode or utilize Chronicle to build task memory.
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Advanced API Techniques (Harness Optimization)
6:51
Implement programmatic tool calling (using a JavaScript sandbox) and prompt caching to significantly reduce input token costs and processing time. Persistent reasoning and compaction also boost performance and cache efficiency.