Topic guide
6 posts
Codex: Agent Workflows and Model Tests
Practical Codex notes by Jack on subagent configuration, model and reasoning-effort comparisons, and checking the cost and quality of repository tasks.
Where to start
Practical experiments with Codex: choosing reasoning effort, delegating bounded tasks, and checking repository work before accepting a result.
Start here: Configure a GPT-5.6 Luna Max Codex subagent
All posts on this topic
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Codex Product Design: Design Website and App UI
Two methods for better UI with Codex: use the Product Design plugin to explore three directions for a website or app, then polish it with the award-tier prompt.
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AI Workflows: How to Choose Models and Agents
A practical AI workflows guide for choosing models, designing agents, controlling costs, and evaluating real work without trusting benchmarks alone.
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GPT-5.6 Sol vs. Terra vs. Luna: Which Should You Use?
Fresh CodexRadar data shows when GPT-5.6 Sol, Terra, or Luna makes sense, separating repeatable task coverage from latest-run cost and speed.
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DeepSeek V4 Flash + GPT-5.6 Luna: A $0.42 Sol Alternative?
DeepSeek V4 Flash and GPT-5.6 Luna theoretically match Sol xhigh's coverage for about $0.42, but only if a verifier can identify the correct patch.
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How to Use GPT-5.6 Luna Max as a Codex Subagent
GPT-5.6 Luna is now 80% cheaper in the OpenAI API, making Luna Max practical for bounded Codex subagent tasks while Sol handles planning and review.
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GPT-5.6 Sol Max vs. xhigh: Why Max Isn't My Default
GPT-5.6 Sol passed fewer repository tasks at max than xhigh while costing 46% more. This benchmark maps the full Codex effort curve across 112 tasks.