I spent some time reading through OpenAI's GPT-6 model guide, and honestly, the most interesting part wasn't the models themselves. It was the operational stuff they kept coming back to.
Caching, compaction, mid-task steering, async tool calling. These aren't glamorous features. They're the unglamorous problems that eat up real time when you're trying to ship something that actually works at scale.
The guide essentially maps out where developers hit walls: figuring out which model to use for which task, keeping costs predictable when usage spikes, and managing workflows that take hours or days to complete. Every one of those sections reads like a feature request for tooling that doesn't quite exist yet, or at least doesn't exist in a form that's easy to integrate.
I'm starting to think the real opportunity in AI development isn't another wrapper around a language model. It's the infrastructure layer underneath: observability for AI workflows, smarter cost allocation, better ways to handle multi-step tasks that don't fall apart when you step away for a meeting.
Has anyone else looked at an API best practices guide and immediately started thinking about what you'd build to solve the problems it documents? Curious what pain points people are running into that still feel unsolved.
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