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Manifest ran an LLM router for four months, classifying requests into complexity tiers and routing them across providers to cut costs, then killed it. A prompt alone does not reveal how hard a task is, prompt caching (cache reads 75 to 90 per cent cheaper) undercuts the savings, and switching models mid-workflow hurts consistency. Their conclusion: a good router stays sticky to one model, doing its job “by, ironically, not doing it”.

The HN discussion broadly agrees that frontier models are now interchangeably good for everyday work, and that nobody has time to learn each model’s quirks when a new one lands every week. The consistency point rhymes with cognitive coverage: switching tools constantly means never really knowing them.

The poster can’t reach flow state with coding agents: “a bicycle that just brakes abruptly every couple minutes. I stop, wait, review, prompt again.” The thread is a good sample of 2026 workflows people have built around the problem: TODO files as free-text prompt queues, JSX templating languages for context piping, custom harnesses running in VMs with their own email accounts and Linear boards, and agents generating diagrams of their changes at different abstraction levels to speed up human understanding.

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