Jev 的另一条路线：让 AI 成为可组合的软件组件，而不只是聊天助手

When software is the primary consumer of intelligence, predictable decisions and composable interfaces can matter more than a convincing conversational persona.

A useful training objective should reflect the task that customers need solved rather than merely improve performance on a familiar public leaderboard.

Robustness requires similar inputs to produce suitably consistent behavior, while determinism only concerns repeated execution on exactly the same input.

Typed semantic primitives can expose uncertainty to application code, allowing developers to set thresholds and control flow instead of relying on persuasive prompts.

Decomposing a large prompt into small semantic decisions can improve inspectability, provided that the combined latency, cost, and error rate remain acceptable.

The best trade-off between intelligence per dollar and intelligence per second depends on whether a workload is asynchronous or latency-sensitive.

A fast decision model should expose uncertainty on tasks beyond its reliable range rather than pretend that every problem can be solved without deeper reasoning.

Existing software companies may gain from embedded intelligence because they already understand the workflows, data, and decisions that customers value.

Multi-model agents become more practical when state is explicit and selectively shared, rather than repeatedly passing an entire growing conversation to every subtask.

A promising demonstration or a surge in token usage can justify further testing, but neither establishes durable customer value or production reliability on its own.