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TypeSafe released Jev 1.14.0 with a split approach: stable improvements for production use and experimental "danger zone" features (image support, bounding box type, longer context) for developers willing to accept less reliability. The company is also sharing internal intelligence metrics while acknowledging their limitations.
- Image support is now composable across Jev's JSON interface (state, instructions, criteria), though multimodal performance is weaker than text-only and requires careful prompt decomposition.
- New "box" type returns bounding boxes without type errors, representing the first Jev type designed beyond binary decisions and signaling exploration into broader System One Model capabilities.
- Jev supports longer context lengths than previously enabled in production; developers can opt in via a danger zone flag, but TypeSafe intentionally limits context by default because reliability degrades at scale.
- Internal benchmarking data is being shared for the first time, limited to open-weight models only, with explicit caveats that the methodology will evolve and developers should run their own evals.
Fast, general-purpose classifiers like Jev can be used as temporary solutions for specific tasks, then replaced with cheaper, faster custom classifiers trained on the input-output data the Jev instance generated. This creates a practical path for engineering teams without ML expertise to eventually build optimized task-specific models.
- System One models solve the immediate problem of deploying classifiers without ML expertise or large datasets — you just prompt them like an LLM
- Once a System One model proves a feature works, you can collect its decisions as training data to build a specialized classifier that's faster and cheaper to run
- This pattern lets teams validate whether a feature is worth building before investing in custom ML development, reducing risk and upfront cost