Routing OpenCode Tasks with Jev
After the release of the TypeSafe AI Jev model and its availability in OpenRouter, I decided to immediately test its capabilities and rewrite my minimalist model router in OpenCode. To do this, I wrote a tool for OpenCode that sends requests to Jev through the OpenRouter API. The main idea is to build the solution based on three criteria. After testing the classifier on about a hundred different…
Jev, the TypeSafe AI model, has been integrated into OpenRouter, prompting the author to develop a tool for OpenCode that utilizes Jev's capabilities. The tool employs three key criteria: Coordination, Uncertainty, and Consequences. Coordination assesses whether the successful implementation demands reasoning about interactions across multiple system parts or localized changes.
Uncertainty evaluates if the implementation necessitates open-ended investigation to uncover unknown root causes, bottlenecks, or design strategies. Consequences weigh the potential impact of an incorrect implementation, such as broad regressions, compatibility breakages, security issues, or data loss. The complexity is determined by the higher value between Coordination and Uncertainty, with a threshold value based on the Consequences.
If the complexity surpasses or equals the threshold, a powerful model is selected; otherwise, a weaker model is chosen. The tool's algorithm computes a weighted score for each decision alternative and selects the highest-scoring option. The complete source code is provided, incorporating the OpenRouter API key and defining the "lite" and "build" strategies based on the assessment of the implementation plan.
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