What it is
An LLM gateway and benchmark comparing TypeSafe Jev with Qwen 3.8 27B on Cerebras across seven synthetic workloads, recording latency, cost, and judgment quality for developers evaluating structured-output approaches.
How it uses Jev
Jev handles one application decision per request by batching native Choice/Noul questions, mapped to the same application output as Qwen's schema-constrained response. Results are validated before simulated actions; native probabilities remain in exports.
Primitives:choicenoul
Technique worth stealing
Compile application fields into compact numeric slots and batch native typed questions instead of one joint schema response.
Try it
npm ci; set CEREBRAS_API_KEY and JEV_KEY in .env; npm run build; npm run start:demos:live
Evidence
Each line is one question put to Jev about the README. ≥ 0.60 reads as yes, ≤ 0.40 as no; in between Jev is not making a call.
- Jev-centricno0.31
- Shows a System One patternno0.21
- Handles uncertaintyno0.07
- Measuredno0.17
- Runnableno0.19
- Worth recommendingno0.24
- Model replicayes0.62
- Problem scopescore on a 0–2 scale1.17
- About Jevyes0.97
Signals by Jev jev-1.13.0, card written by DeepSeek V4.1 Flash from the README on 20 Sept 2026.