Jev + System-One 十阶段落地路线图
从 RLCD 校准、类型化输出到置信度路由与 WebGPU 边缘部署，给出用 Jev 做低延迟决策引擎的十步工程路径。
Mastering Jev and System-One infra ensures permanent employability. Stage 1: The System-One Paradigm Contrast Kahneman’s System 1 (fast, instinctive) with System 2 (slow, deliberate). Explore non-autoregressive architectures and why text generation is an incorrect abstraction for automation. Stage 2: RLCD and Calibrated Decisions Implement Reinforcement Learning for Calibrated Decisions (RLCD) using Brier scores and probability calibration. Distinguish training for "decisions" from RLHF for "chat." Stage 3: Typed Output Schemas Map Jev’s typed choices, scores and probabilities directly to state machines, enums and API payloads. Stage 4: The Confidence Router Intercept probability scores: high confidence triggers instant execution: low confidence routes to System-Two models (e.g., Opus, o1). Stage 5: Sub-Millisecond Serving Optimize for ultra-low latency using TensorRT, ONNX and batched matrix multiplications for parallel decision-making. Stage 6: Edge and Browser Deployment Deploy to edge nodes and WebGPU. 200x speedups enable real-time decision engines in browsers, mobile and IoT devices. Stage 7: The Decision Harness Build deterministic wrappers enforcing business logic thresholds. Validate calibrated confidence scores before executing any agent side effect. Stage 8: High-Frequency Data Flywheels Log Jev’s low-confidence edge cases, route them to a heavy LLM for labeling and feed results back into the RLCD training loop automatically. Stage 9: Hybrid System 1 + System 2 Orchestration Architect a cost-efficient system where Jev handles 95% of traffic instantly at low cost, while an LLM handles the 5% requiring deep, multi-step reasoning. Stage 10: Evals for Non-Text Models Replace standard text evals with harnesses measuring decision accuracy, calibration error and latency/cost tradeoffs instead of BLEU/ROUGE scores. Stage 11: Real-Time Agent Triage Use Jev to instantly route, classify or moderate thousands of concurrent agent actions per second before they reach the expensive reasoning layer. Stage 12: Portfolio and Benchmarks Build a real-time routing engine, high-frequency moderation system or game AI using Jev. Publish latency and cost benchmarks compared to standard LLM APIs. Text generation bottlenecks automation instant, typed decisions are the future. Jev (TypeSafe AI) is a "System One" model that bypasses token-by-token generation, returning typed decisions and probabilities 200x faster and 400x cheaper than standard LLMs. While most optimize text generation, builders own the decision layer. Bookmark it.