Run Ultra-Fast Probabilistic Decision Models on Your Laptop CPU with jevos

TL;DR

jevos is a high-performance C++ engine that serves as an open-source alternative to Jev and Microsoft-Decision-1. It eliminates the need for power-hungry GPUs by executing binary decisions, multi-choice routing, and probability scoring entirely on standard laptop CPUs with sub-millisecond latency.

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Key Features & Benchmarks

• Zero-GPU Footprint: Built purely in modern C++, delivering optimized CPU execution without requiring PyTorch, CUDA, or heavy runtimes.
• Probabilistic Outputs: Generates calibrated confidence scores, yes/no outcomes, and multi-class decisions natively with explicit probability distributions.
• Ultra-Low Latency Inference: Designed for local edge systems, robotics, and fast routing pipelines where API round-trips to frontier models are too slow or costly.
• Direct Microsoft-Decision-1 Alternative: Provides open drop-in architectural primitives for decision tasks that previously required closed or heavyweight enterprise endpoints.
• Minimal Dependencies: Lightweight binary compilation that fits seamlessly into existing C/C++ backends, CLI utilities, or embedded runtimes.

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Quick Start

Clone the repository and build the engine using standard C++ build tooling:

# Clone the repository
git clone https://github.com/feder-cr/jev.git
cd jev

# Build with CMake
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
cmake --build .

# Run a sample decision benchmark
./jevos --input "sample_decision_data.json" --threshold 0.75

You can also integrate it into your C++ project directly:

#include "jevos/decision_engine.hpp"
#include <iostream>

int main() {
    jevos::DecisionEngine engine;
    engine.load_model("decision_model.bin");

    auto result = engine.evaluate({"latency_sensitive", "local_only"});
    std::cout << "Decision: " << (result.choice ? "YES" : "NO") 
              << " (Confidence: " << result.probability * 100 << "%)" << std::endl;
    return 0;
}

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Why It Matters

Not every task requires a 70-billion-parameter LLM. Routing queries, evaluating policy gates, triaging system alerts, and determining deterministic fallbacks can be handled locally and instantly on commodity hardware.

jevos is ideal for:

• Edge and IoT Engineers: Needing deterministic decisions with strict power and memory budgets.
• Local AI Architects: Looking to build layered agent architectures where a CPU-based gatekeeper decides whether to invoke an expensive remote LLM.
• Security-Conscious Teams: Enforcing data sovereignty by ensuring classification and routing logic never leave the local machine.

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