ML Quant Trading

A 24/7 market-making system built around Jev: deterministic code computes the market state, Jev answers six atomic judgments with calibrated probabilities, and the policy engine applies thresholds while hard risk vetoes stay in code.

A Bangla-focused adaptation of Laya, the non-autoregressive typed-decision model: targeted data preparation, fine-tuning and evaluation for classification, routing and scoring on Bangla, Banglish and code-switched text, with the checkpoint still to come.

Open-source Rust decision engine that turns model judgments into versioned, tested rules: Jev reads text while Ordo decides refunds, discounts and risk thresholds, and shows which decisions a change moves before you ship.

A frozen decision server that serves Gemma 4 31B — quantized to FP8 at startup — in TypeSafe's /v1/systemone format, reading the next-token distribution instead of generating tokens. It needs one FP8 NVIDIA GPU with 80 GB and has no authentication.

An open-source, non-autoregressive System One decision engine inspired by TypeSafe AI: it evaluates state context against typed questions in a single forward pass, returning probability distributions and calibrated confidence without generating text.

A System One layout optimizer for decision-model workloads: it reorders rows and fields so a prefix-caching backend reuses input computation, then returns per-record decisions in original order. Its bundled backend serves AutoTrust JEV-9B through vLLM.