Andrew Jewell Sr.

We present TMC (Thermal Momentum Control), a nonlinear feedback controller for heterogeneous HVAC equipment. TMC combines a signed-demand PI core with wrong-direction-gated velocity feedforward, a thermal-coasting brake zone derived from first-order plant coasting distance, back-calculation anti-windup augmented by overshoot-triggered integrator reset, online adaptation of the thermal time constant via a rate-based midpoint-velocity estimator, and runtime Lyapunov monitoring. The controller is implemented as a single Rust core shared unchanged across ten HVAC equipment classes. We provide a closed-loop stability analysis via second-order ODE reduction, and validate against a Ziegler-Nichols tuned PID baseline. On a cascaded two-stage thermal plant, TMC limits overshoot to 0.34°F where an aggressively-tuned PID overshoots by 2.01°F—a 6× reduction. We further present field-validation results from a production deployment of 113 active HVAC units across six buildings via the NexusEdge control platform.

We present Trident, an end-to-end implementation of a BitNet b1.58 ternary language model in pure Rust, including the ternary linear layer, absmean weight quantization, Straight-Through Estimator backward pass, training loop, checkpoint serializer, and generation utility. The implementation contains no Python in the dependency tree and no C++ wrapper around libtorch; all components are built on the AxonML framework with native CUDA acceleration. We train a 616{,}448-parameter model on the complete works of Shakespeare (~5.4M characters) for 100 steps, observing cross-entropy loss decrease from 7.9 to 2.61 (perplexity 13.6) with ternary sparsity stabilizing near 25%. We report storage compression ratios of 9.71× to 11.99× for the Shakespeare configurations and 2.89× for a 162M-parameter variant. Released under MIT and Apache-2.0.

We present AxonML (v0.6.1), an open-source deep learning framework implemented entirely in Rust, spanning 22 crates and 226{,}373 lines of code. The framework provides a complete ML stack: N-dimensional tensor operations with CUDA GPU acceleration via 15 PTX kernel modules, reverse-mode automatic differentiation with gradient checkpointing and automatic mixed precision, 41 neural network layer types including state-of-the-art mechanisms (rotary embeddings, grouped-query attention, mixture-of-experts, differential attention, ternary quantized layers), 5 optimizers, 7 learning rate schedulers, and 7 loss functions. AxonML includes nine LLM architectures, six quantization formats with 1.58-bit ternary support, distributed training (DDP, FSDP, pipeline parallelism), ONNX import/export, and a 33-subcommand CLI. The workspace contains 2{,}285 tests. Dual-licensed under MIT and Apache-2.0.

We address a critical architectural challenge in commercial building HVAC systems: decoupling safety-critical control loops from orchestration infrastructure. The implementation separates a control daemon from a Tauri-based desktop application running on Raspberry Pi controllers, ensuring that orchestrator updates or restarts do not disrupt active equipment management. The system comprises 4,307 lines of Rust backend and 7,681 lines of Leptos/WASM frontend with 59 typed IPC commands, and integrates AegisDB time-series storage, NexusVault credential management, NexusShield security, protocol bridges for BACnet and Modbus, and nexus-serve inference for predictive maintenance. Production deployment spans 60+ controlled equipment units across 16+ commercial facilities.