Projects

Focusing on network automation, high-performance signal processing, and secure multi-agent architectures.

🌐 Network Engineering

View Ecosystem → High-performance tools for topology modeling, deterministic protocol simulation, and visualization.

ANK Workbench

Not started (defining requirements) · Python backend (FastAPI or Flask) · React or Vue frontend — Leverages existing Python ecosystem for ANK components · meets modern UX expectations

An orchestration platform that integrates the ANK ecosystem tools (TopoGen, ank_pydantic, Network Simulator, NetVis) into one seamless workflow. Network engineers can design, validate, and visualize network changes in one complete workflow without switching between separate tools or manually gluing components together.

AutoNetkit

PhD 2017 · Python

NetVis

40 - Integration Surface & Diagnostics · Rust

A Rust-based network topology layout and visualization engine. Takes complex multi-layer network topologies (via petgraph) and renders them using advanced layout algorithms that reduce visual complexity while preserving structural clarity.

Outputs static formats (SVG, PDF, PNG) for v1, with interactive browser embedding planned for future integration with other tooling. Transform network topologies into clear, information-dense visualizations using algorithms that minimize edge crossings, bundle related connections, and respect hierarchical/geographic structure — enabling understanding of networks that would otherwise be visual noise.

Network Simulator

52 (in progress) — daemon-mode-interactive-console · Rust

A Rust-based network simulator that models packet-level behavior for routing protocols. It provides a middle ground between pure algorithmic analysis (like C-BGP) and full emulation (like Containerlab) — larger scale and smaller footprint than emulation, higher fidelity than algorithmic simulation.

Used for smoke testing and design validation of network configurations. Validate network configurations at scale with protocol-level fidelity before deploying to real infrastructure.

TopoGen - Network Topology Generator

Phase 18/24 (15%)

A Rust-based network topology generator with Python bindings that consolidates scattered topology generation logic from AutoNetKit, simulation tools, and visualization tools. Generates realistic data center, WAN, and random graph topologies with proper structure, design patterns, and realistic parameters.

Outputs custom YAML format for use across the network engineering tool ecosystem. Network engineers can quickly generate realistic, validated network topologies without implementing complex algorithms from scratch.

ank_pydantic

Phase 59/62 (100%)

A Python library for modeling and querying network topologies, backed by a high-performance Rust core (ank_nte). Features a two-stage transformation model (Whiteboard → Plan → Protocol Layers), type-safe Pydantic models for nodes/edges/layers, and a composable lazy query API with Rust-backed execution.

Ships with “batteries-included” domain models (ISIS, MPLS, EVPN, L3VPN, IXP) in the blueprints/ module. A clean, consistent API where there’s one obvious way to perform each topology operation — predictable naming, return types, and method signatures across the entire public surface.

📊 Data Science & Simulation

View Ecosystem → High-performance tools for large-scale geospatial analytics and time-series pattern discovery.

Tileserver Polars (Rust Optimized)

Active Development

Serve dynamic vector tiles (MVT) from massive geospatial datasets (millions of points) with sub-second latency, enabling interactive visualization in Kepler.gl without pre-rendering static tilesets.

Weather (BOM ACCESS Pipeline)

Phase 1/4 (50%)

A data engineering pipeline to fetch, process, and serve high-resolution weather model data from the Australian Bureau of Meteorology (BOM). Specifically targeting the ACCESS (Australian Community Climate and Earth-System Simulator) model outputs.

The primary goal is to bypass the complexity of BOM’s FTP delivery and binary formats (GRIB2/NetCDF) to provide a clean, queryable interface (API/DuckDB) for localized weather insights, starting with South Australia.

matrix-profile-rs

Phase 2/5 (50%)

A high-performance Rust implementation of Matrix Profile algorithms for time series analysis. Matrix Profiles enable pattern discovery, anomaly detection, and similarity search in univariate time series without domain knowledge or parameter tuning.

Time series analysis requires identifying:

• Repeating patterns (motifs): “This sensor pattern happened 15 times before failure”
• Anomalies (discords): “This heartbeat segment is unlike any other”
• Similar segments: “Find all sequences similar to this known good pattern”

netflowsim

Active Development

netflowsim provides rapid, massive-scale network performance analysis by using analytic queuing models and Monte Carlo simulations instead of packet-level discrete event simulation. It enables network engineers to validate topologies and routing strategies against billions of flow iterations in seconds.

🤖 AI & Agents

View Ecosystem → Security-first architectures for multi-agent coordination and isolated automation.

Cycle Agent

Phase 1/5 (80%)

A native SwiftUI training application for iPad and Apple TV that bridges professional cycling hardware (KICKR Core) with dynamic AI-driven workout logic via NATS, visualized in a high-performance SceneKit environment.

Secure Multi-Agent Personal Assistant

Phase 18/20 (84%) · Agents can be Go · Python · or Rust

A security-first multi-agent system that coordinates specialized containerized agents (health monitoring, home automation, data aggregation, workflow automation) through a message broker architecture. Each agent runs in isolation with minimal privileges and communicates only through validated message queues, demonstrating production-ready patterns for deploying AI agents in security-critical infrastructure environments.

The orchestrator uses cloud LLM reasoning (GPT-4/Claude) while agents remain lightweight and deterministic. Complete isolation between agents such that compromise of one agent cannot cascade to others or the orchestrator—demonstrating that secure multi-agent systems are practical for both personal and production infrastructure use cases.

📡 Signal Processing & RF

View Ecosystem → SDR spectrum monitoring and biometric signal processing using modular acquisition pipelines.

HealthyPi Ecosystem

Phase 6/6 (87%)

A modular, agent-aware health monitoring ecosystem that translates raw biometric data from HealthyPi hardware (6 and Move) into actionable insights and automated interventions.

RF Signal Reflection Experiments

Phase 3/4 (100%)

An experimental signal processing project exploring how to analyze reflections of ambient radio signals. The system uses coherent multi-channel RF data to study bistatic geometry and Doppler effects.

Practical exploration of bistatic radar concepts and signal processing techniques using existing RF infrastructure. Clean, stable codebase for processing multi-channel RF reflections.

Project Context: rtltcp-rust

Active Development

A cross-platform (targeted at Raspberry Pi) server that interfaces with multiple SDR devices (RTL-SDR, AirSpy HF+) and streams raw IQ samples over the network using the industry-standard rtl_tcp protocol. It features a built-in TUI for live configuration and device management.

The ability to reliably and efficiently stream high-fidelity IQ data from multiple SDRs over a network with a modern management interface.

Project Spectra

** Phase 3: Autonomy

Transform raw radio spectrum data into an actionable “Signal Census” through automated detection, ML classification, and distributed acquisition.

Wi-Fi Reflection sensing

Active Development

System that utilizes existing Wi-Fi signals for through-wall detection and localization, leveraging a coherent radio array.

🔭 Experimental & Hobbies

ASIAIR Import Tool

Phase 1/1 (0%)

A Python script that automates post-imaging-session file organization for astrophotography. It batch-imports FITS files from ASIAIR backup locations, organizes them by target and observation night, copies matching calibration frames, and prepares the directory structure for PixInsight’s WBPP (Weighted Batch Preprocessing) workflow.

Eliminates manual file sorting after imaging sessions - scan hundreds of frames, organize by target/filter/date, validate calibration frame availability, and go straight to PixInsight processing.

AuroraData - Aurora Planning & Substorm Advisor

** 1 - Substorm Trigger Engine

A specialized tool for Australian aurora observers that solves the “should I drive 60 minutes?” problem. It combines real-time solar wind data (NOAA), substorm trigger logic (Bz/HP trends), and local weather (ACCESS-G model) to provide actionable advice.

Providing a single, definitive “Go/No-Go” score that accounts for both space weather potential and local terrestrial conditions (travel time, clouds, moon).

AuroraPhoto

** Phase 1: Star Sharpness Foundation

An automated astrophotography system designed to capture high-quality aurora and night sky imagery. The project uses Raspberry Pi “nodes” connected via USB to Sony a7R V/a7 IV cameras, controlled and assisted by an iPhone companion app.

Provides precise, automated control over exposure and focus specifically optimized for aurora “bursts” and star sharpness, while offering field-ready composition tools.

EclipsePhoto

** 1 - Hardware & Data Foundation

A “fire and forget” Raspberry Pi-based controller for autonomous solar eclipse photography. It coordinates a camera (via gphoto2) and a high-end mount (ZWO AM5 / Benro Polaris via INDI) to capture a complete eclipse sequence from C1 to C4 without manual intervention.

Reliability and autonomy for a “one-shot” astronomical event. The system handles guiding, exposure ramping (Holy Grail), and error recovery (watchdogs) so the photographer can experience the eclipse while the system secures the data.

EclipseStack

** 1 (Ingestion & Foundation)

EclipseStack is a Rust-powered utility (with a web-based UI) specifically designed to align hundreds of RAW solar eclipse images taken during totality. It addresses the challenge of tracker drift by combining image feature detection (solar disk and flares) with temporal extrapolation based on EXIF data.

The goal is to produce a perfectly aligned set of frames ready for HDR stacking in professional tools like PixInsight. Enable high-fidelity HDR solar composites by providing sub-pixel alignment of eclipse frames through a combination of computer vision and temporal drift modeling.

OmniFocus DB CLI (omnifocus-db)

** Phase 1: Foundation & DB Safety

A Python-based CLI that bypasses slow AppleScript/TypeScript layers to read directly from the OmniFocus SQLite database on macOS. It provides structured, token-efficient data (JSON/Text) to agents for lightning-fast project listing, inbox analysis, and context gathering.

• Zero-Latency Context: Near-instant retrieval of projects and tasks without the overhead of the OmniFocus app or AppleScript. - Agent-Optimized: Focused on providing dense, low-token representations of the user’s task list.

• Safety First: Read-only access by default to prevent database corruption while OmniFocus is active.

OpenAstro Core

Active Development

OpenAstro Core is a high-performance Rust library providing shared astronomical logic, hardware drivers, and protocol implementations for the OpenAstro ecosystem. It exists to keep coordinate math and device/protocol behavior consistent across downstream OpenAstro apps.

Downstream apps can rely on correct, consistent coordinate math and device/protocol primitives.

OpenAstro Node

** 2. Control

A headless, autonomous astrophotography controller designed for low-power Linux devices (RPi/Jetson). It manages hardware, executes imaging sequences, and ensures rig safety.

Photo Tour

Active Development

Photo Tour is a smart, interactive photography assistant designed for field use. It helps you compose shots, automate repeatable workflows, and progressively adds intelligent triggering and transition logic.

In the field, you can see what the camera sees and get actionable guidance/control fast enough to improve the shot.

Wave

Active Development · Swift (SwiftUI)

Wave is an evolutionary ambient audio ecosystem designed to manage the user’s sensory environment across rest and work. - StillState: Reclaiming silence and rest in shared or noisy environments through intelligent, adaptive audio

• FlowState: Achieving and maintaining a “Steady State” of focus through task-linked audio and genetic evolution

Wave (StillState & FlowState)

21 (Bluetooth Output Enforcement Hardening)

Wave is an evolutionary ambient audio ecosystem designed to manage the user’s sensory environment across rest and work. - StillState: Reclaiming silence and rest in shared or noisy environments through intelligent, adaptive audio.

• FlowState: Achieving and maintaining a “Steady State” of focus through task-linked audio and genetic evolution.

nascleanup

Active Development · Rust

A Rust-based CLI tool for deduplicating and organizing large file shares. Optimized for Docker execution on DSM, it uses an indexing layer for fast file comparison and metadata management.

soundarray

Active Development

An exploration-focused audio processing system using Raspberry Pi and microphone arrays. It focuses on spatial audio (ToA, beamforming) and classification (vehicles, wildlife) using an “analyst” agent approach.

The ability to capture, localize, and classify complex soundscapes on edge devices or via remote streaming, providing structured insights to an agent framework.