About Terence

• Designed and implemented Harness, a modular, safety-first robotics control platform with formal state machines (SAFE_IDLE, ARMED, ESTOP), command aging, watchdogs, and authority arbitration between operator and autonomous sources.
• Built a networked real-time control system using WebSockets and structured JSON protocols, including heartbeat monitoring, connection lifecycle management, and replayable command streams for post-mortem analysis.
• Implemented fail-safe defaults and kill-path logic inspired by industrial and robotics safety architectures, ensuring that loss of input, stale commands, or subsystem failure always degrade to safe states.
• Designed a human-centered command language and input abstraction layer (CLI, joystick-style input, replay), reducing operator cognitive load and preventing dangerous low-level actuation commands.
• Developed a clean, modular architecture with strict separation between intent, authority, and actuation, enabling future hardware, autonomy, and ROS-style integration without rewriting core logic.
• Applied structured logging and telemetry thinking to make system behavior observable, diagnosable, and auditable under fault conditions.

Replicator AI Terminal — Full-Stack Systems Debugging & Product Architecture

• Debugged and stabilized a broken multi-agent execution graph UI (graph.html) by treating it as a real production incident rather than a toy problem.
• Built a non-invasive runtime diagnostic layer that instrumented the full render pipeline:
  • Run ID resolution
  • LocalStorage payload selection
  • JSON parsing & schema validation
  • Task/agent/link contract verification
  • DOM layout & viewport sanity checks
  • Render-stage counts
  • Camera & transform safety validation
  • Post-render snapshot confirmation
• Added on-demand debug snapshot tooling to allow live inspection of the system without reloads, enabling reproducible diagnosis of rendering and state failures.
• Implemented visible, user-facing failure modes (error banners, placeholder cards, layout warnings) instead of silent UI collapse — improving observability for non-technical users.
• Identified and fixed a critical TDZ runtime crash caused by a function being referenced before initialization, restoring full graph rendering without refactoring the system.
• Demonstrated full-stack reasoning across:
  • Data contracts
  • DOM state
  • Layout geometry
  • Render timing
  • UI behavior
  • Persistence
• Designed the long-term architecture for Replicator as a multi-agent orchestration platform, including:
  • Agent recommendation and compatibility analysis
  • Skill gap detection
  • Bottleneck identification
  • Visual execution tracking
  • Automated and manual agent assignment
  • Future AI API integration (OpenAI, Claude, Gemini, etc.)
• Defined future infrastructure needs:
  • Execution state engines
  • Agent lifecycle models
  • Async task hooks
  • Permission sandboxing
  • Secure credential handling
  • Scalable persistence

This project demonstrates real debugging discipline, safe instrumentation practices, and product-level thinking — not just feature coding.

AI Cabin App — Automated Concierge & Smart Reminder Platform

• Designed a concept for an AI-powered concierge and automation system for cabins, hospitality, and short-term stays.
• The app is intended to:
  • Automatically manage guest reminders and requests
  • Act as a virtual tour guide for nearby activities, shops, and attractions
  • Provide location-based recommendations
  • Support staff workflows (housekeeping, maintenance, supplies)
  • Coordinate housing and occupancy logistics
• Focused on creating a system that blends:
  • Automation
  • Scheduling
  • Context-aware recommendations
  • Human-in-the-loop workflows
• Designed with the same systems-first philosophy:
  • Clear task ownership
  • Transparent state tracking
  • Failure-tolerant workflows
  • Non-technical user experience

This project emphasizes product design, automation thinking, and real-world usability — not just UI.

Research-Grade Systems Thinking & Technical Communication

• Produced TR3C-style system architectures connecting propulsion, electromagnetics, control, materials, and diagnostics into coherent, testable engineering proposals.
• Authored research-grade rebuttals integrating: Plasma physics, Electromagnetics, Metamaterials, Control theory, Systems engineering.
• Designed test matrices and measurement plans specifying required data to validate or falsify claims (field strengths, Q-factors, thrust-to-power ratios, latency bounds, failure thresholds).
• Demonstrated working knowledge of electric propulsion families (Hall, pulsed, MPD-class concepts), including control, diagnostics, and power-system implications.
• Applied control and autonomy principles including PID fallback logic, filtering concepts (Kalman/particle awareness), and latency-aware design for real-time systems.
• Integrated AI-for-physical-systems concepts such as system identification, hybrid physics-ML modeling, and edge-inference constraints into architectural discussions.
• Demonstrated awareness of embedded boundaries, including PWM signaling, watchdog hardware, ESC behavior, MCU offload patterns, and hardware-dominant safety chains.
• Built systems with replayability as a first-class feature (fault injection, time-dilated simulation, reproducible debugging).
• Consistently document assumptions, safety guarantees, and failure modes rather than hiding them — aligned with professional safety and systems engineering.