Developing Foundborne, a planned AI systems venture, and SteadLamp, a separate Home-first household app. Explore current work across AI, infrastructure, automation, and software.
Windows infrastructure often brings multiple administrative services together into one environment. This project demonstrates server management, directory-based user and computer administration, name resolution, centralized policy configuration, and update management across core Windows Server tools.
Identity & Access Management
Active Directory provides centralized administration of users, computers, organizational units, and security groups. A structured directory environment supports account management, permissions, device organization, and consistent access across network resources.
Policy & Configuration Management
Group Policy allows common workstation, security, access, and system settings to be applied from a central management point. Organized policies help standardize configurations across users and computers while reducing repetitive manual administration.
Core Services & Updates
DNS supports internal name resolution and service discovery, while Windows Server Update Services provides centralized visibility and control over operating-system and Microsoft software updates. Together, these services support reliable day-to-day administration of Windows environments.
Windows Server Administration — Server roles, local configuration, system status, services, and infrastructure health.Active Directory Administration — Management of users, computers, organizational units, and security groups within a centralized directory structure.DNS & Name Resolution — Administration of forward and reverse lookup zones, host records, aliases, and internal network name resolution.Group Policy Administration — Centralized workstation, security, access, and configuration policies applied across directory-based systems.Centralized Update Management — Update status, computer groups, synchronization, approvals, and patch-management visibility across managed Windows systems.
Effective Windows infrastructure administration depends on understanding how identity, policy, name resolution, server roles, and system maintenance work together. Managing these services from centralized tools provides greater consistency, visibility, and control across users, computers, and network resources.
Firewall administration and network security across multiple platforms, including traffic policies, segmentation, VPN connectivity, monitoring, and security visibility.
Firewall management is a core part of maintaining a secure and reliable network environment. This project highlights administration across pfSense and FortiGate platforms, including interface management, traffic policies, VPN connectivity, network segmentation, system health, and security monitoring.
Firewall Policy & Segmentation
Firewall rules and network interfaces are organized to control traffic between internal networks, internet-facing services, VPN users, and isolated segments. Clear policy structure helps reduce unnecessary access while maintaining reliable connectivity.
VPN & Secure Access
VPN services provide protected connectivity for remote users and site-to-site communication. Monitoring tunnel status, authentication, routing, and access policies helps maintain dependable and secure remote access.
Security Monitoring
Firewall dashboards provide visibility into traffic, blocked connections, system resources, security events, application activity, and network health. This information supports troubleshooting and helps identify unusual or potentially unwanted activity.
Working with different firewall platforms reinforces the underlying concepts of policy-based access control, segmentation, routing, VPN connectivity, and network monitoring. Familiarity with multiple interfaces also makes it easier to adapt to existing security infrastructure and troubleshoot mixed-vendor environments.
Modern network environments often combine equipment from multiple vendors, each with different management tools and workflows. This project highlights experience working across managed network platforms to organize switching, wireless access, VLANs, connected devices, PoE resources, and overall network health from centralized administration interfaces.
Network Architecture
Network infrastructure is organized around clear device roles, logical segmentation, and reliable connectivity between users, servers, wireless systems, and network services. Centralized management provides visibility into device health, connected clients, traffic, and infrastructure status.
Switching & Segmentation
Managed switching supports VLAN configuration, port administration, PoE devices, uplinks, and separation of traffic across user, voice, server, wireless, and management networks. These controls help maintain an organized and manageable network environment.
Monitoring & Troubleshooting
Network dashboards provide real-time insight into switches, access points, connected clients, interface utilization, alerts, and performance trends. This visibility helps identify connectivity issues, overloaded interfaces, offline devices, and other conditions requiring attention.
Centralized switching, wireless, client, and network-health visibility through the UniFi management interface.Multi-device monitoring of switches, wireless access points, clients, VLANs, traffic, and infrastructure alerts.Switch-level administration including ports, VLAN configuration, PoE, spanning tree, and interface statistics.
Working across different network platforms requires understanding the underlying networking concepts rather than relying on a single vendor interface. Familiarity with multiple management environments makes it easier to adapt to existing infrastructure, troubleshoot mixed-vendor networks, and support technology as organizational needs change.
A discontinued experiment combining language models, retrieval-augmented generation, and cryptocurrency data. This is separate from the current SteadLamp Home-first app; visuals are illustrative and do not show live prices or trading performance.
This prototype explored how large language models and retrieval-augmented generation could be combined with cryptocurrency information and an automated trading component. The concept was intended to make market information easier to query through an AI-driven interface while also exploring how LLM-based systems could interact with external data and application logic.
LLM & RAG Architecture
The project incorporated LLM and retrieval concepts so the system could work with external information instead of relying only on a model’s built-in knowledge. This provided hands-on exposure to retrieval pipelines, context handling, model integration, and the surrounding application architecture.
Application Shell & Integration
My work focused primarily on portions of the application shell, configuration, integration, and understanding how the system components fit together. I was not the primary developer of the core trading logic or the underlying model technology.
Lightweight Model Testing
I also configured and tested smaller language models, gaining practical experience with deployment considerations, resource requirements, model behavior, and the tradeoffs involved in running AI systems outside of a fully managed platform.
LLM + RAG Market Intelligence · Concept Mockup
Market Feed
External Data Input
Retrieval
RAG Context Layer
Model
LLM Analysis
Prototype
Trading Integration
Illustrative Market View — No Live Prices
Pipeline
1. Retrieve Market + reference context
2. Analyze LLM-assisted interpretation
3. Integrate Prototype application logic
LLM & RAG Crypto Prototype — Concept Interface — AI-generated illustrative mockup representing market-data retrieval, RAG context, LLM analysis, and prototype trading integration. It contains no live market data and does not represent trading performance.
Prototype Evaluation
The project was ultimately discontinued after evaluating the development effort, operating cost, maintenance requirements, and whether continued investment made sense relative to rapidly advancing commercial AI platforms. The experience was still valuable as a technical prototype and provided direct exposure to the practical complexity behind maintaining an AI-enabled application.
What the Project Demonstrates
The project represents hands-on learning across LLM integration, RAG concepts, smaller model deployment, application architecture, AI-enabled interfaces, and the practical decision-making involved in determining whether a prototype is worth continuing into production.
An internal R&D concept exploring AI-assisted administration, structured knowledge, and repeatable workflows. The interface visuals are illustrative; this is not a launched product or customer deployment.
This project demonstrates a practical AI-assisted administration and knowledge system built around real files, documents, trackers, recurring responsibilities, research, and day-to-day workflows. The goal is to move beyond using AI only as a chat interface and instead organize information so an assistant can work with useful context, repeatable processes, and structured systems.
Assistant & Administrative Workflows
AI-assisted workflows support research, planning, document creation, reminders, recurring tasks, project follow-up, and administrative work while keeping the user in control of the underlying information and decisions.
Structured Knowledge Systems
Google Drive, documents, spreadsheets, trackers, and reference material are organized into a clearer knowledge structure so relevant information can be located, interpreted, updated, and reused more efficiently.
Automation & Monitoring
Repeatable workflows can include scheduled tasks, reminders, monitoring, status checks, reports, and multi-step research or document processes designed to reduce repetitive manual work.
AI Admin Console · Concept Mockup
Workspace
Today
Knowledge
Workflows
Reports
Archive
Knowledge
Drive + Docs + Sheets
Automation
Recurring Workflows
Control
Human Review
Sample Task Queue
Organize project files Review → classify → update tracker
Prepare weekly research brief Collect → summarize → flag decisions
Check recurring responsibilities Monitor → remind → record status
AI Assistant & Knowledge System — Concept Interface — AI-generated illustrative mockup showing how structured knowledge, recurring workflows, research, and human review can come together in a single administrative workspace. This is a portfolio visualization, not a screenshot of private data.
Applied AI Rather Than a Standalone Chatbot
The system is designed around how the person actually works: what information they maintain, what tasks repeat, what needs review, and where time is being lost. AI becomes one component in a larger operating structure rather than a separate tool that has to be prompted from scratch every time.
Privacy-Aware Demonstration
Portfolio visuals use sanitized, illustrative structures rather than exposing private documents, account information, or personal records. The emphasis is on system design, workflow architecture, and practical implementation.
