01
Cloud Platform
Learning AWS fundamentals, deployment models, networking, security, and scalable architecture patterns.
Cloud • Data • AI Systems
Hi, I'm Logan.I turn messy problems into structured systems.
I'm an Information Systems student focused on Cloud, Data Platform Engineering, and AI-native Solution Architecture. My work sits between business analysis and engineering: understanding the real problem, modeling the system, and building practical solutions that can scale.
Business Analysis thinking→Solution Architecture direction
Current direction
Where I'm Heading
I'm building a long-term path toward becoming an AI-native Solution Architect - combining cloud infrastructure, data systems, business analysis, and automation.
02
Data Systems
Exploring databases, data warehouses, analytics pipelines, and the way information flows through organizations.
03
Business Analysis
Understanding user needs, business rules, workflows, and constraints before implementation.
04
AI-native Workflow
Using AI tools to accelerate coding, documentation, reasoning, and system design without losing architectural control.
Selected work
Systems I'm building and studying
A mix of implementation projects, cloud learning, and architecture reflections. The goal is to show how the system is understood, not just what tools were used.
Full-stack SystemIn Progress
Leaf Creme
A full-stack commerce-style system focused on backend logic, database modeling, and frontend-backend consistency.
FastAPIPostgreSQLSQLAlchemyReactTypeScriptTailwindMUI
Focus
- API design
- Database modeling
- Business logic
- Frontend-backend integration
Learning
The hardest part of full-stack development is keeping the mental model, database model, API contract, and interface aligned.
Cloud LearningIn Progress
AWS Learning Journey
A collection of notes, labs, and reflections from AWS learning and FCAJ AWS Bootcamp.
AWSIAMCloud FundamentalsSecurityArchitecture Notes
Focus
- Cloud fundamentals
- IAM and security basics
- Deployment mindset
- Scalable infrastructure thinking
Learning
Cloud is not just renting servers. It changes how we think about reliability, scalability, security, and cost.
AI-native WorkflowExploring
AI Workflow Experiments
Experiments using AI tools to design, debug, and document software systems with better context and constraints.
ChatGPTCodexPrompt EngineeringContext Engineering
Focus
- AI-assisted development
- Prompt design
- Context engineering
- Human-in-the-loop development
Learning
The quality of AI output depends less on how much context we give, and more on how clearly we define goals, constraints, and decision boundaries.
System Design ConceptConcept
Startup Credit Scoring Multi-Agent Concept
A system design reflection inspired by enterprise-grade multi-agent architecture for startup credit scoring.
Multi-Agent SystemsBusiness RulesComplianceRisk Analysis
Focus
- Single-agent vs multi-agent decision design
- Virtual credit committee model
- Guardrails and compliance
- Risk analysis
Learning
Some business problems are too complex for a single model or single workflow. Multi-agent systems become useful when the problem requires specialized judgment, separation of concerns, and controlled decision-making.
Thinking in public
Writing as a way to clarify systems
Short reflections on cloud, data, business analysis, AI workflows, and what full-stack bugs reveal about architecture.
Context Quality > Context Quantity
A reflection on why better AI systems depend on clear goals, meaningful constraints, and the right context - not just more information.
AIContext EngineeringSystem Thinking
Why Cloud Changed the Way I Think About Systems
From local development pain points to scalable infrastructure thinking.
AWSCloudArchitecture
Business Analysis Is Not Just Documentation
Why understanding workflows and constraints matters before building software.
BARequirementsSystems
From Full-stack Bugs to System Design Lessons
What Leaf Creme taught me about data consistency, backend logic, and architecture.
Full-stackBackendSystem Design
How I think
Practical principles for learning and building
01
Understand before building.
I prefer clarifying the problem, users, constraints, and business rules before choosing tools.
02
Model the system.
A good solution starts with clear entities, flows, boundaries, and trade-offs.
03
Build with context.
Technology only makes sense when it serves the real use case.
04
Learn in public.
I document what I learn, including blockers, mistakes, and architectural decisions.
05
Use AI as leverage, not autopilot.
AI is most useful when paired with clear goals, good context, and human judgment.
Tech stack
Tools grouped by current use, learning, and exploration
No skill bars. Just the technologies and concepts currently shaping the work.
Currently Building With
FastAPIPostgreSQLSQLAlchemyReactTypeScriptTailwind CSSMUI
Currently Learning
AWSCloud ArchitectureData WarehouseAnalyticsDistributed DatabasesSystem Design
Exploring
AI AgentsContext EngineeringSpec-driven DevelopmentAutomation Workflows
Contact
Let's connect around systems, cloud, data, and AI workflows.
I'm open to internship opportunities, cloud/data learning communities, hackathons, and projects related to business systems, AI workflows, and scalable architecture.