Documenting cloud projects and foundational insights on my path to Cloud / AI architecture.
I'm developing skills in cloud, operations, and applied AI through hands-on projects, structured learning, and real technical work. Right now that means strengthening fundamentals, improving how I think through complex problems, and using systems thinking to build reliable, repeatable results.
My foundation spans Linux, networking, APIs, service and log investigation, Git/GitHub, AWS fundamentals, Bash/Python scripting, and LLM-assisted workflows. I'm currently enrolled in the AWS re/Start program at Per Scholas, deepening that foundation through structured, scenario-based cloud training.
Admitted B.S. Cloud & Network Engineering student at WGU (Target Q2 2028)
FastAPI backend that grows from local development into an AWS-deployed application with LLM integration, automated delivery, and security hardening.
Scenario-based cloud training covering Linux, Python, networking, security, databases, automation, AWS Cloud, and professional readiness.
Completed + Active
+ Planned
Linux, Networking, Git, AWS, Python, AI Workflow
System investigation through services, logs, processes, and shell automation.
Connectivity troubleshooting across DNS, routes, ports, and protocols.
Foundational AWS practice across EC2 access, VPC networking, and core cloud concepts.
Python for operations tooling, API checks, and log analysis.
LLMs as thought partners and accelerators for learning and technical execution.
Version control habits for clean iteration and collaboration.
Rebuilding the basics: keys, permissions, and the habits behind reliable SSH access.
Article on building in public and the role visibility plays in a self-directed technical career path.
Study system, resources, and the approach that helped turn preparation into a passing result.
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![Cloud With David [.com] project preview.](assets/projects/cloud-with-david-com/cover.png)
