I'm a Solutions Engineer II at Datadog, focused on Kubernetes, containers, and cloud infrastructure. I got into tech at 40 through Flatiron School's software engineering bootcamp. Before that, I spent 15 years in real estate — that background ended up shaping what I build.

Texas homeowners get overassessed on property taxes every year. Most don't protest because the process is intimidating, and hiring an attorney costs $1,000+. I built Tax Corrector to solve that — a homeowner enters their address, gets a professional PDF report with comparable home analysis for $27, and takes it to their county to protest.

Under the hood it's async — Celery task chains handle scraping, PDF generation, and payment webhooks through EventBridge and SQS. I built it solo, using AI throughout. (Private repo.)

KaI is a structured context system for AI collaboration. It gives your AI a foundation that carries forward between sessions — your goals, your decisions, what you've both learned — and loads what's relevant instead of everything at once.

It includes collaboration principles, structured project tracking, skill progression, and a messaging layer between Claude instances. I run three of them across three repos, each with different focus areas. The repo has the full breakdown.

It's one approach to a problem a lot of people seem to be hitting. I don't know if it's the best approach, but it's been working for me and I wanted to share it.

I help customers instrument, monitor, and debug containerized infrastructure. Spending a lot of time reading Datadog Agent source code in Go and digging into Kubernetes internals to understand the systems I support at a deeper level.

I also use Tax Corrector as an observability sandbox — instrumenting my own production app with the same tools I help customers use.

Cert: AWS Certified Cloud Practitioner (2024)

• LinkedIn
• GitHub
• joshhayles07@gmail.com
• Colorado Springs, CO