My collected technical writing: production-tested guides, runbooks, and architecture documentation from real infrastructure work.
This is my personal archive of technical documentation I've written throughout my career. Every guide here:
- Solves a real problem I encountered in production
- Contains tested commands that actually work
- Reflects hard-won knowledge from debugging, deploying, and maintaining live systems
- Is maintained actively as tools and best practices evolve
These aren't theoretical tutorials—they're the runbooks I wish I had when I first tackled these problems.
My approach: Treat documentation like production code. If it's worth writing once, it's worth versioning, testing, and keeping current.
| Document | Last Updated | Status |
|---|---|---|
| JIRA Worklogs Bot | Jan 2026 | ✅ Active |
| Route Planning with LLM | Jan 2026 | ✅ Active |
| Deploying to AWS EKS | Dec 2025 | ✅ Active |
The complete end-to-end playbook I developed for deploying containerized services to Amazon EKS, refined through multiple production deployments.
What's inside:
- Terraform infrastructure provisioning (ECR, IAM, networking)
- Docker containerization best practices
- Helm chart development and deployment
- GitHub Actions CI/CD pipeline setup
- EKS cluster management and troubleshooting
- Production-ready configuration patterns
Who it's for: Platform engineers, DevOps engineers, SREs
Level: Intermediate to Advanced
Time: 2-3 hours (first deployment), 30 min (subsequent)
A complete system design document for a Microsoft Teams bot that simplifies JIRA time tracking. Born from weekly frustration with JIRA's clunky worklog UI.
What's inside:
- Full system context and integration architecture
- Microsoft Teams Adaptive Card interface design
- AWS Lambda + Spring Boot 3 backend design
- Data flow diagrams and API contracts
- Load simulation and performance SLOs
- Production readiness considerations
Who it's for: Backend engineers, System designers, Teams integration developers
Level: Intermediate
A hackathon post-mortem documenting how we built a working route optimizer in 4.5 hours using GitHub Copilot as a third team member—and the constraints we used to keep it from derailing us.
What's inside:
- The constraint prompt that kept the AI agent productive
- Real-world lessons on LLM-assisted coding
- AWS Location Service integration patterns
- Why "we drive, you execute" matters
Who it's for: Engineers exploring AI coding assistants, Hackathon participants
Level: All levels
- Browse what I've written above
- Pick a guide that matches your problem
- Follow along—all prerequisites are listed upfront
- Jump around—each guide has a table of contents for quick navigation
I appreciate corrections, updates, and improvements:
# Fork and clone
git clone https://github.com/yourusername/documentation-library.git
cd documentation-library
# Make your improvements
# Submit a PREvery guide I write follows the same pattern—because it works:
- Clear prerequisites upfront (tools, access, assumed knowledge)
- Time estimates so you know what you're getting into
- Quick navigation for when you need to jump to a specific section
- Step-by-step instructions with the actual commands I used
- Troubleshooting for the gotchas I hit (so you don't have to)
- Context and reasoning not just "what" but "why"
- Direct and imperative: "Run this", not "you might want to consider running"
- Code-first: Real commands that work, not pseudocode
- Annotated: Comments explaining what each command does
- Version-aware: I note when something is version-specific
- Security-conscious: Production best practices, not shortcuts
- ✅ I've run every command in a real environment.
- ✅ Code blocks use proper syntax highlighting.
- ✅ Placeholders are obvious (like
<YOUR_CLUSTER_NAME>). - ✅ Anything based on work done for employers has been thoroughly redacted.
- ✅ Security implications are called out.
- ✅ Production considerations aren't afterthoughts.
My documentation spans the stack I work with day-to-day:
| Category | Technologies |
|---|---|
| Cloud | AWS (EKS, ECR, IAM, VPC, CloudWatch) |
| Orchestration | Kubernetes, Helm, Docker, Atlantis |
| Infrastructure | Terraform, CloudFormation |
| CI/CD | GitHub Actions, GitLab CI |
| Languages | Python, Go, Shell scripting |
| Observability | CloudWatch |
This list grows as I document new systems and tools.
Spotted a typo? Command didn't work? Know a better way? I'd love your input.
What helps:
- Bug reports when commands fail
- Updates for newer tool versions
- Clarity improvements
- Additional troubleshooting tips
How to contribute:
- Fork this repo
- Make your changes
- Test them (seriously—run the commands)
- Submit a PR
I'll review and merge anything that makes the docs better.
MIT License. Use it, fork it, improve it — just give credit where it's due.
What I've Written • Using These Guides • Report Issues
Written and built with ❤️ because documentation is worthy of the same level of care as production code..