Cloud & DevOps Engineer exploring AI, RAG & LLM-ops. I document everything I learn — the notes, pipelines, and architectures — so you can use them to crack the same certs and build the same systems.
A real terminal — type help to see what's available.
Two specializations, one toolkit.
Docker
Kubernetes
Jenkins
Terraform
Ansible
GitHub
Feedspot is one of the largest content discovery & reader platforms on the web — and the team behind it has been a brilliant place to grow as a DevOps engineer.
RSS feed reader & aggregator. Curated feeds, smart folders, full-text search across millions of articles.
Schedule and publish content across social channels at the optimal time. Bulk-upload, queue management, multi-account support.
Tools for content creators & bloggers — distribution, audience growth, ranking insights, and outreach to top blogs & influencers.
One of the largest podcast directories on the web — discovery, ranking, and metadata across a million podcast titles. A proud Feedspot product.
Crawling, deduplicating and serving millions of feeds is a meaningful systems problem — perfect ground to actually understand caching, queues, and rate limits.
The kind of place where curiosity is encouraged. R&D time, room to experiment with new tools, and freedom to bring AWS AI services into the stack.
A pipeline I optimize today serves real users tomorrow. The 30% efficiency improvement on processing pipelines? Felt across millions of feed updates.
DevOps automation meets AI integration.
Multi-node Jenkins agents deploying to AWS EKS via GitOps. SonarQube + OWASP integrated into the pipeline; Helm-based monitoring with Prometheus & Grafana.
Multi-stage Docker builds for an Nginx frontend, automated tests & linting, Trivy vulnerability scanning before pushing to GitHub Container Registry.
Amazon Bedrock chatbot deployed to Slack via Lambda + API Gateway. Full infrastructure managed with Terraform, observability through CloudWatch.
Retrieval-Augmented Generation bot built on Bedrock Knowledge Bases for instant answers across internal documentation. Embeddings indexed in OpenSearch.
Amazon Q integrated with CloudWatch Logs to auto-summarize production incidents and surface root causes in plain English to on-call engineers.
Reusable Terraform modules for multi-region AWS deployments: VPC, EKS, RDS, monitoring stack. Drift detection via Atlantis on every PR.
Four free study sites — notes, cheat sheets, PDFs, and hands-on labs. Built while learning in public, hosted on GitHub Pages, free forever.
Containers, images, Dockerfiles, multi-stage builds, Compose, volumes & networking.
Pods, deployments, services, ingress, Helm, kubectl, and real cluster labs.
Full study notes for the CLF-C02 exam — services, pricing, security, and the Well-Architected Framework.
AIF-C01 study guide — Bedrock, SageMaker, generative AI fundamentals, and RAG architecture patterns.
Star a repo, share with a friend who's prepping, or connect on LinkedIn — I post study breakdowns weekly.
Updated monthly. Building in public.
Working through Bedrock, SageMaker basics, and generative AI fundamentals. Targeting cert by Q3.
Hands-on with vector stores, retrieval pipelines, and the production patterns that make RAG actually reliable on top of Bedrock.
Going through Kelsey Hightower's classic to deepen K8s internals beyond managed EKS.
Studying for the same cert? Building a RAG pipeline? Just want to swap notes on what worked? My DMs are open — find me on the channels below.
