ChatGPT CLI
A modern, extensible command-line interface for ChatGPT with subcommand support, configuration management, and comprehensive logging.
- Go
- OpenAI
- CLI
I build production-ready ML platforms and resilient cloud infrastructure. I specialise in taking ideas from notebooks to reliable products. From opinionated MLOps frameworks and distributed runtimes, to hybrid multi-cloud architectures and developer tooling, I focus on delivering systems that people actually love to ship with.
Opinionated tooling, hands-on infrastructure, and products that turn experiments into outcomes.
A modern, extensible command-line interface for ChatGPT with subcommand support, configuration management, and comprehensive logging.
Anonymous teacher and course review platform built for DIMES students, combining a modern React frontend with a FastAPI backend and PostgreSQL.
LLM-powered CI/CD pipeline analyzer that validates pipelines and recommends fixes across multiple platforms.
An end-to-end MLOps framework that automates experimentation, deployment, and observability through a unified CLI and Python API.
A Raspberry Pi powered lab with Kubernetes, observability stack, and GitOps delivery for experimentation and edge workloads.
CLI-first AI assistant that generates contextual quests and tasks using OpenAI, packaged for repeatable deployments.
Reusable Terraform + SageMaker setup that trains, evaluates, registers, and deploys regression models on serverless infrastructure.
Multi-cloud expense tracking platform deployed on GCP with serverless components and automated governance.
Big data lab that orchestrates Spark, Hadoop, and Hive locally to surface actionable insights from aviation datasets.
Hands-on Terraform lab that teaches reusable infrastructure patterns across AWS, GCP, and Kubernetes.
Full-stack social platform that mirrors Instagram and Twitter experiences with secure authentication.
Android app that unlocks Genius API metadata with offline-friendly search history and rich song insights.
Professional history.
Community support and extra activities.
Volunteering activities.
Strong theoretical foundation that complements hands-on engineering work.
Continuously investing in community-recognised credentials across cloud, Kubernetes, and automation.
Available for collaborations, advisory roles, and teams that need a pragmatic engineer to turn ML projects into resilient products.