I’m actively learning and experimenting with AI-assisted coding, automation and data analysis using GitHub as a place to document progress through practice, projects, and experiments to accelerate how product ideas move from concept to impact.
The AI shift changes how product teams learn and deliver. I’m deliberately developing hands-on skills to work faster, validate earlier, and increase the leverage of product work.
- Applying AI-assisted workflows to product discovery, delivery, and developer productivity
- Building practical experience through GitHub certifications, automation, and real project work
- Prototyping tools and environments that reduce friction and speed up learning loops
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- PowerShell for automation and experimentation
- Azure Data Explorer (KQL) and Power BI for querying and understanding product data
- Python (early-stage) for analysis and synthesis
- GitHub Actions and Copilot workflows for modern AI-assisted development
- Prompt design for turning raw inputs into insights and design artefacts
- 🦞 Pinchy — is my OpenClaw instance, built as a personal AI assistant for hands-on experimentation. It’s where I explore agent workflows, automation, and practical ways to make AI tools feel personal and useful. The goal is to turn an adaptable assistant into something I can actually build with and develop over time.
- Pinchy on Pi
— I'm building Pinchy on a Raspberry Pi 5 setup to make it more portable and experimentation-friendly. This version is focused on creating a safer test environment where I can iterate on the build to replace my Echo Show. - PinchyOnPi.md — scratchpad notes, setup steps, and next tasks.
👉 More context and background on LinkedIn.
As a Product Manager (started as an Engineer) here are some of the languages I developed through in the past, and maintain a knowledge of. In the dynamic realm of cloud computing, I am gaining experience in leveraging leading cloud platforms and technologies to architect, deploy, and manage scalable, highly available, and fault-tolerant systems.
