Building NiallOS with Claude Code
“AI will save you so much time!” Silicon Valley promised. So I tried everything. ChatGPT for writing assistance. Claude for research summaries. Gemini for project management. Copilot for planning. Every morning started the same way: Ten minutes explaining my context to whichever AI I was using that day.
“I’m a Product Lead at Cambridge Education Futures…”
“I’m working on a Master’s…”
“I need this in British English for an internal audience…”
“Keep it practical, I’m building training programmes…”
Then I’d finally get to the actual work. AI was supposed to save time. Instead, I felt busier than ever.
I was drowning in AI tool overload while still doing everything manually.
The AI Promise vs. Reality
2023-2024: AI will transform knowledge work! Automate everything! Work 4 hours a week!
That was the promise. My reality as a Product Lead at Cambridge looked quite different. I was designing training programmes for education leaders working with organisations like UNICEF and KFAS, and managing a Master’s research project. Creating workshop materials and training curricula. Publishing reports, papers and research outputs. Tracking professional development and learning goals. Staying current on EdTech and AI in education.
AI was supposed to save time on routine tasks, help me focus on strategic work, make complex work manageable, and give me breathing room.
What actually happened? Every morning became an AI context-switching marathon. ChatGPT for brainstorming workshop activities. Claude for analysing research papers. Back to ChatGPT for LinkedIn post drafts. Gemini for curriculum outline suggestions. Each tool required a fresh start and re-explaining everything from scratch.
I calculated the daily context tax. I wasn’t using AI to work smarter. I was managing a portfolio of AI tools, each one requiring setup, context, and translation. It felt like having five junior assistants who all had amnesia every morning.
