Background
About This Site
Independent analysis from someone who has been building systems since before most of the current AI hype cycle was born.
Background
30+ Years in IT
The career started before commercial internet access was a thing — running BBS systems, managing small communities across dial-up lines, and learning that networks break in the most inconvenient ways possible. Those habits of thinking about failure modes and edge cases have never left.
Across three decades the work has spanned systems engineering, network architecture, large-scale automation, cloud platform engineering, and now AI systems integration. The common thread is complex systems with real consequences when they stop working.
The current engagement with AI is not a career pivot. It is a continuation of the same work — understanding how new technology actually performs under production conditions, what the real limitations are, and where the engineering effort belongs.
There are no vendor relationships here. No advisory board seats. No consulting agreements that create conflicts of interest. What is written is an honest assessment of what the technology does and does not do.
Philosophy
How This Site Approaches AI
Real-World Usage
Theory without production experience is speculation. Everything written here is grounded in what actually works when you move past the demo and hit real scale, real data, and real failure modes.
Cutting Through Hype
The AI space is saturated with marketing dressed as analysis. Benchmarks are curated. Press releases omit limitations. Independent assessment requires no loyalty to vendors or their narratives.
Practical Outcomes
The question is never just "can it do this?" but "should we do this, at what cost, and what breaks when it fails?" Engineering is about trade-offs, not capabilities in isolation.
Experience
Technology Timeline
Early 1990s
BBS Sysop
Running bulletin board systems. Dial-up networking, FIDONET, early peer-to-peer file sharing, and community management before the public internet.
Mid–Late 1990s
Systems & Network Engineering
Transitioning from BBS to TCP/IP networks. Windows NT, Novell, early Linux deployments. First exposure to enterprise-scale infrastructure.
2000s
Enterprise IT & Automation
Infrastructure architecture, scripting and automation, data centre operations. Building systems that had to survive years of production load.
2010s
Cloud & Platform Engineering
Cloud migration, DevOps, containerisation, CI/CD pipelines. The shift from owning hardware to orchestrating services.
2020s – Present
AI Systems & LLMs
Hands-on work with large language models, inference infrastructure, RAG systems, AI automation, and agent orchestration in production environments.
Mission
Why Does This Site Exist?
The AI space has a signal-to-noise problem. Most of what is published is either vendor marketing, breathless speculation, or academic work that never touches production systems. There is very little from engineers who are actually building things and have enough scar tissue to know what matters.
This site is an attempt to contribute to that smaller category. It will not always be right. It will be honest about uncertainty. It will not be comprehensive — there is no ambition to cover everything. But what it does cover will be grounded in real experience and written without an agenda.
If you are an engineer, architect, or decision-maker trying to separate genuine capability from marketing noise, this is for you.