We use cognitive vocabulary to describe these systems, and that sets expectations that later break in production. Here I go over the basics — LLM, MCP, AGI — what really happens inside a transformer, why reasoning isn't a new module, and the Artificial Analysis metrics I use to measure instead of argue.
Keep reading: What an LLM really is — and how to measure it with Artificial Analysis →
AI dev tools are great for a PoC, but shipping to prod is another story. Here I share my approach to the AI SDLC and the best practices I apply, based on my own experience.
Keep reading: AI SDLC: a compendium of best practices for developing with AI →
A written companion to agents.zerogap.uk — where I share RAG architectures, agentic orchestration patterns and how to pick the right one for your problem.
One possible design for production-grade agent infrastructure on AWS: ECS Fargate, GitLab CI/CD with automated evaluation, Lambda ingestion, self-hosted Phoenix observability, a single RDS instance with pgvector, pilot-light multi-region DR and a continuous improvement loop — with monthly costs.
Keep reading: Agent Architecture — Production Infrastructure →
A pattern for letting SharePoint users work with back-end data without exposing secrets or trusting the client: an SPFx extension gets an Entra ID token silently, an Azure Function validates the JWT and writes to Azure Table Storage.
Keep reading: SPFx, Azure Functions and Table Storage with Entra ID →
The HyperAgent Cognition Engine is a dual hyper-agent architecture in which two complete cognitive pipelines — a Solver and a Critic — work together, combining lateral thinking, domain-expert reasoning, independent verification and iterative refinement in one coherent system.
Keep reading: HyperAgent — Cognition Engine: Building an AI System That Thinks Like a Human →
A practical checklist for securing AI systems: choosing between LLMs and deterministic logic (and when to use MCP), securing inputs, sanitising outputs, sandboxing agents and tools, using local models safely, and continuous evaluation and observability.
Today you can build modern websites using only Python on both the frontend and the backend, and plug in AI for advanced experiences. Here's how to do it securely, using a children's story generator as the example.
Keep reading: A complete website in Python: frontend and backend with AI →
How to set up a remote MCP (Model Context Protocol) server over Streamable HTTP with secure Okta authentication and DPoP, protected by a reverse proxy in Python.
Keep reading: How to set up an MCP server with Okta and a reverse proxy in Python →
A Linux server running fail2ban, ClamAV, rkhunter, maldet and lynis, with their logs analysed by Azure OpenAI through LangChain: per-log reports, an aggregated report, email delivery and archiving.
Keep reading: How to build an AI security suite from open source tools →
