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- June 4, 2025
Articles By This Author
The Shifting Sands of the AI Landscape
In navigating the current AI landscape, it’s become clear that relying on off-the-shelf platforms, even those offering some level of customisation like OpenWebUI, presents significant limitations. These platforms, while useful starting points, often bake in assumptions and rigidities, particularly around critical features like RAG, that don’t cater to the diverse and dynamic needs of real-world business applications. Similarly, the promise of quick-fix AI solutions often seen in online tutorials, while great for proof-of-concept demos, glosses over the true complexity and customisation required for robust, production-ready systems.
Understanding the Brains (or lack thereof) Behind Your Chat App: Why LLMs Aren’t What You Might Think
Large Language Models (LLMs) are incredible pieces of technology, capable of generating remarkably human-like text, answering complex questions, and even assisting with creative tasks. It’s easy to interact with a chat application powered by an LLM and feel like you’re talking to a truly intelligent, aware entity with memory and understanding. However, this is where a common misconception arises, and understanding the reality is key to having a good experience.
Building a Comprehensive N8n Command Center with Grafana: The Detailed Journey
N8n provides two main views of your workflows. The workflow list shows you basic information – workflow name, when it was created, when it was updated, and whether it’s active. That’s it. No execution statistics, no performance metrics, nothing about the actual behavior of your workflows.
Extracting n8n Workflow Node Execution Times and Displaying in Grafana
I wanted to extract accurate execution times for all nodes in all my n8n workflows, including cases where nodes run multiple times (either due to loops or because they’re sub-nodes like “Postgres Chat Memory”).
The Art and Science of Syncing with n8n: A Technical Deep-Dive
Syncing seems easy, right? Just grab data from System A, push it into System B, and you’re done. Bahahahaha! If only it were that simple. This document serves as both a practical guide to syncing strategies in n8n and a technical deep-dive into the specific challenges of synchronizing Microsoft Teams messages. It’s meant to be comprehensive, detailed, and reflect real-world implementation challenges beyond what you’ll find in typical integration guides.
Multi-Model, Multi-Platform AI Pipe in OpenWebUI
OpenWeb UI supports connections to OpenAI and any platform that supports the OpenAI API format (Deepseek, OpenRouter, etc). Google, Anthropic, Perplexity, and obviously n8n are not supported.
Previously I had written pipes to connect OWUI to these models, and n8n, but now I’ve combined all four into a single pipe.
This technical walkthrough explores the implementation of a unified pipe that connects OpenWebUI with Google’s Gemini models, Anthropic’s Claude models, Perplexity models, and N8N workflow automation.
The Open WebUI RAG Conundrum: Chunks vs. Full Documents
On Reddit, and elsewhere, a somewhat “hot” topic is using OWUI to manage a knowledge base / files and take advantage of OWUI’s built-in RAG (Retrieval Augmented Generation) functionalities. The thing is, sometimes, you’re not trying to retrieve snippets for context; you’re aiming for summarization, translation, file comparison, or brainstorming. I often see people struggling with system prompts or RAG prompts to get an LLM do process documents in ways that RAG simply doesn’t support. You can’t “chat to your PDF” and ask, “Take the grand totals section from my Excel file and re-write the summary in the financial report to reflect those numbers.” RAG isn’t built for this. It’s built to merely return chunks of text to the Agent that are hopefully semantically similar to the user’s request.
Code Smart in n8n: Programming Principles for Better Workflows
As a coder of 30+ years, I’ve learned that coding isn’t really about coding – it’s about logic, efficiency, and creativity under constraints. These fundamental principles are just as relevant in n8n as they are in traditional programming.
Sure, you can quickly learn n8n without any coding background. That’s one of its strengths! But understanding basic programming concepts can transform how you build workflows. Why? Because these time-tested principles help you create more robust n8n solutions.
Case Study – provider agnostic AI chat interface
A client wanted a “chatbot” to interface with all of the providers (Google, OpenAI, Perplexity, Anthropic, and image generation models) so they could easily compare
Creating a “Custom GPT” with Open Web UI
Why pay $20 per month to OpenAI when you can do it for “free” using OpenWeb UI.
People say “free” a lot in the AI space. It’s not really free, unless run OWUI locally, and you have the time and skills to set it up.