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- December 12, 2025
A Framework for API Data Syncing: The Ontraport to Supabase Case Study
This document outlines a detailed, real-world process for syncing data from a third-party API into a Supabase database. It is designed to prepare the data
The AI in Automation Puzzle: Why “Learning” is a Myth and How to Get Real Results
AI and automation are two distinct concepts that can work powerfully together, but they are not interchangeable. The excitement around AI has led many to believe it can replace the entire automation process, but this often leads to complex, expensive, and unreliable solutions.
Real LLM Streaming with n8n – Here’s How (with a Little Help from Supabase)
n8n, for all its power in workflow automation, is NOT natively built for streaming HTTP responses. Its sequential, node-by-node execution model is fantastic for many tasks, but it’s a fundamental blocker for true LLM streaming.
The good news? I’ve been wrestling with this and have landed on a robust architectural pattern that brings that smooth streaming experience to n8n-powered UIs, primarily by leveraging the power of Supabase Edge Functions and Realtime subscriptions.
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.