Sankar's Blog

Lately, I’ve been experimenting with automating deep research using n8n. The goal was simple: type a prompt, wait a few minutes, get back a structured research report with summaries and sources — no fluff, no extra work. It took time to get right. I watched a bunch of research automation videos, borrowed what made sense, and hacked the rest together. Now it works reliably. Here’s a breakdown of how I did it, what tools I used, and what came out of it. What I Wanted A prompt like: "Clinical Communication Market in the US" In return, a structured report within 8 minutes. Ideally with bullet-point insights, links to sources, and export options. Tools I Used OpenRouter This is where the prompts go. I used OpenRouter to access GPT models that could break down broad topics into sub-queries and generate clean summaries from source material. Tavily A powerful search API that goes beyond just returning links. It gives back structured results — perfect for feeding into the ...

A few mornings (months!) ago, I read that Meta has made LLaMA available to the public and you can host the LLMs on your local machine. I have always associated AIs with GPUs, and GPUs are expensive. So the extent of my understanding of AI was that it was expensive—and ChatGPT is free. Then, out of sheer curiosity, I decided to search if I would be able to run a local LLM on my i5, 16 GB Windows machine. Voila, apparently I could do that in less than half an hour. Trust me, I was neck-deep in work when I decided to take this route. So there you go—a step-by-step (no code required!) way to run a LLaMA 3.2:1b model on your basic Windows machine. I’m not sure if anything above this model will work with such a configuration, but LLaMA 3.2:1b does. Step 1:  Installing Docker Desktop  If you don’t have Docker Desktop, install it. It’s just a few “Next” clicks and one “Install” button. You can download it from here —just choose your desktop configuration, download, and install. Now,...