My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":

n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.

RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.

Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.

Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.

The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.

Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.

AI News Digest

I built an automated AI News Digest with n8n that collects the latest AI & automation news from RSS sources, selects the most relevant items, summarizes them using Groq AI, and delivers a structured daily digest directly to Telegram.

The workflow runs automatically every day, eliminating the need to manually check multiple sources.

Built with n8n, RSS, Groq API, Telegram API, and JavaScript.

A simple example of how AI automation can turn information overload into a useful daily workflow.