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.

𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝗠𝗮𝗸𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗘𝗮𝘀𝗶𝗲𝗿 At the end of the day, a website has one major job: Make it easier for someone to decide. Decide to trust. Decide to inquire. Decide to book. Decide to buy. That is why I focus so much on user flow, structure, clarity, and presentation. When a website removes doubt, it becomes powerful. When it simplifies the next step, it becomes effective. When it helps a business communicate clearly, it becomes valuable. That is the standard I build toward. Wishing everyone a productive week ahead. 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗵𝗲𝗹𝗽𝘀 𝘃𝗶𝘀𝗶𝘁𝗼𝗿𝘀 𝗱𝗲𝗰𝗶𝗱𝗲 𝗳𝗮𝘀𝘁𝗲𝗿: 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗼𝗿 𝘁𝗿𝘂𝘀𝘁?