When you make ai agents for clients do you open n8n account specifically for their workflows or do you have 1 account in which you have all the workflows created for each business specifically

Can anyone tell me why am I getting this error? Although the Expression is correct, and everything else is properly configured. Then WHY?

Vector DBs became the default answer for anything involving context or memory… but do most projects truly need them? A lot of devs are using Pinecone, Weaviate, or Qdrant even when a simple JSON store or SQL table might be enough. So let’s discuss: Where do vector databases actually provide real value, and where are they just adding cost and complexity? Have you built anything that worked better after removing a vector DB? Or do you think they’re essential for any scalable AI workflow? Curious to hear real examples — both successes and failures.

Lately, I’ve been posting general discussion questions every day — interesting topics that should invite conversation — but usually only 3–4 people respond. I’m trying to understand why. Is it because I’m posting at the wrong time, when few people are active? Or are my discussion topics simply not engaging enough to spark replies? Could it even be that a lower rating affects visibility or reach? I’d really appreciate honest feedback — especially from people who’ve managed to create active discussions. How do you make a post that gets the community genuinely talking and sharing ideas?

Anyone building serious AI systems knows this pain — same prompt, same model, different result. Sometimes it’s formatting drift, sometimes it’s reasoning quality suddenly dropping for no clear reason. So here’s the question for experienced builders: How do you stabilize LLM performance across runs and updates? Do you rely on prompt enclosures, structured parsers, few-shot consistency prompts, or custom model fine-tuning? And how often do you re-validate outputs after OpenAI or Anthropic silently tweak model behavior? Let’s compare methods that actually work — not theory, but real practices for keeping agent workflows stable.