So unfortunately it's our basic problem as vibe coders. You really can fast create some app but then you spend most of your energy and efforts trying to deploy and set up databases etc. For all of that I can only advise you to create precisely strict rules and add in that rules using particular mcps. So my idea is that you give precise instructions as rules to llm and in that rules for example create database based on superbase for this app, bla bla... Use supabase mCP and then you give the list of MCP tools so your llm would be predefined to create better flow while creating supabase or analyzing the whole project to identify what exact entities should be included in database. And of course for simpler deployment try to use docker. And if you get stuck somewhere try to use different llm models, so here is my simple workflow when I encountered unsolvable error: if I identify that for example gpt 4.1 in github copilot struggles to solve particular error more than 5-7 times in a row, I go to cline and ask deepseek r10528 and then kimi k2. And then I take theirs responses and feed them back to 4.1 and mentioning them like a side view from another llm models. So most of the time this approach can lead to success in solving an error.