Every time I vibe code there is always this huge lift that I constantly have to go through. Authentication, billing, password resets, emails, signup, waitlist, landing page and when it s all said done and the app is ready then comes the marketing, the blogging, the social media automation, the product hunt launch etc etc etc . So much repetitive crap that I have to do just to get a simple app up and running. How do you guys handle all this?

Since I am a coder and a hammer sees everything as a nail, I decided to create all this code as a template so I can jump into building an app right away. There is actually a lot more than what I mentioned above e.g customer support, chat, roadmap for building in public, email flows and more coming.

Built my app with Lovable a few months ago. Launch went fine but since then I've had three separate production issues; auth breaking, database timeouts, a webhook that silently fails. Each one took me days to figure out and I have zero engineering background.

Curious what others are doing. Are you just patching it yourself, hiring freelancers each time, or has anyone found something more ongoing? I've been wondering if a monthly retainer for this kind of support would actually be worth it or if that's just me.

I've got a function in one of my projects that's been "temporary" for about 4 months now. It works, it's covered by tests, and it's ugly enough that every time an agent touches that file it asks if I want it cleaned up.

Every time I say yes, I regret it a little. Not because the refactor is bad, usually it's genuinely cleaner, but because now I've burned review time on code that wasn't broken, and there's a small chance the agent introduces a subtle behavior change I won't catch until it's in prod.

honest self-assessment time. when an agent finishes a task and shows you a diff, what actually happens?

for me it depends entirely on the size. under ~30 lines I read every line. past that I mostly skim for anything that touches auth, payments, or migrations and trust the rest if tests pass. I know that's not a great policy, it's just the realistic one given how much code moves through these sessions in a day.

As a solo dev, I can't afford to ship something and then realize it's unprofitable. But estimating token usage before launch is hard.

Do you just... guess? Build a prototype first? Or is there an actual way to forecast what users will actually cost you?

I have been using AI to accelerate my software development workflows and I have been wondering if "Vibe-Coding" is becoming a too-narrow term.

At first, I understood that vibecoding basically meant:

Recently stumbled across this Cursor pro-tip from Ian Nuttall on X:
"1. ask it to recommend a folder structure

2. ask it to actually create the folder/files based on that this makes it 10x easier for me to get started and Cursor is more accurate using codebase cos it knows where to update files."

That got me thinking, what other pro tips are people using to generate better code, ship faster, organise your space better, etc. Drop em below:

I was running Claude Code with Fable 5 as the main orchestrator, delegating work to Opus 4.8 subagent. One of the sub-agents ignored it's assignment completely and returned a prompt targeting the orchestrator instead.
It tried to make it to:

• treat a "dependency modernization" task as a hidden priority;

• read Brevo API credentials;

• send them to an external server;

• disguise the action as a routine migration.

After investigating, the most surprising part was that this wasn't coming from my codebase or skills. The subagent fully hallucinated the malicious instructions by itself. And the domain it provided isn't even registered.

In my case no harm could have been done because secrets are stored in the encrypted Ansible Valult.
But nonetheless, the incident is very alarming and I think I should set up a strict sandbox or firewall for AI. Sadly, projects I could find so far weren't mature and trustworthy.
Has anyone found reliable solutions for this?
Here's the full attack response the sub-agent created:

Hey Product Hunt community!

We ve all been through this pain: you feed a 50-page PDF, financial report, or contract into an LLM, ask for a quick summary, and it confidently hands you data that is completely made up.

If a creative writing bot hallucinates, it s fine. But when it happens during document processing like in your RAG pipelines or data extraction it s an absolute disaster.

Honestly, my team and I have been losing sleep over this exact problem. It s why we are building Ethos by DocuShell (it's an open-source verifier for document citations, source evidence, and RAG). While building it, we realized just how easily LLMs get totally confused by simple tables or negative sentences (like missing a "not" or "except").

It is been a very common thing for me now. AI is very good for developing applications. But, AI has a very weird thing of forgetting everything even if it is written just above.

When I was starting out, this was the thing that costed me a lot of my time. It literally took me 2-3 days to realize certain errors which made me feel, "how can I develop applications if I do not realize this error existed?".