hi everyone,

I have been hearing about the vibe coding app's are entering the market and anyone can do it now,however i still have few doubts about the survival of the app as anyone like me who do not have much experience with building app and trying one.
What are the chances of that app competing and the security of the app against the one who is well known tech person build the app and know what to look for,what to add,what to remove,how to make sure this app will not have any loop holes which will land me in trouble atleast until i have any tech team or tech co-founder.Being a solo founder who is building an app for students it is hard to know all the things and ai only answer to us for what we ask it not what should we be doing instead,what to avoid,what are we already doing wrong.

I'm a freelance consultant. Tried Folk, Attio, HubSpot free, Google Sheets. Never stuck with any of them. The problem wasn't the features, it was that I never went back to the tool.

So I built a CRM inside my AI assistant (Claude + MCP server + Supabase). Six contact lists, email drafting, a Chrome extension that scrapes LinkedIn profiles at $0.001 each. Total cost: $10.

The whole thing lives where I already work. That's why I actually use it.

For the past couple weeks I've been on my #indiehacking coding grind

All the tools out there to go from just vibecoding to actually launching can be quite overwhelming when just starting out

I'm a UX designer and Product strategist. Not a fullstack developer.
And I just shipped a full SaaS product.
Here's how I did it.
I work as a UX and Product SaaS strategist and also run a remote digital agency Koylabs.
I always had product ideas but never the engineering background to build them myself. That changed this year when I started seriously experimenting with AI tools and vibe coding.
I picked a real problem I kept seeing with clients they wanted Google reviews on their website and every existing solution was clunky, complex and a bit dated. Simple problem. Clear market. So I decided to build the solution myself.
The stack:
Next.js + Supabase for the backend
Stripe for payments and subscriptions
Cursor as my AI coding environment
Vercel for hosting
What surprised me:
AI is incredible at boilerplate and repetitive logic
The hard parts were still hard embed systems, iframe height communication, Stripe webhook edge cases
Product thinking was my biggest advantage.
Knowing exactly what to build and what to leave out saved weeks
Vibe coding is real but you still need to understand what you're building
What I actually shipped:
Full auth and multi-tenant architecture
Stripe subscriptions with trials, webhooks and billing portal
A custom iframe embed system that works on Framer, Webflow, WordPress and any platform
Automated review sync
Four widget layouts
Start to launch: a few weeks.
The result is Review OS a simple clean way to show your Google reviews on your website. One embed code, done in under a minute. Only $9/month.
If you or someone you know needs this try it free for 7 days at www.reviewos.co 7 day free trial
And if you're thinking about building your own project follow along.
Give this tool a try and share your thoughts I'm looking forward to your feedback.
My next post will be a full playbook on how I did it, tools, process, mistakes and all.
What are you currently working on? Drop a comment would love to know.
Launching on Product Hunt this Wednesday would love your support

Do any of the more experienced coders ever feel like closing the loop in vibecoding is a bit too much control to let go off? I'm not sure if I can categorize myself as an experienced coder. I'm been mainly self-taught and been doing it for almost a decade now, so I have strong opinions about what I consider best practices. (I actually love refactoring, because it feels like order is once again restored in the codebase).

However, with AI becoming so good at so many parts of the product development, I'm starting to feel like a project manager, or god forbid, scrum master... On the one hand, it's nice that I get to spend most of my time thinking about the problems themselves rather than the implementation of the solution. But on the other hand, I think my coding style is a bit like trying to find a way out of a dark room by running into the wall repeatedly as opposed to planning a way out. And I kind of missing running into the wall to be honest. Now, I'm enviously looking at AI hitting the wall.

Most of the vibe coding conversation is about speed and quality, how fast you can build and how good it turns out. Nobody really talks about the exit question. If you build inside a platform that hosts everything for you, how easy is it to actually leave.

Some tools give you a clean codebase you can download and run anywhere. Others keep the important parts, database structure, auth, backend logic, wired into their own infrastructure in a way that's hard to fully untangle. You don't notice this while things are going well. You notice it the moment you want to switch tools, bring in an actual engineer, or move to your own hosting and suddenly realize how much of your app doesn't travel with you.

Has anyone hit this wall for real. Did you end up rebuilding from scratch somewhere else, or did you manage to get everything out cleanly. And if you checked this before you started building, what did you actually look for.

Over the last year, I ve been experimenting more with local-first patterns , apps that prioritize offline functionality and sync later instead of depending on constant server calls.

What used to feel experimental now feels surprisingly stable. Faster UI, fewer loading states, and a smoother user experience overall.

I rebuilt a small side project recently with a local-first approach, and the difference in responsiveness was noticeable. But it also introduced new challenges around conflict resolution and state consistency.

It makes me wonder:

Spent the last few weeks building three connected apps and shared microservices with Lovable/Cursor. The first 80% went shockingly smooth, the demo-stage stuff always does. Then real usage started, and the integration between services and the admin dashboard started throwing constant sync errors. A few different AI coding tools could patch individual symptoms but never quite nail the root cause.

For anyone who's taken an AI-built app past the demo stage: what was the first thing that actually broke once real users or data hit it, auth, data consistency, service integration, something else? And did you end up digging into the code yourself, or bring in outside help to sort it out?

For the past few months I've been juggling three AI coding agents Claude Code for architecture, Codex for implementation, GLM for review. The pain wasn't any single one of them. It was the switching cost.

Every time I switched engines I had to re-explain the project: which framework, which conventions, where the old conversation left off. None of them remembered what the others knew. I'd waste 10 15 minutes per switch re-loading context, and half the time I'd just give up and stay on whatever I opened first.

So I started building a small thing for myself. Local desktop app. Lets me run all three engines from one window, with a shared memory layer underneath.

The thing I underestimated: persistent memory is the whole game. Once Claude and Codex can both see "this project uses SwiftData + strict concurrency + @Model on iOS 18+," switching becomes free. I stopped caring which engine I was on and started picking per-task.

Two days ago I saw this thread about how we are having more launches in post-GPT era.
And a question was born in my head: what quantity is optimal now? Of course, you can often see a trend among builders on X, where they launch a project per month, then roughly 4 months later 1 project takes off and we don't see new projects for the next 6 months because the person is busy scaling (and that's ok, testing a hypothesis shouldn't take much time)
But still, what pace should be considered right? 12 in 12 months slow in modern reality. Launch a product in a day? Unrealistic (SEO, ads, app approvals, various settings and optimizations). Theeeeen...48 products a year?
Or should we look at this from another angle, where LLMs allow us to create 12 products in 12 months with more features and better quality? What's the community's opinion?