Hey fellas, I am testing and vibe coding different ideas and projects for months now and I would like really to start something seriously and professional.

Because I am not a traditional developer (no coding experience) I am stuck in the process to get a vibe coded app/Platform into life with safety and real visitors to starting generate revenue.

Do you have some advices so I can open my eyes into this process?

Thanks,

Manos

Hear me out… what if, 4 or 5 people teamed up to work on 1 project.

I mean that’s 5 different creative design architects that are constantly feeding ideas into 1 shared project

In my mind I was thinking of a MMORPG zombie apocalypse. Yes I understand that genre is definitely overused, BUT the idea was - that either 5 people all add into the central idea/systems that break away from it OR 5 different people are working on 5 different systems that make of the entirety of the game.

Now of course this could be used for any large game, I just know that a zombie game would be easy to recall and identify with.

Would anyone be interested in that? Could this framework possibly be the future of low level ‘studio’ games? Could this be the new “indie development” wave of design, if so I think I’ll give it a name - “band development”

Welcome Band Devs!

Hopefully there aren’t major falling outs… maybe some can stick together like Metallica

End of June Vibecoding report. Does it look ok?

Three months in vibecoding, three products live - $244 in revenue (Recurring + One-time)

April and May - learning and building
June - Go to market and building

Welder AI - $159
Countly - $85
Bitcoin Gate - $0

(I can drop the links in the comments)

Claude Code + Codex subscription - $660 (3 months)

I am a 19-year-old aspiring entrepreneur looking for guidance on building a startup. I want to understand how to identify future trends and predict which technologies are likely to become important in the coming years. How can I stay ahead and recognize emerging technologies before they become mainstream?

I am particularly interested in Artificial Intelligence. I would like to know what opportunities exist in AI that could help me build a unique and successful startup. Please guide me on where to start, what skills I should focus on, and how to find real problems that AI can solve.

Every week, same thing: “Built a SaaS in 3 days with Cursor 🚀”, pretty dashboard, 400 upvotes. Then you start charging for it and find out the entire hard part is still missing.
A few things that are absent from basically every one of these projects. Honest question: how are you handling this?
Auth. Someone increments the user ID in your API and sees other people’s data. Does your backend actually check that or are you hoping the AI wired up “something with auth”?
Secrets. Stripe key or DB credentials sitting in the frontend bundle or a public repo. You wouldn’t even know.
Migrations. Live DB, 200 real users, you need to change one field. Now what?
Compliance. Real users means real data laws where it’s stored, who you share it with, deleting it on request. For EU users that’s GDPR and it’s not optional. “I’ll deal with it later” is a fine with a date on it.
Cost. Every click fires a GPT-4 call, no rate limit. Someone writes a 10-line script and you wake up to a four-figure bill. Who’s paying that?
Backups. “I have backups.” Have you ever tested a restore? No? Then you don’t have a backup, you have a feeling.
When the AI can’t fix the bug. It loops, changes random lines, makes it worse. You can’t read the code because you never read it. Who debugs that? hope?
This isn’t gatekeeping. It’s the bare minimum between “prototype” and “something you can actually charge money for.” And it’s exactly the part no tool does for you, because it’s the part where you have to understand what you’re doing.
So, genuinely: how do you deal with this stuff? Or is the plan “I’ll fix it when it breaks”?
(And before someone says “works fine for me” yeah. Everything works fine at 5 users.)

A lot of people talk about vibe coding in terms of shipping fast, getting users, or hitting $1k MRR.

But I think one of the best parts of vibe coding is that you don't need any of those reasons.

You can build an app that's only for you.

It maybe a personal journal with AI features. Maybe it's a dashboard for your investments, a meal planner, a reading tracker, or just a weird little utility that saves you 10 minutes every day.

No customer interviews.
No market validation.
No worrying about churn.

Just: "Would I use this every day?"

That freedom is something software development hasn't always had. A few years ago, building even a small personal tool meant days or weeks of work. Now you can have an idea after dinner and be using it before bed.

Ironically, some of the best products probably start this way. You build something because you genuinely want it, not because you're chasing metrics.

Not every project needs to become a startup.

Sometimes the best outcome is opening an app you made yourself and thinking, "Yep, this makes my life better."

To me, that's one of the most beautiful parts of vibe coding.

I joined a startup 2 days ago as a fresher, and I’ve noticed that almost everything here is vibe-coded/AI-generated. I honestly hate this approach because I’m worried I won’t actually learn proper development.

Is this normal in startups now, or should I be concerned about my growth?

 I see the word vibecoding in every comment section lately. People use it to sound trendy and pretend they do not care about the underlying code.

They are entirely missing the point.

Tech is changing at lightspeed. This is not a fun little trend or a cool buzzword. It is the biggest shift in leverage we have ever seen.

While everyone was busy arguing over whether using AI makes you a real developer I put my head down. I used AI to accelerate my workflow and launched products that made me $1000 this month alone.

People do not realize how big this change is. The barrier to entry has vanished but the focus shifted from writing syntax to actually solving problems.

If you stop worrying about looking cool and start using these tools properly the leverage you get is insane.

I used this exact approach to build my latest project. I am trying to figure out if the core loop actually solves the problem well. If you have two minutes to test it out at https://getmindfuel.vercel.app/,I would love your honest feedback.

I've been heads-down on this for months and only now feel ready to talk about it properly.

LoopTroop is a local, open-source GUI orchestrator I built. It uses OpenCode (right now, more backends in the future) to create new apps or add features, fix bugs, and improve existing codebases. It leans heavily on context engineering, Ralph loops, beads, LLM councils, and git worktrees.

Think Lovable or Replit, but with the opposite philosophy: it cares more about getting things right than shipping as fast as possible.

The Problem That Started Everything

Early this year I kept hitting the same wall. I'd try to add meaningful features to my own apps and the normal IDE route just became overwhelming on anything complex. Tools like Replit didn't deliver what I needed either — they were even worse.

Around the same time Ralph loops were getting popular. I thought: What if I combine Ralph loops with a proper LLM council and a full development lifecycle? Maybe I could finally solve the real problems I was facing instead of fighting the tools.

I told myself it would take a month. Maybe six weeks max.

Boy, was I wrong.

(I also have a day job, kids, a house — you know, normal life.)

So me and my partner just started working on it whenever we could.

120 Hours of Planning (The Most Important Part)

The first 120 hours were pure planning. No code at all.

We looked at every similar project we could find and asked: what did they do well? Where did they fall short? I basically threw the initial idea at almost every model available back then and kept asking "What's missing? What's dumb? What should we push to later?"

Some of the Chinese open models actually gave the best suggestions. I'd take their feedback, update the plan, switch to another model, ask again, and repeat.

Once the core plan felt solid, I started digging into the actual tech stack behind it, this time leaning more on frontier models, then bouncing the new version back to the others for another round of criticism.

That whole back-and-forth took the full 120 hours.

multiple views from inside the app

Actually Building It (And Why One-Shotting Was Impossible)

Then we started implementing the plan.

I used a bunch of different harnesses — hammering GitHub CLI (insanely useful at the time because one request could just keep running for hours), Claude Code, Codex, Droid, and a couple others. I'd keep the best output from each run, steal good UI ideas from the rest, and then go back to the strongest harness (mostly Claude Code with Opus back then) to fold everything together.

Another month+ passed before it actually worked. This thing is genuinely complex. There was never any chance of one-shotting it.

After the core started working, I spent the next couple of months adding features and refining until it finally felt like the thing I had originally pictured in my head. Only then did I feel comfortable starting to talk about it publicly.

interview

execution

The Tools & Models I Actually Used

Because LoopTroop itself runs an LLM council, I needed access to lots of different models at the same time for testing and building. Here's what I leaned on:

• Multiple Codex Pro subscriptions + Codex-lb (the open-source load balancer so I wasn't constantly logging in and out)
• VS Code + Copilot + Copilot CLI (mainly for Claude Opus/Sonnet)
• OpenCode Go subscription for smaller tasks
• Antigravity (with Google Pro) for UI work and occasional Opus. Now that Flash 3.5 is out I use it constantly; it's fast and good enough for most non-complex work
• Kilo and OpenRouter to pull in free models for the council

For big architectural decisions I would spin up GPT-5.5 (or 5.4) Pro and cross-check the plan across a ton of other models: MS Copilot, GLM, Qwen, Minimax, Grok, Gemini, Deepseek, Kimi, Xiaomi, and sometimes even Mistral or Ernie. Same technique I used in my planning phase (described before) — moving the plan between different model families to catch edge cases and collect good ideas. I ended up with an architecturefile with 10000+ lines that got so changed when I made the app actually work.

When I just wanted to know "What's the current best way people are doing X right now?", I usually started with Grok or Gemini.

Out of the open models, I'd currently recommend Kimi and GLM (on top of the big three).

Anything that got too complicated went straight onto the roadmap (it lives on GitHub and in the docs). That's how I actually managed to launch.

infographic from notebooklm

What I'd Recommend Now (After Going Through It)

If you're building something genuinely complex with AI right now, here's what I'd tell you:

1. Spend way more time on initial planning than you think you need. Run your plan through as many different models as possible. Different model families catch completely different things. Push the big scary features onto a roadmap and get as close to a real MVP as you can. Tens of hours, hundreds even if you want something serious — not a to-do app.
2. After planning, use the strongest model you can afford for the first real iteration. I'd honestly just use LoopTroop for this now, with whatever the current top model is as the main implementer.
3. Then settle into the real forever coding loop: Complex work? Use LoopTroop. Seriously, it's built for exactly this. Small stuff or quick fixes? Use whatever fits your budget (full Claude/Codex/Cursor plan, OpenCode Go, or free models via OpenRouter). Just keep repeating this forever.
4. Market and build at the same time. Don't wait until it's perfect. Keep shipping updates and talking about it in parallel with the development work.

See It In Action

Here's a full walkthrough of LoopTroop doing its thing:

Watch the full demo (16 minutes, after 6:50 the demo) on YouTube:
https://www.youtube.com/watch?v=LYiYkooc_iY

That's the story so far. It took way longer than I expected, cost more in subscriptions than I want to admit out loud, and taught me a ridiculous amount about how different models actually think.

If you're building something complicated with AI agents or orchestrators right now, I'd genuinely love to hear how you're approaching it.

• Repository: github.com/looptroop-ai/LoopTroop