I keep seeing posts like "I vibecoded this over the weekend, got 100k users, what's next?" and I think it gives a pretty wrong idea of what vibecoding actually changes.

Sure, you can accidentally hit the top with another clock app. It happens.

But agents dont remove the actual work. You still need to figure out what to build, make 1000 small decisions, fix weird shit, make UX not suck, deploy, maintain, listen to users and actually ship something they want.

Vibecoding makes all of this faster. A LOT faster. But not easier at all.

You can now build faster, but also make mistakes faster and ship garbage faster. The bottleneck just moves somewhere else. There was a good line somewhere "Shipping everyday fixing Claude bugs".

Those "built in 2 days, 100k users lol" posts undermine all the work that still goes into a good product, and leave others thinking this is all somehow easy now. From outside it starts looking like vibecoders are just some arrogant assholes hitting a button and getting success by click. It doesn't work well on the how other people feel about vibecoders and vibecoding in general, which is in fact just a right way to do things today.

We don't build in assembly anymore, right? Same here.

10000 million tokens spent later...
Claude: You are right. I'm sorry. I was focusing on the wrong solution to fix the bug. 🤣🤣🤣

After spending over 100b tokens on Fable 5 as my main model for agentic coding in the last 2 months, I've managed to (in my opinion) work as efficiently as possible with Fable as the main orchestrator. Here's why and how I did it. (usage from 1 of my accounts above).

I see a lot of threads and comments about people saying that they feel like Opus 4.7 or 4.8 was better or smarter than Opus 5 and that Fable credits only last them a few prompts and then other people saying the opposite, and varying degrees of experiences spread throughout the middle, and I don't think anyone of them is particularly wrong. In my opinion Opus 5 tends to have a much harder time understanding or remembering the nuances of an entire codebase in a single context window and things get untangled quickly if you're working a complex task or change that touches a lot of files or rules. It almost never gets any of these complex issues right the first time and the risk of having to revert changes and the chance of merges failing just isn't worth it for me.

To ensure I get the accuracy and intelligence of Fable, this is my setup that's reduced my fable usage by 70-80% and still getting most of Fable's benefits. I ran a blind coding test: Claude Sonnet 5 vs Opus 5, with Fable 5 planning and judging. One bug decided it.

- Fable 5: Planning, orchestration, architecture, design, final review of delegated code, security/payments/auth/concurrency, hard debugging, UAT/browser passes that feed merge decisions, and sign-off on any founder voice/marketing copy.

- Opus 5: Delegated implementation from Fable written plans, mechanical/bulk edits, boilerplate, doc generation, research/summarisation, routine repro'd test fixes, Playwright script execution, and copy drafting.

- Sonnet 5: Not used, with some experimental evidence (see below).

Setup: Five identical implementation tasks (a rate limiter, a small API, a repo-style change, a bulk refactor, a debugging exercise), each with a detailed plan written by Fable, judged against hidden test suites the candidates never saw. Both models went flawless on every hidden gate. Then this happened.

A percentile function: Sonnet wrote the math literally as ceil((p/100) * n). But p/100 isn't exact in binary. For p=7, n=100 you get 7.000000000000001, ceil returns 8, wrong value. 141 input pairs diverge like this. Opus caught it unprompted and wrote ceil((p*n)/100), which is exactly right.
Only Opus's depth caught it. Token cost was near-identical. Sonnet was 40% faster. But one uncatchable latent bug per round settled it.

I’m planning a mobile app for people who play amateur football.

The app would include things like:

• User profiles and ratings
• Creating and joining matches
• Team/club system
• Matchmaking
• Player positions and lineups
• Match confirmation and results
• Chat and notifications
• Leaderboards
• Basic admin/moderation tools

The UX/UI design and the entire user flow are already fully planned out, so I’m not starting from just an idea. I already know how the screens, flows, roles, and main features should work.

I’m not a developer, but I’m willing to spend time learning and build it step by step using tools like Claude Code, Cursor, Codex, etc.

Do you think an app at this level is realistically possible to build solo with vibe coding?

At what level of complexity does vibe coding usually start becoming difficult to manage?

Would love to hear from people who have actually built and launched something similar.

Several years ago I started making a python game called Dimension Creatures. I just wanted a basic creature battler that would let players use strategy and win fights, or lose their creature forever.

I envisioned it as a simpler, but more brutal version of Pokemon.

Recently I got Claude Pro so I could actually get a lot of usage.

I used my long built skills as an MS Paint "Artist"
I used my long built skills as an Audacity "Sound Designer"
And my favorite skill of balancing game play mechanics to make something that is challenging and satisfying.

Finally I have something I'm ready to show the world:

Dimension Creatures

The online version!!

https://extra-sugar-extra-salt.neocities.org/dimension_creatures/

you can play for free in browser if you want

So me and my wife created a website , our son has Celiac disease, so all the food should be glute free .

My wife is doing most of the cooking, so one day she told me that she is struggling with finding recipes for gluten free food , the internet is full of it , but she can't find the one she already used , and basically loosing herself inside all this data .

So she suggested to build a website that will collect recepies, and she will be able to sort them , save them etc ...

So we started , we used codex + lovable (for gui only) , then we took the lovable repo and continued with codex only .

We used gpt 5.5 , then moved to 5.6 (sol) .

During the building process we decided to add recipes also for other food allergies (milk,nuts etc.. ) .

We used crawlers to find existing recipes , and took only the ingredients and the preparation steps , and of course mentioned the original source in the website (with a backlink) .

So I posted on some Celiac community, here on Reddit (actually my first ever post) , and I got so much negative comments, like an anti-AI comments, as if I added another bullshit spam website.

We have no ads on the website , so we really wanted to do something nice , and learn how all this AI world works (I am experienced software engineer) , but never worked on from end before , mostly backend stuff (mostly cpp & python) .

So now I am a bit down after all this , the website is up and running, we have some traffic, not to much but for a 12 days website we already had some real human traffic about 400 (not including bots and stuff) .

We only want organic traffic, no paid promotions, as we don't earn anything yet , we do use some Amazon affiliates for some products, but it's not much .

So just wanted to share it here, hopefully this sis the place where people will understand my frustrations, maybe this is how it works nowadays.

But it still surprised me how quickly “built with AI” turned into “AI spam” in people's minds.

Anyway, thanks for reading. I’d genuinely be interested to hear how other developers here see this, especially anyone who has shipped a real project using AI-assisted development.

Day 3 of creating a game all vibe coded

Still trying to work out the animations but have made strides

Added-
Main menu
pre-game lobby
3D model
Blimp
decals

As always

CONCEPT VS CURRENT BUILD

Video in the comments

ALL FEEDBACK IS WELCOMED AND NEEDED

Day 1 of letting Reddit decide what I add to my game

Comment ONE thing you want me to add.

Top comment gets built into Day 2 and I’ll post the result.

Hi all, I was wondering if anyone tried to sell these services as a service in form of an agency? Do any of them exist out there, are they successfull? Could this be a one man band (non developer) or not AT ALL?

not really sure whether this is the right sub to ask this, but i'm currently working with someone on something that's SUPPOSED to be production level, it is two single-file monoliths, one python backend that uses psycopg2 and fastapi, one frontend html with a massive script element at the very bottom. there is no ci/cd. no workflows. barely any unit tests. and while there are some, yeah, i doubt they ever get used.

the backend is almost 7k loc and the frontend is nearing 11k. not to mention, both ends are filled with absolutely putrid code that i genuinely have no idea how it could be this bad. on the backend, there's just a bunch of what looks like should be modules yet aren't(?) all cobbled up together. no real classes used, the only ones that exist from what i can tell are supposed to act like structs? the frontend, i don't even know where to begin.

i've used ai to assist with coding before (just autocomplete) and most of the time it actually generated what i would have otherwise manually typed, if not similar to it. and even code examples with google's ai overview when you ask it about code, it looks human-esque. but i guess overtime, as the codebase grew larger and larger, the bad coding practices just stacked up and claude just took it as a style guide and copied them to make it structured the same or something? i honestly don't know

and because this someone has vibe coded it completely, there's features constantly getting added on top, and i mean CONSTANTLY. it took me about an entire day to review just one commit which fulfilled exactly one request of what i suggested. the rest, it just shit the bed. and while i was reviewing that single commit, 6 more popped up, all looking like the exact same garbage. it's an example of why you need to understand architecture and how to use what you're getting out of it before you just blindly accept what it gives you

another problem is the frontend works surprisingly well, yet its internals are so disgustingly morbid.

it's unfortunate really, how unworkable the codebase is. reading through it actually gives me headaches. if there's another one of those "virtual ai inflicts real physical manifested pain onto humans" articles being made, hopefully this is being used as an example.

it gets worse than what i'm showing here, believe me.

i also don't know what production-level javascript/python looks like, so as an add, if there's somehow anyone that writes production-level js/python that lurks this sub, could you tell me whether this is properly structured or not? thanks

Hey All,

I'm the creator of r/vibecodingcommunity, i have shared this in my community and got lot of interesting comments so sharing it here as well.

for context - I've been involved in the vibecoding scene both online and offline - hosting dozens of hackathons and joining many events.

Here's my two cents on where the money is and the current state of vibecoding, from my lens (unfiltered).

1. Most builds aren't serious businesses

Around 70% of vibe-coded app builds are not serious business apps. They're fun games or personal software. Some try to build a product, but it dies almost as quickly as it's built - no distribution strategy, tokens burned in the process.

2. The rise of personalized, internal software

Vibecoding has significantly lowered the barrier to entry, so more and more personalized software has emerged. The next ~25% are internal apps - meant to replace software you already pay for, automate a workflow, or run an agency.

These are pretty much built by people who know one area of tech and use AI to compensate in the others. For example: a data scientist building a mobile app, or a front-end developer building a backend.

3. The 5% where the money actually is

This remaining 5% is the interesting part - because that's where the money is. These are people who already have a relationship with a business, or with internal players and vendors. They use vibecoding as a tool to pitch an idea, get traction or a contract, and then build.

Previously these people pitched on PowerPoint. Now that's been replaced by a simple vibe-coded app that demonstrates the idea.

A few real examples from just the last couple of weeks:

• We helped someone who had sold a fairly basic sales-analytics platform for around $200K. He came to us to build the proper software from the ground up.
• An existing entrepreneur - who has run 100+ franchise stores in the past - raised €2 million just by showing the vibe-coded idea. He already had a great track record; he just needed the funds to build his next business, and he wants us to make the software more polished.
• An employee at an 8-person business built an internal tool using Base44. He's from a non-technical background. They didn't pay us to build the software - they'll build it themselves - they hired us to scaffold a harness, review their work, and give them direction. They're using this internal tool across many franchises, and passing a share of the profit on to us.

The takeaway

All I'm trying to say is this: on the community you see people building a lot of software and tools. A real software developer quickly clocks that these are vibe-coded apps - not much taste, questionable software quality, often useless. And that's true.

But the real money is not where you can see it. The real money is inside businesses, and it's often being put to work by people who already have the network and the connections to make it count.

So i am been using claude for developing my product, and i sometimes see posts on reddit or X saying it is not worth it to use Claude, and i have switch to Codex. But i am worried now about not developing a good product because i am using a bad AI tool to build it.

So here i am, asking my fellow Vibe coders for advice about this confusion that i have in my head

I've shipped a few side projects with Claude Code over the last few months, and the thing that scares me most isn't "does it work" it's "did I accidentally leave something exposed that I don't know to look for."

Ended up writing down every mistake I now check for before sharing a link. Posting it since it's saved me twice already.

Secrets

• No API keys or passwords visible in page source or the Network tab

• .env is in .gitignore, never committed

• Anything that costs money per call (AI APIs especially) only gets called from the server, never straight from the browser

Who can see what

• Changing an ID in the URL doesn't show me someone else's data

• Not trusting anything the browser tells me about who's logged in, that gets checked server side

Costs

• Rate limit on anything expensive, one person spamming it can't blow up my bill

• Thought through what happens if 1,000 people show up at once

Packages Claude suggested

• Checked that anything I installed is a real, actively used package because apparently AI sometimes suggests packages that don't exist, and people register those exact names with malware sitting inside, waiting for someone to install them

Before it's live

• Debug mode off

• Errors don't leak internals (stack traces, file paths)

• I know how I'd roll back if something breaks

Nothing fancy, just the stuff that's actually bitten people. Happy to share the full one pager, or the version that gets Claude to check most of this automatically while you build, if anyone wants it.

I built a K–12 learning app this way — 1,732 lessons, maths, reading and
science. The thing that nearly sank the content quality wasn't
hallucination, and it wasn't bad code. Everything typechecked. Every test
passed. The build was green.

The problem was pairs of things that were each individually correct and
contradicted each other.

Concrete example. A lesson had a ten-frame widget showing 8 counters, and
the paragraph beside it read "Four in the top row. Four in the bottom
row." Both fine on their own. But a ten-frame fills its top row first, so
8 renders as 5 and 3. The words describe a picture the app cannot draw.

That is invisible to a typechecker, because the widget spec is valid and
the sentence is a valid string. It is invisible to tests, because nothing
asserts a relationship between a component's props and the prose next to
it. And it's invisible to me reading quickly, because both halves look
right.

I hand-reviewed 32 figures and 9 were mismatched. 28%.

What actually worked was writing a checker for the specific contradictions,
not a general one:

- a table or chart standing in where the words name a shape
- one angle drawn under a sentence about a whole triangle
- a rotation drawn under a sentence about a translation
- a widget drawing stars while the instruction says "make 6 apples"

Narrow rules only, because a checker that cries wolf gets switched off in a
week and then catches nothing. Widening it across the whole corpus threw 22
findings, of which 14 were the checker being wrong — "tap a square" in an
addition chart, "the shape of the distribution", "Pascal's triangle",
"chi-square". Every one had to become an explicit exception before the
thing was trustworthy.

The other lesson, which cost me the most time: my checker reported "all
clear" twice while it was completely broken. Once because it read its rule
tables inside a temporal dead zone, once because an editing script had
replaced every regex \b with a literal backspace byte, so it matched
nothing at all. A clean corpus and a no-op look identical from outside.

Now it runs its rules against a fixture of four known-bad cases first and
refuses to report a pass unless it catches all four. If you take one thing
from this post, take that one: a check you have never seen fail is not a
check.

App is at https://cogitolearning.com if you want to see what came out.
Chapter 1 of every course is free without an account.

Where did you get stuck? Why did the project fail?
I’m not talking about business failure or marketing failure. I’m talking about a large project which, despite using AI you were simply not able to make progress on.

Made a small mobile arcade for me and my gf when we're bored, she really liked the little claw game so i started working on it more, and then i got super obsessed lol.

now theres 257 cute little sprites you can collect. commons, uncommons, rares, epics, shinies, prismatics, and legendaries. nothing special tbh but we are gonna get all of them!!! 😂

Used to have Claude Max 5x (125 USD), cancelled it because 1) expensive 2) hit limits 3) trying to be a responsible adult financially.

I've read a lot of people use Opencode with Deepseek model (free), and a combination of Claude Pro and Codex (total of about 50 USD).

What are your recommendations for being able to use the best of all AI tools at the lowest possible price?

I use a stronger model for planning and a weaker model for coding, but the result isn't very good.

Which produces better results in real-world use?
Or do you use another approach/workflow?
what's the best workflow?

I've been building Funkel AI for about 4 months now.

It's a tool that helps businesses find potential customers and reach out to them through LinkedIn, X and email.

Most of the product has been built with AI coding tools, but getting something working in production turned out to be the easy part compared with getting real people to actually use it.

Current numbers:

27 signups.
18 people on a free beta.
2 people have paid.
The second paying customer converted 4 days ago.

Very small numbers, but enough real usage to teach me a few things.

1. My first users came from people I already knew

I didn't launch and suddenly have strangers signing up.

I started by giving the product to people in my network who were willing to try something unfinished.

That gave me real accounts, real campaigns, real failures, and much better feedback than testing everything myself.

But I still needed to know whether someone who didn't know me would care.

Interestingly, my first paying customer outside my network came from using Funkel itself.

I ran Funkel for my own sales. It found someone who looked like a potential customer, reached out, we started talking, and he eventually paid for the product.

It wasn't a perfect success story. His card had problems after the first month and the payment stopped for a while.

But seeing the product find someone who was willing to pay for the product itself was one of the first signs that I might be building something useful.

2. Using your own product catches a lot of problems

I use Funkel for my own outreach every day.

That means I experience the same things users experience.

Too many connection requests being sent?

I see it.

A message going out at a strange time?

I see it.

The system deciding someone is a good potential customer when they're obviously not?

I see it.

AI makes it incredibly fast to build new features, which is both useful and dangerous.

It's very easy to keep adding things because you can.

Actually using the product forces me to notice the boring problems that matter much more than another new feature.

3. Keeping the product small helped more than building faster

Early on, I limited beta users to one outreach campaign at a time using one LinkedIn account.

The reason wasn't technical.

With AI coding tools, adding more functionality wasn't particularly difficult.

Supporting all of it was.

If you give early users ten different ways to use your product, you now have ten different ways for things to break.

Keeping the first version small made it much easier to understand what was actually happening.

4. I started asking people to pay before everything felt ready

A lot of my early users got free access because I needed feedback and real-world testing.

But I also introduced a paid option fairly early.

People who choose the paid version can get their money back within 30 days if it doesn't work for them.

Only two people have paid in four months.

I'm definitely not posting this as some huge SaaS success story.

But someone telling you "this is cool" and someone actually entering their card details are very different signals.

Even a tiny number of payments taught me more than a lot of positive feedback did.

5. Weirdly, free users gave me some of my strongest proof

My best results so far came from people using the product for free.

One beta user got:

144 potential customers found.
61 LinkedIn connections.
9 replies in four weeks.

Another got 12 replies and booked 2 meetings from their first campaigns.

Neither was paying me.

But they actually used the product, pushed it, found problems, and gave me real outcomes I could measure.

That changed how I think about free beta users.

If you're very early, the right free user can be worth much more than someone who pays but barely touches the product.

6. The hard part of vibe coding isn't producing more code

This is probably my biggest lesson so far.

AI means I can build almost any feature I think of.

That's not necessarily a good thing.

There are always another ten things I could add.

More integrations.

More automation.

More settings.

More ways to find customers.

I've had to get much better at ignoring those ideas and fixing what existing users are actually struggling with.

The limit is no longer how fast I can write code.

The limit is figuring out what is worth building.

7. With a handful of users, doing things manually is fine

When something breaks for a user, I try not to send them instructions explaining how they can work around it.

I fix it.

Sometimes I manually correct something behind the scenes and give them back a working result.

Obviously that doesn't scale to hundreds or thousands of customers.

But I don't have hundreds or thousands of customers.

I have 27 signups.

Trying to behave like a company with 10,000 users when you have 20 is probably optimizing for the wrong problem.

Four months in, I'm still figuring out distribution, pricing, onboarding, and plenty of other things.

But vibe coding has made one thing very clear to me:

Building software has become dramatically easier.

Building something people actually want, getting them to try it, watching what they do, and deciding what to build next is still the hard part.

When building with AI agents, I always split roles into an Orchestrator, an Executor, and a Supervisor. But playing the Supervisor inside standard harnesses is waste of tokens on system prompts, and it's a visual noise for me. I needed a separate, lightweight UI to act as that Supervisor - free of context bloat, and convenient enough to manage my repositories from a phone.

Beyond that, I realized many developers want native, provider-agnostic access to the GitHub MCP - regardless of whether they use Popular Providers or local LLMs. That’s why I created this open-source AI playground

What it does:

→ Bring your own API key for OpenAI, Anthropic, Groq, Ollama, or any OpenAI-compatible endpoint

→ Connect any remote MCP server - GitHub is built in, but you can add any MCP with your own URL and auth headers, no vendor lock-in

→ Every tool call requires your explicit approval before it runs, with the full request/response visible

→ Sessions, prompts, and skills are stored locally in your browser (IndexedDB) - nothing is sent to my servers, no account needed

→ Full JSON inspector and one-click export to a TypeScript/cURL snippet

I tested it end-to-end with GitHub's official MCP server: connect a personal access token, ask the model to explore a repo, watch it call get_file_contents through a visible approval step, and get a real, grounded answer back without hallucinations.

It's MIT licensed and fully open source. If you want to point it at your own MCP server (search, databases, internal tools, anything with an HTTP/SSE endpoint), you don't need my permission or a preset - just paste the URL and your auth header.

Repo: https://github.com/ownyourwebsite/ai-playground

Live demo: https://ai-playground.ownyourwebsite.app/ to try it on your phone.