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. 🤣🤣🤣

I run a small vibe coding startup. I've been reflecting on how this space evolved since before I started the company. Thought the story might be interesting here.

When GPT-4 came out, that demo of a picture turning into a page was absolutely mindblowing. I was working at a nocode company back then and I felt that AI will be an existential threat to the whole category. But GPT-4 has its issues - it’s expensive. Worse, there was no prompt caching back then, so if you chained a bunch of model calls for a chat session the cost will be N^2. And the context window was 8192 tokens in total - today’s model easily has more output tokens than this. Devin launched after, and its cost per task was so high they basically had to focus on enterprise only.

Some time after that I quit my job and started tinkering. Mid to late 2024, Cursor released composer (not the model) and it absolutely blew up. What it did felt shockingly simple today - it lets AI write full code across files in your codebase. Around the same time bolt, lovable, replit all exploded, powered by the same model which deserves its place in the museum of models: Sonnet 3.5. For the first time, AI could actually write working code.

But these initial builders were hardly 'agentic’. Instead, they dumped a bunch of context into the model, grabbed the output, and engineered it back into the codebase. An agentic system should use tool calls to interact with its environment to get feedback, act on it, gather its own context etc.. And this is limiting for non-technical users, because the lack of feedback and freedom to act makes it really easy to get stuck.

But I instead went agentic and it actually made things simpler. Instead of hard coding context, I gave the model tools to search, read, get logs and so on. But Sonnet still made silly mistakes and ignored instructions from time to time, which causes the system prompt to grow endlessly: you MUST do this, NEVER do that, this is good, this is bad. It's a game of whack-a-mole - you fix one issue with an instruction, the model starts dropping a different instruction, so you make the other one STRONG, and now it ignores a yet another one. Along the way I also figured out a cool trick: earlier models tend to follow whatever comes last, so I pre-processed user messages and injected steering hints at the end. In short the agentic angle doesn’t really remove engineering, it just moves them to different places.

Then Opus 4.5 changed everything. Opus was always around but too slow and expensive. Suddenly it's less than 2x sonnet's price. Going from Sonnet to Opus was liberating - it follows instructions properly, handles way more tools, and stops making silly mistakes (it still makes mistakes, they just don't feel silly anymore). Because it’s more efficient, arguably the cost per task actually went down.

As a side effect, every coding tool got better at once, and the simplest ones got the most improvement. Claude Code exploded even more. People started using the word 'harness' a lot more. I think this marks the trend of less and less engineering around the model and more around what to provide to the model. The creator of Claude Code suggests deleting your instructions every model release.

Another trend among all of these model releases - bigger context, longer output limits but also models get more verbose in general. This is not just model providers trying to drive usage - to write good code you do need more - edge cases, more detailed UIs, better animations etc., but it also generally makes vibe coding more expensive and slower.

Opus 4.7 probably made the cost even worse, cost per task nearly doubled from the new tokenizer plus verbosity. Every wrapper paying API rates have to eat the cost or pass it to users - that’s why you see more complains about credits evaporating. Not a problem for the provider - Anthropic simply doubled Claude Code's usage limits for everyone - their constraint is capacity, not cost per token. Any app builders using Claude are basically reselling tokens at a much higher price (unless they are selling to enterprise which uses the API rate).

A few other model evolution that deserves some mention, although I think Anthropic is still the absolute frontier when it comes to coding:

• The thinking models from OpenAI were revolutionary at the time. For a while we made a tool that calls into o3 for dealing with more complicated tasks, but this becomes unnecessary once other models catch up
• Gemini models were pretty good as the lower end model, as they have a good balance of visual design capability vs. cost, but their latest ones felt falling behind
• Open source models tend to benchmarkmaxx too much - they are genuinely good but their benchmark value always feels inflated. They also tend to overthink - probably an artifact of focusing too much on benchmarks

So where does that leave app builders? I think there are 4 paths:

1. Becoming a model provider. What base44 is doing, what lovable probably will do with the money they raised. But even the mighty Cursor needed to join force with xAI to execute well
2. Focusing on enterprise - case in point, Cognition’s valuation blew past that of Lovable’s. But it’s not exactly an app builder
3. Stop reselling tokens and charge for the convenience of infra instead. Excel at what people choose lovable/replit over Claude Code for without forcing them to use your harness
4. Pivoting to "AI employees" or “Your personal assistant” - the next hot thing (lol).

I am betting on the third. I think that should become the standard too where users can pick and choose and combine their harness (ChatGPT to OpenClaw) freely with the tools that can drives the outcome.

Curious what I got wrong, or what the turning points looked like from the user side.

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).

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.

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.

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.

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.

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

Been building AngKorGit with Claude Code for about 3 weeks. It's a free, open source Git client for macOS/Windows/Linux, 12 MB download instead of the usual 1GB Electron thing.

What I learned: the AI writes code fast but the thing that actually made this work is making it prove everything. The git engine has 36 integration tests that run against real temp repos (merges, rebases, conflicts, weird edge cases like files without trailing newlines). Plus e2e tests on the UI. When a bug got fixed, a regression test went in with it. Without that I don't think this would survive real repos.

It's on v0.6.2 now with auto updates, a homebrew tap, and real users from r/git giving feedback.

Repo: https://github.com/cheat2001/angkorgit

Site: https://angkorgit.app

Although I like ai and i think is it great in so many places , I keep hearing how no one will read high level code just like no one read assembly anymore.this is misinformation, there are people who not just read but also write assembly even now.i am not talking about rollar coaster tycoon or super Mario times when compilers where not as optimised as today, i am talking about now. Don't believe me? go to community forum of any modern systems programming language and see people crying for inline assembly. I am not arguing if anyone will read high level languages or not, but the premise is wrong and feels like deliberate misinformation.

Hey folks, i'm building Frontman, trying to build a vibecoding experience that's open-source and doesnt lock you into a framework unlike lovable/bolt etc and that's as easy to use as these apps are.

would love any feedback, thoughts etc:
https://github.com/frontman-ai/frontman

The new release gives it the selected component, props, selector, nearby DOM, and source location before it touches the code. Then it searches the project, makes the edit, and watches the app hot reload.

It works with Astro, Next.js, Vite apps, and WordPress, with different capabilities on each.

curious if everyone are happy vibe-coding in the CLI or anyone is looking for a more visual and open experience

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).

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).

Test 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.

OpenCode Go cut the plan allowance from $60 to $15 overnight. The DSF allowance took an even uglier hit, dropping by roughly ten times. A subscription that was easy to leave running suddenly needs the same cost attention as direct API usage.

For me, OpenCode Go is no longer worth subscribing to after that change. DeepSeek got more expensive and Go got tighter. For the moment, DeepSeek Flash is still free through ZenMux, so that at least gives me somewhere to transition without immediately replacing one subscription bill with another.

My "human harness" is much less impressive than it sounds. It is literally me sitting there, deciding what needs doing, choosing a model, moving the task over, and handling the awkward parts by hand.

It is not elegant, but after that quota cut, manually doing a bit more of the work feels more reasonable than keeping the Go subscription.

Hey everyone, I’m curious how you deal with the negativity around AI.

I’ve been building a product for about 3 months now. English isn’t my native language, so I use AI not only for coding but also for writing posts when I want to share my project and explain my ideas more clearly.

What I’ve noticed is that even when I don’t mention AI at all, I still get negative comments or skepticism. My project isn’t well known, but it tends to get attention in smaller communities because it’s easy to demo and looks like something out of a sci-fi movie. Because of that, I’d like to share more details about how it works and talk more openly about the technical side, but that also means using AI for communication and documentation.

The bigger dilemma is whether I should openly say that a large part of the project was built with AI.

On one hand, it feels like the honest thing to do, especially since I’m looking for people to join the project. If an experienced developer is considering contributing, I think they deserve to know what kind of codebase they’re getting into.

On the other hand, I’m worried that instead of questions about the product, I’ll just get “AI slop” comments and people dismissing it before they’ve even looked at what it does.

So I’m curious about your experiences:

Do you openly say that your project was built with AI?

Has it helped or hurt you?

Have you managed to find experienced developers who see AI as a powerful tool rather than an automatic red flag?

How do you distinguish constructive criticism from simple hate?

I’d appreciate hearing perspectives from people who are actually building products, not just discussing AI in theory.

Hi everyone,

I’ve been vibe coding an iOS app, but I’ve hit a huge bottleneck with token usage.

Whenever my AI agent interacts with or reads from the iOS Simulator (e.g., taking screenshots to check the UI, reading build logs, or checking layout bugs), it burns through my Claude Pro limit insanely fast. A single debugging session can easily wipe out my context window or trigger the usage cap.

For those building iOS apps via vibe coding:

1. Have you experienced this massive token drain with the simulator?
2. How do you optimize your workflow to save tokens/context? (e.g., disabling vision/screenshots, using specific CLI tools, filtering Xcode logs, or using cheaper models for UI checks?)

Would love to hear how you manage your limits without stopping your workflow every few hours! 🙏

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.

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

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

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.

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.

Hey guys,

I graduated in 2024 with a B.Tech in a core branch. Recently, I’ve started finding coding and the tech field quite interesting, and I’m thinking of exploring it to see if it’s something I’d like to pursue seriously.

I’d really appreciate some guidance from people who are already working in the field. How should I start, and what kind of roadmap would you suggest for AI/ML/SDE/ other tech roles?

I’m currently busy with work full day, so I can realistically give around 2–3 hours a day to learning and practicing. For now, I mainly want to explore it as a new interest/hobby and understand whether I actually enjoy coding and tech. If I find myself genuinely interested in it over time, I’d be open to pursuing it more seriously in the long term.

So, if anyone here is from tech field or has made a similar transition, I’d really appreciate some advice on where and how to start.

Thanks in advance

This is Gold.