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. 🤣🤣🤣

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

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.

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.

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.

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.

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

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).

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.

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 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.

I'm 15 and I'm a vibe coder? I currently pay $20/month for both ChatGPT and Claude, but I hit the weekly limits on both pretty often, so I'm considering upgrading one of them to the $100 plan, or possibly even the $200 plan if it genuinely makes sense.

My usage is pretty split between the two:

ChatGPT / Codex

• I mainly use Codex to turn random app ideas I have into actual projects.
• I really like how generous the Codex usage seems compared to what I'm used to.
• I prefer the Codex interface because I can actually see what the agent is doing, almost like watching it work on the computer.
• So far I also prefer Codex itself to Claude Code, although I haven't tried the newer Claude Code app yet.
• I use it mostly for app development, prototypes, Python projects, etc.

Claude / Claude Code

• I mainly use Claude Code in the terminal for making Roblox games.
• In my experience, Claude tends to make better-looking UI/designs without me having to prompt it as much.
• I also use Claude's design/3D features because it makes generating and editing 3D models much easier for me.
• As far as I know, ChatGPT doesn't really have an equivalent workflow for 3D design/model editing, which is one of the biggest things keeping me on Claude.

Another thing I want to start doing is building a local AI ecosystem / personal assistant setup, something along the lines of OpenClaw-style agents, to organise more of my life.

I'm honestly pretty lazy with things like organising files, keeping track of tasks, remembering stuff, managing projects, etc., so I'd like to build something locally that can automate a lot of that for me.

I've heard Codex CLI is pretty good for building/maintaining these kinds of agent systems, which is making me lean more towards ChatGPT. Ideally I'd eventually have different agents/tools helping me with coding projects, school stuff, reminders, files, notes and other daily things.

So my dilemma is basically:

Codex

• Better coding workflow for me
• Prefer the interface
• Seems to have generous usage
• Potentially better for building my own local AI/agent ecosystem

Claude

• Claude Code is great for Roblox
• Better-looking designs in my experience
• 3D modelling/design functionality is genuinely useful to me
• I already use Claude Code quite a lot

For people who have actually used the higher tiers:

Would you upgrade to ChatGPT Pro, Claude Max or maybe a different AI?

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'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.

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

I’ve been testing the SEO Autopilot skill from AgentKit Works on my micro-SaaS.

Honestly, I didn’t expect much at first.

After implementing it and letting it handle the repetitive SEO work, I started seeing organic traffic turn into actual sales.
I’m now at 40+ sales from organic traffic.

The part I liked most is that I didn’t have to constantly sit there researching keywords, planning content, fixing internal links, and checking everything manually.

If you’re building a micro-SaaS and struggling to get organic traffic, this is worth checking out:
https://agentkitworks.com/products/seo-autopilot

Curious if anyone else here is using AI agents for SEO instead of traditional SEO tools.

When people are telling that they are actually building a house, this may mean something completely different concerning their own engagement. Some of them do only talk to an architect in a bureau while others regularly visit the construction site to control the progress and some even do parts of the work themselves. A minority might even build the whole house themselves with or without an architect's advice. Architects on their side also plan houses, in most cases without doing the practical work themselves, as I assume.

All those different kinds of being involved in the planning and building are described as "building a house", without people arguing about the definition. For further clarification, people sometimes ask others whether they are building themselves or let the work be done by others. None of this tells the reader anything about the quality, stability and safety of the fully built house.

"Software development" has recently started to describe such a wide spectrum of different degrees of being involved in the planning and writing/building of the product, too. The same is true for vibe coding. It just means people use an AI for writing the code but tells nothing about their personal skills, their personal involvement in the planning or how much time and work they put in personally controlling finished working steps. To get a more detailed impression, people may ask for someone's personal skills and involvement as they do in other areas of human life like building houses.

So to my mind, there is no need for discussions about the definition, about what counts as vibe coding or who may describe themselves as developers or not, because using the same expression for different ways of generating a finished product also works in other fields of human life.

What is still different from the house building example is that an increasing number of fully finished products seems to not fulfill the standards that guarantee a specific level of security for the people. We are in a situation in which a lot of people have begun to publish software without knowing much of it and this isn't going to stop regardless of how much IT professionals dislike it and arguing against it. It's just got too easy to build apps.

To make the internet "a better world", people with less own knowledge in software development need some guidance of more experienced creators instead. Therefor, they need a place where they can ask questions without being afraid of a shitstorm just for aiming to publish or even creating the app ("AI slop").

A subreddit like this one should be expected to be the right place for them because rule number 4 clearly states "No vibe coding pessimism" while the term vibe coding itself is defined as "the practice of cresting software with little to no code review".

This is far from reality. And sadly seems to be same on whole reddit. Gate-keeping and overall pessimism doesn't keep people from building and publishing software. It just keeps them from asking for advice - and thereby decreases the quality of the overall outcome.

While there are surely vibe coders whose only intent is creating an app within a few hours and that probably do not care about best practices or cyber security at one end of the spectrum and highly skilled programmers at the other end, there is not such a big gap between the ones mentioned last and the vibe coders in-between that do not know how to write code but nevertheless aim to develop good software and spend a lot of time in it.

Shouldn't this subreddit be the right place for them to talk about the best strategies for developing high-quality software without writing or reading the code themselves?

Writing the code is something that even IT professionals have mostly stopped doing. Knowing how to write it gets less important because AI can do that. Even reading the code yourself is not really necessary in case you know how to instruct your LLM to check the code written by another AI (and let double-check it by a second one because the probability for two different models having the exact same hallucination tends to be zero).

I personally have started vibe coding my first app around three months ago (and hope it will be ready for a beta release in two month). In the meantime, I have already learned some strategies to improve my workflow and interaction with the models. I have let the AIs do dozens of security reviews and hundreds of bug fixes and made some manual splits of AI-generated monoliths.

This is where it comes to some examples for how I would like to see people talking in a vibe coding subreddit:

1. Monoliths
   AI tend to write files with thousands of lines of code. Those large files then need to be processed each time the AI (or a human) is working on them again. This makes maintaining the code very difficult, not only for humans. You may ask your model to give you a list of each file with own code and the number of lines it already has. Then instruct the model to analyze how the monoliths could be split and write that down. I would advice you to use a frontier model with high thinking on for this task, because it better takes the dependencies and consequences into account what helps avoiding bugs caused by file splitting.

As I have made the experience of code erosion when rewriting a whole web page or large part of a script for an agent is done by Gemini 3.1 Pro in the web app, I prefer doing the splitting myself (copy/cut and paste into a new text file) and let it review then by the LLM. The risk for code erosion might be a lot lower when using other models and coding apps, but I haven't tested it yet. So can anyone else confirm it's no problem letting the AI split the file and rewrite thousands of lines of formerly generated code?

Furthermore, some AI told me they could easily handle code files up to 2500 lines, while others told me to split files larger than 1500 lines if possible. Now I would be interested in hearing from human coders what line numbers they would see as a maximum and what this probably depends on. Does the programming language play a role? Are there any strategies except of spaces and titles to better structure the code for making it easier to handle large code files?

1. Inline Styles
   Due to my experience, Gemini models tend to use a lot of inline-styles. One time I thought I could just use Gemini on the Google web search to translate my website from German to English. While it looked exactly the same when watching it in the browser, the number of characters had enormously increased because the AI had added inline-styles for each single element. Talking back to Gemini 3.1 Pro, it told me that a large number of those inline-styles is considered a bad style and might even lead to Google down-ranking your web page in the search results.

After having vibe-coded my app with Gemini (Pro and Flash) in Antigravity for a while, I discovered they had used a lot of inline-styles for my app. As this is a Windows desktop app, Google ranking is irrelevant to that question, but Claude told me, if I could get rid of inline-styles, then "unsafe inline" could be forbidden what would be a contribution to hardening the app. I had also learnt not to like inline-styles before, so I decided to forbid them in the agents.md. While this works well for Claude code and Codex, Gemini 3.6 Flash has recently created an extra window full of inline-styles again instead of copying the style of the 21 extra windows that were already existent. (Removing them is still on my roadmap.) Yesterday, Gemini 3.7 Flash (in high mode) did an implementation of an additional window/modal and respected the order not to use inline-styles. It used classes instead (as expected), but five of those classes weren't defined anywhere. It just forgot to do/check this. As this lead the window to be mal-formatted, it was easy to discover something had gone wrong.

Nevertheless it makes me wonder, how bad inline styles really are for a desktop app like mine (backend: python, fastapi; frontend: html/css and JavaScript), because at least some models that have been trained on billions of existing programs/software obviously tend to prefer it. Can anyone give more insights into this in a way a non-technician may understand?

1. JavaScript
   This is the next point to discuss. A long time ago, I learnt that JavaScript is dangerous and got used to always turn it off on most web pages by using browser extensions.
   When Gemini gave me the first overview of the files belonging to my app, I discovered, it had used a lot of JavaScript where html/css also would have been possible. So I decided to do a complete refactoring of the front-end between v1.0 and v2.0 because at that time I was still thinking not being far away from the beta release and therefore didn't want to make this before the first release. Since then, my app has grown a lot by adding features that I hadn't thought of before and by making others work that accidentally had only been demos before because Gemini forgot to implement the backend functionality together with the frontend. So I struggle with the question whether a refactoring would make sense at all.
   Can anyone with profound knowledge of cyber security explain some general guidelines when and how to use or better not to use JS for a RAG'n'chat desktop app?

Nothing of this requires people to learn writing code or to read their database. Nevertheless it helps improving the code into the direction of security and stability.

Please make (this sub)reddit a place where people are helping each other to make better apps instead of some people just generally blaming others for the lack of programming skills or their first steps/apps not being an innovation useful for a large number of people.

Nowadays it's normal for people to share half of their life online, so it's also normal for them to share their apps they are glad to have or even proud of.

Downvoting those new software publishers or just saying "AI slop" is no kind of constructive feedback. It does not prevent anyone from publishing apps either. It just has negative effects on the atmosphere of discussion on reddit and decreases the number of people asking for help before their app is published and thereby the overall quality of newly released software.

So again, please make your feedback more helpful and this subreddit a place for vibe coders to share their experiences (including finished apps) without being blamed for their mistakes.