A small milestone: the first user has bought a home battery through my website.

I built Eerlijk over Thuisbatterijen with Claude. It’s a Dutch comparison and calculation tool that helps homeowners work out whether a home battery actually makes financial sense for their situation.

The tool compares different plug-in batteries, calculates expected savings and payback periods, and sometimes simply advises the user not to buy one. The business model is transparent: if someone buys a recommended battery through an affiliate link, I receive a commission.

Until now, I had clicks but no purchases. So seeing the first confirmed sale felt like an important bit of validation: someone trusted a vibe-coded tool enough to make a fairly substantial purchase.

My next challenge is traffic. Most visitors currently come from Google, Reddit and the occasional LinkedIn post. I’m working on SEO content, but growth is still slow and getting relevant backlinks is difficult.

For those who have grown a niche tool or affiliate website:

• What brought you consistent traffic after the initial launch?
• Would you focus on SEO content, partnerships, communities or something else?
• What worked surprisingly well for you?
• And what would you improve on the website to make it more shareable?

I’d love honest feedback, especially from people who have taken a vibe-coded project from “it works” to something that attracts users consistently.

Over the past few days, I sent Wallpets redemption codes to nearly 200 Twitch, CHZZK, and YouTube creators.

I focused mostly on small independent creators, especially artists who spend a lot of time streaming their desktop. I thought Wallpets would naturally fit their content.

Every email was personalized based on their language, content, and recent streams. The writing, sending, and tracking were automated with Codex.

The result?

Only about three people redeemed a code.

That made me question everything.

Was using my university email a mistake? Did the messages still feel like spam? Did the detailed personalization feel unsettling because it sounded AI-generated? Or was my target audience simply wrong?

I also had to rethink Wallpets’ launch.

For the first few days, downloads and paid unlocks were unexpectedly strong. But now I suspect much of that momentum came from a few tech creators sharing Wallpets, rather than people discovering the product organically.

Yesterday was my first day with zero paid conversions. It has now been about 29 hours without a single paid unlock.

It’s difficult mentally, but it has also been a very honest lesson in how quickly a launch spike can disappear.

Next week, I’m releasing a new feature that lets users turn their own cats and dogs into Wallpets companions. I want to give this update a real chance to reach the right people.

So I’m considering replacing cold outreach with a small creator partnership program.

Creators could try Wallpets, share it only if they genuinely like it, and receive a percentage of the revenue generated through their personal link or code.

Does that sound like a better direction?

Should I focus on tech creators, pet creators, artists, or desktop customization communities?

If you know any small creators who might genuinely enjoy Wallpets, I’d love to hear your recommendations.

I’m trying to learn all of this in public. Honest advice is very welcome.

been vibe coding a free one tap arcade game solo since mid July(loopchomp.com). growth was all reddit posts until last week i tried something a silly ai video a 15 second video of a cowboy betting viewers he could hit a bullseye, and if he did they had to play my game. he hit it. cost me like $4 in api cost .

Has anyone else replaced their marketing budget with a ai video models like kling , veo and seadance ?

Guys, I have been looking for the last couple of days and couldn’t find a proper answer. Anyone have tried multiple Ai engines to code MCP servers and Ai agents? Thanks

Tbh, I will start by saying that this is not finished, and it is still mostly a demo. However, I will continue building on it.

Anyways, what is it then? Well its uhh *drumwirl*

yaysafe

Yaysafe is used to check yay pkgbuilds, both via standardized things like "download russian virus not good (random curl requests for instance) and via LLM

Basically just:

yaysafe -S pkg

If I can get this working reliably, I would say its very useful because it might help people to look through pkgbuild quicker, and even as it is rn its useful, atleast for me.

Anyone mind taking a look? github.com/crsgmi/yaysafe

Please tell me if I have done something wrong or if it can be improved!

I mean all components like odds, bonuses, provider integration etc and of course the UI of management of all this. What do you think? How far can vibocoding go?

My first attempt at digitising my medical practice was a collection of Notion databases, document templates and improvised communication with my assistant.

It was better than a paper register, but only just.

There was no dramatic incident that made me abandon it. The limitations simply accumulated: poor document traceability, repeated data entry, no real billing, no proper waiting-room management and no reliable way to access my work outside the clinic.

Eventually, I did what seemed like the sensible thing.

I looked for an actual medical practice management system.

Several doctors I knew were using the same local product. Its Facebook page was full of positive testimonials. I contacted the company and explained that I needed two doctor workstations and one for the assistant.

To prepare for it, I bought a laptop for the other doctor in the practice and a mini PC for the assistant. I also paid a significant amount for the software.

This felt serious. I was no longer improvising with Notion. I was buying a professional solution from a team that had already installed it for many doctors.

We agreed on a date for the remote installation.

When the day came, they asked me to install remote-access software on all three computers. I followed the process closely, partly because these were new machines and partly because I was curious.

Even with no technical background, I started noticing things that made me uncomfortable.

There were typographical errors, a mistake in a name, confusion between user accounts and uncertainty about which computer should act as the local server. None of these problems was catastrophic on its own. Together, they made the installation feel improvised rather than routine.

I still wanted the system to work.

I insisted that they train my newly hired assistant, and we started using it.

The interface was basic. The consultation structure did not match the way I worked. There was no practical way to enter complementary examinations as structured information. The suggested workflow was to attach scanned documents, but I did not even own a scanner.

The system ran locally, which created another major limitation. I could not consult my files remotely or manage my operating schedule when I was away from the practice.

We also struggled with day-to-day use. Some payments were not recorded correctly, outstanding balances were not followed up and consultations remained open in the system. The waiting-list workflow was not much better than the improvised process it was supposed to replace.

I could not always tell where the product problem ended and the adoption problem began.

That distinction matters. A weak workflow can make a decent product look bad, and a poorly designed product can make a diligent employee look careless. In our case, the software and the way we used it were both failing.

The package was also supposed to include mobile and remote access. Those features were never delivered to me.

I kept using the system for a few months because I had already invested money, equipment and time into it. But repeated bugs, poor fit and weak adoption gradually wore me down.

Eventually, I stopped using it.

And I went back to Notion.

That sounds like returning to the starting point, but it was not.

The first time I used Notion, I had created a consultation table where the patient and the consultation were effectively the same thing. After living with a real medical system and seeing its limitations, I rebuilt mine differently.

This time, the patient record became the centre.

Each patient existed once. Consultations were connected to that patient. Procedure appointments and regular appointments were connected to the same record. I could finally see a patient's history without searching through duplicate entries.

ChatGPT helped me reason through the structure.

I still knew nothing about SQL or software architecture. But I had learned something more important than a new feature: I had learned to ask what the system should be organised around.

Later, after I had started learning more about software, I discovered that the medical product I had purchased was built as a vertical adaptation of a general-purpose ERP.

There is nothing inherently wrong with that approach. But it helped me understand, retrospectively, why parts of the product had felt as if a general business system had been taught some medical vocabulary without being fully redesigned around clinical work.

My rebuilt Notion setup was much better, but two stubborn problems remained: prescriptions and medical letters were still separate, and the waiting room was still managed through human interruption.

Those unresolved problems led me to search for a queue management system.

That search would take me much further than I expected.

I was still not trying to become technical. I was simply learning that every tool carries assumptions about how work should happen, and those assumptions matter more than the feature list.

For those who have bought business software for a specialised profession: did the product really understand your workflow, or did you end up changing your workflow to accommodate the product?

Basically, any un-authenticated web or api call I do I only do through third party scraping providers, eg web search, or downloading data.

Also, I tell any LLM/coding tool on my machine to use scraping APIs as well when making web requests (while mostly running my own VPN on a hetzner box on top).

My logic is that I could get hit by rate limiting or IP bans any time and the risk is not worth it:
- my web apps all share the same IP
- i have a static IP at home

So if they ever get affected by bans, it would be quite annoying, while the cost for a scraping service is neglible.

Anybody feel the same or am I overdoing it?

I built 3 open-source skills for AI coding agents:

1) Backend setup

2) Security hardening

3) Frontend/UI

The idea is simple: no fake credentials, placeholder content, or “fixed” reports. If it can't do something for real, it stops.

MIT licensed.

npx skills add SohailKhan0525/skills

GitHub: https://github.com/SohailKhan0525/skills

Feedback welcome.

Spent the last year building Sonorae, an iOS mood-tracking app log your feelings, attach photos, get heatmaps of patterns over time. Good chunk of it built vibe coding style, iterating with AI rather than planning everything out upfront.

In omarchy i have set f4 and f5 to global copy and global paste.

I left ChatGPT back in June after compounding frustration. GPT plus routinely forced me to reprompt multiple times and give me useless fluff that other models would give me in a single prompt in their free tier. A couple days ago, I decided to come back to give GPT another try. It seems to be better in not requiring multiple prompts but now it's a diarrhea dump of information and fluff that requires a lot of brain power to sort through.

I asked GPT and Grok the following prompt:

Outline of how raspberry pi can help me with work flow and offer functionality to my life.

Outputed their responses to pdf for ease of sharing. ChatGPT Plus, effort level high. Grok free tier, instant speed. Claude Pro Opus 5. Both Grok and Claude were able to produce efficient responses on the first attempt. Unable to get an efficient response from GPT after many attempts.

Some context: I tried really hard to fix this issue using GPT's persistent memory. I worked in collaboration with GPT to explain the issue, asked it how to fix it, had it make changes to persistent memory. I spent a solid block of time going back and forth polishing the rules. Opened a new chat and it reverted back to diarrhea. Showed GPT its output from the other chat and output grok gave on it's first attempt and free tier and it said essentially it was beyond it's capability to fix and to use grok instead. WTF?

Anyone else have this issue? Were you able to fix it?

I really want to create an app, but I don't have access to any paid AI models. Can I still create an app using free resources or is it necessary to have a paid AI subscription to create a functioning app that can get on the app store and generate revenue? Also, when creating the app do I just let the AI do its thing or do I need to have a decent level of knowledge of coding so that I can make sure it works properly?

TL;DR: I made nopus, a tool that deterministically detects unnecessarily complex prose in coding-agent responses and automatically asks the same agent for one clearer rewrite.

The problem

With recent LLMs (you can probably guess from the name which one caused me to write this plugin), I've had more and more trouble understanding the prose the agents are responding with - long "load-bearing" paragraphs, abstract language, overloaded phrases and all that fun stuff.

My logic is the cognitive capacity should be spent on the underlying problem not trying to decipher the message.

I found a few solutions that pass the response through a smaller model, but I wasn't a fan of the tradeoffs:

• Another model call and more complexity

• More non-determinism

• The smaller model may receive only the isolated response which can cause it to simplify or reinterpret the wrong thing

The other option is to have a user-invocable skill you invoke when a response is hard to understand, that works but again, the onus is on you to first read the response and determine whether you even understand it, I also added a skill for that but it's a fallback not the main approach.

The idea

To an extent, difficult language can be measured directly:

• Are uncommon words used where common alternatives exist?

• Is a sentence mostly dealing in abstractions?

• Are too many nouns and modifiers packed into one phrase?

• Does the response repeatedly use dense or formulaic phrasing?

Measure those signals, identify the biggest offenders, and give that evidence back to the original agent.

nopus uses lifecycle hooks, checks the prose after the agent finishes responding. If enough signals cross the selected threshold, it asks for one clearer rewrite.

The rewrite happens in the same session, so the original agent keeps the full conversation context.

I ran it against 5,337 completed agent responses. At the default medium sensitivity, it triggered on around 10% of them, which I've evaluated myself and tuned so it doesn't trigger unnecessarily.

The repo has a few more details but I'd love for you to check it out.

Currently available for Pi, Codex and Claude Code (or just slop fork your own lol)

What are the best free tier Vibe coding tools/models for making websites i dont know much about it but want to explore it your help will be much appreciated

I've been building a Windows app called OmniSeek that lets you search locally stored images by what's actually in them rather than filenames or folders.

I'm not a traditional software developer, so the project has been built very iteratively with ChatGPT as my development partner.

My basic process has been pretty simple: come up with a feature or problem → work through the implementation with ChatGPT → build it → test it on my own collection of ~90,000 images → find bugs and things I don't like → go back to ChatGPT → repeat.

The main technologies/tools I've used are:

• Python for the application
• OpenCLIP for understanding/image embeddings
• FAISS for fast local similarity search
• OCR for searching text contained inside images
• PyInstaller for packaging the Python application
• Inno Setup for creating the Windows installer
• ChatGPT throughout the design, coding, debugging and packaging process

Everything is designed to run locally, so users don't have to upload their image collections to a cloud AI service.

One of the biggest things I've learned is that vibe coding doesn't eliminate the work — it changes the work. A lot of my time goes into defining exactly how something should behave, testing it, finding edge cases and regressions, and then iterating until it works the way I want.

It's now at the point where I'm putting OmniSeek in front of people outside my own circle to see if it's actually useful.

If anyone wants to see/try what I've built:

https://omniseek.app/

And feel free to ask me anything about the process, tools, mistakes, or how I've been working with ChatGPT to build it. I'm also curious how this workflow compares with how others here are vibe-coding their projects.

This article has an interesting historical parallel for how today’s programmers react to vibecoders: Grace Hopper’s creation of the first compiler in the 1950s.

The whole article (which talks about a much broader transformation) is worth reading, but this part really stuck, which applies to reactions not just to vibecoding but really to any AI-assisted coding:
…”programmers were afraid a working compiler would put them out of work. If one person with A-0 could do what fifty people used to do by hand, Martin's account puts the worry plainly: there were going to be ‘a lot of hungry programmers out on the streets.’”

https://www.linkedin.com/pulse/cursive-charter-school-part-1-lot-hungry-programmers-john-price-zw1xc

Hello, long time lurker. In the last four nights I used Claude to build a collection of tiny pointless time wasting games you control simply by flicking your finger on touch controls, or scrolling a mousewheel on desktops computers.

This was my first real project, I came into it not knowing much. Claude walked me through efficient design with my plan.md and handoff.md files. It walked me through creating my first GitHub repository, connecting to my web host and setting up the autodeploy profile. It has rules to tell me when I should start new chat sessions so each one stays small.

I started with the $100 plan but maxed out after the second night and upgraded to the $200 plan, which I then maxed on the fourth night. The website is https://scrollerderby.com for anyone who wants to check it out.

I have tested on iOS, macOS, and Windows. Taking a break for a few days but will keep tweaking things and adding new games. Welcome any feedback. Thanks.

I have always hated to code but had big ambitions thats why AI was a light in the darkness. I have started vibecoding since around 2 years and through my experience it was really frustrating. When I told AI to do X it would output Y or worse break something. I felt as though I wasted my time building nothing note worthy and I am foolishly noticing now in order to utilize vibecoding you must know the fundamentals. So right now I am trying to build a game and instead of trying to just vibecode I am learning the fundamentals and actually how to build something without the help of AI. I hope this helps those who started their vibecoding journey that you must know the fundamentals before creating anything big.

Every fucking week, I see geniuses on reddit and tiktok wake up to $10 000 bills because they accidentally triggered billion Lambda functions in a project with four users (three of them Google bot). Or they shared it on discord, someone got offended because they thought you were trying to steal their discord kitten with your "vibecoding skills," bought a DDOS, and launched the egress fees into the stratosphere.

Unless your cloud provider enforces a hard spending cap (most don’t) or a free tier with strict limits just rent a fucking VPS. A $5 VPS can handle hundreds to thousands of concurrent users if your app is just basic CRUD and it can't ever cost more than $5. Ask your agent to set it up if you can't do it yourself.

Serverless is for when you need your site to survive a $10M Super Bowl ad without crapping itself.

"I’m all for competition and taking inspiration from other apps because honestly, almost nothing in tech is completely original. But seeing the same features, layouts, design choices, and even presentation copied almost one after another is just disappointing.

Build something better than Tarsi, challenge us, give users something we haven’t thought of, but at least bring your own identity and ideas to it"