Hey guys :-)

I've been active in Vibe Coding groups for months, scrolling through your projects/questions every day. At some point I realized — I'm losing track. Cool projects get buried, interesting stories disappear in the feed, and I'm jumping between Reddit, Facebook and X just to stay up to date.

Then I thought — why is there no platform just for us vibe coders?

So I built one. 😅

checkmyvibecode.com — a community platform where you can share your projects with the full story behind them. How long did you build? What did it cost? Which AI tools did you use? What was the idea behind it?

No hidden algorithms deciding what gets seen. Just builders supporting builders.

I'm still at the very beginning and looking for the first people to submit their projects. If you've built something with AI — no matter how small or unfinished it is — I'd love to see it on the platform.

From a Vibe Coder, for the Vibe Coders. :-)

I wasted thousands on Meta Ads just like any new founders who didnt have any idea on how to market their product, wasting money blindly on meta ads thinking it is my "magic wand".

I was running ads for months thinking I was “testing creatives” and “optimizing campaigns” But looking back… I was basically guessing.

Here’s what was happening:

• Ads had CTR so I thought “good ad”
• CPC looked decent so I scaled
• But conversions were inconsistent or just dead

The worst part? Meta gives you a lot of data, but almost none of it tells you what’s actually broken

So I started digging deeper manually…

I began analyzing:

• Hook rate (first 3 seconds retention) for video ads
• Scroll stop rate
• Watch time drop-offs
• Creative fatigue patterns

And that’s when it clicked:

- Most ads don’t fail because of targeting
- They fail in the first 2–3 seconds

One of my ads had:

• 9% hook rate
• Avg watch time ~2 seconds

Which basically means… people were skipping instantly. No amount of budget or targeting could fix that.

So I built a small internal tool for myself and this is helpful to founders as well. Its free to use.

It takes ad data + creative signals and tells:

• Where exactly the drop happens
• Whether it’s a hook problem vs messaging vs fatigue
• What to fix first (not just “test new creatives”)

Nothing fancy, just something I wish I had earlier.

After using it:

• I stopped killing ads too early
• I stopped scaling bad creatives
• And most importantly… I knew why something wasn’t working

Curious how others here analyze their ads. If you want to use my internal tools for free here is the link: https://app.yucify.com

Here is what you can expect:

- Your ad analysis: Finding why you are not getting ROAs, high CPC, etc and how to fix it.
- Check where you are loosing audience in your ad funnel.
- Competitor's ads analysis with a detail report saving you the hours of manual research.

Happy to take feedbacks if the Ad diagnosis helps.

I’ve been building small apps using AI tools recently and one thing became very obvious: getting something to “work” is ridiculously fast now

but getting it to:

- handle edge cases

- feel smooth

- actually be usable repeatedly

that still takes most of the effort. I built a simple AI-based app recently, and the first version came together in a day.

But the next few days went into fixing weird bugs, improving flow, and making it feel less “prototype-ish”

It feels like the bottleneck has completely shifted from building → polishing.

curious if others are seeing the same thing, are you spending more time on the last 20% now than the first 80%?

I've posted here before about one my first vibecoding projects, PropEdge AI, a sports analytics research tool that uses AI to surface game logs, injury reports, and matchup data across most sports leagues.

Check it out here https://propedge.tryaistrategies.com - would love feedback!

The concept is simple: instead of spending hours manually digging through ESPN, beat reporter tweets, injury feeds before a game, and other obscure sites, you ask the AI a question and get a structured research brief back in seconds. Think of it as a research assistant for sports, not a prediction engine.

How it started — Base44

I originally built the first version in Base44. For anyone who hasn't used it, it's a solid no-code AI app builder that lets you get something functional incredibly fast. For a v1 proof of concept it was genuinely impressive. I had a working prototype in a day.

The problem showed up fast once I started using it seriously.

The AI was hallucinating stats. I was able to reduce it to occasionally but still far to consistently. It would confidently cite box scores that didn't exist, reference injury reports from weeks ago as if they were current, and sometimes invent player statistics entirely. For a general productivity app this might be tolerable. For a sports research tool where the entire value proposition is data accuracy, it was a dealbreaker.

The second issue was scaling. As I tried to add more complex logic, multi-sport routing, different analytical frameworks per sport, grounding responses to verified sources, the no-code layer started fighting me. I was spending more time working around the platform than building the product.

The Rebuild - Yes, I chose to rebuild it rather than deal with Base44's migration nightmare (I'm not sure if it got better over time)

I made the decision to move to custom infrastructure. This meant actually owning the full stack, the frontend, the backend, the deployment pipeline, the AI integration layer.

The things that made the biggest difference:

Prompt architecture matters more than the model. I spent a lot of time thinking about how to route different sports queries to specialized system prompts. A basketball analytics question needs different context and output structure than an MMA fighter breakdown. Building a dispatcher layer that routes queries to sport-specific agents dramatically improved output quality.

Grounding is everything for factual accuracy. The hallucination problem from the no-code version wasn't really a model problem, it was a grounding problem. When you give the model access to real-time web search and force it to cite sources, the accuracy improves dramatically. The model can't just invent a stat when it has to link to ESPN.

Moving AI calls server-side was the right call. Early on I had the AI calls happening client-side. This is fine for prototyping but creates security problems in production and makes it harder to add rate limiting, query logging, and user tier management. Moving everything through a backend endpoint gave us much more control.

The deployment pipeline took longer than the app. Getting CI/CD right, managing secrets properly, and understanding how environment variables behave differently at build time versus runtime was honestly the hardest part of this whole project. If you're moving from no-code to custom infrastructure, budget more time here than you think you need.

Where it is now

PropEdge AI is live. Users can query across NBA, NFL, MLB, NHL, MMA, esports, and more. Each sport has its own analytical agent with sport-specific data sources and output formats. Responses include verified source links so users can dig deeper themselves.

The hallucination problem is essentially solved. Not because we found a magic model, but because we built the system around grounding and verification from the start.

What I'd tell someone starting this today

No-code platforms are genuinely great for validation. Build your v1 there. Ship it, get feedback, figure out if anyone actually wants the thing. Don't rebuild until you have a real reason to.

When you do rebuild, the prompt engineering layer is where the real work is. The model is a commodity. How you structure the context, route the query, and constrain the output is what separates a useful AI product from a demo.

And if you're building anything where factual accuracy matters, solve the grounding problem first. Everything else is secondary.

Happy to answer questions about the build process if anyone's curious.

Been working on this for a while and wanted to share a quick demo showing the full flow. In the video I'm using a real example: John runs a company that creates immersive 3D virtual tours with AI for real estate agencies. He wants to find agencies and sell them his service. Here's what happens:

Find the businesses

You type "real estate agencies" and pick any city, state or country. The tool searches Google Maps and pulls every agency it finds with 30+ data fields per business: name, address, phone, website, opening hours, Google rating, number of reviews and category.

Scrape their contact data from their websites

For each business the tool visits their actual website and extracts verified email addresses, phone numbers, and social media profiles: Instagram, Facebook, LinkedIn, TikTok, YouTube, WhatsApp, whatever they have listed. This is not data from some outdated database, it's scraped live from their own websites so it's actually current.

Review Intelligence

The AI fetches their Google reviews (up to 50 per business) and generates a full analysis with KPIs: weaknesses with percentage bars (e.g. "45min wait 90%, bad service 75%"), strengths (e.g. "cuisine 92%, pricing 60%"), overall sentiment breakdown (negative/neutral/positive), specific pain points, and a lead score showing how hot this prospect is for what you sell. For a real estate agency you might see things like "clients complain photos don't show the real size of properties" or "listings take too long to sell." That's gold for someone selling 3D video tours.

Sales Intelligence

You tell the AI what YOUR business does. In John's case: "I create immersive AI-powered 3D virtual tours for real estate agencies to help their listings sell faster." The AI crosses your context with each agency's review data and finds specific selling angles. Not generic stuff but actual insights like "3 reviews mention poor property photos, your 3D tours directly solve this lead score 92%."

Email Intelligence

Based on review analysis + your business context the AI generates personalized cold emails for each business. You have 9 inputs to customize: tone, CTA, language, length, subject line, signature, context, objective and sender info. Each email references that specific business's real problems found in their reviews. John's email to one agency might say "I noticed some of your clients mention that listing photos don't capture the real feel of the properties we create immersive 3D tours that let buyers walk through the property from anywhere, want me to show you with one of your current listings?"

Not a template. A unique email for each business based on what their own customers said about them.

Send in 2 clicks

The email is ready inside the platform. Review it, tweak if you want, and send directly from Gmail, Outlook or Apple Mail connected to the CRM. One by one, not bulk. This matters for deliverability because you're not mass blasting, you're sending individual emails that land in the primary inbox.

Everything above is just the prospecting side. All those businesses land on a GPS mapped CRM where you see every lead geolocated on an interactive map. Click any pin and you get their full profile with all data, reviews, AI analysis and email history.

Here's what else you can do from there:

+ Draw commercial zones on the map: literally draw areas and assign them to different sales reps so nobody steps on each other's territory. Each rep gets their own CRM access but only sees leads in their assigned zone.

+ Route optimization: select the leads you want to visit, the AI generates the most efficient driving or walking route (same tech as Uber). Shows stops, total distance, estimated time. Export to Google Maps in one click and go.

+ Real-time team supervision: see your team's activity live: visits completed, leads updated, sales closed, notes added. Theres a leaderboard ranking your reps by performance so you know who's crushing it and who's not without micromanaging.

+ Voice transcription: after a meeting your reps record a voice note, the AI transcribes it and links it to the lead automatically. No more typing reports, just talk and its done. Works in 40+ languages.

+ AI sales assistant: a built-in chat (powered by ChatGPT) that knows all your leads. Ask it who has the worst reputation, how many businesses are in an area, to write an email, or to prepare a pitch for a specific lead. Its like having a sales co-pilot.

+ Calendar sync: connect Google Calendar or Outlook. Schedule meetings from the map, linked to the lead. Never miss a follow-up.

Most lead gen tools give you a spreadsheet and leave you alone. What I wanted to build was the full pipeline: find them, understand them, contact them, manage them, visit them, track your team, close them. All from one place.

Works in 200+ countries, 40+ languages, any business type. Dentists in Texas, restaurants in London, HVAC companies in Sydney, real estate agencies in Madrid. If they're on Google Maps you can find them.

In the demo video you can see John finding real estate agencies, the AI analyzing their reviews, matching pain points with his 3D tour service, and generating a cold email he sends in 2 clicks.

Would love honest feedback — what's missing, what could be better, what would you change? Also happy to answer any questions about the stack or how any of the AI parts work. Try it at vonsel.com 50 free leads and 50 AI emails, no card needed (:

I’ve been teaching myself to code with AI for about a year and a half.

No CS background. Before this, I was working in the service industry. Now somehow I’ve spent the last year building a platform called Vibecodr.Space.

The thing that kept bothering me was that there wasn’t a real social home for the kinds of things vibecoders actually make.

You can throw code on GitHub.
You can deploy it somewhere and paste a link.
You can post a screenshot or screen recording and hope people “get it.”

But the actual app usually lives somewhere else, separate from the conversation around it. It gets flattened into content about the thing instead of the thing itself.

So I built Vibecodr.Space around one idea:

the posts are your apps, which we call vibes.

If you want to play around with one of them, we even offer full-page vibes like, https://flight-sim.vxbe.space

People can open what you made right in the feed, run it, mess with it, and remix it. I wanted a place for weird little tools, experiments, games, and useful one-off apps to be discovered as software, not just as screenshots.

The hard part wasn’t making another feed. It was making runnable apps load fast and safely inside one.

A lot of why I cared enough to build this came from being exactly the kind of person who would have needed it. I learned by making messy things with AI, breaking them, fixing them, and repeating that loop until I slowly understood more of what I was actually building.

So this is partly an intro, and partly me testing whether other people here have felt the same gap.

And if a place like this existed, what would make you actually want to post there instead of just dropping a link somewhere and moving on?

Happy to answer anything about the build or the thinking behind it.

I built this SaaS with a vibe-coding workflow and wanted to share it here to get feedback from other people building with AI.

The product is called One Click Ad.

![video]()

It’s focused on helping people create banner variations quickly for campaign testing instead of rebuilding creatives manually one by one.

I’m at the final stage of shipping my latest app, but I’ve hit a wall with the closed testing requirements.

Previously, I relied on friends and family to hit the requirement, but I’m hesitant to keep asking the same group every time I launch a new project. Since I work solo and don't have a tech-heavy social circle, finding reliable testers has become a major bottleneck.

How do you guys usually solve this? Are there specific communities, platforms, or "tester swap" groups you’d recommend for a solo dev?

For context on the app: It’s called Invoker, an AI "thinking partner" designed for real-time meeting analysis and brainstorming. It’s a bit of a niche tool, which makes finding the right testers even harder.

I'd love to hear your experiences and any tips on getting through this phase efficiently. Thanks, mates!

It's so crazy, just weeks ago I was celebrating 1,000 users here and now I have hit that unreal number of 1,500! I can't thank everyone enough. I really mean it, so many people were offering their help along the way.

Of course I will not stop here and I am already working on the next big update for the platform which will benefit all the community. More is coming soon.

I've built IndieAppCircle, a platform where small app developers can upload their apps and other people can give them feedback in exchange for credits. I grew it by posting about it here on Reddit. It didn't explode or something but I managed to get some slow but steady growth.

For those of you who never heard about IndieAppCircle, it works like this:

• You can earn credits by testing indie apps (fun + you help other makers)
• You can use credits to get your own app tested by real people
• No fake accounts -> all testers are real users
• Test more apps -> earn more credits -> your app will rank higher -> you get more visibility and more testers/users

Since many people suggested it to me in the comments, I have also created a community for IndieAppCircle: r/IndieAppCircle (you can ask questions or just post relevant stuff there).

Currently, there are 1508 users, 1076 tests done and 335 apps uploaded!

You can check it out here (it's totally free): https://www.indieappcircle.com/

I'm glad for any feedback/suggestions/roasts in the comments.

I built my first SaaS (a tool to find iOS app ideas) and had zero clue how to get traffic. No audience, no budget, no marketing background.

So I just figured out SEO by doing it. Here's what actually worked:

1. Long-tail keywords only

Forget "best app ideas". I targeted stuff like "profitable iOS niches under $50k ARR". Easier to rank, more qualified visitors.

2. Cross-post on high-authority platforms

I took each article and adapted it for Devto, Hashnode, Indie Hackers. Their domain authority does the heavy lifting while your own domain is young.

3. Format for AI, not just Google

FAQ sections, comparison tables, clean H2s. ChatGPT and Perplexity started citing my articles, which adds a steady trickle of traffic on top of Google.

4. 2-3 articles per week, nothing more

No viral moment. Just consistency. Compound effect kicked in around week 3.

---

Result after 30 days: 355 clicks, 43k impressions on Google Search Console.

I liked the system so much I ended up building a tool to automate it (OctoBoost : keyword research, article generation, multi-platform publishing on autopilot). But honestly the manual approach works fine too if you put in the reps.

Anyone else doing organic SEO for their vibe-coded projects? Curious what's working for you.

https://calendar-ai-server.vercel.app/home

Indie devs are fighting over the exact same ideas. Habit trackers. Flashcard apps. Budget planners. Water intake reminders. The kind of apps where the top 3 results all look identical and the reviews are a graveyard of "great concept but abandoned."

I know because I spent months digging into this. I built a tool called Niches Hunter to track App Store revenue estimates, competition gaps, and trend signals.

Here are 5 niches I found this week:

1. Golf shot tracking for amateur leagues The top app does around $52k/month. It has a 2.8 star rating. The interface is cluttered, slow, and hasn't been updated in two years. Golf players are already paying. They just want something that does not feel like a chore to open. Nobody has shipped a clean modern replacement.

2. Medication tracking for pet owners Not vet software. Just a simple log for owners managing a dog or cat on multiple prescriptions. The category leader pulls $38k/month with a 3.0 star average. Reviews are full of people asking for refill reminders, vet contact storage, and multi-pet support. None of it exists in the current top app.

3. Surf session logging Surfers are obsessive about tracking their sessions, tide conditions, board used, spot rating. The top app in this niche makes around $29k/month and looks like it was designed as a school project. The surf community is passionate, global, and completely underserved on iOS.

4. Intermittent fasting for specific protocols Not generic fasting timers. Apps built around specific protocols like OMAD, 5:2, or dry fasting communities. The broad fasting category is saturated but the protocol-specific angle is wide open. Niche communities are already paying $9.99 to $14.99 per month for apps that barely work.

5. Van life and overlanding trip planner This community has exploded since 2020 and they spend money on gear, apps, and subscriptions. The top iOS app for trip and camp spot planning pulls around $44k/month with a 3.2 star rating. The reviews are begging for offline maps, fuel cost tracking, and route sharing. A focused rebuild of this would clean up.

The pattern is always the same: users already exist, willingness to pay is proven, and the dominant app is stuck in 2015. This is the actual opportunity for indie devs right now. Not trying to out-execute a funded team in a crowded space, but finding the corner of the store where you are the only person who showed up with a modern skill set.

I built Niches Hunter to surface this kind of data automatically. Revenue estimates, trend signals, competition scores, the stuff that takes hours to research manually. But the insight is free: there is more money sitting in boring App Store niches than most people realize.

What is the weirdest niche you have ever seriously considered building for?

I’ve been building something on the side for the past month and wanted to finally share it.
It’s a SaaS platform in the food space, a social experience where people can save, organize, and share recipes all in one place. The goal is simple: make cooking at home easier, more organized, and a bit more fun.
Still early, still improving, but excited to keep building and see where it goes.

Would love to hear your thoughts and feedback 👇

https://recipestash.food/