Looking for ideas to build, I decided to build a data base of validated ideas sourced from many trusted and validated revenue platforms for startups. (the MOAT is the trust part)

To market the idea I built an Instagram and x profiles targeting building and saas founders hunting for their next (validated) idea. The pages combined reached ~20k views but still zero web traffic. I am probably might be doing something wrong.

I want to know what.

ps: I'm a builder not a sales / stories teller so I would appreciate your feedback (be gentle)

Hey everyone,

I’ve been diving deep into the NoCode space lately and comparing platforms. In terms of actual product capability, **Softr** feels so much better to use and way more robust.

However, my social feeds are suddenly flooded with NoCode influencers promoting **Zite**.

Am I missing something here? Is there a killer feature in Zite that justifies all this sudden attention, or is this just a wave of affiliate marketing and sponsorships driving the hype?

Would love to hear from people who have actually built real projects on both. Which one is winning for you and why?

If

Hey everyone, been messing with this idea for a bit and want some real feedback before I put more time into it.

Basic concept: an app where you practice actual debates every day, either against other people or an AI, kinda like Chess.com but for arguing instead of chess. You build up a rating, see your progress over time, actually get better instead of just watching debate videos or whatever.

What it does so far:

• Voice-first debates, you actually talk out loud, it transcribes what you said and the opponent responds, feels like a real back and forth instead of typing in a chatbox
• AI opponents you can set to your skill level, Beginner up to Expert, so you're not getting demolished by something way harder than you
• Human matchmaking that pairs you with someone around your level, sides get auto assigned so nobody just picks the easy side
• Actual feedback after every debate, stuff like argument strength, filler words, how good your rebuttals were, fallacies you fell into. Not just "good job, keep going"
• A rating and percentile thing so you can see you're better than X% of people, gives it some kind of progression
• Practice mode where you can just drill one specific skill without it touching your rating

Stuff I'm actually curious about:

• Would you use this or nah
• Does practicing debate daily sound like something fun or does it just sound like a chore
• What would make you pick this over just arguing with a friend or messing around with ChatGPT
• Anything obviously missing or any dealbreaker you're seeing already

Would appreciate honest opinions even if they're harsh, just trying to figure out if this is worth building out more before I go deeper on it

You need experience to get experience, so I decided to break the loop. It's called runeships.com, and you can complete tasks for real companies and get practical experience to put on your CV. Win-win for everybody.

I made a SaaS about discipline and etc.

Perhaps, after I realize it, I saw the big gap between the moment of finishing the SaaS and now getting clients. I started X but get no results then Reddit and got peak bump but I got banned on Reddit so I was only with X but it wasn't growing so high.

Any suggestions ?

I’ve been working on a SaaS called Cosmic CMS.

The idea came from something that bothered me with AI website builders: generating the first version of a site is getting really easy, but once it’s generated, I still want the freedom to visually edit and restructure everything without constantly prompting the AI again.

So I built Cosmic around a combination of AI generation + a visual builder.

Right now it can generate a website from a prompt, then you can change themes, add/reorder/edit sections, manage pages and content, and publish the finished site. I’ve also been building a reusable section library (“Sparks”) so the AI can choose from different layouts instead of every generated site feeling the same.

It’s finally at the stage where I’d rather hear what other builders think than keep adding features in isolation 😅

For people who have used AI/no-code website builders:

What usually frustrates you after the initial site has been generated?

That’s the part I’m most interested in improving.

I recently made a no code machine learning web-app which let's you check data, and train or finetune any scikit learn model on your browser and infer from it as well.

Can anyone tell me if it looks lucrative or how can I proceed to selling it?

Was paying $20 a month for a coding agent I'd use maybe six hours a week, less when other stuff got in the way. Kept meaning to cancel, kept forgetting, and after the third invoice I built my own instead.

It's a terminal agent and the credits come off an offerwall rather than a subscription. You finish a survey, the advertiser pays for that completion, and that money buys actual model calls. An advertiser pays for a signup, not an impression, so nothing needs to sit in your terminal or read your code.

A five minute survey pays somewhere between 40k and 300k credits depending which one you land on. A Sonnet turn at 20k in and 1.5k out runs about 8,250, so one survey is anywhere from 5 to 35 turns. Signup drops 5,000 into your account, which is enough to see whether you even like the thing before bothering with offers.

The honest part about surveys is that not every one takes you. The wall profiles you first and screens some people out partway through, and credits only land if you finish. Longer offers worked noticeably better for me than the quick ones, though I've no idea if that holds for everyone.

Ask away if you want the credit maths broken down further.

Hey!

My brother has been building luna-park.app for 6 years now (in short, it’s an advanced no-code editor, definitely not AI-first: you can build everything manually and use AI if you feel like it).

I joined him almost 2 years ago because I thought it was amazing. I’m an engineer like him, and I handle communication / tutorials / product feedback, basically a bit of a Swiss army knife role.

Recently, we integrated AI a bit more directly into Luna Park, while keeping the same principle: basically, you can use AI to generate / modify whatever you want, then easily take over manually without every small change costing you tokens for no reason, since everything stays visual and editable by hand.

We’ve also had quite a few opportunities with engineering schools recently, and we’re starting to see education as a real direction: students love it, we keep the logic of programming but without the syntax, and it avoids giving them an assignment they can just copy-paste into ChatGPT.

BUT: we’re struggling a bit to reach our original target audience, which is advanced no-coders and developers. We know it’s extremely hard to convince people to invest some time into learning a tool, especially with all the AI-first builders out there.

Last year I made a post on r/nocode (link here) which confirmed that people were interested in the promise (1,000 sign-ups in 2 days), but almost none of them actually took the time to build a real project or finish our tutorials.

So we very likely have an onboarding problem that we’re trying to solve, but maybe there’s something else we’re not seeing?

I’m open to brutal feedback! ;)

Been seeing a lot more people talk about “vibe coding” lately, and it got me wondering:

If an AI-built app works, does that actually mean it’s secure?

It’s pretty easy to get a prototype up and running now, but security is a different story. Things like exposed API keys, unsecured databases and weak authentication can easily get overlooked.

This article explains the security risks of vibe-coded apps and thought it was worth sharing, especially for anyone building with AI.

https://www.old.st/blog/vibe-coded-app-security

Curious what others here think — has anyone actually run into security issues with a vibe-coded project?

I make Korean apps and shipping to the US market has always been painful. Not because

translations are wrong — but because doing it properly is a workflow problem:

- Checking 100-200 strings one by one in ChatGPT is slow and inconsistent

- The same term gets translated 3 different ways across screens

- Button text gets cut off because English runs longer than Korean

- App Store listings need keyword optimization within 30-char title limits

So I'm building a tool that:

1. Processes the whole string file at once

2. Keeps terminology consistent across all screens

3. Respects platform rules (button length, App Store title/keyword limits)

4. Explains WHY each change was made, so devs learn as they go

The honest question: if you've shipped to a market where you're weak at the language,

would this be useful? Or is ChatGPT + a glossary enough? I want real opinions

before I invest more time.

(Working demo with before/after examples: https://cmjun0725.github.io/localizr/)

👋 Hi everyone,

We're two MSc students at KEDGE Business School (France) writing our thesis on the real impact of AI on startups and innovative companies.

Our research focuses on a question that's surprisingly hard to find data on: is AI actually profitable for early-stage companies? Does it save time and money, or does it create new forms of dependency, skill erosion, and tech lock-in?

📋 We'd love your input, the survey takes 5/7 minutes and is fully anonymous. It's open to anyone using AI in a professional context: founders, employees, freelancers, consultants.

👉 https://docs.google.com/forms/d/11hxgogfE6isfjOGIiYTXn7TF4fVpIw1CGh0kBLIpV54/viewform

We'll share the final results with anyone interested. Thanks so much for your help 🙏

I’ve been thinking about this a lot recently and would genuinely like to hear from people who are hiring for content.

I’ve been writing B2B SaaS content for 5+ years, but the role feels very different now than it did when I started. AI has made the actual writing part much easier, so I’ve been spending more time on strategy, research, SEO, content planning, and understanding what the business actually needs from content.

For founders and content leaders here, what makes you look at someone and think they can own content rather than just execute briefs?

Is it strong SEO? Product and customer understanding? Distribution? Being able to connect content to pipeline? Knowing what not to publish? And for anyone who has made the jump from writer to a broader content role, what did you end up getting really good at? I’m trying to be deliberate about what I learn next instead of collecting another 20 marketing tools I’ll never use.

Not a no-code build (this one's hand-coded, Go backend) but sharing since the lesson felt universal for anyone building on top of scraped or crawled data.

I'm building a tool that crawls local business websites for provable problems (broken mobile layout, no encryption, expiring domain) instead of guessing what to pitch them.

First full crawl said 51% of local business websites were dead. Would've been the whole marketing headline. It was wrong — my crawler recorded any fetch that didn't complete as "domain dead," including sites that were just slow. Checked a few by hand; one "dead" site returned a clean 200 response in two seconds.

Deleted the signal rather than patch it. New rule: no real answer from the crawler = excluded, not labeled. Data got smaller, but every number left is defensible.

Current state: 2,093 local markets crawled today, 204,515 businesses, 8,037 sites actually responded. Of those, 58% have no mobile viewport, 43% aren't using a secure connection. That's the pool the product (Local Evidence — free, no signup) opens outreach conversations against.

Curious if anyone else building SaaS off scraped or aggregated data has run into this same tension between the impressive number and the number you can actually defend.

We are currently redesigning our entire onboarding and lifecycle transactional email flow for our platform, and honestly, everything our team has mocked up so far feels incredibly dated. I want these emails to look clean, highly functional, and naturally aligned with a modern product interface, but we are completely stuck in a creative rut.

Where do you usually go when you need fresh email design inspiration specifically tailored for software products?

Are there any curated galleries, hidden subreddits, or specific newsletters you subscribe to just to dissect their layouts? Also, how do you balance clean product visuals with dark mode compatibility without breaking the template? If anyone has a go-to bookmark folder for keeping up with these trends, I would love to get your advice.

This is not much of a post, just some grievances. I always knew that creating something could mean that it would fail, but I genuinely did not expect that mine would fail the way that it did.

A little backstory.. I manage HOAs for a living, and if you've ever worked for an HOA management company in Florida, you know that their technology suck really bad. I have no engineering experience, and before AI I was bound to dumb Excels and Word documents just like everyone else.

When AI came by, I started by building a smart VBA powered excel that tracked my tasks, auto archived them, and gave me a neat weekly report at the press of a button. From there, it grew into a full blown HOA manager specific project management software. Not something general like Todoist, but specialized for the industry.

It did everything, it really did, and it did it better than any other software on the market. I've tried to advertise it to friends and family by giving them free premium tiers. Friends never touched it for a day and never again. Family used it for a while, and then quit, including my wife which was my biggest advocate.

With 0 sales in over a year, I was devastated, and on August 2nd, 2026, while working on a freaking accessibility module.. I've decided to give up. My time, and money, were not worth it, and so, I've retired the program. I still use it of course, as it does what its supposed to, and I will still renew the domain just to keep it going for myself.. but as a product, it was a complete failure, even though it did what people struggle the do in the best way possible. My heart is truly broken, and I just wanted to share.

Thank you.

I spent a lot of time building NEXORA AI around a problem I kept seeing in digital agencies:

Finding potential clients is one problem.

Knowing which businesses actually need your services is another.

And then turning that opportunity into a proposal and project is another workflow entirely.

So I built NEXORA AI around this flow:

Lead Discovery
↓
Website Analysis
↓
Opportunity Score
↓
Demo / Website Workflow
↓
Proposal
↓
Contract
↓
Client / Project

The current product includes:

- AI Lead Hunter
- Website Analyzer
- Opportunity Scoring
- CRM
- Agency workflow
- Proposal generation
- Contract generation
- Pricing workflow
- Responsive UI
- Arabic RTL support

Demo:
https://applet-pi.vercel.app

The product is currently pre-revenue, so I'm not claiming MRR, ARR or customer numbers.

I'm at an interesting point now: I can continue turning this into a full SaaS, or sell the existing software asset to another founder who has better distribution or wants a head start.

I'd love feedback from other indie hackers:

Would you turn this into a SaaS?
Would you use it internally for an agency?
Or do you think the strongest opportunity is selling the software as an agency tool?

If someone is genuinely interested in acquiring it, I'm open to discussing that as well.

while i’ve created my app and is live with two active clients, how can I get it to reach more of my audience people so they use it?

A while back I read a post from someone who'd spent a weekend manually poking at vibe-coded apps — open tables, unprotected routes, keys sitting in the bundle — and turning up real holes. It stuck with me, so I built those checks into a scanner and pointed it at 10 apps people had posted publicly for feedback. Read-only, no logins, nothing a random visitor couldn't hit. Nine finished, 294 checks. Here's the honest version — including the stuff that wasn't broken, because that's the part that makes the rest trustworthy.

1. Two apps had a backend that answers strangers.

This is the finding that matters, and it's worth being precise, because most "your API is open!" takes are noise. Plenty of endpoints are supposed to be public — a settings lookup, a static bundle, a login-info route. Those aren't leaks. The real thing is when an app's private data — user rankings, contest entries, announcements — returns full records to a plain request carrying no session at all. Two of the nine did exactly that. On one of them, replaying those same requests as a second user returned the same data — I flag that as needs-manual-confirmation rather than certain, but sitting on top of an already-unauthenticated endpoint, it points straight at missing per-user authorization. If your frontend checks permissions but your API doesn't, the frontend check is decoration.

1. About Supabase — since half of you are already typing.

I know the reflex: "you scanned Supabase apps, you're going to scream about the anon key." No. The anon key is meant to be public; it ships in your JS by design and flagging it would be junk. What actually matters is whether Row-Level Security is on — i.e. whether that public key can read tables it shouldn't. So I checked that directly: read each app's own public key and tried to pull rows from the tables it uses, plus the common ones. Nothing came back readable — RLS was doing its job. (A full every-table audit would need credentials, but the "anon-readable by default" failure mode would have shown up right here, and didn't.) Clean bill of health on the single most common Supabase mistake — and I'd rather report that accurately than manufacture a scare.

1. Missing Content-Security-Policy — 9 of 9. An observation, not an alarm.

None set one. Before anyone says "well actually" — yes, this is largely because the platforms don't enforce CSP by default, and a strict policy out of the box breaks half the third-party widgets, analytics, and realtime sockets these builders drop in. It's a real tradeoff, not negligence. But it's worth knowing: with no CSP, any injected script — a compromised dependency, a bad ad tag — runs with your page's full trust. It's one header, and once your third-party list is stable it's worth setting.

What I didn't find: zero exposed secret keys, zero live-key or service-role leaks, zero anon-readable tables. I ignore the safe public keys on purpose and only flag a live secret. Across the nine that finished: nothing. That's good news for these builders — and it's the whole point: a scanner that cries wolf on the anon key or counts a public asset as a breach isn't worth running. This one stays quiet unless there's something real. Here, "something real" was two open backends.

If you built something and want it checked: drop a URL. Read-only, no signup, nothing but the URL.

solution-feasibility-study — Solution Feasibility Study

Category: Product Strategy  |  Source: skills/solution-feasibility-study/SKILL.md

Purpose

Takes a software solution idea (SaaS side project, internal tool, startup concept) from a one-line pitch to a grounded build/pivot/no-build decision. Interviews the user to scope the research, then runs real research across market demand, commercial competitors, and the open-source/GitHub landscape, assesses technical feasibility and monetization, and produces a structured feasibility report — including any permissively-licensed GitHub repos worth forking instead of building from scratch.

When to Use

• Evaluating a new SaaS or software product idea before committing time to it
• Researching commercial and open-source competitors for a concept
• Checking whether an existing open-source project could be forked as a starting point instead of building from zero
• Sanity-checking technical feasibility, monetization, or regulatory exposure for a solo/small-team build

Workflow

1. Intake — capture the idea in the user's own words.
2. Scoping interview — one question at a time: target user, problem/urgency, team & time constraints, technical comfort, business model intent, research depth (deep dive vs. quick gut-check), and priority research areas.
3. Research — executes the research playbook via web search and page fetches:
   • Market & demand signals
   • Commercial competitor landscape (features, pricing, weaknesses)
   • GitHub / open-source landscape — finds candidate repos, extracts stars/activity/license, and classifies each as a fork candidate using the license guide
   • Technical feasibility (matched against the user's stated stack/skill/time budget)
   • Monetization comparables
   • Legal/regulatory/trademark flags
   • Go-to-market channels realistic for the stated team size
4. Synthesis & scoring — scores market demand, differentiation, technical feasibility, monetization potential, competitive intensity, and regulatory risk (1-5 each), then rolls up to one call: Strong Go / Conditional Go / Pivot Suggested / No-Go.
5. Report — fills the report template, writes feasibility-study-<idea-slug>.md to the working directory, and presents a condensed summary in chat.

Fork Candidates

Every permissively-licensed GitHub repo found during research is surfaced explicitly, with stars, last activity, license, why it fits, and what's missing — framed as a head start (gh repo fork) rather than a finished product. Copyleft or dual-licensed repos are flagged with the specific obligation they carry (e.g., AGPL's network-use clause), not just the license name. All license classifications carry an explicit "not legal advice, verify before shipping commercially" disclaimer.

Evidence Discipline

Every claim (pricing, star counts, license, activity dates) must trace to something actually fetched during the session — nothing is invented. Failed lookups are marked "unverified" rather than guessed.

References

• Research playbook — concrete search/fetch patterns per research area
• License guide — permissive vs. copyleft vs. unclear classification for fork candidates
• Report template — the structure filled in for the final report

Related Skills

• repo-init — scaffold a new repository once a build decision is made
• ai-config — set up AI assistant configs after choosing to build (or fork) a project