I built a tool that tells you if your startup idea has real demand — before you write a line of code
Most ideas die in Notion. Not because they were bad, but because founders skip the one step that would have told them whether anyone actually cares.
I kept seeing the same pattern: months of building, a ProductHunt launch, and then silence. The problem wasn't execution — it was that nobody validated demand before writing line one of code.
So I built spark.
How it works:
- Describe your idea in one sentence
- spark generates a live landing page in under 60 seconds
- You share it — Reddit, Twitter, Slack, wherever your audience is
- Real people sign up (or don't)
- Unlock the AI analysis for $19: verdict, next steps, top risk, full email list
The insight I kept coming back to: there's a fundamental difference between an AI telling you your idea sounds promising and 14 strangers leaving their real email address. One is a simulation. The other is evidence.
What you get from the analysis:
- Conversion rate benchmarked against thousands of tested ideas (cold traffic average is 3–7%)
- AI verdict: Build / Pivot / Drop
- Your next 3 concrete steps
- The top risk to watch
- Full email list + CSV export
- Page extended from 7 to 30 days
Zero signups? You owe nothing. That's also signal.
Would love feedback from this community — especially anyone who's validated (or killed) ideas before building. What's your current process for testing demand?
→ usespark.net
An upvote on product Hunt would be appreciated
→ https://www.producthunt.com/products/spark-validate-your-startup-idea?utm_source=other&utm_medium=social
The ability to edit the page before launch is a fantastic feature! Overall, this is a very solid concept, congrats on the launch.
My only concern is data ownership. Currently, Spark essentially becomes the 'owner' of thousands of unique business ideas and sensitive email lists. This creates a potential conflict of interest and security risks for founders. I think users would feel much more secure if Spark offered direct integrations via Webhooks or Zapier. That way, the emails could flow directly into their own CRM systems instead of just sitting on your servers.
Keep the good work!
Thank you — really appreciate it.
On data ownership: fair concern, and worth being transparent about. The emails are stored in your account, not ours to use — we don't contact them, sell them, or use them for anything outside of showing them to you. You can export the full list as CSV the moment you unlock.
The webhook/Zapier idea is genuinely good and on the roadmap. Right now the flow is: test → get signal → export and move to your own CRM. But a direct integration that skips the manual export step makes sense, especially for founders who are running multiple tests.
Thanks for the concrete suggestion — this is exactly the kind of feedback that shapes what gets built next.
The distribution point is so true. I hit the exact same wall — built something, shared it to the wrong audience, got silence. Then I realized the idea wasn't the problem, the test setup was. The headline insight is spot on too — as long as you describe the solution instead of the pain, people just scroll past.
That silence after a launch is the worst kind of feedback — it feels like rejection but it's usually just the wrong room.
Most people fix the idea when they should fix the audience or the first line. Once those two are right, the same idea that got nothing starts converting.
What did you end up changing — the channel, the framing, or both?
Great approach to validation — the email signup metric is a much better signal than AI-generated opinions. At ClipForge, we took a similar approach by validating through creator communities first before writing any code. The key insight you're highlighting about the difference between "AI says this looks promising" vs "14 people gave their email" is exactly right.
One thing I'd add: the most common reason for zero signups I've seen is poor distribution, not bad ideas. If you're only posting to generic subreddits or your own Twitter following, you're not reaching your actual target market. The tool itself looks solid — have you considered adding a "best channels for your specific idea" recommendation based on the niche?
Completely agree on distribution — it's probably the #1 reason for zero signups and the hardest thing to give generic advice on.
The "best channels for your niche" idea is actually something I've been thinking about. Right now the AI verdict mentions where to find your audience, but it's fairly generic. Making it specific to the idea (e.g. "post in r/solotravel, not r/entrepreneur") would be way more useful.
Good validation story with ClipForge — creator communities are underrated for early signal. Did you go wide across communities or focus on one first?
Automating the bridge between a Notion brain-dump and a live conversion test is a great way to debug founder hubris before the first commit. You are effectively offering a shortcut through the valley of silent launches by prioritizing real email signups over polite AI feedback.
What is the most common reason you see for an idea getting zero signups during the testing phase?
Honestly? Distribution almost every time — not a bad idea.
Founders share to their existing audience (who are too polite to ignore it) or post once in a generic subreddit and read zero signups as "the market said no." It wasn't the market. It was 12 people.
The second most common: the landing page headline describes the solution, not the problem. "AI-powered habit tracker" gets nothing. "Stop planning your day at 11pm when it's already wasted" gets signups.
The idea rarely kills itself. The test setup does.
We used to have the exact same mindset you mentioned: 'Post once in a subreddit, and if no one signs up, the market has spoken.' That was exactly how we felt during our first Product Hunt launch.
For our second launch, we got a bit more savvy and tweaked our copy to something more relatable, like, 'Don't try to plan your day at 11 PM when the day is already wasted.' We’ve learned a bit since then, but we still have a long way to go.
If you're open to it, could I ask for your brutally honest critique of our product, Bunzee.ai? We only got 8 upvotes on Product Hunt and are currently stuck in the cold-start phase, so we really need a sharp perspective.
To give you a quick intro, we built Bunzee.ai for founders asking: 'Who are my competitors, what do their users hate, and what exact features do I need to compete?' It pinpoints competitor pain points using 100% real, hallucination-free 'human' review data. Based on that, it tells you exactly what to build (generating a PRD) and how to build it (providing an MVP prompt).
This sounds like a legitimate way to test the market before putting in a bunch of time and energy to your product. With more people jumping into building products these days, I can see your tool doing really well.
Question: How does the AI find the appropriate channels to post, like how does it accurately know where those 14 people would be located online? Do they keep retrying a couple of times, or is it a one-time thing?
Good question — worth clarifying. spark. doesn't distribute your page automatically. You share it yourself, wherever you think your audience is. The AI part comes after: it looks at who actually signed up and tells you what that signals about your real market and where to find more of them.
So the loop is: launch page → share it → collect real signups → get a verdict on whether it's working and where to double down.
Most founders run it a few times — tweaking the angle or targeting a different community — until they find the version that converts. The page stays live for 7 days, extendable if you're getting traction.
This is really relevant, especially the line about the difference between AI saying an idea is promising and strangers leaving their email. That gap between “analysis” and real trust is exactly where a lot of founders seem to get stuck.
I’m researching this for Tradi right now: how early-stage founders decide which tools or validation methods to trust before they spend money or build too much.
Curious, when founders use Spark, what do you think gives them the most confidence: the AI verdict, the conversion rate/signups, seeing the email list, or feedback from where they shared the page?
Honestly, it's the email list — every time.
The AI verdict and conversion rate give context, but founders don't really believe it until they see an inbox row with a name and email they don't recognize. A stranger with no social obligation to be nice gave you their email. That's the moment the doubt breaks.
The conversion rate matters more in hindsight — founders use it to compare iterations or benchmark against their next idea. But the raw list is what makes it feel real on day one.
Curious what you're finding with Tradi — are founders more likely to trust tools with social proof, or ones that give them a concrete artifact (like a list) they can point to?
That’s a really useful distinction, “context” vs “belief.”
The email list being the moment the doubt breaks makes sense because it turns validation from an abstract score into real evidence from strangers.
For Tradi, I’m starting to think the same may be true for software buying: founders may not fully trust a recommendation unless there’s some concrete artifact behind it, comparison notes, risk checklist, founder use cases, or evidence that similar teams chose it.
When you say “a concrete artifact they can point to,” what kind of artifact do you think would create the most trust before buying a tool: a ranked shortlist, side-by-side comparison, risk report, user examples, or something else?
For buying decisions, I think it's user examples — specifically ones that match your exact situation. Not "team of 50 at a Series A" when you're a solo founder. The more specific the match, the more it collapses the "but does this apply to me" doubt.
A risk report is underrated though. Most buying content is upside-focused. Something that honestly says "this breaks down when X" builds more trust than a glowing comparison, because it shows the artifact isn't just marketing.
Combination that probably works best: one matched example + one honest limitation. Short, specific, no spin.
What's Tradi's current format for surfacing those?
That “one matched example + one honest limitation” framing is really useful.
Right now, Tradi is still early, so the format I’m thinking through is something like a lightweight buying/diligence note for founders:
The part I’m still trying to understand is what format founders would actually trust and use. A long report may feel too heavy, but a simple ranked list may feel too shallow.
Your point makes me think the useful version may be short and specific: “someone like you used this for X, but watch out for Y.”
When you’re evaluating a tool, would you rather see that as a short note, a side-by-side table, or a more detailed diligence style memo?
This really hits.
I am seeing the same thing while building. The hardest part is not knowing what to do, it is choosing what not to do. There are always more ideas, more features, more places to push, especially when things feel slow.
What I am learning is that progress usually comes from staying with one thing long enough to actually make it work. Not jumping when it gets uncomfortable. A lot of the frustration seems to come right before something starts to click.
Doing less is not easy, but it is probably the only way to do something properly instead of just adding more noise.
This is the actual hard part. Anyone can start — staying with something through the quiet period before it clicks takes a different kind of discipline.
"Doing less properly" sounds simple until you're two weeks in with flat metrics and three shiny ideas in your notes. The pull to pivot is strong exactly when you should be holding.
What's helping you stay with it right now?
Nice idea!
Thank you
Demand validation before building is smart. Wish I'd done this before my last project.
And does it work for niche B2B ideas or mainly consumer stuff?
Totally get that — learned it the hard way too.
It works for both, but differently:
B2C → faster signal (more traffic)
B2B → slower, but higher-quality signups
Even 2–3 relevant B2B signups can be a strong validation.
That B2B vs B2C split makes sense. The signal quality difference is real.
I'm actually experiencing this right now with my own launch - targeting freelancers and small business owners (B2B-ish). Getting feedback faster than expected but converting that into actual sales is the next challenge.
Appreciate the insight. Going to check out the tool.
Good luck with the launch — that feedback-to-conversion gap is its own puzzle, usually a positioning or urgency problem more than a product one.
Let me know what you think of spark. after you try it.
Interesting! Good luck.
Thank you — appreciate the kind words
The "14 strangers leaving their real email vs an AI telling you it sounds promising" line is the whole product in one sentence. That's the landing page headline if it isn't already.
The zero signups = you owe nothing model is smart too. It removes the biggest friction point which is people feeling like they're paying to be told their idea is bad. You're making the failure case free and that changes the psychology entirely.
Question I'd genuinely think about — what happens after the $19 analysis? Someone gets Build verdict and 3 next steps and then what? The most valuable thing you could do is own that next moment. Even a simple "here's where founders who got this verdict went next" would extend the relationship beyond a one-time transaction.
Good luck on the PH launch.
"14 strangers leaving their real email" isn't the headline yet but it should be — taking that.
The post-$19 moment is the exact gap I'm thinking about. Right now the relationship ends at the verdict and that's a mistake. Someone who just got a Build signal is the highest-intent founder I'll ever have — they have real demand, a warm email list, and momentum. Dropping them at "here are your 3 next steps" is leaving the most valuable moment on the table.
The direction I'm thinking: a lightweight continuation layer. What did founders with similar signals do next, what resources actually helped, optional check-in in 30 days. Not a full platform, just enough to make the verdict feel like a starting line rather than a finish line.
Thanks for pushing on this — it's the most actionable thing anyone's said today.
This topic is very relevant; nowadays, AI increasingly dominates the market, and with its help, everything becomes easier. A quick question: can we provide topics for the landing page before it's created, or can we make changes and adapt it as we wish after it's done? What were and are your biggest challenges? Good luck on the long road ahead.
Yes to both — you can guide the page before it's generated by describing your idea in detail, and you can edit any section after it's live directly from your dashboard.
Biggest challenge so far: making the signal trustworthy. Generating a page in 60 seconds is the easy part. Making founders actually trust what the data tells them — especially when it says "drop it" — is harder. People want validation, not truth. Building a product that delivers the second one is the real work.
Thanks for the kind words.
Really interesting approach — especially the part about measuring real signup intent instead of relying on AI “simulated validation.” Totally agree that nothing beats strangers willingly dropping their email.
Quick question for you:
How accurate have you found Spark’s validation to be after founders actually build the product?
Do you have examples where a landing-page test strongly predicted real demand later?
I’m asking because I’m currently building a tool myself, and I’ve been trying to validate it through conversations + small experiments instead of landing pages. Curious how Spark’s results compare to other early-signal methods.
Love the philosophy behind this — removing months of blind building.
Honest answer: I don't have post-build outcome data yet — the product is too new. What I can say is that the signal is directionally consistent with what validation research shows. A stranger typing their email because they want something is a stronger behavioral signal than a conversation where someone says "yeah I'd use that."
Conversations are easy to say yes in. A signup requires a small but real act of commitment — they're giving something (their inbox) in exchange for something they actually want.
That said, your approach of conversations + small experiments is complementary, not competing. Conversations tell you the why behind behavior. A landing page test tells you the rate. The strongest validation combines both — use conversations to sharpen the copy, then use the page to measure whether strangers act on it.
Would be curious what you're building — happy to run a test and see what the data says.
That's a fair and honest framing — and the distinction you're drawing between behavioral signals and conversational ones is exactly right. Saying yes in a conversation costs nothing. Typing an email costs a little. And that small friction is actually the point.
The "conversations tell you why, the page tells you the rate" framing is how I'd think about it too. Neither one alone gives you the full picture. Conversations without a page test can leave you optimizing copy for people who are already warm to you. A page test without conversations can leave you with a conversion rate you can't explain or improve.
What I'm building is a document analytics platform — think DocSend but with more depth on the analytics side. Signature tracking, per-page engagement, CC recipient tracking, that kind of thing. The validation question I'm sitting with right now is less "will people pay" and more "which use case pulls hardest" — is it the sales team sending pitch decks, the legal team tracking NDAs, or the ops team managing file requests. The conversations have been useful for surfacing that the jobs-to-be-done vary a lot by role. The page data should tell me which angle to lead with.
Would genuinely be curious to run the test you're describing. What does that look like on your end?
This is good - but I would think an intermediate step is required that expands upon the idea and creates a "value proposition" because users will typically not provide that.
Then the output could be even better.
That's actually what happens under the hood — the idea text goes through a value proposition expansion before the page is generated. spark infers the target customer, the core problem, and the benefit framing before writing a single line of copy. The user just provides the seed.
The output quality depends heavily on how specific the input is, which is still the main friction point. Better input guidance is something I'm working on.
I think that would be useful. As it stands now, I entered one para and it picked out things verbatim and some of the Copy was too technical so knobs to adjust those as well might be useful
That’s really helpful feedback 🙏
Right now it leans a bit too close to the input, especially for technical ideas.
Working on adding more control (tone, level of abstraction, etc.) so it can simplify and adapt the copy better.
Intersting, Would love to read more in future.
Thanks for the kind words, feel free to test it with your ideas
Interesting tool. This will definitely is something good.
Thank you — means a lot at launch.
interesting, but there is one thing we can't validate an idea in just 48H
I advise you to go for at least a week for that
Good point, you're right, validating an idea takes at least a week, thanks for the advise
Interesting tool. This will definitely save a lot of time for a new founders.
Thanks, feel free to test your idea, it's free!
A great initiative. This tool can eliminate later frustration.
That's exactly the goal — catch the wrong bets early, before they cost months of your life. Thanks for the kind words.
Good direction. Too many tools validate ideas with opinions instead of behavior. Real signups > AI validation. I’d be more convinced seeing case studies where people actually pivoted or killed ideas based on your data.
People love seeing real case studies. Myself included. A site that shares bad ideas is very interesting.
Spot on — and that's the gap I need to fill. The behavioral signal is there, the proof-of-outcome stories aren't yet.
If you've run a test (or are willing to), I'd love to document what happened — whether it was a pivot, a kill, or a build. First real case studies would mean a lot at this stage.
Home people enjoy it! But you need to create something more fix to a group of people oe niche!!
Fair point — right now spark is for anyone with an idea, which means it's optimized for no one specifically. The niche question is real. Early thinking is indie hackers and solo founders who move fast and don't have a team to gut-check ideas with. Still figuring out where the sharpest fit is — usage will tell me.
I'm sorry, but this just looks like one of the 100,000 websites generated today by AI with no real proof that it works. I guarantee you there are a hundred other projects also called Spark that think they're doing the same thing.
Fair. The proof isn't on the page yet — that's on me.
On the name: you're probably right there are others. Doesn't change whether the product works.
The only real answer to both points is case studies and retention. I don't have them yet. That's what the next 30 days are for.
This is tapping into a real pain, but the strongest version of this idea is not “validate before you build”—it’s “separate curiosity from commitment using real behavior, not simulated interest.” The difference matters because landing page signups are still a low-friction signal, not true demand; people will drop an email for curiosity, free value, or even just good copy, but they won’t necessarily pay, switch tools, or change behavior. So the risk is positioning “14 emails” as validation when it’s really just weak intent data unless you anchor it against something stronger (like return visits, reply-to-email rate, or willingness to take a next step like pre-ordering or booking time). The interesting direction here isn’t just generating pages fast—it’s building a system that tells founders how close they are to real purchasing intent, not just interest. What’s your current threshold for calling something “validated” versus just “people were curious”?
This is the most useful critique in this thread.
You're right that an email signup is weak intent — it's the lowest-friction signal that exists. We're transparent about that: the benchmark we show (3–7% on cold traffic) is explicitly framed as "interest signal," not demand proof. But you're pointing at something deeper — the product shouldn't just measure interest, it should help founders understand how far they are from purchasing intent.
The honest answer to your threshold question: right now we don't have one. A 10% conversion rate on cold traffic is strong signal that something resonated. It's not proof anyone pays.
The direction you're describing — return visits, reply rate, willingness to pre-order or book time — is exactly where this should go. A layered signal stack instead of a single conversion number. That's a meaningfully harder product to build, but it's the right one.
Keeping this comment for the roadmap. Genuinely.
The framing of "14 real emails beat any AI verdict" is sharp, and I think it points to something deeper: writing about an idea can make you feel like you've stress tested it when you've mostly just rehearsed it.
The fastest pre-build signal I've found is asking one person in my target market to say the problem back to me in their own words, not "does this sound good" but "when did you last feel this?" If they can answer that naturally, the idea has roots. If they can't, the idea might be more interesting to me than to them.
One thing I use to catch ideas before I lose them: DictaFlow. I dictate the raw impulse the moment it hits, before I have time to polish it into something that sounds impressive but no longer has the original conviction. Reviewing cold dictations a few days later is a surprisingly honest test, does this still feel urgent, or did I just like how I wrote it?
"When did you last feel this?" is a better qualifying question than anything I've seen in a validation framework. The difference between a problem someone can date and locate versus one they can only describe abstractly is everything.
The dictation idea is interesting for the same reason — raw impulse before the pitch reflex kicks in. Most people by the time they're typing an idea into a tool like spark have already half-sold themselves on it. The honest version was 20 minutes earlier.
What you're describing and what spark does are actually complementary — yours catches the idea before polish, spark tests whether the polished version lands with strangers. Both check different kinds of honesty.
nteresting framing, agree that 14 real emails beat any AI verdict.
Curious about the methodology side: when spark gives the Build/Pivot/Drop verdict, what's it actually weighing? Is it purely conversion rate against the 3-7% benchmark, or are you also factoring things like time-on-page, traffic source quality (Reddit vs cold ads vs warm Slack share would tell very different stories), or self-reported intent from signups?
Also wondering how you handle the noisy cases like 50 signups but all from one Reddit thread that just liked the idea entertainment-wise vs actual buyers. That false-positive risk feels like the hard part of demand validation.
Good questions, and I'll be straight about where the methodology is solid vs where it's still thin.
Right now the verdict weighs conversion rate against the benchmark, traffic source (we track referrer), and signup volume relative to views. What we don't yet factor in is time-on-page or self-reported intent from signups — those are on the roadmap but not live.
The Reddit thread problem is real and it's the hardest case. 50 signups from a thread that went viral because the idea was entertaining is a different signal than 12 signups from a quiet Slack share to actual practitioners. We flag traffic source concentration in the analysis, but we don't yet automatically discount it. That's a gap.
The honest answer is that no single conversion rate number tells the full story — which is why the analysis includes next steps rather than just a verdict. "You got 8% conversion from a single source" should lead to "now test with a different audience" not "start building."
The stronger version of this product is exactly what you're describing — a signal stack that weights source quality, return visits, and downstream intent. That's where this goes.
This is quite wonderful….I have learnt to realize a problem then build a solution, instead of creating a solution and looking for problems or customers.
How are you currently handling pushing your product to the market Saya?
I would surely give an upvote at product hunt
Thank you — that means a lot.
On distribution: mostly organic right now. Sharing in communities where founders already talk about ideas — Reddit, Indie Hackers, Twitter. The product is self-distributing to some extent because every spark page has a public URL that people share themselves to collect signups.
Would genuinely appreciate the upvote . Here's the link: https://www.producthunt.com/products/spark-validate-your-startup-idea?utm_source=other&utm_medium=social
The useful part here isn’t idea validation.
It’s compressed demand proof.
Most founders don’t need more idea feedback.
They need faster evidence.
That’s the real product:
not “validate your startup idea”
but “get proof before you build the wrong thing”
spark is clean, but still sounds early and lightweight for that job.
If this becomes the default pre-build demand layer, the name likely needs more weight.
Exirra.com fits that better.
Stronger signal, more durable, less MVP-coded.
spark works for a quick test.
Exirra works if this becomes the system founders trust before committing months of build time.
Exirra does not exist.
I meant Exirra.com as a brand/domain direction, not an existing product.
Point was: “spark” feels right for a lightweight idea test.
But if the product becomes something founders trust before committing months of build time, the name needs to carry more authority than a quick-start name.
Exirra.com gives it that stronger software-brand feel.
"Compressed demand proof" is a better frame than what I'm using — genuinely stealing that.
On the name: spark is intentional. The lightness is the point. Heavy names signal commitment before you've earned it. If this becomes the default pre-build layer, I'd rather grow into the weight than start with borrowed credibility.
Exirra sounds like enterprise software. That's a different bet.
That’s fair.
The real question is not whether Spark is wrong.
It’s when the product stops being “light enough to try” and starts needing to feel “serious enough to trust.”
Spark is doing the first job well.
It gets weaker the moment the product stops being:
“quick validation”
and starts being:
“evidence I’d trust before spending 3 months building”
That’s usually the inflection point.
Not a better name now.
Just a heavier one when the product earns it.
Agreed. And that inflection point is exactly the one worth building toward — not naming toward.
Really like the focus on actual validation signals vs AI-generated “this sounds promising” — that distinction is spot on.
One thing I’d be very curious about though:
how are you seeing the conversion from “people signing up on the generated page” → actually paying the $19 for analysis?
Because from a user perspective, the core value already happens at the moment of:
“people left their email → there is demand”
So the paid step might feel slightly secondary unless the value of the analysis is extremely clear and immediate.
Also wondering:
before someone shares the page, is it obvious what kind of result they’ll get after?
Feels like small clarity + expectation gaps here could have a big impact on conversion.
Happy to take a closer look at the flow if helpful — this is a really interesting space.
Really appreciate this — you're pointing at something real.
On the paywall: you're right that "people signed up = demand confirmed" could feel like the job is done. The bet is that signups create urgency rather than satisfaction — you can see the count but the emails are blurred, the verdict is locked. That tension is what's supposed to drive the $19. Whether it actually does, I'll know soon.
On expectation setting before sharing — that's the sharper critique and I think you're right. There's a gap between "your page is live" and "here's exactly what you'll learn when people sign up." Working on making that more explicit.
Would genuinely take you up on the fresh eyes — feel free to run an idea through it and tell me where it breaks.
Makes sense — the “blurred value creates tension” approach is interesting, I can see the logic behind it.
I tried to look at it from a first-time user perspective, and one thing that stood out:
before sharing the page, I’m not fully sure what kind of decision I’ll be able to make after the analysis.
Right now it feels like:
“you’ll get a verdict”
but not fully clear:
“what will I actually do differently after I see it?”
For example:
– will it tell me why people didn’t sign up?
– will it suggest how to improve the idea or positioning?
– is it more like validation or iteration guidance?
I think making that part more concrete before the sharing step could increase both:
→ motivation to share
→ and willingness to pay after
Happy to run a real idea through it and give you a more structured breakdown of where the flow feels strong vs where it drops.
This is the clearest articulation of the gap I've heard so far — thank you.
You're right. "You'll get a verdict" is too abstract. The question people actually need answered before they share is: "if this works, what will I know that I don't know now?"
The analysis does answer the iteration question — it tells you specifically what held people back, whether the problem is the idea or the positioning, and gives you 3 concrete next steps. But none of that is visible before you share. That's the fix.
Taking you up on the structured breakdown — run a real idea through and tell me exactly where the motivation to share drops. That's the most useful thing I could get right now.
Got it — I ran a quick test flow with a real idea, and I can already see where the motivation to share starts to drop.
The main point isn’t the tool itself — it’s the moment right before sharing.
Right now it feels like:
“I’m about to send people to a page… but I’m not fully sure what I’ll get back that justifies asking for their attention”
A couple of moments where friction shows up:
– before sharing, the outcome still feels a bit abstract (“verdict” vs a clear decision I can act on)
– I don’t fully know what kind of insight I’ll get beyond “there is / isn’t demand”
– so the emotional push to actually share isn’t as strong as it could be
I think if that part becomes more concrete and outcome-driven, it could significantly increase both sharing and conversion after.
Happy to map this out properly — this feels like one of those flows where small changes can have a big impact.
The conversion rate benchmark is what makes it useful — knowing whether 5% is good or bad on cold traffic is something most people have no reference point for.
Exactly, most people don't understand the difference between this website, and a regular landing page builder, the thing is, this website isn't a landing page builder at all, the page is the bait. The real product is what happens after: real strangers see your idea and either sign up or don't.
You get a conversion rate + AI verdict based on actual humans, not AI guessing if your idea sounds good.
Demand validation before building is the right order. The gap I see is between 'people say they're interested' and 'people actually pay' — those two signals are very different. How does your tool distinguish between expressed interest and real willingness to pay?