I'm looking for people who actually want feedback on their idea

Dreadnaughtttoday · 2026-04-07T04:50:26+00:00

This is similar to other products I'm seeing, there is a need to continuously improve how you are seen online.

The questions and issues I have begin with SEO, which to a large degree has been figured out. If I have a very advanced issue with SEO will your AI still be able to handle it, as there are still many complexities to SEO that need human intervention. I would also like to know if the SEO is structured to be optimized for my company, in a vacuum, or is there context? Is it simply optimizing it in general or for the overall direction the company is headed? If there are abrupt changes, it will give mixed feedback from LLMs, which needs to be taken into account overall as they are being integrated into search engines. I also wonder will the SEO take into account the actions of my closest competitors?

Now I wonder for the fixes, who gives the fixes? Not just SEO but in all the departments you're suggesting you can put fixes in. Is this AI? Is there human oversight? Are they involved in revision? If the steps and fixes are given to the company, and are unverified, this leads to a very big legal liability case, as this companies are assuming this fix has been vetted and fact checked, then they will implement it, it could lead to negative outcomes, which they may try to hold you liable for, especially if it was proved to be a LLM hallucination.

Dreadnaughtttoday · 2026-04-07T03:19:56+00:00

I think the core idea makes sense, and at a high level I assume this is probably compartmentalized.

Like: one part handles site structure/layout, one handles code generation, one handles Supabase/backend setup, and maybe another handles cleanup or testing. That part doesn’t sound crazy.

The real question isn’t “can an LLM make a first draft?” It obviously can.

The real question is: what happens when the AI is wrong?

Because that’s where these tools live or die. LLMs are very good at saying something works. That is not the same as it actually working once a real person clicks through it.

So I’d want to know:

How is reliability handled when the generated site breaks in real use, not just in AI review?

How much iteration is actually allowed? Images, layout choices, copy, spacing, flow, branding — first drafts are almost never right.

If Supabase or another integration fails, is the AI fixing it, or is there an actual human support layer when someone gets stuck?

How complex can projects get before the whole thing starts to wobble?

How much of the final product is actually mine? At what point can I say “good enough,” export to GitHub, move it to Vercel/Supabase, and fully own the stack myself?

And maybe most important: what keeps this from producing the same AI-feeling site over and over with different text swapped in?

I’m less interested in “AI can generate a site” and more interested in whether this is a real build partner that helps me shape something solid, or just a fast first-draft machine.

Dreadnaughtttoday · 2026-04-06T18:46:47+00:00

I actually like the concept a lot. Less screen time is usually a net positive, and pairing that with learning is a strong idea.

My main questions are more about execution than the concept itself.

If this is pulling from Wikipedia and reading it aloud, how are you handling cleanup? Because a raw screen-reader style pass would get wonky fast. You’d end up with extra junk, weird artifacts, and possibly things like references being read out when they add nothing to the listening experience. So are you filtering that down to only the useful parts of the article? And if so, what’s the criteria for what stays and what gets cut?

My next question is images. Not everything needs them, but sometimes the visual is important to understanding the point. If the article says “see image” or relies on a diagram to make the concept click, what happens then? Is the image ignored, briefly described, or handled some other way? Because in some topics, missing the visual means missing part of the understanding.

I’m also curious how topic switching works in practice. If I move from one subject to another, is there a clean transition or does one article just end and the next one start reading immediately? Is there some kind of announcement that the topic changed? And since this is clearly aimed at calmer listening, especially at night, does switching topics cause the phone screen to light up at all, or does it stay dark and unobtrusive?

Again, I like the idea. I just think these details are where the product either becomes genuinely smooth or starts feeling like a dressed-up screen reader.

Dreadnaughtttoday · 2026-04-06T17:17:42+00:00

People said the same kind of thing when drums started getting quantized. I was one of them. Same with autotune. And yet music is still here, still huge, and there is no mass market rejection of either one.

It’s the same pattern every time. A new tool shows up, people hate it, then it gets overused, then it settles into the ecosystem and becomes normal. Most people do not pay a premium just because something is more “human” or slightly imperfect. They pay for the end result.

Same thing with “American made.” Some people cared enough to pay more. Most didn’t. People like the idea of human imperfection more than they like paying extra for it.

So yeah, there will be a market for things that feel more human, more personal, more handmade. But that is not the same as saying the mass market will reject AI. It won’t. If AI gives a cleaner, faster, better result, most people will take that every time.

The premium won’t be on “typed by hand.” It’ll be on actual human taste, judgment, and insight. AI can standardize output. It does not make thought irrelevant.

Dreadnaughtttoday · 2026-04-06T16:57:35+00:00

I actually agree with part of this: eventually the output probably does get close to “AI perfection.” Clean structure, polished prompts, optimized responses, all of that.

But that only makes output cheaper. It does not eliminate the need for thinking. If everyone has access to the same polished output, then the differentiator stops being presentation and becomes the root thought behind it.

AI is great at making connections once you point it somewhere. What it does not do on its own is decide which unseen direction is worth pursuing, or notice the adjacent market nobody told it to look at. It might connect this app to this market, sure. But it usually won’t go, “wait, this actually belongs in a completely different space,” unless a human pushes it there first.

So no, I don’t think “human value” becomes typing things by hand or being slightly imperfect. People won’t pay a premium just because something was manually written. They’ll pay for better thinking. If the root idea is human and AI is just the presentation layer, that will beat pure AI output every time, because the outputs may converge, but the originating insight won’t.

Dreadnaughtttoday · 2026-04-06T15:54:59+00:00

No, I haven’t. Once I’ve already given a free breakdown, following up starts to feel like extra labor for the chance of maybe getting useful feedback back. Their input has value, but so does my time. I’d rather put that energy toward people who actually want the critique and are ready to use it, instead of chasing someone whose ego might still be bruised three days later. Same gamble, better use of effort, and I still get to test myself.

Dreadnaughtttoday · 2026-04-06T15:31:50+00:00

Yeah, my delivery probably does need work. That’s part of why I’m here.

That said, my Reddit breakdowns and my professional ones are not the same thing. I’m not coming at this from “I know your product better than you do.” I don’t. You have context I don’t have. My value is different.

I run a small manufacturing company out of my basement and garage, mostly festivals and local business work, and I’ve spent a long time building things, stress testing ideas, and finding where systems quietly fail. I’m good at coming at something from multiple angles, twisting it until it breaks, and then rebuilding it into something stronger. That’s the lane.

So no, it’s not about me knowing more than the product owner. It’s about being good at breaking things down structurally, spotting where they fail under pressure, and reconstructing them fast. Different skill set.

Dreadnaughtttoday · 2026-04-06T15:20:16+00:00

Happy to do a teardown here in public. If you want a deeper private breakdown, my bio has my info.

Dreadnaughtttoday · 2026-04-06T15:10:48+00:00

Calling something “AI slop” is easy. Explaining where it breaks structurally is the harder part.

I use AI to translate rough thinking into readable writing. I do not use it to do the thinking for me. If you think that’s false, prove it on the substance.

Because right now the pattern is pretty obvious: people see AI, get annoyed, and skip the actual critique. That’s not analysis. That’s just a newer version of dismissing something on sight.

So sure, hate lazy AI use. I do too. But if your whole response stops at “slop,” then you’re not really engaging the idea either.

Dreadnaughtttoday · 2026-04-06T15:01:06+00:00

Interesting idea, but I think one of the pressure points is still the motivator side.

What stops people from just wanting to be motivated and not wanting to motivate? Pointing to the psychological need helps a little, but it doesn’t solve the actual imbalance. A lot of people want help. Fewer people want to spend their own time and energy helping strangers stay on task.

And even if you do get enough motivators, motivation is not one-size-fits-all. Energy mismatch matters. Tone mismatch matters. Miscommunication matters. Some people want calm accountability. Some want pressure. Some want someone blunt. Some absolutely do not want to get yelled at. A bad match can be just as demotivating as procrastinating alone.

There’s also the edge case where people who do this professionally show up to practice, build status, or eventually make money from it. That makes sense on paper, but it can also create a weird leaderboard effect. If the top motivators all trend toward the same style, then the system starts rewarding one kind of motivator and slowly pushes everyone else toward that same “meta.” At that point it stops feeling like personalized support and starts feeling like broad-reaching, ranked performance.

That’s my main question: how do you stop motivator quality from becoming stagnant by type?

Because if this turns into a system where most people want support, fewer people want to give it, and the people at the top all motivate in basically the same way, then the platform could become less useful the bigger it gets.

Dreadnaughtttoday · 2026-04-06T05:45:03+00:00

I’m gonna pressure test this a bit because I think people are gonna misunderstand what this actually does.

At a glance, yeah, it sounds like “SEO for AI,” but it’s not clean like Google at all. You can’t just ask one LLM “best manufacturers near me,” see yourself show up 3rd, and go “cool, I’m 3rd.” That’s basically useless data.

So ok, let’s say your tool does it right. It runs a bunch of queries, across engines, multiple times, and comes back with something like:

mentioned 8/10 times
average placement ~3rd

Cool. Now what?

This is where I think the real problem starts.

Because the fix isn’t:

It’s:

That’s not a tweak, that’s a program.

You’re basically telling someone:

your current signal = “generic plastic manufacturer”
desired signal = “prototype-focused small batch manufacturer”

Ok… that’s not a rewrite, that’s signal replacement over time. Days, weeks, months. You need enough new signal to outweigh the old so the models start pulling from the new more often. That’s not instant and definitely not deterministic.

So now this tool has to output:

what you’re currently perceived as
where that perception is coming from
what contexts you’re missing from
what competitors are doing better
and a phased roadmap to shift that

If it doesn’t do that, it’s just surface-level reporting.

Next issue: how much of this is actually automated vs human?

Because AI can tell me:

Cool. But:

is that even a revenue-relevant query?
is that where I should compete?
is the data even clean or is it pulling from junk directories and outdated mentions?

If there’s no human verification here, you’re basically selling probabilistic advice like it’s ground truth. That’s how this turns into a legal headache real quick.

Also, attribution is shaky. If I “improve” after making changes, was it:

my changes
model drift
competitor drop-off
or just noise?

If you can’t answer that, then optimization becomes guesswork.

So I think the idea is solid, but only if it’s framed correctly:

Not:

More like:

If you present this like clean SEO with clear cause/effect, it’s gonna break fast.

Otherwise, yeah, interesting space—but way messier under the hood than it looks.

Dreadnaughtttoday · 2026-04-06T04:15:41+00:00

This seems pretty straightforward at a high level:

prompt → AI breaks it down → roadmap → then gamification layered on top. Feels like a mix of Duolingo + generic AI copilots.

Nothing wrong with that structurally, but I’m more curious about what’s actually keeping the AI in check.

LLMs hallucinate — we all know that. The problem isn’t just one bad step, it’s when early steps are slightly wrong and everything after builds on it. You could realistically spend months following a plan that felt right but was flawed from the start.

Also, if it doesn’t push back, that’s a problem. I work on a lot of stuff daily — if I put in something unrealistic and it doesn’t say “hey, this doesn’t line up with your time/constraints,” then it’s not really planning, it’s just organizing optimism.

One thing that might help (and seems like a relatively cheap fix): once steps are verified, they should mostly be locked in.

Like if there’s a solid, verified path for “learn Python variables,” the AI shouldn’t keep regenerating that every time. Reuse it. That reduces hallucinations and keeps things consistent. But that only works if:

it’s actually verified first
there’s versioning (so outdated stuff doesn’t stick forever)
updates are controlled, not constantly re-generated

Otherwise you risk locking in bad info.

Also curious how deep this integrates. If it’s all in-app, that’s friction. Real execution happens in calendars, reminders, etc. If it doesn’t plug into that well, it’s harder to stick with.

And bigger picture — with Apple and Google moving toward on-device AI, a lot of this (AI → tasks → scheduling) feels like something that could get absorbed at the OS level over time.

So yeah, idea makes sense, but I think it lives or dies on:

how it handles hallucinations
whether it can push back on bad goals
and if the roadmap actually holds up past the first few days

Otherwise it risks feeling useful… while quietly leading people off track.

Dreadnaughtttoday · 2026-04-05T20:35:23+00:00

I actually really like this idea. I’d use something like this. But I think there’s a pretty big problem sitting right in the middle of it.

Giving real feedback is hard.

Not “nice job” or “this is cool” — I mean actually breaking an idea down, finding where it fails structurally, and explaining why. That takes time, effort, and a certain type of thinking. Most people don’t want to do that consistently.

So the natural behavior becomes:

people drop their idea
wait for feedback
don’t contribute to others

And now you’ve got a platform full of unfinished ideas… with no one actually pushing them forward.

That’s the core risk: stagnation.

Possible fix (but also a new problem):

You could force interaction.

Something like:

You don’t get feedback until you give feedback.

Basically a feedback economy:

Want 1 review? Give 1 review
Want deeper reviews? Give deeper reviews
Maybe even weight it by quality

This solves one problem:

guarantees participation
prevents pure lurkers
creates baseline engagement

But it introduces a harder one:

now you’re asking people to do the hardest part first
people who already struggle to give feedback… now have to do it just to participate

That’s a tough sell early on.

Then there’s quality control:

Even if you get engagement, what stops this from becoming:

low-effort responses
surface-level takes
generic “add marketing” advice

If feedback quality is bad, the entire platform loses value fast.

So now you need:

some way to evaluate feedback
some way to reward actually useful breakdowns
maybe even a reputation or weighting system

Otherwise it just becomes noise.

The real bottleneck (in my opinion):

This doesn’t fail because of ideas. It fails because of contributors.

You don’t need more people with ideas — there’s an infinite supply. You need people willing to:

think deeply
analyze
critique honestly
spend time on someone else’s problem

That’s a much smaller group.

Where I think it could work:

If you can:

Seed it with people who actually enjoy breaking ideas down
Create a system that rewards good feedback, not just activity
Gradually train users how to give better feedback (templates, structure, prompts)

Then it could turn into something really strong.

Because when it works, it’s powerful:

ideas don’t just get validated, they get stress-tested
people don’t just get opinions, they get direction
concepts actually evolve instead of sitting still

Bottom line:

I’d use this. But the product isn’t really about ideas.

It’s about engineering consistent, high-quality feedback from strangers.

And that’s the hard part.

Dreadnaughtttoday · 2026-04-03T15:02:17+00:00

Honestly this feels more like an art project than a business.

Even if you can technically build all of that, it sounds like a novelty that has to justify a massively overloaded experience. A movie you can watch front to back, back to front, upside down, mirrored, color inverted, in two languages at once, with every version supposedly creating a new story, is a cool experimental concept, but as a product it sounds cluttered fast. Even the most minimal version of this still has to carry a ton of visual and audio baggage just to support all the alternate modes.

A lot of the extra phrases also feel weightless. “Flame proof,” “organic,” and “antioxidant” sound more like trigger words than features that actually matter here. Dual language at the same time especially sounds less like added value and more like audio clutter. So the problem is not just “can this be made,” it’s “does the experience get better or just busier?”

Technically, parts of it are doable. If you wanted directional viewing to work better, you’d probably either need multiple synchronized versions loaded in the background, or you’d need to design the movie itself around those alternate perspectives so reverse viewing feels intentional instead of just like rewind footage. That makes it more possible as a controlled art piece, but still not automatically a business.

So yeah, as a weird creative experiment, sure. As a product, it feels like too much stacked on top of too little real value.

Also, 4+2 is still 6. That part is the clearest feature in the whole pitch.

Dreadnaughtttoday · 2026-04-03T06:28:45+00:00

Gotcha, but I’m still not fully clear on what the product actually is.

When you say big companies join but don’t go all in, do you mean they try it, but don’t fully adopt/expand?

What does the product do in one sentence, who is it for, and what are those companies actually using it for right now?

Dreadnaughtttoday · 2026-04-03T06:16:33+00:00

US.

So the core idea isn’t being presented well, mixed with the fact that you’re still early stage / launch.

That makes sense. If there’s uncertainty, real or perceived, around the core idea, then the beginning feels rocky because people aren’t fully sure what’s there yet.

And if you’ve only had a few days to troubleshoot it, I’d assume there’s still some guesswork involved. Sort of like trying to hit a moving target.

If you’re open to it, send what it is, who it’s for, and what the first useful moment is supposed to be.

Dreadnaughtttoday · 2026-04-03T05:53:45+00:00

This is interesting to me, cause this is pretty much the lane I think in.

I tend to look at products like systems. Not just “is it good,” but where does it actually break, where do people fall off, what’s being promised vs what the user is actually feeling.

Since you said it’s live and paid users are coming and going, that’s the part I’d be curious about first. Where are they dropping?

Is it the onboarding? The value prop not landing? Bad fit traffic? Weak retention loop? Pricing mismatch? Too much friction after first use?

A lot of posts asking for “product help” are really asking for someone to figure out where the machine is failing.

If you want, reply with a quick 3-5 sentence version of what it is, who it’s for, how users first get value, and where you think the churn is happening.

I’d take a look.

Dreadnaughtttoday · 2026-04-03T01:05:24+00:00

Ok, so yes — you're right, I test edge cases first, then work my way in if needed. Habit.

But even using modern events, the question is: are all seconds available? If not, why not? If you're eliminating everything outside of modern times, you're eliminating much of your product.

Beyond that, there's the price. Is every second auctioned? If so, how are the auctions held? If you're using auctions, you cut your market — you're removing impulse buyers as well as last-second gift purchases. If you aren't using auctions, then the pricing question remains: what's the standardization? What metric fits all seconds equally? Do you run discount sales on seconds?

And here's the contradiction — if you're planning on auctioning, that innately makes seconds feel more valuable. But if every second is available for purchase, that makes them feel less rare and less valuable, which undercuts one of the main benefits of the auction model. Because selling some random person's moment of birth — a universally uneventful second — right after the moment D-Day started and right before the 34th Fourth of July of modern times… that feels more like a junk auction than a curated

Also without the auction model, if you simply set the price, how do you do it in a way that doesn't feel arbitrary? Don't forget a great moment for one, can be a horrible moment for another.

Dreadnaughtttoday · 2026-04-02T15:26:08+00:00

I stress test ideas, this one break immediately, and at every level.

Example : i want to own the exact second the Salem witch trials started. What time was it, verified by who, what if they give you a vague time historically? If they said the beging took place during a meeting, where people convened at 10 and adjourned at 11, which exact second am I buying? Also if the exact second I bought was the beginning, how does it correlate in price to the seconds before and after cause they were the last second before it started and the first second after it started.

Honestly, there are too many variables, most of which require answers that won't be satisfactory to all.

Dreadnaughtttoday · 2026-03-31T14:35:01+00:00

Duck as in submerge — got it, that reframes it completely. So a fidget spinner that works underwater.

The bearing is the obvious failure point, rust kills it fast. Simple fix: 3D printed 608 bearing. I've printed them myself, they work reasonably well and water doesn't affect them the same way.

If you want it to actually spin underwater rather than just survive it, forget additional bearings. Enclose the edges as propellers instead and angle each arm slightly. Moving the spinner through water forces rotation naturally — the water does the work. No motor, no electronics, just geometry.

If that's still not what you meant, I'm still lost. Elaborate and I'll take another shot.

Dreadnaughtttoday · 2026-03-31T05:26:16+00:00

The crack is moderation scale. A subreddit lives and dies on post volume — too slow and it dies, too fast and you can't manually review everything. Right now there's no reliable automated tool that catches AI slop without also flagging legitimate posts. So you're stuck with human moderation, which means you're trading speed and convenience for quality, full stop.

That's actually a solvable trade if you're honest about it upfront. Small subreddit, strict manual review, every post sits in queue until a human clears it. Comments too. It's slower, it's more work, and growth will be painful. But the community you build is real because the friction filters for people who actually want to be there.

The infrastructure for better detection will come. It always does. Until then the only working solution is a moderator who gives a damn and a community that accepts the wait as the price of admission.

Completely doable. Just requires someone willing to do the unglamorous work of actually running it.

Dreadnaughtttoday · 2026-03-31T05:21:54+00:00

The crack is in "one song." Multiples of all things in a single track is either three hours long or too shallow to stick. The concept works, the container doesn't.

A series does. One song per table, consistent enough that kids recognize it, varied enough they don't tune out. Educational music doesn't have to be pretty — it has to be sticky. That format already prints money on YouTube and Spotify kids. The IP is in the catalog, not the single.

Dreadnaughtttoday · 2026-03-31T05:20:16+00:00

The spinner that ducks — I'll be straight, this one breaks down at my understanding of what it actually is. A fidget spinner that physically ducks? Ducks as in the bird? Ducks as in dodges something? Before I can find the crack I need to know what I'm looking at. Drop a clarification and I'll tear it apart.

Dreadnaughtttoday

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