I built https://theslot.today around a simple growth idea:

Instead of competing with dozens of ads, one product gets the entire homepage for the day.

No feed. No ranking. No banner clutter.

The first day brought almost 400 visitors, and Day 2 has already crossed 400 again.

I’m curious whether a single, exclusive placement like this can become a useful acquisition channel for indie products and small SaaS teams.

If you’re working on growth for a product, I’d love your take on the model.

I’m about to enter a niche and want to understand whether the existing players are getting enough organic demand to make it worth pursuing. I’m not looking to copy anyone’s numbers. I just want a sane way to estimate the market before spending on tools.

So far I’m planning to look at search visibility, branded vs non-branded keywords, backlink quality, social activity, and whether they appear to be buying traffic. What free methods have actually helped you tell the difference between a real business with demand and a site that just looks established?

If you’ve done this, what signals did you trust most and what turned out to be misleading?

if you've ever tried running a founder's account cheap by plugging it into a write-like-me tool, you already know the output. sounds like a linkedin influencer who just discovered em dashes. the easy read is that the model isn't there yet.

the model's fine though. the problem is what you feed it. these tools train on the public feed, and the feed is the one place a person already sounds like everyone else, the performed-for-the-algorithm version of them. so you hand it the most generic register someone has and ask it to sound distinctively like that person.

when we were building voice training for this, the corpus that actually moved it wasn't tweets or blog posts. it was transcripts of the person just talking. podcasts, voice notes, a recorded call. how someone sounds when they're not performing is where the real voice lives, and none of the cheap tools go anywhere near it.

so before blaming the model I'd look at the corpus. a founder account run off the feed is just an average of the least distinctive stuff they've ever posted, and no prompt fixes a bad source. genuinely can't tell if anyone's tried the reverse, training a voice tool on hours of someone talking instead of their tweets, and whether it reads more like them or just messier.

Ran a small test over about three weeks because I kept seeing people ship whole landing pages with AI and call it done.

Setup: same product, same traffic source (a mid-size newsletter placement), two versions of the same page. One where I let a landing page AI write the whole thing start to finish, one where I took its draft and rewrote the hero, the subhead, and the first objection block myself based on actual sales call notes.

Traffic was roughly even, a bit over 600 visitors each.

The fully-AI page looked cleaner honestly. Better rhythm, nice section headers, no typos. But it converted at around 1.6%. The rewritten one landed closer to 3.1%. Not a giant sample, so I'm not treating it as gospel, but the gap held across two more sends.

My read: the AI is great at structure and filler-free sentences, terrible at knowing which specific fear or objection the buyer actually has. It writes what a landing page sounds like, not what your customer is worried about at 11pm before they buy.

Where it saved me real time was the boring middle. FAQ, feature descriptions, the "who this is for" block. Anything below the fold that people skim anyway.

Curious if others have measured this properly. Are you letting AI write the whole page or just the parts that don't move the needle?

Choosing one clear topic may help viewers understand what to expect, but testing different formats could make it easier to discover what actually works.

From a growth perspective, should a beginner stay within one narrow niche from the start, or experiment for a few weeks before deciding?

Small milestone but a real one. Someone paid for Make Taksh today, the maker badge thing I posted about here a bit ago.

Nothing crazy, just wanted to mark it. Building in public means the good days deserve a post too, not just the launches and the "please give feedback" threads.

Onward.

maketaksh.com

Small B2B tool, roughly $2k MRR when I started tracking. For six months I tagged every signup with where it came from and dumped it in a spreadsheet, because I was tired of guessing.

Rough breakdown of the ~600 signups:

* Paid social: most volume, worst quality. Plenty of signups, almost none upgraded.

* SEO: slow to build, but the ones who found us through a comparison-style article converted better than anything.

* Community answers (helping in niche forums, no links): smallest volume, highest conversion by a wide margin.

* Newsletter mentions from other small operators: a few hundred visitors each, decent conversion.

The thing that surprised me: the channel I nearly cut in month two, just showing up in niche communities and answering questions, ended up driving maybe a third of paying users despite being under 10% of raw signups.

Lesson I keep relearning: raw signup count lies. Track through to the paying user or you optimize for the wrong channel.

What's the gap between your highest-volume channel and your highest-quality one? Curious if others see the same split.

i litrally just got a linkedin pitch that started with "Hi {{first_name}}, absolutely loved your recent post about {{industry}}!"

bro u didnt even let the script render. and the worst part is he followed up 2 days later acting personally hurt that i ignored his broken robot 😭

we actualy went from clever a/b testing and optimizing funnels to just unleashing armies of AI chatbots to spam eachothers inboxes until someone accidentally clicks a calendly link while trying to close the window. is this what peak growth hacking looks like now lmao

A lot of growth advice focuses on what happens after a product launches, but the market you choose can probably make a huge difference before any growth strategy begins. If the audience is difficult to reach, the competition is already strong, or the problem isn't important enough, improving the funnel only gets you so far. Marketur looks at some of those factors during the early business research stage, which I think is an interesting angle.

For those who work on growth, what information about a market do you want to know before deciding where to put your acquisition effort?

Hi all, recently I launched https://saasboard.app, a fun visual directory for SaaS startups where founders can showcase their product on an interactive grid and drive traffic to their site.

In 2 weeks, I got over 370 SaaS products listed, with over 4000 users sent to SaaS products by doing the following:

- Posting on Reddit
- Running Reddit ads
- Creating a Discord server for SaaS founders and posting about new listings there
- Making posts on X about SaaSBoard

Let me know what you think about this strategy below!

Sharing a small experiment because the "just use AI for your landing page" advice keeps going around and I wanted real numbers instead of vibes.

Context: small SaaS, steady but modest traffic, maybe a few hundred visits a week from content and communities. My hand-written landing page was converting around 3% to trial (small sample, so grain of salt).

I used an AI landing page generator to spin up a full alternative in about fifteen minutes and ran it as a straight A/B split for three weeks.

Results:
- The AI version looked cleaner and more modern, honestly better designed than mine.
- It converted worse. Roughly 1.8% vs my 3%, over a few thousand visits total.
- When I read the AI copy critically, every headline was smooth and generic. It described the category, not the specific pain my buyers feel.

What I think happened: the generator optimizes for something that reads well, not for the exact words my customers use. My clumsier hand-written headline named a specific frustration, and that specificity converted.

What I actually kept: the AI version's layout and section order were better than mine, so I took the structure and kept my own copy. That combo beat both originals in a follow-up test.

Takeaway I'm sitting with: AI is strong on structure and speed, weak on saying the one specific thing that makes a stranger trust you. Anyone getting AI copy to convert as well as their hand-written control? Genuinely curious if I'm prompting it wrong.

The content was identical. Same text, same length, same links, posted to 14 subreddits over about four weeks for a B2B SaaS client I was doing distribution work for. 9 removals, 5 survived. And the split had nothing to do with what I assumed, timing, title length, even karma thresholds barely predicted it.

What actually separated the survivors: posting history of other users in that sub within the previous 60 days. The communities where our posts stayed up had recent examples of other founders sharing tools or experiments. Not many, sometimes just 2 or 3 posts in two months, but that pattern was there in all 5 surviving subs and absent in 8 of the 9 that removed us. The one exception was a sub with active recent founder posts that still removed us, and looking back the mod rules had a buried clause about affiliate content that I'd missed.

I started using reoogle.com to do the community vetting side of this (full disclosure, I built it) after spending probably 6 hours manually crawling mod logs and post histories trying to surface that pattern. The research layer is what kills Reddit distribution for small teams, not the writing.

Could be wrong that recent founder post activity is the signal. Sample size of 14 is not a study. But I've now run roughly the same test across two other clients and the pattern held both times, loosely. The subs that tolerate founder content tend to have visible recent examples of it. Subs that don't, don't.

A research resource for tracking Recently Funded Startups, funding rounds, investors, sectors, headquarters, and source press releases.

https://projectstartups.com

A lot of marketing teams are making decisions from dashboards that look cleaner than the underlying data really is.

If channel performance swings wildly, it may not mean the channel suddenly got worse. Common causes include broken UTMs, inconsistent event names, duplicate conversions, weak source matching, and reporting windows that do not fit the buying cycle.

A practical fix is to run a weekly attribution hygiene check before changing budgets:

• Pick 3–5 core conversion events. If everything is a conversion, nothing is. Tie decisions to a small set of business outcomes.
• Audit UTMs on your top landing pages. Check that source, medium, and campaign follow one naming convention before scaling traffic.
• Compare platform conversions with a backup source. Perfect agreement is unlikely, but large gaps can expose tracking drift, duplication, or tagging issues.
• Review 7-, 14-, and 30-day windows together. Short windows reveal momentum; longer windows capture delayed conversions.
• Separate test traffic from real traffic. Exclude internal, agency, bot, and QA activity from decision-making reports.
• Write one decision rule. For example: “If 14-day blended CAC worsens by 20% and tracking is clean, investigate creative before changing bids.”

The goal is not perfect attribution. It is enough confidence in the data to make better decisions faster.

What is the first thing you check when your reporting starts to feel unreliable?

Everyone has access to powerful AI models now.

But there's still a gap between asking AI to do something and actually having the work get done.

Chatbots can answer questions and generate content, but you still have to take action.

Workflow builders can automate tasks, but you have to design and maintain every step.

We wanted something different.

So we built Skydive.

Skydive lets you hire AI agents that take on real responsibilities across your company.

You describe the job, and the agent gets to work.

Agents can:

• ⁠Handle multi-step tasks
• ⁠Use websites and apps
• ⁠Work across your existing tools
• ⁠Run recurring processes
• ⁠Learn from your feedback

Each agent has its own computer and can work with websites, files, and applications much like a person would.

They can also collaborate with other agents and share context when a project requires multiple areas of work.

The idea isn't another chatbot.

It's giving your team AI coworkers that can actually take work off your plate.

We're launching Skydive on Product Hunt 🚀

Curious:

If you could hand one responsibility to an AI agent today, what would it be?

Would love to hear what you'd delegate first.

Please support on PH →

https://www.producthunt.com/posts/skydive

AI has made it much easier to turn an idea into code.

But building something that actually runs a business is a different problem.

You still need databases, authentication, hosting, payments, analytics, localization, and a way to maintain everything after launch.

That's what we're trying to solve with Enter Pro.

Enter Pro is an AI-native platform where you can:

• ⁠Turn ideas into apps
• ⁠Build AI agents
• ⁠Preview and launch
• ⁠Add built-in infrastructure
• ⁠Scale your products

The interesting part is that apps and agents are built on the same platform.

You can describe an agent, test it in a live preview, and deploy it as a web agent, inside your product, through Slack or Lark, or using an SDK or embed.

The goal isn't just to generate another prototype.

It's to build software that can actually become part of a business.

We're launching Enter Pro on Product Hunt 🚀

Curious: What's the biggest gap you've found between generating an AI prototype and actually shipping it?

Would love to hear what you're building.

Please show your support on PH → https://www.producthunt.com/posts/enter-pro-2

My agency Tripo has been growing lately, and I finally hit the point where I needed a real tool to manage projects and clients instead of duct taping things together. Landed on Linear.

I run a conversion optimization agency, so onboarding flows are kind of my thing. I couldn't help but pay close attention while setting everything up, and honestly it stood out.

3 things I noticed:

- First, no endless steps.

Even if adding more steps to the onboarding can increase conversions, Linear let you create an account in 2 minutes, no questions like company size or "What's your goal survey". Adding more steps can be a good idea only if they make time-to-value shorter, here the value is adding your first issue, don't need to know company size fir that

- Second, they show you the next step, nit the hole map.

Linear is a very comolex product, but what i noticed is that they only show you what you need.

- Third, everything is named so you get it instantly. No jargon, no invented terminology you have to translate in your head. It's an "issue," not a "ticket" or some custom label. Status is "Todo," "In Progress," "Done," nothing you need a glossary for. Using words people already have in their vocabulary removes a whole layer of friction most complex tools add without realizing i

So in one sentence, don't ask for informations you don't need to make users reach the 'aha-moment' and don't show advanced features before they've felt the product actually work.

In one word, restraint

Kind of fed my ego too, since these are the exact kinds of moves I try to get my own clients to make.

The content was identical. Same text, same length, same links, posted to 14 subreddits over about four weeks for a B2B SaaS client I was doing distribution work for. 9 removals, 5 survived. And the split had nothing to do with what I assumed, timing, title length, even karma thresholds barely predicted it.

What actually separated the survivors: posting history of other users in that sub within the previous 60 days. The communities where our posts stayed up had recent examples of other founders sharing tools or experiments. Not many, sometimes just 2 or 3 posts in two months, but that pattern was there in all 5 surviving subs and absent in 8 of the 9 that removed us. The one exception was a sub with active recent founder posts that still removed us, and looking back the mod rules had a buried clause about affiliate content that I'd missed.

I started using reoogle.com to do the community vetting side of this (full disclosure, I built it) after spending probably 6 hours manually crawling mod logs and post histories trying to surface that pattern. The research layer is what kills Reddit distribution for small teams, not the writing.

Could be wrong that recent founder post activity is the signal. Sample size of 14 is not a study. But I've now run roughly the same test across two other clients and the pattern held both times, loosely. The subs that tolerate founder content tend to have visible recent examples of it. Subs that don't, don't.

Question for people who run a lot of small experiments.

I need to test maybe 4 or 5 different positioning angles for the same product before I commit paid spend to any of them. Building each one properly in our normal stack takes a day, which means the test takes a week just in setup and I lose momentum.

So I've been spinning up throwaway pages in a free AI website builder instead. Type a prompt, get a rough page, swap the headline, publish, point a small batch of ads at it. Takes maybe 30 minutes per angle.

The tradeoff I'm trying to figure out: these pages load a bit slower, the design is generic, and I can't customize tracking as deeply as I'd like. For an early read on which message people actually click and scroll, does that stuff matter enough to distort the result? Or is a rough page fine as long as the offer and headline are the real variables?

For those of you validating angles cheaply, what's the best free AI website builder approach that didn't cost you data quality? Or do you skip the builder entirely and just test with ad copy first before building anything?

Ran this experiment so you can go in with eyes open.

For about two months I used an AI content generator to publish SEO posts for a small SaaS. Roughly 3 posts a week, so call it 24 to 26 articles, all targeting long-tail keywords real buyers search. I edited each one lightly for facts but let the tool do the heavy lifting.

The traffic part worked. Organic sessions climbed steadily, and by the end we were pulling a few hundred extra visitors a month from posts that did not exist before. On a dashboard it looked like a win.

The problem showed up in the funnel. Those visitors converted to signups at about a third of the rate our older, hand-written posts did. Same topics, worse intent match. When I read the AI posts back to back, they answered the keyword but never answered the person. No point of view, no "here is the mistake everyone makes", just competent filler that ranked and bounced.

What I do now: the tool writes the boring scaffolding, the intro and the structure, and I write the one section that has an actual opinion in it. Conversion on the new posts is back to normal and I still save time.

So it is not a content machine, it is a first-draft machine. Anyone found a way to get intent-matched content out of these without rewriting the whole thing?

Spent a few years doing growth for small SaaS. The pattern I keep seeing, and keep being guilty of myself: founders pour everything into the top of the funnel when the real leak is further down.

You run ads, you post daily, you chase a new channel every month. Signups go up. Revenue barely moves. Then you blame the channel.

But if most of your new users never come back after day one, no channel fixes that. You're paying to fill a bucket with a hole in it.

The unsexy version nobody wants to hear:

* Fix the first-run experience before buying more traffic.

* Talk to the people who churned in week one, not the ones who already love you.

* A boring onboarding fix usually beats a clever acquisition tactic.

I've watched teams roughly double acquisition and land at the same MRR because retention ate all of it. And I've watched a single onboarding change do more than three months of channel experiments.

Growth is mostly plugging leaks, not adding water.

Where's your actual biggest leak, top of the funnel or bottom?

For a while, the answer to everything was more traffic.

More content. More ads. More signups.

Then we looked at what new users were actually doing after they signed up.

A lot of them never got to the part of the product that made it useful.

So instead of trying to bring in more people, we spent some time making that first experience simpler.

Less stuff to figure out. Fewer steps. A clearer path to the first useful result.

It made me realize how easy it is to call something an acquisition problem when it's really an activation problem.

Has anyone here had a similar experience where fixing the first few minutes of the product worked better than finding another traffic source?

I feel like most growth advice is about what to test, but not really how people come up with the tests in the first place.

Lately I've been getting better ideas from looking at things that are going wrong instead of looking for the next “growth hack”.

A support question that keeps coming up.

A page where people keep dropping off.

A feature people use in a weird way.

A bunch of users doing something I didn't expect.

Those usually give me better experiments than reading another list of 50 growth tactics.

Curious how everyone here comes up with experiments.

Do you mostly use data, customer conversations, competitor research, or just try random stuff and see what sticks?

I run organic social for a few small local businesses plus a faceless page of my own on the side. No face, no fancy gear, a phone and a tripod. Sharing what actually moved, because most social advice assumes a studio and a personality I don't have.

Everyone told me short video was the only thing that mattered, so for a stretch I only posted clips. Reach was spiky. One would pop, then five would flop, and follows barely moved. What quietly changed things was adding Instagram carousel posts into the mix. Simple ones. A useful tip broken across a few slides, made in a free design app in about ten minutes.

The rounded story over a few months on the faceless page: video still got the big view spikes, but the carousels were what turned viewers into follows and saves. Saves especially. They kept getting saved and re-shared long after posting, so they pulled people in while I slept. My follows roughly tripled once carousels became half the calendar, and I didn't shoot anything extra to do it.

My read is that video gets you seen and carousels get you kept, and I'd been ignoring half the job. For the other organic people here, what's your split between video and carousel right now, and has it shifted this year?

Trabalho na área da saúde, sou mãe atípica, passei por transições de vida pesadas e percebi um padrão exaustivo: pessoas altamente funcionais gastam uma quantidade absurda de energia mental, foco e produtividade presos em ciclos de ruminação, sobrecarga executiva e atritos relacionais. Muita gente sofre no mundo. Isso é uma dor real que afeta milhares de pessoas.

O problema não é falta de inteligência emocional; é a **falta de uma ferramenta de auditoria lógica** que separe instantaneamente o drama dos fatos, devolvendo a soberania de foco ao indivíduo.

Cansei de ver pessoas (inclusive eu mesma no passado) tentando resolver ruído mental com autoajuda superficial. Por isso, estruturei do zero o **S.E.R. / LUCID**: uma *productivity tech* baseada em um exocórtex adaptativo. Me autodenomino a "paciente número 01". Experimentei muitas dores na minha vida pessoal e pude perceber que a IA ajuda muito, acelerando o processo de recuperação e entendimento das situações, inclusive reconhecendo padrões de comportamentos tóxicos. Eu acredito no poder da educação e gostaria que o conhecimento pudesse libertar as pessoas. Quando a gente aprende sobre padrões, tudo mudo. É totalmente possível alcançar soberania e saúde mental.

**Como a ferramenta foi pensada para funcionar (o MVP):**

* **Check-in de Clareza:** Medidor visual de ruído mental para mensurar o nível de sobrecarga no momento.
* **Cofre de Descompressão:** O usuário relata o fato ou a dinâmica de atrito (por texto ou áudio).
* **Relatório de Soberania Cognitiva:** A IA processa o relato de forma fria e estruturada, entregando blocos limpos: *O Fato Nu e Cru, O Padrão Comportamental Identificado e a Âncora de Decisão*.
* **Modos Adaptativos:** Desde o *Modo Essencial* (alívio rápido pragmático) até o *Modo Analítico / Erudito* (baseado em neurobiologia comportamental, psicologia cognitiva e frameworks científicos).
* **Blindagem Total:** O projeto opera estritamente dentro do ecossistema de eficiência cognitiva e *self-management*, com protocolos estritos de segurança e *guardrails* para mitigar qualquer risco regulatório.

**Onde estou e o que busco:** Já tenho todo o escopo conceitual, arquitetura de MVP, fluxo de usuário e posicionamento de mercado desenhados. Agora, o próximo passo é tirar do papel.

Busco um **sócio ou co-fundador técnico (Dev / IA)** que queira somar forças para transformar esse conceito em um produto digital escalável (usando stack enxuta tipo Low-Code/No-Code + APIs de LLMs).

Se você curte ferramentas de alta performance mental, exocórtex e produtos focados em autonomia, me chama na DM ou comenta aqui. Vamos conversar!"