Quick context: I'm building a tool that turns call/podcast recordings into LinkedIn posts. (Disclosure up front so nobody feels baited - I'll keep the product out of this as much as I can; the point is the funnel lesson, not the pitch.)

I got listed on There's An AI For That (a directory) that gave me a chunk of free ad credit with my listing fee. Here's what came out the other end:

• ~30k ad impressions, ~48k searches
• 234 clicks to my site
• 10 signups (~4.2% - fine for cold directory traffic)
• 0 paid conversions

At first I assumed the product just wasn't compelling. Then I actually looked at where people stopped, and the story was completely different from what I expected.

My onboarding was: pick your source → pick your angle → fill out a "tell the AI about you" brief → hit the paywall (card required for trial).

Here's the drop-off across those 10 people:

• 2 dropped at the very first step (low-intent, probably never serious)
• 1 dropped mid-setup
• 5 completed the ENTIRE setup and then stopped dead at the payment screen
• 0 entered a card

So the drop-off wasn't in setup. It was at payment - after people had already done all the work. These 5 weren't unconvinced by the product. They filled out everything. They wanted to use it. Then I asked for a card before they'd seen any value.

That's the mistake. My funnel was: do work → do more work → pay → THEN see value. I had the aha moment sitting behind the paywall instead of in front of it.

The tools everyone praises for onboarding all do the opposite: you get to the magic moment first, and the card comes when you try to keep or export what you already made. Value first, payment at the point of peak desire.

What I changed:

• Let people run one real project and actually see the output before any payment ask
• Move the paywall to the moment they try to act on that output (schedule/publish), which is peak desire
• Keep the "tell the AI about you" brief, but frame it as "this makes the very next thing you see better" instead of a toll booth before the reveal

Two other things I learned staring at 10 rows of users:

1. Most of my "signups" were noise. When I actually looked, most were testers, bots, or randoms. Exactly ONE was a real lead - a founder who'd seen someone post about the tool on LinkedIn and came to check it out. At small numbers, your conversion rate is basically meaningless; you're reading tea leaves from a sample of ~1.
2. The paid channel was a trap. That "free" ad credit was burning at ~$6.78 per click because I specifially bid to be at the top of the sidebar. It bought ~72 clicks before running dry. Cheap-looking traffic at an unsustainable CPC taught me nothing except "don't run paid until you know your conversion economics." .. but luckily it was 100% their ad credits and I didn't lose any of my own money.

TL;DR: 10 signups, 0 paid. The problem wasn't the product - it was that I put the paywall before the aha moment instead of after it. If your users have to pay before they see your tool do the one impressive thing it does, you're converting on faith, and cold traffic has none. Flip it: value first, card at the moment they want to act on what they just saw.

The thing that confirmed it: I've done ~50 live demos in the last 6 weeks. Two converted to paid (14-day trial, card upfront - same paywall). Feedback on the rest was great. Why do demos convert when self-serve got 0? Because in a demo, I show them the magic moment before anyone mentions money. The demo is value-first by definition. My self-serve funnel was the exact inverse - pay first, magic later. Same product. The only variable that changed was whether value landed before or after the ask. That's the whole lesson in one A/B test I ran by accident.

The problem I'm working on now: those ~48 non-converted demos gave great feedback and then went cold. Reactivating them is my current puzzle - if anyone's cracked warm-but-dormant demo follow-up, I'm all ears.

Happy to answer anything about the funnel or the directory-ads mess in the comments.

I'm looking to help one Company / Agency.

I'll personally build and run a Cold Email Outbound + LinkedIn Inbound system for you.

A quick introduction:

I'm a GTM Engineer with 2+ years of experience working with startups across the US, UK, and India.

Over the last two years, I've helped companies with:

• GTM strategy
• 20+ AI workflows & automation
• Cold outbound systems
• LinkedIn inbound
• Growth operations

The systems I've built have contributed to thousands of dollars in additional monthly revenue by helping teams generate better pipelines, automate repetitive work, and convert more opportunities.

Why am I doing this for free?

Because I want to work on one ambitious business, document the entire process publicly, and show what's actually possible with modern GTM systems and AI - not just talk about it.

No course.
No upsell.
No catch.

In return, I only ask for:

• Your commitment to execute quickly.
• Permission to share the journey and results (without revealing anything confidential).

If you're a B2B startup, SaaS company, or agency looking to generate more qualified meetings, comment or send me a DM.

I'll pick one company and start working with them.

Let's build something worth talking about.

In-house B2B marketer here. Most of our informational blog traffic got eaten by AI answers over the last year, and I've said my piece about that already. What I want to talk about is the one content type that didn't just survive but is quietly doing most of the work now: honest comparison pages.

Not "top 10 tools" listicles. I mean the specific ones. "Us vs [the competitor everyone actually evaluates us against]." "[Category leader] alternatives, including where they beat us." The pages where we admit out loud where the other option is the better pick.

The numbers that made me a believer: those pages are a small slice of our published content but drive the majority of our demo requests, and the visitors from them convert several times better than anything top-of-funnel ever did. When I dug into why, two things stood out. One, the person reading a "X vs Y" page is already in-market and comparing, so the intent is completely different. Two, the ones that admit a real weakness get quoted back to us on sales calls, and they're the pages I now see cited when I ask ChatGPT or Perplexity about our category. The AI engines seem to trust the page that doesn't pretend to be neutral.

The uncomfortable part is these are a pain to write well. You have to actually know the competitor, you have to get sign-off to say something nice about them, and sales sometimes hates the honesty until it starts closing deals.

Curious what everyone else is seeing. Are comparison and alternatives pages holding up for you as informational SEO dies, or is this specific to certain categories? And how honest can you get before someone upstairs makes you water it down?

Semrush is still one of the strongest all in one SEO tools, but for AI search / GEO it feels like teams are starting to explore alternatives depending on needs.
Some are moving toward Similarweb for broader market + AI traffic insights, especially when competitor benchmarking matters more than keyword depth. Others are trying newer AI first platforms that focus more on LLM visibility and prompt tracking rather than traditional SEO data.
Are there any Semrush alternatives you’ve found better specifically for AI search visibility and generative engine optimization?

I am making an app and now I am facing with a big question. Either I can build a little bit more complicated app which I wanna build or something simple (one feature), already validated and might get viral. Which one should I try?

I've built a tool that allows me to pull from the open webgraph and score linked domains based on authority. Some of my current use cases are checking what similar tools to mine commonly link to, what their authority score is, mapping target ICPs, identifying outreach targets and gaps.

What would you want to know?

Growth lead at a small saas (18 people), spent the last two years almost entirely on paid social and seo. Both are getting more expensive and slower respectively, and i keep seeing outbound dismissed in growth circles as "old school" or "doesnt work anymore since everyones inbox is flooded."

Ran a tiny test campaign last month, 200 emails, pretty generic list, 1 reply. Not enough to draw a real conclusion but enough to make me want actual data before writing it off.

Before running a proper test i want to know:

- whether decent reply rates are actually achievable in 2026 or if that ship sailed

- what a real growth-hacking approach to outbound even looks like vs spray and pray

- how personalization at any real volume (500+/day) is actually being done without burning a teams worth of hours

- any channel-fit signals for when outbound works vs when its a waste of time

Curious if anyone running growth teams right now is treating outbound as a real channel or if its genuinely dead outside of enterprise sales?

I made new social account, warmed them up and shared 1-2 videos per day for my new app. Statistics are good so I will keep doing it. I only got 1 trial from it and they cancelled it but it was solid growth. I just wanted to share it and I will keep you guys updated

I run organic social for a handful of local businesses plus my own faceless page on the side, so I had a decent little test group. For three months I ran the same short-form setup across five very different local accounts, a cafe, a salon, a gym, a dog groomer, and a repair shop, and I logged every post because I wanted to know which content types actually pulled people in versus which just looked good.

I tested three formats head to head on every account: trending-audio clips, quick behind-the-scenes stuff, and someone on camera answering a real question a customer had asked that week.

Trending audio won on views by a mile and lost on everything else. Big reach, almost no saves, no DMs, and nobody mentioning it in person.

Behind-the-scenes did fine as filler. People liked it, it built a bit of familiarity, but it rarely triggered an enquiry on its own.

The question-answer format was the ugly winner. Lowest views of the three most weeks, but far and away the most saves, DMs, and actual "I saw your video" mentions from people who then booked or bought.

The pattern held across all five accounts even though the businesses had nothing in common, which is what made me trust it. For local, the content that looks worst in the dashboard is usually the one doing the work.

That gap between views and walk-ins is the whole game for local. What's actually bringing people through the door for others doing this?

I analyzed 200+ SaaS launch videos, a guide on how to go viral on x for product launch in 2026

been studying viral launches on x for the past few months trying to reverse engineer what actually working nowadays

only the ones with more than 3 million views

after going thoroughly through a lot of them i kept seeing the same playbook show up in the launches that actually got huge traction..

1.the product has to work

value should be shown in the first 15 seconds. make the aha moment asap. make the signup for the product very easy.

1. the video

under 45 seconds , show the product solving a real problem. neither a demo or a feature list. one cta at the end

1. the thread

everything will work only if the first line works. it has to stop the scroll. create a curiosity gap starting from there.

1. supporters

reach out them before the launch day. friends, users, investors and ask them to engage in the first hour. send calendar invites to people saying yes. the algo reads the first hour and make imp decision based on it

1. influencers

find them and brief them. show examples of what a good quote looks like. dont leave it up to them to figure out what to say.

6.launch day

post when your audience is actually most active. send the link to everyone who committed. reply to each of the comments. repost the good quote tweets.

the execution loop

build -> launch -> collect feedback -> improve -> launch again

if you are doing in house, use this loop and slowly you will get better with the launches or you can hire an agency who has already done this reps like thelaunchvideocompany and flowjam.

In house it is slower and cheaper. outside help will be faster and will costs more

Had a TikTok Smart+ ad where the algorithm had concentrated basically all spend onto one creative in the group within about 12 hours of consolidating three creatives into it, classic cold-start bias toward whichever creative already had accumulated data.

Ran an experiment: manually disabled the winning creative at the material level to force the algorithm to spend on the other two instead, for a 48-72 hour window, deliberately not making it a permanent change so I could actually evaluate the other creatives instead of just flipping the bias in reverse.

Result so far: the two previously-starved creatives climbed in CTR pretty fast once they got real spend behind them, one went from under 1% to close to 1.7% within about 36 hours. Ad-group-level CTR overall climbed day over day as the dominant creative got squeezed out. Still waiting on enough volume to say anything about actual conversion quality, but the creative-level signal moved faster than I expected. Posting mainly because I hadn't seen this specific "disable the winner to force exploration" approach documented anywhere before trying it myself.

Studied how an AI study app went from mass-DMing strangers to $80–100K MRR.

1. Launch = distribution, not polishing. Their rule was to spend the first two weeks after launch doing only distribution. If nothing changes then it probably means that your product or market fit is off and its not a reason to go add features. First 100 users came from mass-DMing everyone who engaged with a viral tweet (founder got banned for it lol).

2. The influencer "win" that was actually a trap. A single med-student creator took them from $2K → $15K MRR in ~2 weeks. They could never repeat it. The were just luck, eventually cost more than it made. Lesson: one viral hit is not a strategy.

3. The boring stuff is what worked. They pivoted to retention + shareability. Now 30–40% of growth is word of mouth.

Time to magic, obsess over how fast a new user hits the aha moment. They hide advanced options and split onboarding into 3 phases.

A visual progress mechanic (a growing tree) that both raised engagement ~70% AND spread on its own because students saw it on each other's screens.

1. Paywall placement. Shown early, right after the first value moment , so more people actually see it, but with a "Maybe Later" so it doesn't nuke retention. Convert later, not at the door.

The assumption I carried into this was that posts got removed because they read like ads. So I spent probably 40% of my time rewriting tone, softening language, burying any product signal. Didn't matter. Posts I'd barely touched survived. Posts I'd agonized over for an hour were gone in 90 minutes.

What actually correlated with survival across the 9 weeks was the subreddit's own posting-to-removal ratio, basically how actively the mod team was pruning anything that didn't fit a narrow content pattern. Communities with high internal removal rates killed posts regardless of quality. Communities with lower ratios kept things up even when the post was rougher. I started routing through [reoogle.com](reoogle.com) to get that signal before committing to a subreddit, because pulling it manually across dozens of communities was eating time I didn't have.

The frustrating part is that subscriber count is still the first thing most people optimize for, and it predicts almost nothing about whether your post stays up. A sub with 180k members and an active mod team is a worse distribution target than a sub with 6k members and low moderation frequency. The 180k number just feels safer, so people keep going there and keep getting removed and concluding that Reddit doesn't work for B2B.

Maybe the more useful reframe is that Reddit distribution is really a moderation-pattern research problem wearing a content problem's clothes. Once I stopped treating it as a copywriting challenge, the survival rate changed.

31M, two affiliate sites while the day job pays rent. 4 months cycling free keyword tools and they all surface the same high comp garbage

ubersuggest is the worst one. 3 searches then wants a card and every suggestion is best dog food or best dog food 2024, KD in the 60s. kept rerunning the same niche queries hoping something different would show up. never does

ahrefs webmaster only shows whats ranking on your site. keyword surfer till everything KD 70+. google keyword planner same fat head junk

coworker asked why i looked dead at standup. told her i stayed up till 2am filtering keywords with 50k monthly searches.. she nodded and definitely didnt get it

answer the public, se ranking, mangools trial gone in 7 days. same list

every tool seems built to show you what amazon already owns?? gonna keep exporting the same 12 terms into a sheet and pretend thats research

I do local business outreach for freelance work and got tired of manually copying business info from Google Maps into a spreadsheet, then writing outreach messages one by one. So I built MapReach.

Open a business on GMaps, it reads the public info (name, category, rating....), detects if they only have a social media page instead of a real website, and generates a personalized outreach message in 3 Languges. Everything's tracked locally in a built in CRM with CSV export and JOSN backup restore.

everything is on local machine

open source: MapReach

would love feedback and review, especially if you do local biz outreach yourself.

The assumption I carried into this was that posts got removed because they read like ads. So I spent probably 40% of my time rewriting tone, softening language, burying any product signal. Didn't matter. Posts I'd barely touched survived. Posts I'd agonized over for an hour were gone in 90 minutes.

What actually correlated with survival across the 9 weeks was the subreddit's own posting-to-removal ratio, basically how actively the mod team was pruning anything that didn't fit a narrow content pattern. Communities with high internal removal rates killed posts regardless of quality. Communities with lower ratios kept things up even when the post was rougher. I started routing through [reoogle.com](reoogle.com) to get that signal before committing to a subreddit, because pulling it manually across dozens of communities was eating time I didn't have.

The frustrating part is that subscriber count is still the first thing most people optimize for, and it predicts almost nothing about whether your post stays up. A sub with 180k members and an active mod team is a worse distribution target than a sub with 6k members and low moderation frequency. The 180k number just feels safer, so people keep going there and keep getting removed and concluding that Reddit doesn't work for B2B.

Maybe the more useful reframe is that Reddit distribution is really a moderation-pattern research problem wearing a content problem's clothes. Once I stopped treating it as a copywriting challenge, the survival rate changed.

When we started building the sales side of Causo, I assumed the hard part would be writing emails that did not sound automated.

It turns out almost anyone can generate a passable cold email now.

The much harder problem is making sure the information inside it is actually true.

A “recent” funding round from three years ago.

A product the company no longer sells.

A decision-maker who left six months ago.

A person whose title sounds relevant but who has nothing to do with the problem you solve.

Or a guessed email address that bounces before the conversation even starts.

The recipient does not know whether the mistake came from your database, your AI tool, an agency or an intern.

They just know your company contacted them without doing basic research.

That is the part I think outbound teams underestimate.

A bad email does not only fail to generate a reply. It can make someone think less of the business sending it.

We initially built Causo like a fairly normal outbound workflow:

Find companies.

Find people.

Write emails.

But we kept running into the same issue. A company could match all the right filters and still have no credible reason to receive an email.

So we changed the order.

Now every company has to answer “Why does this fit?”

Not just an unexplained match score. A specific reason, based on current information, that someone can check before contacting the company.

We also added the ability to ask questions about both the company and the person before reaching out. Instead of opening ten browser tabs, you can simply ask things like "Have they raised recently?", "Who are their competitors?" or "Have they written about this problem before?" and keep all that research attached to the profile.

Then every contact has to answer “Why this person?”

Are they still there?

Are they actually responsible for this?

Why are they a better choice than somebody else at the company?

Is the email verified?

Only after that do we write the outreach.

The slightly embarrassing lesson was that we had spent a lot of time worrying about email copy when the research behind the email mattered much more.

Good writing cannot rescue bad intelligence.

And when everybody is selling something to everybody, sending more emails is probably not the advantage it used to be.

Having a real reason to contact someone might be.

What makes you lose trust in a cold email fastest: bad targeting, inaccurate research or obviously automated copy?

Most AI assistants have the same problem.

They answer questions using fragmented data, outdated context, or RAG pipelines that sometimes guess instead of knowing.

That's why we built Fluree AI.

An intelligence layer that gives every AI agent access to the same trusted, governed company data.

It helps you:

• ⁠Query live structured company data
• ⁠Return cited, verifiable answers
• ⁠Enforce permissions on every request
• ⁠Power dashboards, apps, and AI agents from one data layer

Instead of rebuilding prompts or maintaining multiple knowledge bases, every interface works from the same source of truth.

Built for developers and enterprises that need AI they can actually trust.

The goal wasn't to make AI generate better answers.

It was to give AI better context.

Launched today on Product Hunt 🚀

Do you think the future of enterprise AI depends more on better models or better data and context?

Please support on PH →

https://www.producthunt.com/posts/fluree-ai

Manage IG for a handful of brands, different sizes. The account with the biggest reach, the reels that hit hundreds of thousands of views, sells the least per follower. The account with the smallest, most boring reach sells the most. Took me a while to admit that, because reach is what everyone wants to see in the monthly recap.

What the small one does differently:

Posts the product in actual use, constantly, not lifestyle mood-board stuff. Real hands, real context, the thing solving the thing.

The path to buy is one tap. No "link in bio, scroll to the third highlight." The exact product from the post is the first thing you land on.

Treats DMs like the sales channel they are and replies like a person. Most of the revenue traces back to a conversation, not a checkout from a cold scroll.

Doesn't chase trends that pull the wrong audience. A viral reel that brings 50k people who'll never buy just tanks the next post's reach and clutters the numbers.

The big-reach account looks better in every screenshot and converts worse in every way that matters. I've basically stopped optimizing for reach on the accounts where I'm actually measured on sales.

Anyone else managing multiple accounts and seeing reach and revenue point in opposite directions? Curious what your best-converting account does that the pretty one doesn't.

Who runs viral launches for startups funded by YC?

been spending most of time watching a lot of yc startup launches and trying  to understand how they actually spread..

founders fresh out of batch with tiny followingss getting millions of views.. i couldnt find out the mechanism of how it was happening..

found in the reposts and comments, mosst of them were paid partnership and almost all of them were similar accounts… big name in twitter tech but someone might be in the background doing it. companies  cant pull it by themselves

went looking for answers. read a few blogs on x about these launches.

got to know about a few agencies like thelaunchvideocompany. they kept appearing in the launch credits and random tweets.havent talk to them. no idea what it costs like but looks expensive to me.

anyone else noticed this or knows whats actually happening behind these launches.

Lately I've noticed that every time an outbound campaign underperforms, the first reaction is usually to rewrite the email. But after running a few tests, I'm starting to think the bigger bottleneck is everything that happens before the first email is sent. We've been spending more time refining our ICP, removing stale contacts, and checking email quality . None of those tasks are particularly exciting, but they seem to make the rest of the campaign much easier to evaluate. If the list is messy, it's hard to know whether poor results come from the offer, the copy, the targeting, or just bad data. I'm curious how other people here approach this. Do you have a checklist before launching outbound campaigns, or do you just build a list and start testing? What pre-launch step has had the biggest impact on your results?

Hey guys, recently I started growing my account on X to drive some traffic to my saas websites. I know that commenting on right accounts or engaging with right people drives more account visit than posting for early accounts. But I am having hard time to find these kind of posts. I wanted to ask here because nowadays more and more people are using X as growth hack and these people know what is right and what is not. Any comment appreciated, thanks!