How we got 42 beta testers in 2 weeks

Love this approach. Dogfooding is the absolute best way to prove the model. We actually just had a similar timeline but in B2C HealthTech- we launched Phoebe (an iOS engine that predicts migraine attacks via Apple Watch biometrics) and hit 30 paying users organically in week 1. But our next phase is B2B2C, specifically targeting neurologists and clinics for partnerships. Does IbexAI work well for highly specific medical niches and identifying active clinicians on LinkedIn? Might need to DM you to test this out for our upcoming outbound push.

Congrats on the first sale though Curious though,how did you handle beta testers who gave feedback but never converted to paying users? And was LinkedIn outreach sustainable long term or did it get exhausting?

That's a sign that your product works. Curious to know how would i use it to get actual LOI for my pre launch service ( at Xseth we are building penetration testing powered by AI )

nice approach

did you personalize messages much or mostly rely on timing?

This is exactly what I need at this moment...

The "reaching them at the exact moment they were thinking about the problem" line is the part most beta-tester guides miss.
Curious how you'd adapt this for a creator audience (YouTubers, not B2B) — would you scan recent posts in r/NewTubers or YouTube comments for similar intent signals? My tool is for video creators so LinkedIn    

  isn't the right channel, but the principle clearly works.

Really interesting approach — especially the timing piece.

Reaching people when they’re actively thinking about the problem feels like the real unlock here vs. traditional cold outreach.

Curious how you handled the transition from initial interest → actual usage. Did most of those beta testers stick around beyond first interaction?

I’m working on something in a very different space (helping families evaluate whether kids’ activities are actually worth the time/cost/energy), and one of the biggest challenges so far has been turning early interest into consistent usage.

Your numbers on acceptance/reply are impressive — but I’m wondering how that translated into retention.

Congrats on the beta customers. Hope you make it far!

The timing angle is what makes this click - reaching someone 20 minutes after they engaged with a lead gen post is a completely different conversation than a cold DM three weeks later. They're already in the headspace.

72% acceptance is wild though. Was that with a personalized note on the request or blank? I've found that one detail swings the number pretty dramatically.

Quick question - I'm building a SaaS tool for freelancers and trying to figure out the right moment to reach them. Would IbexAI work for a use case like that, or is it better suited for B2B/lead gen niches? Wondering if there's enough LinkedIn content activity in the freelancer space to make the signal useful.

Congrats on the beta testers, that’s a strong early signal.

I’m researching how founders choose tools while building and launching. Curious: what tools did you use to get/manage those beta testers, and how did you decide on them?

"This is a goldmine. I’ve been looking for the best spots to share my new tool (BurnCheck) (burncheck. github. io/burncheck/) and this list saves me hours of research. I’ve found that r/SaaS and r/SideProject are great for my niche since I'm focusing on AI cost optimization. Thanks for sharing this resource with the community!"

This is really helpful. I’m working on getting my first early users now, and I’m realizing that beta users are not just about numbers — they teach you what messaging people actually respond to.

The biggest challenge so far is making the value clear enough that someone understands it immediately.

This is super relevant to what I'm doing right now doing customer discovery before building an AI support agent. The beta tester approach is exactly what I'm trying to figure out. How did you find your first 10?

Solid numbers — 72% accept and 55% reply is way above cold averages.
Curious about one thing: how did you define the ICP criteria that the AI agent scans for?
Did you pre-define it manually or did the tool learn from early engagement data?
Building something adjacent (reverse-engineering ICP from company data rather than intent signals) so genuinely interested in how you approached the targeting layer.

This is exactly what I needed to read. I created this app called Onfoot and getting beta testers has been my wall. The insight about timing — reaching people at the exact moment they're thinking about the problem — makes total sense, but I'm in B2C territory where those moments are harder to surface. For something like a location app, people aren't posting about it on LinkedIn right before they need it. How would you adapt this approach for B2C? Or is community-based targeting (Reddit, niche groups) your recommendation there?

This is the insight most people miss entirely. The timing-over-personalization principle is something we've validated repeatedly building Closr. We track LinkedIn engagement patterns to find the moment someone is actively in a problem, not just profile-browsing looking for someone who might have it eventually. One thing we've noticed: the difference between a liker and a commenter isn't just intent level, it's problem acuity. Commenters are often mid-discovery, meaning they're still forming their opinion. That's actually the best moment to reach them. Did you notice any difference in the quality of beta testers from each group, not just quantity of replies?

Thank you for your advice. Now what is the best strategy for launching?

Congrats! Do you plan to extend your tool to X? We’re doing the exact same strategy there but manual. I know how challenging this is to automate and we’d definitely pay for it.

"Monitoring intent signals on LinkedIn is a goldmine, but doing it manually is a full-time job. Having an AI agent handle the 'interest' filtering instead of just spamming keywords is a massive level-up for outbound. Coming out of the Harvard Lab is a hell of a pedigree, too!

The timing angle is the part most people gloss over but it's actually the whole insight. Cold outreach fails because it interrupts people who weren't thinking about the problem. What you described is the opposite you found people at the exact moment the problem was top of mind. The 72% connection acceptance rate makes sense when you frame it that way, that's not a cold ask, that's a relevant one. Curious about the message itself were you transparent about using a tool to find them or did you frame it purely around the content they engaged with? That line between 'I saw you liked X' and 'my tool found you' seems like it would matter a lot for the reply rate.

Love the approch : I am right on a stage where i have build a product and worked for one of my friend but dont know how to start the marketing where to start.
Just getting confuse everyday.

Love this approach — timing beats volume every time. We did something similar but for content validation. I broke down the exact connection message + follow-up that got us 68% replies on my blog "Easy AI Profit" (just search easyaiprofit on Blogger, it's the first result). Happy to share the template here too if useful.

Looking for Seed Users!

We are building a platform called "Wave-Making Engine". Think of it as a "Creative DiDi"​ — a place to post promotion tasks and claim creative bounties. It makes great ideas instantly profitable and gets good products genuine exposure.

It solves the problem of new products (personal projects, products, music, films, apps) lacking visibility.

We are now recruiting our first 100 Founding Users​ to join our Discord. You'll get to participate directly in the design process and receive a permanent, exclusive status.

Dogfooding your own product to hit a 55% reply rate is incredible! Catching users at the exact moment of intent completely changes the game. But finding where those high-intent users actually live before building is usually the hardest part. To skip that guesswork, we actually built an AI agent that automatically validates those global market gaps for you from day one. Congrats on the launch!

Using your own tool to recruit beta testers is the cleanest product-market fit signal there is. If it doesn't work on your own use case, you've got a real problem.

We're doing something similar for StoreMD's 10 Pro beta spots right now. our channel is IH posts and direct store audits rather than LinkedIn automation, but the logic is the same: find the merchants already feeling the problem, not the ones who might eventually care about it.

72% acceptance rate on cold LinkedIn is strong. The segment breakdown on that number probably tells you more about your real ICP than anything else you've measured.

really useful appreciate it

Really interesting approach — especially the timing aspect.
Reaching people exactly when they’re already thinking about the problem makes a huge difference compared to cold outreach.

I’m currently running into a similar challenge from a different angle: trying to find the right app developers to talk to without just messaging random people.
This idea of “context-based outreach” feels like a much smarter way to do it.

Curious how you balanced personalization vs. scalability when reaching out?

So is this open to everyone yet

was there a difference in the number of replies from those who commented vs liked the post?