What Pain-Point are you Solving and How did you discover it?

Honestly, Takivo started the way a few people here have described, not from a vision, but from watching the same thing go wrong over and over on my own team.

We were running on a communication platform with a task tool bolted on the side and a few AI tools somewhere in the mix, and I kept noticing that the actual work was already being decided in the conversation. Someone would commit to something in a channel, a decision would land in a thread, and then it would just scroll away. No follow-up, no record, gone by the next morning. No one's fault. That's just what chat does.

The signal for me was how normal everyone treated it. People had their workarounds, their pinned messages, their "let me just note that down" habits, and they'd accepted that things slipping through the cracks was part of working in chat. That usually means a problem is real and nobody's actually looking at it.

We didn't validate with surveys. We showed people what it caught in their own threads, a task they'd have lost, a decision that would've quietly disappeared, and the reaction told us more than a survey would have.

The core hasn't really moved since then, but one thing did: it had to propose, not act. Nobody trusts a tool that runs off and does things on its own. So Takivo surfaces what it finds and you decide, that turned out to matter more than any feature. It reminds, followups and once a mandate is set, it knows what the work is about. The more you use it the better it understands your organization.

What surprised me most was how much useful work was already sitting in the conversation, unread, the whole time.

If you're curious, it's Takivo.ai still early, still learning from what people send back.

How did you uncover the problem? I was one of the people doing it — manually reformatting CSV exports into client-ready PDFs every week, freelance work on the side. The "there has to be a better way" moment was realizing I was spending more time formatting than analyzing.

What signals told you it was worth solving? Talking to other freelancers/small agency owners, the same complaint kept coming up almost word for word: "I know what the data says, I just hate turning it into something presentable."

Validation? Honestly, imperfect — launched on Product Hunt today without a pre-built audience, which taught me fast that validation-by-launch-day-metrics is a weak signal. Real validation is happening now, one conversation at a time with actual users.

Has it stayed true to the original idea? Mostly — CSV/Sheets in, branded PDF report out, AI insights and anomaly detection built in. What's evolved is the AI reliability side: getting a model to consistently follow a structured output format (not just a good prompt) turned into its own rabbit hole today.

What surprised me most? How much of "AI-powered" features actually come down to boring engineering — prompt compliance isn't guaranteed, so today I ended up building delimiter-based parsing just to force a consistent output structure instead of trusting the model to follow instructions on its own.

What I'm building: Naxely — naxely.com

My wife discovered the problem. I just had the misfortune of understanding its scale.

Dagmara runs a purpose built trichology clinic in a premium hair salon salon. She had spent 18 months building a custom salon app to capture medical-grade trichoscope imaging at four magnification levels for a clientele of more than 600 clients.

The problem worth solving was in that data. Clients arriving with blood test results their GP had marked normal while the trichoscope was showing something different. Not occasionally. Consistently. No tool existed to connect those two pictures, and every client left the salon without a single piece of their own data.

Validation was unusual. We didn't validate whether people would pay for a consumer product first. We validated the clinical gap with 18 months of real data before a line of consumer code was written. The commercial question came second.

The product has stayed true to the original problem. The biomarker layer wasn't in the original spec. It came directly from what the data kept showing us. So it evolved, but in the direction the evidence pointed, rather than away from it.

What surprised me most was the hardest decisions weren't architectural. They were about language. We spent three days arguing about whether to use the word "deficient" for a flagged blood result. Clinically accurate. Also alarming enough to make people close the app. We use "needs attention" now. She was right and I was wrong. A happy wife is a happy life :-)

I came out of early retirement from a 30-year career in medical diagnostics to build this. My wife's clinical observation was a better brief than anything I encountered in those three decades.

Orga Hair is live on Android now.

I am building Cogni, a tool that essentially intervenes when you start over-relying on AI tools like Claude and ChatGPT and asks you to insert your own independent thoughts before you let AI make a decision for you.

I caught myself asking ChatGPT whether I should take a client project. Not asking it to help me think it through. Asking it to decide. And I realised I had no opinion of my own ready. Six months earlier I would have had one.

I come from marketing, and the uncomfortable part was noticing this in my own field first, everyone's strategy decks started sounding the same, everyone's positioning read as if it came out of the same prompt.

Then I found the research 10+ papers talking about how such overreliance on AI is starting to cause a wave of cognitive decline. These included researchers from Harvard, MIT, UPenn and MICROSOFT ITSELF.

One of the papers even suggested that a lot of users failed to even recognise that this was happening to them.

So the pain point: AI answers arrive before you've formed a view, and slowly you stop forming views. I'm building Cogni, a Chrome extension that catches the prompts where you're outsourcing judgment (decisions, strategy) and asks what you think first. Thirty seconds of your own reasoning before the AI's.

Discovered the problem by being the problem, basically.

@jake_friedberg Mine started with a problem I was tired of having.

I have ADHD, and one of the things that comes with it is time blindness. I'd get completely absorbed in what I was doing, look up at the clock, and realize I had missed a Zoom meeting or was already late for an appointment.

The strange part was that I already had Google Calendar reminders turned on. They just weren't enough. Notifications are easy to dismiss or overlook when you're hyper-focused.

I kept thinking, "Why can't my calendar trigger a real alarm instead of just another notification?"

I looked for an app that did exactly that. I couldn't find one. Well, I found some but they didn't work.

So I built Never Miss Meetings for Android. It syncs with Google Calendar and lets you create actual Never Miss Meetings alarms for the events you choose. Simple on purpose.

As for validation, I admittedly did it backwards. I built the app because I needed it. Then I found out I wasn't alone. People with ADHD, busy professionals, students, salespeople, PARENTS—anyone who lives by their calendar immediately understood the problem.

The biggest surprise? Building the app wasn't the hard part. I used a no-code platform, but getting Google OAuth approved, navigating Play Store requirements, and polishing the user experience took far longer than I expected.

One thing I learned is that simple products can solve big problems. I deliberately resisted the urge to turn it into another bloated productivity app. Sometimes the best feature is knowing when to stop adding features.

It's launching on Product Hunt tomorrow, so we'll see what the community thinks!

Love this thread.

My pain point was honestly pretty personal. I kept seeing families — especially parents with young kids and teens getting their first pet — completely overwhelmed by the naming process. Every resource online is just a list. No personality, no context, no fun. It felt like such a missed opportunity for something that should be a really special moment.

So I built My Petaverse — not to make money, but because I genuinely wanted to create something families could do together around a pet. Something a 12 year old and their parent could both enjoy.

I also loved the idea of sneaking in a little language learning through the name meanings and personality descriptions — available in five languages so kids around the world could use it.

The biggest surprise? Building it with zero coding experience through vibe coding and Google AI Studio. I had an idea and just... figured it out as I went. Shipped a real iOS app and I'm still kind of shocked that worked

I wanted something that felt genuinely fun for families and first time pet owners, not just another utility app. Honestly the money was never the point.

ours started from noticing a friend's voice go flat for weeks before they'd admit anything was wrong — the tone changed way before the words did. validated it by pulling old recordings where we already knew the outcome (diagnosis, layoff, breakup) and the vocal markers were there weeks earlier than anyone said anything out loud. hasn't drifted from that: still just trying to catch the change before the person notices it themselves.

Timing in outbound sales... I discovered it working as an Account executive!! I was tired to reach out too late to clients... So I built a tool for myself called clicke.io and the my collegues wanted to use it too so... I am here now and launching the 21st of july!!

ArtfulAsthetics started out as just another generic website to sell designs.

1. When starting the company, the first digital products I uploaded were aesthetic to-do lists. However, when I saw just how saturated the market was, I decided to make vehicle outlines instead and listed them on Etsy. After a few months of uploading, I started to think about the multitude of art being generated/prompted with no real creativity. Therefore, I built a platform where you sketch, outline, and then aim for a similarity of 80% or higher before you are allowed to publish your design.
   

2. A couple of signals that the problem was worth solving were consumer frustration over a design they purchased being computer-generated, and all other websites allowing prompted art to still be sold, meaning buyers needed a way to differentiate between real and fake art.

3. We tend to sometimes pay for things we can't find anywhere else. Thus, being able to buy designs you know aren't just generated, but created, will probably make some more willing to purchase hand-drawn designs over prompted ones.

4. Yes, the solution to the problem stayed the same. However, I did make many changes to the core functionality for how designs were published.

5. Up to this point, it has been the domino effect of features and ideas arising from other problems that needed to be solved within the platform.

If you happen to want to check it out, here is the link to the website: https://www.artfulasthetics.com.

Our whole app is built around one question: "Would you buy it again?"

The pain: my wife and I kept finishing bags of coffee we genuinely loved — and forgetting them. Three weeks later we're back at the roastery or coffee shop, staring at the shelf, no idea if it was this bag of Ethiopian or the other one.

And we weren't unusual. Specialty coffee has gone fully mainstream — every city has ten roasters now, every office has three people with strong grinder opinions — but nobody really had a "system" to keep track of what they liked and what they would buy again.

So we built Tamp. Log a bean in 30 seconds. When the bag's empty, answer the one question that matters: would you buy it again?

Here's where it gets interesting: those answers add up to your actual taste profile — not what's trending, what you rebuy. Tamp uses it to point you at your next bean, from a directory of 1,000+ roasters with live stock from their webshops. Loved that washed Ethiopian? Here are three you haven't tried, in stock right now, from roasters you've never heard of.

Journal in, discovery out. The more you log, the better it finds your next favourite.