What Pain-Point are you Solving and How did you discover it?
We’re all builders here, which usually means at some point we looked at something clunky, slow, or frustrating and thought, “there has to be a better way.” Most products don’t start with a grand vision; they start with irritation, curiosity, or firsthand pain.
I’d love to learn more about how others here have navigated that journey:
• How did you uncover the problem you decided to work on?
• What signals told you this problem was worth solving?
• How did you validate (if at all) whether people would actually pay for a solution?
• Has your product stayed true to the original problem, or did it evolve into something different?
• What surprised you the most along the way?
If there’s anything else you’ve learned, good or bad, feel free to share. The honest stories are usually the most helpful.
And of course, feel free to plug what you’re building as well as you may have the solution to a problem somebody else is looking for!
Replies
mine came from pure irritation rather than any grand vision, which matches what you said. i kept hitting the same dumb wall in my own workflow and finally got annoyed enough to build the fix ngl. the signal that mattered showed up when i caught other people complaining about the exact same thing in passing, research never told me anything that direct. the grand-vision starts almost never survive contact, the irritation ones tend to
The pain: Generating personalised images at scale is surprisingly broken.
If you've ever needed to send 5,000 event certificates, localised ad creatives in 12 languages, or personalised social cards for every user you know the drill. You either:
- Sit in Figma/Photoshop manually swapping text and exporting (works for 10, breaks at 1,000)
- Stitch together a Puppeteer script that breaks every time Chrome updates
- Pay for an enterprise tool that takes weeks to integrate and costs more than your hosting
I ran into this building a campaign for a client. We had one clean design and a spreadsheet of 8,000 names. The "just automate it" path led straight into a brittle Playwright setup that I spent more time maintaining than actually using.
The insight: the design-to-image pipeline is always the same one template, variable data, rendered output. But every team solves it from scratch.
So I built PixelDrive: design the template once in a visual editor, mark what changes, then hit a URL with your data as query params. The rendered image comes back. No SDK, no setup. First render in about a second, cached repeats in 19ms.
The "URL is the API" design was deliberate I wanted something a developer could use in 5 minutes and a marketer could understand by reading the URL.
Would love to hear if anyone else has hit this what did you end up building/using?
Very simple: We were already building something similar (working in AI and video production) and then people from companies with a clear pain point approached us and spoke of it.
There is a real pressure to automate and hence accelerate video production processes. TV channels are still too dependent on old people watching television. Now, however, they do not have the resources to adapt, as new formats require much more video output at a much lower ROI.
Which is why we developed OntoVision, which helps production teams automate their workflows turning well-analysed video footage into completed productions.
How did you uncover the problem you decided to work on?
I lived it. I was the guy organizing weekly football games for a group of friends — and every week it was the same WhatsApp chaos. "Who's playing Saturday?" — 47 replies, half of them memes. Someone confirms, then drops out. You're left manually figuring out who's on the waitlist, calling people one by one. I thought: this has to be an app. So I built it.
What signals told you this problem was worth solving?
Every person I described it to immediately said "we have the exact same problem." The pain was universal and recognizable in under 10 seconds. No one needed convincing that the problem existed — only that my solution was better than just living with it.
How did you validate whether people would actually pay for a solution?
Honestly — I didn't do classic validation before building. I built a working MVP first, then put it in front of real people. The signal I watched for: would anyone actually use it consistently over WhatsApp? They did. Paid plans came after that — and the 14-day trial with no card required removes the biggest barrier to finding out.
Has your product stayed true to the original problem, or did it evolve?
The core stayed the same — kill the group chat chaos. But I didn't expect coaches and amateur league organizers to be such a strong use case. They need attendance history, PDF reports, season stats for sponsors. That pulled the product up-market from "app for friends" into something closer to lightweight team management software.
What surprised you the most along the way?
That I built the entire thing solo — no team, no agency — using AI as my coding partner. What surprised me wasn't that it was possible. It was how fast it was possible. Full SaaS product: payments, push notifications, PDF reports, admin panel, 8 languages, E2E tests, CI/CD — built by one person. That genuinely changed how I think about what a solo founder can ship.
The pain I was solving: digital fragmentation.
I counted my apps one day — Instagram, Twitter, Strava, Duolingo, WhatsApp groups for community, a separate notes app... I was living across 10+ apps and none of them "knew" the full version of me.
I discovered it when I realized I couldn't answer "who am I online" without switching between 5 different apps.
So I built Maleu — one app for social sharing, learning, fitness, communities, and AI camera direction. India's first life OS.
The insight: it's not about features, it's about a unified identity across everything you care about.
The pain I'm solving with Maleu: your digital life is scattered across too many apps — and none of them talk to each other.
I discovered it personally. I was using Instagram for photos, Twitter for thoughts, WhatsApp for staying connected, Strava for runs, Duolingo to learn, and a notes app for everything else. At some point I realized I felt like different versions of myself existed on different platforms.
The signal it was worth solving: every person I talked to described the same feeling without me prompting it. The word "scattered" kept coming up.
Validation approach: I didn't wait for revenue signals — I started building because I genuinely needed it. The real-world validation came from the fact that the average person uses 9+ apps daily just to live a "normal" life. That number alone felt like enough.
Has it evolved? Absolutely. What started as a feed app became a full ecosystem — communities (Circles), a learning layer (Bloom), fitness tracking (Wrex), AI photography guidance (Poses), and more. The core problem stayed the same; the solution grew to match it.
What surprised me most: people don't know they need this until you show them something unified. The "aha" moment isn't in explaining features — it's when someone sees their entire life in one place for the first time.
Building Maleu solo from Hyderabad. India's first life OS.
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.