From product to people

Great products don’t come from clever solutions. They come from deeply understanding problems.

I’ve seen it over and over again. The teams that build things people actually want are the ones that obsess over the problem, not the technology. They talk to users. They watch how people struggle. They ask why five times until they get to the real pain point.

But here’s the thing. Most teams don’t do enough of it.

The research problem

Talking to users is one of those things everyone agrees is important. It’s also one of those things most people skip or shortcut.

Why? Because it takes time. A lot of time.

Setting up interviews, finding the right people, scheduling calls, conducting the conversations, taking notes, analyzing patterns. For a small team trying to ship fast, this can feel like a luxury you can’t afford.

So what happens? Teams rely on assumptions. They build on gut feeling. They talk to five people and call it research. Or worse, they skip it entirely and go straight to building.

And then they wonder why nobody uses what they built.

“If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.” — Albert Einstein

This quote gets thrown around a lot, but it’s true. The understanding part is where the magic happens. The building part is just execution.

Building Mira

This is exactly why I built Mira.

The idea was simple. What if AI could conduct customer interviews at scale? Not replace the human insight, but handle the heavy lifting. The scheduling, the conversation, the transcription, the pattern recognition.

You define what you want to learn. Mira talks to your users. You get the insights.

I built it as an experiment, really. I wanted to see if AI had gotten good enough to have meaningful conversations with real people and extract genuine insights from those conversations. Turns out it has.

The interviews feel natural. People open up. And because AI doesn’t get tired or biased or rush through the last three interviews because it’s Friday afternoon, you actually get consistent, high-quality data.

It’s the kind of tool I wish I had years ago when I was running user research for different projects and products.

Using it on Minutemailer

Naturally, I started using Mira for my own startup, Minutemailer. We needed to understand our users better, figure out what features mattered most, and identify friction points in the product.

Mira helped us get answers faster than we could have through traditional interviews alone. We learned things we wouldn’t have discovered otherwise, simply because we could talk to more people and ask better follow-up questions.

But here’s the honest part. While the product works and the insights are valuable, I haven’t really put Mira out there yet. It exists. It works. People who try it find it useful.

But almost nobody knows about it.

The go-to-market challenge

And this is where the real challenge begins.

Building a product in 2026 is easier than it has ever been. AI accelerates everything. You can prototype in hours, ship in days, iterate in real time. The technical barriers that used to slow everything down are disappearing fast.

But getting people to actually use your product? That’s as hard as ever. Maybe harder.

There’s so much noise now. Everyone is launching something. Every week there are new AI tools, new startups, new products competing for attention. The barrier to building has dropped so low that the market is flooded.

Which means the real bottleneck isn’t building anymore. It’s distribution. It’s getting your product in front of the right people at the right time with the right message.

The irony isn’t lost on me. I built a tool that helps you understand users better, and my biggest challenge is finding those users in the first place.

Marketing is still hard

Here’s something people might not expect. I have a background in marketing and branding. I’ve worked with brands, built campaigns, thought deeply about positioning and messaging for years.

And I still find go-to-market incredibly hard for my own products.

Knowing the theory and executing it for yourself are two very different things. When it’s your own product, you’re too close to it. You know every feature, every edge case, every technical decision. Communicating the simple value to someone who has never heard of it requires a completely different mindset.

I did one LinkedIn post about Mira a while back. It got some genuine interest. People reached out, wanted to try it. That felt great. But one post isn’t a strategy.

I’ve been thinking about launching on Product Hunt. It feels like a natural fit. A new AI tool for customer research, the Product Hunt audience would get it. But even that requires preparation. The right timing, the right assets, the right community engagement leading up to it.

Marketing isn’t something you can just turn on. It’s a constant effort, and for someone who loves building, it can feel like the hardest part of the job.

The next frontier

So here I am. I’ve built something I’m genuinely proud of. A tool that solves a real problem I’ve experienced firsthand. And now I need to do the part that doesn’t come as naturally: getting it out there.

I think a lot of founders are in the same boat right now. AI has made us all more capable builders. We can create things faster and better than ever before. But the human side of business, connecting with people, earning trust, building an audience, that still takes real work.

Maybe that’s the next frontier for AI to help with too. Not just building products, but helping founders figure out how to reach the right people. Understanding not just what to build, but how to talk about it.

For now, though, I’m going to do it the old-fashioned way. Write about it. Talk about it. Put it in front of people and see what happens.

Because the best product in the world doesn’t matter if nobody knows it exists.

If you’re curious about Mira, check out the case study. And if you have thoughts on go-to-market for AI tools, I’d love to hear them.