It's weird having a decent number of active users while having zero clue what your typical customer actually looks like. Some people log in every single day for work while others signed up months ago and completely vanished. Trying to build features for a mixed crowd like that makes roadmaping really tough because you can't please everyone at once.
If you've been through this stage, at what point did you decide to narrow down your target market instead of trying to appeal to everyone?
I m not an expert researcher, but I use AI daily to pull summaries, market trends, and quick facts for my projects. It s lightning-fast, but it has one terrifying flaw: it sounds just as confident when it s completely wrong.
A few weeks ago, I wrote an article using statistics an AI gave me about a local market growth. It looked solid and cited plausible-sounding sources. Right before publishing, a colleague pointed out that one of the core numbers was entirely fabricated by the model. On the surface, the prose was flawless, so I almost missed it.
It made me wonder if we are trusting AI-generated insights too easily just because the writing sounds professional.
How do you verify facts and data when AI hands them to you? Do you cross-reference multiple tools, ask it for actual links, or just trust your gut? And has a fact-checked AI response ever turned out to be completely made up for you?
Most AI features feel like gimmicks because they're a button looking for a reason to exist. Someone in a meeting said "we need AI" and three weeks later (or maybe hours now with Claude Code) there's a sparkle icon in the corner that summarizes things nobody asked to have summarized.
The test I use: if you stripped the word "AI" off it entirely, would people still want it? If the answer is no, it's a gimmick.
For the first half of this year, we were starving for revenue. So when a mid-sized company reached out about our platform, I went full "enterprise mode." We hopped on 6 different Zoom calls, built a custom CSV importer for them, and even shifted our roadmap to prioritize a feature their team specifically asked for.
After three months of back-and-forth, they finally signed a $500/month contract. I was thrilled. I thought we had finally cracked the code for landing bigger accounts.
Fourteen days into the contract, they canceled.
When I asked for feedback, the answer stung: "It's too hard for our team to learn." We had spent so much time building the custom features and doing the manual data imports that we completely neglected the core user experience. The tool was clunky, the UI was confusing, and their team just refused to adopt it. I was so focused on winning the deal that I forgot to make sure the product was actually easy to use for the people who had to log in every day.
We often cover software topics on ProductHunt, but sometimes we overlook the fact that tech can be felt most in the physical world (hardware).
As of mid-2024, there have been approximately 3,979 reported incidents involving autonomous vehicles (AVs) in the United States since reporting began in 2019. Source
Heard something on a podcast recently that reframed how I think about retention features.
On average it takes ~60-70 days for a new behavior to feel automatic. But motivation, the thing that gets people to start, decays way faster than that. So there's a gap: motivation dies around week 2-3, the habit isn't formed until week 9-10, and that gap is where most users quit.
Streaks are supposed to bridge that gap. But most of them fail because the daily ask is too big. If your streak requires real effort every day, you're burning motivation the user doesn't have anymore.
I've hit day 40 on an app purely out of guilt, then quit and never opened it again. The streak didn't build a habit. It just delayed the uninstall.
We launched Nimply (AI agents that draft social media replies and posts; you approve before anything goes out) on 23 Sep. Here is what the 24 hours actually looked like, with numbers, because most launch retros only show the wins.
