As users, we all want to try a product before committing. As builders, we want to show real value without over-engineering and investing time 'just for show'. Finding that balance is harder than it looks.
I just shipped my demo for Rewo (https://rewo.app), and intentionally went with a live, real demo:
Fully functional product (same codebase as prod)
Uses demo data instead of real integrations
Some interfacing + sync pieces are disabled
Auto-resets on a schedule so anyone can jump in fresh
I'm trying to get better at competitor research for SaaS products, but there's one thing I keep struggling with: knowing which updates are actually worth paying attention to.
A competitor might change their pricing, launch a new feature, redesign their website, or announce an integration. It's interesting to see all these changes, but I don't think every update should influence what you build.
My current thinking is that a change matters when it affects how a product is positioned or what customers can do with it. I'm still figuring out how to separate those signals from the noise.
I do go-to-market for a product where the customer is a founder, and I keep running into the same wall.
Founders can smell marketing from a mile away. They've written the same emails, run the same ads, built the same funnels. So the usual playbook lands flat. Anything that sounds like copy gets skipped. Anything that sounds too clever gets skipped faster.
What seems to work better is being specific and slightly boring. Less "transform your fundraise," more "here is the exact thing this does and here is what it doesn't do."
But I'm not sure if that's a real insight or just the one thing that happened to work for us.
I was testing how LLMs rank products in our space by asking vague buyer questions like "what's the best tool for X." We showed up on Gemini, but ChatGPT kept listing older competitors that haven't updated in years.
It made me realize how inconsistent these AI recommendations are compared to traditional Google SEO.
If you added comparison pages, structured data, or an llms.txt file recently, did you notice any real difference in how models cite you?
My question may be stupid, but it's very important to me for developing my products. I would like to verify that my website can be viewed by specific users from specified countries such as United States, Canada, Mexico, etc. I tried some web proxy via Google search results. It works but I'm not sure it's really from specified countries, for eg., I checked with United States but YouTube video returns Chinese interface, that means website is accessed from China, not United States.
My site is translated into 10 languages. The language switcher and the internal links on every localised page were supposed to keep visitors in their language. For months they did not. Every locale page quietly linked back to the English homepage.
First post here. Been browsing Product Hunt for a while without an account and finally made one.
I keep noticing apps adding an AI button to something that didn't need one. A calendar app with "AI scheduling." A to-do list with "AI prioritization." A notes app with "AI summaries." Most of the time I poke at it once, it does something vaguely fine, and then I go back to using the app exactly the way I did before. The button just sits there.
I'm not against AI features. Some of them are genuinely good and I use them. But there's a difference between a tool that was built around it and a tool that stuck a chat box on top because it was trendy. From the listing alone, those two look identical.
Curious if anyone else feels this. What's an AI feature you actually use regularly, versus one you tried once and forgot about? And does a listing mentioning AI make you more likely to try something, or less at this point?
The first week felt like validation. 340 customers, $50K in the bank, near the top of the charts. I thought I'd solved the cold start problem.
What I hadn't worked through: I'd acquired 340 customers who paid once and had no incentive to churn. Which meant I had no recurring signal on what actually needed fixing. The feedback was noisy because everyone bought at different price points with different expectations. Support was immediate and permanent. When I raised prices six months later to attract monthly subscribers, existing LTD holders treated it as a personal betrayal.
