... Most product teams run surveys. We did too. We'd ask things like "What features would you like to see?" or "How would you rate your experience?" And we'd get perfectly reasonable, perfectly useless answers. The turning point for Murror came when we stopped designing around what people said they wanted and started paying attention to what they were avoiding. Here's what I mean: in our app, users can share stories and reflect on their emotions. We noticed ... ... ones that feel effortless, but the ones that feel meaningful. It's counterintuitive in an industry that worships frictionless experiences. But I think there's a difference between unnecessary friction (bad UX, confusing flows) and necessary friction (moments that ask

... checked off. The team is busy. Meetings happen. Decisions are made. But the code does not move. That is not a roadmap. That is a wishlist. 2. The founder is the only one who can explain the product. You ask anyone else on the team what the product does. They hesitate. They look at you. They wait for the founder to answer. That means the vision is not shared. It is just one person's idea. When that person ... ... added the features. But the same complaints keep coming. That means you fixed the wrong things. Or you fixed the symptoms, not the cause. The users are trying to tell you. You are not listening. 4. The team stops asking

... different reason. Wanted to share what all I learnt while building this solo in 2.5 months from scratch. As you all know, AI capabilities for coding has seen significant improvement since last 6 months, but still just asking it to build you an app doesn t cut it. You can take the vibe coded software only till certain extent, not to production. You still need to have good understanding of software development in order to get it right. Me being ... ... discussion. - Need a well structured audit loop to challenge what AI codes. Some people use codex to audit what claude writes. But, for me even claude s separate agent works well for auditing. - TDD works well. Instead of just asking

... just shipped something new on Product Hunt. Ask Product Hunt AI is a way to explore launches, products, and discussions without digging through pages or trying the perfect search query. You can just ask

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked

... most important and challenging experiences you ll have as a founder is fundraising. I pitched VCs across London and San Francisco. Some spent 15 seconds on my deck. Some took the call just to diligence a competitor. One told me to ask

... users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried it at least once Retention rate % who used it again after 30 days Request volume how many users asked for it The gap between what people asked

... months, our most requested feature at Murror was a chat function. Users wanted to talk to the AI the way they talk to a friend. It seemed obvious. Every competitor had it. Every feedback form mentioned it. So we built it. And within two weeks, our core metrics started dropping. Session length went down. Return rate went down. The thing users said they wanted was actively making the product worse. Here is what we discovered when we dug into ... ... intentional pacing. It does not let you rush through your own emotions. The result was better than both the old experience and the pure chat. But we never would have gotten there if we had just built what users asked

... chat : 1. Launch before you're ready. We launched on 31st May 2025, my co-founder's birthday , here on Product Hunt. And kind of forgot about it. Then people started signing up, using the tool, and emailing us asking to upgrade. That's your signal. If people pull the product from you before you've even asked

... trying to protect their pet project. Just the raw plan. The prompt: "Analyze this roadmap. Identify the three most likely failure points. Use first principles reasoning. Assume we will follow your recommendations without ego. If you need more data, ask for it." The results were not symmetrical. Failure point #1: The unvalidated feature. The roadmap had a feature labeled "Priority 1." Estimated build: six weeks. Claude flagged it immediately. "No user research cited. No support ticket volume. No search volume ... ... weeks into development. Failure point #2: The integration with no ROI. Another line item: "Integrate with Platform X." Estimated: eight weeks. Claude's analysis: "What outcome does this drive? No stated goal. No success metric. No evidence customers are asking

... seconds. Then refactored a messy function into something readable. We felt like geniuses. By end of day, it had shipped 3 minor improvements. We started talking about cutting engineering costs. Day 2: The first crack. We asked Claude to add a new filter to our dashboard. It wrote the code. It worked locally. We merged. That night, something else broke. A completely unrelated chart stopped loading. No error logs. No obvious cause. We spent 2 hours tracing it back ... ... refactored a shared utility function that 5 other features relied on. It didn't check dependencies. It just assumed. We rolled back. Lesson one: AI doesn't think about side effects. Day 3: The false confidence trap. We asked

... this guide isn't just a quick summary. We're going to go deep. We'll explain exactly what Snaply is, how it works, why privacy matters so much for dictation tools, how it stacks up against every major alternative, who it's best for, and how to get the most out of it. Whether you're a solo professional, part of a team, or evaluating dictation software for an enterprise deployment, this is the most complete breakdown ... ... find. Table of Contents Why AI Dictation Matters in 2026 What Makes a Truly Great Dictation App What Is Snaply? Snaply's Core Features: In Depth Why Privacy Is Non-Negotiable for Dictation Snaply vs

... built a model to generate 1,000 questions that people actually ask. Not random prompts. We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries. We clustered them by intent and generated 1,000 representative questions. We asked

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor

... cuts a little deeper. It's about self-editing. And self-rejection. I think most of us suffer from it. "I could never do that." "I could never say that." "I could never start that." "I could never ask that." I hear it all the time. Usually in response to things I'm doing - which, if you keep reading, you'll find deeply ironic. When I left my job to build @Meet-Ting , my co-workers couldn't imagine leaving ... ... managed to stay on the right side of school so I got an education. I've had opportunities. But outside of that, a lot of it is more basic. It's just doing. Or asking

... There are millions of apps across the App Store, Google Play, and the Microsoft Store. New ones launch every single day. Yet when you ask developers where they can properly showcase their work to real users, most of them struggle to name more than one or two places. When you ask

... sentence. If you can't tell someone what your feature does in 10 words, you don't know what it does. I gave this big rambling pitch about "content planning and scheduling and automation." People nodded. Nobody asked follow-up questions. The second sign: Nobody asked

... surveyed 3,000 B2B buyers who used AI assistants (ChatGPT, Perplexity, Gemini) in their last vendor selection process. The goal was simple: find out exactly what they asked and what actually influenced their shortlist. The results reveal a massive gap between how companies market themselves and how buyers actually discover them through AI. Here is what we learned. 1. Buyers Don t Ask

... genuinely useful: Solved a real problem 4.8 stars on reviews 20% MoM growth Customers who found us, loved us But "found us" was the problem. Organic discovery had flatlined. Referrals carried us. New users from search? Zero. Then someone asked ChatGPT about our category. Screenshotted it. Sent it to me. We weren't there. Not #1. Not #2. Not #10. Nowhere. What I Thought Mattered (It Didn't) What I Prioritized What AI Actually Cares About Beautiful design Structured content ... ... project management tool for remote design teams. Here's exactly how we work." Feature lists Problem-solution paragraphs Vague benefits Specific outcomes Lesson #2: AI Loves Comparisons Even If You Lose This one hurt. I avoided mentioning competitors

... After analyzing 1,000+ SaaS products at Rankfender, I've identified the 3 non-negotiables for AI visibility. If you're not doing these, AI will never know you exist. Thing #1: You Must Answer Questions AI Is Already Asking The mistake: Most products optimize for keywords people type into Google. The reality: People ask

... random 500 from leaderboard Finding #1: The Discovery Gap Is Real Academic research confirms what we found: when users search for products by name, LLMs recognize them almost perfectly 99.4% for ChatGPT, 94.3% for Perplexity . But when users ask discovery questions like "What are the best AI tools launched this year?" the success rates collapse to 3.32% and 8.29% respectively. That's a 30-to-1 gap. What this means: Your Product Hunt launch gets you known . But it doesn ... ... Hunt changed a page title from "The best AI dictation apps" to "The best AI dictation and speech-to-text software." Result: citations tripled overnight. Why: LLM search queries differ from human Google queries. Match the language people actually ask

... spent thousands translating your site. French. German. Spanish. Japanese. Maybe more. Here's the painful truth: Your site is translated. Your AI visibility isn't. When a German user asks ChatGPT in German about your category, you're invisible. When a French prospect searches Perplexity in French, your competitor

... live on the leaderboard today. One thing we kept seeing in user research: teams have dashboards, but still can't get fast answers. "Why are leads down this week?" still takes hours of manual digging. Genie fixes that - you ask in plain language, it finds the right metrics, runs the analysis, and returns an answer with a chart. No SQL, no waiting. A few things I'm curious to hear from this community: What's the first question ... ... ask

... Sure. Transformative? Not really. Then I tried something different, and it rewired how I work entirely. The shift: From "do this" "think through this with me" Instead of: "Write a go-to-market strategy for my product" I started asking: "Here's my current GTM thinking: [dump]. What assumptions am I making that I haven't tested? What would you push back on?" The difference is massive. The first gets you a generic 5-step plan. The second gets ... ... session: "Assume this feature failed after 3 months. Walk me through the 5 most likely reasons why." The answers are uncomfortable. That's the point. Devil's advocate on copy and positioning Instead of "make this better," I ask

... Mistake Most product owners treat AI visibility like a marketing problem. They think: "Let the SEO team handle it." Here's why that's wrong: Your product is being evaluated by AI every single day. When potential users ask: "What's the best tool for [your category]?" "Alternatives