... more useful to this crowd than a launch announcement. Every "AI closet" pitch sounds identical for the first thirty seconds: photograph your clothes, get outfit suggestions. The differentiation always turns out to live one layer down, in which single signal the product picked to be good at and that's also where the category quietly fails. Look at what's actually out there. One competitor

... Play's Age Signals terms limit the signal

... Google still largely shows traditional results. AI search is mostly eating informational traffic, not purchase-intent traffic. Second, the traffic that AI does send is higher quality. ChatGPT-referred visitors spend an average of 15 minutes on a website versus Google-referred visitors at 8 minutes. Sessions from AI referrals grew 527% year over year in the first five months of 2025. Third, only 16% of brands are systematically tracking their performance in AI search. That means 84% of your ... ... competitors

... find will evaporate when the tab closes unless it is recorded where the system can see it. Leave a thumbs-down with specific feedback ("quoted a version we never shipped") on the exact offending message; that is the strongest signal you can give the system, and automatic flaggers do the same job for common failure categories without waiting for a human. A handful of annotations is enough to get the ball rolling. 4. Track it as a Signal

... already launched many things that failed, I now try to understand before building whether an idea is worth investing my time into. For example, in my case, a tool that helps prevent LinkedIn bans ... Here are some signals I look at: The problem happened to me first. I became my own potential user, which is usually a strong starting point. I ask people on Product Hunt. You can see more patterns, learn from people with more experience, and get feedback ... ... nicely! Comment from Nika(@busmark_w_nika): @rnagulapalle  But that's when people already know your product. I need to nail that part when they will learn about my product :D Comment from Arnold Oshenye(@oshylabs): One signal

... actually listening. The issue is structural. When a platform makes it easy to post about your product, people post about their products. When there is no filter between genuine contribution and self-promotion, the promotional content takes over. The signal-to-noise ratio collapses. This is how platforms die slowly. Not from lack of users, but from lack of reason to pay attention. The Algorithm Is a Values Statement Here is something that does not get said enough about social ... ... build an audience by being worth following. Then your product posts land somewhere real. The principle: earn reach by contributing value, not by promoting yourself. Why This Is the Right Call Professional networks are built on trust and signal

... manager already feels that something is wrong but cannot clearly see what is happening. With Pulseboard, we are trying to build something earlier, lighter and safer. The idea is simple: What if every employee could send a small daily signal about how they are doing, without having to expose themselves immediately? Not a long survey. Not a heavy HR process. Not a public confession. Just a small anonymous check-in that helps people notice changes earlier. Why anonymity matters ... ... gets better. That waiting period is exactly where many problems grow. Pulseboard is designed to lower that threshold. Instead of asking someone to immediately start a difficult conversation, we let them give a small anonymous signal

... Product Hunt I m Cheng,the maker of Signal Radar. And today is my first launch. This started with an uncomfortable realization: the moment you need your phone most lost on a trail, injured in the backcountry, stranded in a country you don t know is usually the moment it stops being useful. No signal

... That's a solid mental model actually. Treat agents like services, expect signals on state change. The problem is most agents don't emit those signals

... metrics? Comment from Mohit Gupta(@mohit_gupta138): @busmark_w_nika  Ok, if we were to pull on the time thread a bit. Then maybe think about the balance between launching the second service that was tied to you vs the last one. and the effort that you might have put in each (did it get easier with time and your compounding efforts on personal brand). would love to know Comment from Varun Mishra(@varun1jan): I think the biggest mistake ... ... treating it as a binary choice. Building a personal brand before PMF can give you distribution, but it can also distort feedback because early users want to support you. Building in silence gives cleaner signals

... 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 ... ... this most right now? For us it was solo builders who can't also become full-time marketers. Smaller users, faster feedback loops, and they tell you what's broken in hours not quarters. Your table comparing build time vs

... generation) 68% 52% 11 requests Prioritize now ROSE (on-site optimization) 45% 31% 9 requests Keep, enhance later Social/Community Engine 14% 4% 19 requests Delay, needs more validation RASE (app store tracking) 11% 3% 22 requests Delay, low retention signal Ranklink (backlink discovery) 9% 2% 24 requests Delay, not sticky yet The features people asked for most (RASE, Ranklink, Social Engine) were used by less than 15% of users. The features they barely mentioned (Workflow Engine, RAIVE, RCGE) had adoption ... ... feature request. That's a product. RASE had 22 requests. We built an MVP. 11% of users tried it. 3% came back. We spent 4 months building something people asked for but didn't actually stick with. The signals

... retool it. The problem wasn't the AI. It was the output. RCGE could generate clean, structured content fast. But clean and structured isn't enough anymore. We analyzed our own content library against Google's new signals. Five red flags kept appearing : Zero information gain Our content was paraphrasing what already existed. No original data, no case studies, no expert quotes. Google now measures "information gain" as a ranking factor. Content that doesn't add anything new gets devalued ... ... extract a snippet-ready answer, it stops citing you. This affects AI Overview visibility directly. High CTR, low engagement People clicked our AI content then bounced fast. Under 30 seconds on a long article signals

... 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. Not a parody. A real competitor

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... rolling these changes out across Product Hunt. Product Hunt is becoming part of the AI retrieval layer. Key Lessons We view AI visibility as a new distribution layer. Our goal is to ensure that authentic community signal

... 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 ... ... actually worth pursuing is hard. I d see people talking about problems on places like Hacker News, Reddit, Stack Exchange, app reviews, etc., but there wasn t an easy way to systematically connect those complaints into potential opportunities. The signal

... SIGRANK Check it out Tagline SigRank: The Global Leaderboard for AI Systems and Their Sharpest Users. Powered by MO ES . One-Liner (Subheadline) Ranked in real time. Across all platforms. SigRank reveals who s shaping the AI frontier one signal at a time. What It Is SigRank is the world s first user-centric, cross-platform AI leaderboard. It ranks individual users, AI systems, and ecosystems measured by cognitive signal