... measured community growth only by counting member signups for the first six months. The numbers seemed good on paper. The discussion boards were quiet. We changed our focus to a metric: the ratio of member-created posts to staff-created posts. We also watched how many questions got an answer from another community member within two hours. The quality of community interactions improved a lot once we stopped chasing vanity registration numbers. Engagement became self-sustaining because community members actually ... ... helped each other. Measure

... nobody covered, which is worse, because it changes a line of code a lot of us shipped this year without thinking about it. If your product has a "paste a URL" box, a link unfurler, an enrichment step, a competitor-monitoring job, or anything that fetches a page and hands it to a model, this is about you. What was actually measured

... plainly that AI Overviews need no special markup, no AI text files, no schema. The one independent study of what users actually do is 18 months old and says a citation is worth a 1% click. Everything fresher is measured by the people selling the service. It's Q4 planning season, which means a lot of small teams are about to write GEO on a budget line. Before you do, I'd spend twenty minutes on two documents, because they ... ... Three numbers from it get quoted. Users who saw an AI summary clicked a traditional result in 8% of visits; users who didn't, 15%. Users ended their browsing session 26% of the time with an AI summary present versus

... question I always have around a Product Hunt launch is how do you actually measure whether it was successful? Teams spend weeks preparing for launch day and coming in the top 5 is obviously one way to look at it but I am curious what other signals people pay attention to. For those who have launched before, what did you look at afterwards to decide whether the launch was actually worth the effort? Comment from Arnold Oshenye(@oshylabs): Same experience ... ... first number i'd actually believe. Comment from Devashish Mamgain(@devashish_m): It depends on the primary reason why you re launching on Product Hunt. When we launched @Kommunicate and @AppLozic - Chat & In-App Messaging SDK , we measured

... rate goes up. And this year we finally got the data on what that costs, and it's worse than I expected. In a study published in Science this spring, Myra Cheng and Dan Jurafsky's team at Stanford measured sycophancy across 11 frontier models ChatGPT, Claude, Gemini, DeepSeek, the ones you're probably building on. On general advice and real dilemmas pulled from Reddit's r/AmITheAsshole (cases where the crowd agreed the poster was in the wrong), the models ... ... default on purpose, and it costs us some of the warm-glow metrics, and that's the correct trade. If you're building anything that gives people feedback, advice, or reflection, three things I'd actually do. First, stop measuring

... while back I posted here about counting the sources behind a handful of AI answers. I kept going, and I now have 241 software categories measured. For each category I asked ChatGPT and Gemini the six questions a buyer actually types (best X, best free X, most affordable X, best for small teams, what should I use, recommendations), gave each one a live web search, and logged every product named and every source behind the answer. That ...

... years by people who cared about how it feels to use. Dark, considered interfaces. Whitespace that isn't accidental. Content that is the product, not decoration bolted onto a spreadsheet. The short answer to "what's the best LinkedIn alternative in 2026" is this: builders aren't looking for another feed. They're looking for a professional network that was actually designed, not just maintained. That's the gap forg.to is built to close. Here's why the gap exists ... ... product, the update, the shipped feature is the profile. Not a r sum with a photo attached to it. The Feed Is Drowning in AI Slop, and the Data Backs It Up This isn't a vibe. It's now measurable

... from the same tool. On raw benchmark performance, Claude Opus 4.6 sits around 80.8% and GPT-5.5 powering Codex around 88.7% on the metrics one source tracked, but Claude Code holds an 88.6% SWE-bench Verified score by another measure. The benchmarks genuinely don't agree with each other depending on methodology, which is its own lesson. Price-per-output tells a different story than raw capability. Cursor's Composer 2.5 lands in the top three on capability index while ... ... token? Token counts alone feel misleading once you factor in retries, wrong turns, and the fact that a "cheap" run that needs 3 follow-up prompts to actually work isn't actually cheap. Curious what people here are actually measuring

... were often the ones who left the harshest feedback. Meanwhile, users who opened the app twice a week for five minutes were writing us emails about how it changed how they handle difficult conversations. We were measuring activity when we should have been measuring

... every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor tracking. That told us more than any sales call. 2. Support ticket clusters What problems keep coming up? We used RAISA to cluster 1,200 support tickets. The largest cluster (34%) was "automation rules are confusing." Not "add app store tracking ... ... correlation with retention. That told us where to invest. What we decided Building now: Workflow Engine v2 Conditional logic, scheduled runs, webhook triggers. 78% adoption, 64% retention. This is what keeps people around. RAIVE enhancements Real-time citation alerts, competitor

... 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

... tested them all building the same "Todo" app from scratch using identical prompts, requirements, and a stopwatch. The goal: Which AI tool actually delivers production-ready output, not just pretty demos? Watch the full head-to-heads: JustCopy.ai vs Base44: https://youtu.be/c79HJaWz6Y8 JustCopy.ai vs

... this industry, I still have a tons of things to learn from, doesn't matter who is willing to give me a hand, or I can ask AI, or even coming here. I wish a product is measured by multiple metrics, instead of the popularity, the traffic it receives, and financial earnings. Imagine a product being newly set for a starting program, and from, just say nothing, through all the means of development, testing, renewing, optimizing the products, paying potential ... ... free fun product that will not only be in demand among users, but also somehow refer to that successful paid product. I think that's a cool idea! ) Comment from Olga Aleksiutovich(@monolgaz): agree with Nika. The only measure