... 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

... Many brands measure AI search visibility by running a predefined list of prompts and counting how frequently they appear in the answers. For example, a SaaS company might track prompts such as "best CRM software," "top CRM platforms," and "CRM tools for sales teams." These prompts provide a useful starting point, but buyers often ask more specific questions based on their requirements. Someone might need a CRM for a 10-person team, while another is looking for software that integrates ... ... with Salesforce or supports complex enterprise workflows. Each scenario can produce a different set of recommendations. Tracking prompts across buyer segments, use cases, and purchasing stages can reveal where a brand appears consistently and which competitors

... Sarah, the founder of Operato AI , the self-serve AI Visibility platform. In 2014 I joined AppsFlyer as an early employee and spent 10 years there, building the French market and helping marketing teams measure where their customers actually came from. One lesson stuck with me: every time a new acquisition channel appears, the companies that measure

... Many brands measure AI search visibility by running a predefined list of prompts and counting how frequently they appear in the answers. For example, a SaaS company might track prompts such as "best CRM software," "top CRM platforms," and "CRM tools for sales teams." These prompts provide a useful starting point, but buyers often ask more specific questions based on their requirements. Someone might need a CRM for a 10-person team, while another is looking for software that integrates ... ... with Salesforce or supports complex enterprise workflows. Each scenario can produce a different set of recommendations. Tracking prompts across buyer segments, use cases, and purchasing stages can reveal where a brand appears consistently and which competitors

... everyone! I recently released my Android app, ChargeCheck , and I'd love to hear your feedback. I wanted a simple way to compare chargers and USB-C cables without buying additional measuring equipment. Here's what the app does: Run a 60-second charging test View estimated battery-side charging power in watts Monitor current, voltage, and battery temperature Save results and compare different chargers or cables Check how stable the measurements

... product show up? And more importantly, do you know how often it does? Most founders I talk to are investing in SEO, publishing blogs, and building backlinks. But there's another discovery channel emerging that is much harder to measure: AI-generated recommendations. Here's how I'd approach measuring

... significantly. Complex tasks may require more model calls, tool usage, and retries, even when the price paid by the customer stays the same. I'm interested in learning how AI founders and finance teams handle this today. Do you measure gross margin by customer? How do you account for differences in workload complexity? Are your existing billing and cost-monitoring tools sufficient? I'd appreciate perspectives from people building and operating AI products. I'm exploring this problem through ... ... credits-per-game idea: would you price credits mainly to control shared infrastructure consumption, or would you also use them to reflect the different compute requirements of different games and agents? Comment from Hisashi Space(@hisashispace): Can't you 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

... everyone! I have been working as a developer and project manager for a while now, and this is a challenge I keep running into. We have been doing remote work for years, yet many companies still measure productivity exactly how they did in an office. They look at the hours logged on a screen or how quickly someone replies to messages. But sitting in front of a computer for eight hours straight does not automatically mean you are doing great ... ... shift the focus. Instead of counting minutes, we should look at the impact of the work and the general pulse of the team. Did we hit the milestone? Is the quality of the output high? Are people communicating well? Measuring

... 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 Yogesh Joshi(@yogesh_builds): @niveditha ... ... product - makes sense! Thanks for sharing 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

... Every client conversation this quarter eventually lands on the same question. Why doesn't ChatGPT recommend us. We can never answer it properly, because the measurement underneath is broken. Referrers from ChatGPT and Perplexity are inconsistent, a lot of it arrives as direct traffic, and so the honest answer to "how many of our signups touched an AI tool first" is usually that we don't know. What shifted my thinking: one of the GEO tools that launched here recently ... ... catalogue. The total was never the instrument, the distribution was, and every figure now gets date bucketed before it reaches a report, with a single window spike labelled as crawling instead of folded into the baseline. Which is the measurement

... these metrics don't necessarily tell you whether the conversations were actually good. That's why we created the Inquio Score : one score designed to summarize the quality of real customer conversations. We explain how it works, what it measures, and why we think chatbot QA needs to move beyond activity metrics. Read the article How do you measure

... domain, and the seam between them is a link out. The part I'd read as roadmap. Now put that next to what the same developer site already ships for advertisers. There's an Ads section with a JavaScript measurement pixel, a Conversions API, supported conversion events, and an Advertiser API for campaigns and targeting. A full closed loop for measuring

... that cost me something. Both inputs were already soft and I was treating the result as a decision. A wrong ratio does not look wrong, it is a normal number in a normal range. So which one did you measure wrong? Not on purpose for a deck. The one where you found out later the definition had moved ...

... 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

... suddenly lose access to all AI. But what happens if , one day, access to the leading American models is restricted more broadly? We would still have European models, open-source systems we can operate ourselves, and Chinese alternatives. But there is no guarantee that another major power would never make the same decision to protect its own strategic advantage. AI is no longer just another software category. It is becoming infrastructure. It will shape education, healthcare, national security, research ... ... world are American, and Europe should continue working closely with the United States. But cooperation and dependency are not the same thing. Technological sovereignty does not mean refusing to use foreign technology. It means having a real alternative

... This process becomes repetitive, and many users eventually stop tracking. At the same time, most calorie-tracking applications treat every user similarly. Two people may receive nearly identical meal information even when they have different ages, body measurements, activity levels, weight goals, allergies, or health considerations. Someone trying to build muscle may need different nutrition guidance from someone trying to reduce weight. A person managing high blood pressure may need to pay greater attention to sodium. Someone with diabetes ... ... users can review and correct the result before saving it. They can change the food name, portion, preparation method, quantity, or ingredients. When confidence is low, FitBin can request another picture, ask a short clarification question, or provide likely alternatives

... help with this, I added two tools to agentcad : spec: spec allows the agent to declare different things that should be true about the final shape: it's overall size, cylindrical features, the number of planar shapes + their orientation measure: which allows the agent to measure

... governance posture it is an illusion of one. The question is whether a behavioral floor, even an imperfect one, is better than no floor at all. Curious if anyone here has built or worked with review quality measurement systems and what signals you found actually correlated with genuine engagement vs

... leads from organic. Your finance system says 2 of those leads turned into paying customers. Which number is correct? All of them. None of them. The question is wrong. The tools are not broken. They are just measuring different things. Why they do not match Search Console measures

... measure delivery. We measure