... 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

... like ChatGPT / Claude / Cursor can talk to the site Not replace SEO. Add visibility into a discovery layer that barely existed a few years ago. Curious what this community thinks, especially WP / SEO / Woo folks: If you could only measure ONE thing about AI discovery on your site first, what would it be? 1) which crawlers hit which pages 2) an overall AI Visibility Score 3) whether llms.txt is actually being read 4) something else (tell us) We re both ...

... 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 Arnold Oshenye(@oshylabs): Same experience ... ... without waiting a month to check it. 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

... grid are the ones seeing it for the first time, and they only see it once. The mistake underneath is that I designed the first thirty seconds and assumed the next thirty days would sort themselves out. Everything I measure at signup rewards that grid. Nothing I measure

... 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

... training-blocking switched on. Which, if you're the kind of person who ticks the safe-looking box, you do. Second, Cloudflare sells bot management and is building a market for paid crawling. Every number in their report is measured by a company with a position in the outcome. Read them as direction, not measurement

... 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. Comment from Albert Nelson(@albert_nelson1): We once changed who we counted customers and suddenly growth looked fantastic. Nothing actually changed. That ...

... 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

... patent in 2024 in multi-agent architecture and Advanced RAG systems The problem I kept running into at enterprise scale: hiring people who can actually build with AI is nearly impossible because no one has a reliable way to measure it. Resumes are inflated by AI. Take-home tests are gamed. Legacy platforms test binary tree inversions while your next hire will spend their career prompting agents and steering copilots. I lived this problem firsthand. So I built a platform ... ... wished I had. VibeLevel.ai the first platform that measures

... 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

... thing. Search Console says another. Your CRM says something else. They're all tracking the same campaign, same time period, and they give you different numbers . This isn't a bug. It's how the systems are built. GA4 measures sessions and modeled behavior. Google Ads measures

... 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