... search results on a specific date and seen a specific pattern. The research output contained no such observation. The sentence came out. Reading it more carefully would not have caught that, because the sentence was perfectly plausible. Separately, we measured whether our posts were competing with each other instead of continuing to assume it. 13 posts, 78 pairs, similarity threshold 0.363, highest observed 0.300. The assumption was wrong. If you have ever run a Googlebot user agent fetch against your ...

... fire as three separate events with three different names. Cost: whichever word wins will be wrong for at least one of the three contexts. Keep them contextual and rename the events so they aggregate. Preserves the copy, fixes the measurement. Cost: I'd be 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. Comment from Albert Nelson(@albert_nelson1): We once changed who we counted customers and suddenly growth looked fantastic. Nothing actually changed. That ...

... measured the click collapsing from 15% to 8% when an AI summary appears. A separate read of 35,000 ChatGPT citations shows where the decision moved to and it's mostly places you don't own. Start with the one number here that isn't someone's marketing. Pew Research put browsing software on 900 US adults and watched 68,879 real Google searches in March 2025. Not a survey about how search feels. Actual behaviour. When an AI summary ... ... pages with a summary, against 16% without. An AI summary showed up on 18% of searches at the time; that share has only gone one direction since. So the click is going. That part is real and 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

... prove it moved anything? Screenshotting chatbot answers every morning isn't a metric. The honest truth is that GEO is still largely a black box. But "black box" doesn't mean "unmeasurable." "If you can't measure it, you can't improve it." Peter Drucker GEO starts with one thing: a scoring baseline you can run consistently. Same site, same input, same score every time. That's exactly what the service I'm sharing today does. It measures

... Reach" is basically noise. 10,000 views tells me nothing. What I actually want to know is which post started a real conversation a comment from the right person, a DM that went somewhere, an actual connection. Almost nothing measures that. 2) Cross-posting reads as cross-posting. Most tools take one post and just resize it per platform. But platforms don't differ in aspect ratio they differ in what that audience will tolerate. Same content, different crop, still ... ... feels generic. So, genuine questions for the creators/marketers here: - Beyond "scheduled" and "published," what would you actually want a tool to measure

... scrollbar, no hint, no right-hand edge. Now a too-wide message scrolls sideways inside its own frame like a civilized document. The message stopped growing 8 pixels every time you resized the window. Turns out the frame was measuring its own height, adding a margin, then 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

... world had any reason to know it had a subscriber. Worth separating from confidence, because the repair looks different. It was not overconfident about the facts it had. It was correctly confident about facts that had expired. Calibration is measured against what you know, and no amount of it helps with something that stopped being true after you read it, which is why making the model hedge more does nothing here and a freshness check on the inputs ... ... unrelated things, and no value is. Which is where your question resolves for me. Who it affects should not set the number. It should select who answers, and then the number becomes a property of a person, which is measurable

... built a mastering tool that measures AI exports instead of guessing at them. Reply with a link to one of your Suno or Udio tracks and I'll run it and tell you what it actually measured

... 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

... doorbell. It rings the same whether it's a package or a fire. That's basically the gap Pushary fills, same hook idea you've already wired, but the event carries a real handoff (done vs blocked vs

... pieces does not help. Adding a llms.txt file does not guarantee citation. Paying for mentions is a waste of money. Google is warning you: do not fall for the snake oil. What Bing is saying Bing is talking about measurement. Copilot, ChatGPT, Perplexity, and AI Overviews behave differently from a blue link SERP. The logic of citations is new. If you pretend otherwise, you are flying blind. You cannot optimize what you cannot measure

... 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