... I have a free tier that costs me real money every time someone uses it, and no way of knowing ...

... ship a bot that just says "you're right." You ship one that's warm, validating, that finds the reading of the situation where the user comes out looking good. It tests well. People like it. The thumbs-up 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 ... ... with before you touch anything. Here's the part that should worry any maker. The same study ran the experiment on the other side 2,400 people talking to sycophantic and non-sycophantic versions. The people getting the flattery rated

... them to determine which providers offer the best performance at the lowest cost for different target websites. The main performance metric we've settled on is average success latency: How quickly does a provider return a successful response? Success rate or latency alone doesn't tell the full story. A provider might respond quickly but have a low success rate

... 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. In addition to taking pictures, users can search for predefined foods by name. Search results can include common foods, branded products, restaurant items, recent meals, favorite foods, and previously saved combinations. This allows FitBin to remain useful in situations where ... ... information For example, FitBin may explain that a meal provides enough protein for the user s muscle-building goal but contains more sodium than their remaining daily target. It may recommend a smaller portion or suggest a lower-sodium alternative

... conversion landscape in 2026 looks very different from what it did three years ago. Here is what the data says. The conversion hierarchy (2026) Review sites (G2, Capterra): 12-18% SQL-to-close rate. Buyers arrive already comparing vendors. Referrals / word-of-mouth: 10-15%. Pre-vetted by trust before first contact. Product-Led Growth: 9% median free-to-paid conversion. 25%+ with PQL. Webinars (live): 22-33% CTA conversion. High-engagement webinars hit up to 69%. AI search (GEO/AEO ... ... higher conversion than traditional organic. One B2B study found AI-referred traffic converted at 7.12% vs

... weeks ago, one of our developers pushed a refactor touching one of the agents Latitude runs in production. It looked harmless in review. Every request kept going through and latency stayed flat, but the prompt cache hit rate (how often an AI successfully reuses its cache instead of rereading information), which is normally above 80 percent, fell to 30. For perspective, cache reads cost about a tenth of what fresh input tokens do, so the drop tripled our effective token ... ... spend on the same traffic. Thankfully, a monitor in Latitude was watching that rate

... businesses wanting to stop landing in spam, and start landing in the inbox. Most of my clients come from a checklist I launched on Hacker News 4 years ago and Upwork, where I ve maintained a 100% job success rate and reached Top Rated

... while back, I needed exchange rate data for a small personal project. Nothing fancy just convert a few currencies, maybe pull some historical rates

... Replit scaled from $2.8M to $150M ARR in under a year. 25% of Y Combinator's Winter 2025 cohort had codebases that were 95% AI-generated. The tools are incredible and the speed is real. But the rejection rate hasn't changed. It's actually gotten worse. 50% of apps are rejected on first submission. 40% of all apps get rejected at least once. That number existed before vibe coding. It exists now. And it's about ... ... actually causing rejections (the patterns nobody talks about) Most rejection content online is vague. "Make sure your app follows guidelines." Yeah cool, thanks, who would've thought of that Here's what actually gets apps rejected at disproportionate rates

... 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 ... ... analytics. Feature Build time Customer requests Actual usage after 3 months SSO 8 weeks 4 requests Used by 2 accounts (both internal) Audit logs 6 weeks 2 requests 0 accounts Enterprise tier 10 weeks 0 requests 0 signups API rate

... people said and started tracking what they actually did. The dataset We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried it at least once Retention rate

... months ago, we ran an experiment with our own data. At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard. I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize ... ... Give me the numbers." The answer changed how I think about competition. The dataset: 5 competitors

... 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

... went down a rabbit hole. Product Hunt launches are every founder's obsession. Everyone wants to know the secret formula. So I used Rankfender to analyze 1,000 Product Hunt launches from 2025-2026 tracking their AI visibility, citation rates, and what actually correlated with success. Here's what the top 10% did that everyone else missed. The Dataset Parameter Value Launches analyzed 1,000 Time period 2025 2026 Platforms tracked ChatGPT, Perplexity, Gemini Metrics measured AI citations, share ... ... what we found: when users search for products by name, LLMs recognize them almost perfectly 99.4% for ChatGPT, 94.3% for Perplexity . But when users ask discovery questions like "What are the best AI tools launched this year?" the success rates

... just exposure, but the degree to which AI treats your brand as a credible source . Share of Voice (SOV): Represents your brand s voice share within AI narratives the probability that AI mentions your brand rather than a competitor when answering a topic-related question. Sentiment: Captures the emotional tone of AI mentions positive, neutral, or negative showing how AI portrays your brand. Together, these four metrics answer the two most fundamental GEO questions: How frequently does AI mention ... ... brand? and In what tone and context is the brand represented? 1.2 Trends and Competitor

... many found they were invisible. But here's what scared me more: Of those who WERE visible, 43% had incorrect information in AI answers. Not "suboptimal." Not "could be better." Wrong. Outdated pricing. Missing features. Wrong founder bios. Competitors credited for your work. And the average error sticks around for 4 6 months . Today, I'm sharing the full dataset: 1,000+ SaaS products, 75,000+ AI answers, and the hard truth about what's being said about you when ... ... categories This is not a survey. This is actual citation data from real AI answers. The Hard Truth Only 24% of SaaS products appear in AI answers for their core category keywords. 76% are completely invisible. Company Size Visibility Rate

... Rankfender , we've identified clear patterns. Certain content types get cited 3x more often than others. Here are the 7 content types that win AI citations with real examples you can steal. First, the data: Content Type Citation Rate (vs

... Have comparison pages 82% 31% Use FAQ schema 71% 19% Cite data sources 68% 27% Part 3: What content wins AI citations? This is the question everyone asks. Here's the data: Top 5 content types by citation rate: Comparison pages (X vs

... 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 ... ... clean test case, and a new tool called @Gauge made the impact measurable. Gauge tracks LLM visibility across major AI models using a large, search-informed set of prompts, giving us a statistically meaningful way to measure citation rate

... Limitless enables users to: Browse products across multiple stores and categories Build shopping lists and compare options Choose delivery or in-store pickup at checkout Add custom instructions for fulfillment Track orders live Schedule deliveries within preferred time windows Rate stores and delivery partners From the outset, this Idea Usher review notes that Limitless is not framed as a single-vertical app. It is designed as a local commerce layer that prioritizes convenience through consolidation. The Problem Limitless Is Solving ... ... Journey The user journey follows a predictable, low-friction path: Typical Flow Open the app Browse categories or search for products Add items from preferred stores Choose pickup or delivery Add optional instructions at checkout Track the order live Rate

... growth has been the result of hard work, but we ve also had a secret weapon: an AI agent that acts as both a data analyst and a PM, working 24/7 to optimize our product s activation and conversion rates. For decades, companies have been making product decisions based on intuition and manual data analysis. We wanted to see what would happen if AI could take the wheel completely. We originally built this AI agent as an internal project last ... ... June, and since then, it s improved our activation rate

... automatically picks the best model for you: Before LLM Arbitrage: User request Random model selection Hit or miss suggestion After LLM Arbitrage: User request Analyze user's past preferences Pick best model for THIS user Better suggestion (higher accept rate) How It Works (The Nerdy Bit) Okay, for those who want the technical details... SourcePilot use an algorithm called Thompson Sampling (the same one Netflix uses for recommendations). Here's the flow: USER FEEDBACK LOOP Write AI Suggests Accept/Deny/Retry System ... ... Learns Model Stats DB Model A: 85% Success rates

... saved 1 hour/day on data analysis" That's one of the key findings from our study with 100 thaink Analytics users. The numbers that matter: Average rating: 4.4/5 (technical & business profiles combined) 48% highlight reduced MLOps deployment time 63% of business users confirm immediate efficiency gains But we also share the real challenges our users faced: Data lifecycle struggles (collection, quality, formats) The 3 main barriers to AI adoption (transparency, cost, security) What our study reveals ... ... transform tool adoption Detailed testimonials from a Head of Data and a Retail Demand Planner Manager Ready to experience it yourself? [Start your FREE trial] - No setup required [ Book a demo ] Discover thaink Analytics unfiltered and why 100 users rated