... think it just became something bigger. The Core of Cluing Never Changed From day one, collecting information was only half of the idea. The other half - and the more important one - was adding your unique insight to every find. Not only what you found, but: Why you saved it Why it matters How it connects to something else What your team should do with it A source without that layer is just another link in a folder. Your insight turns ... ... from it. AI sees the source. Context helps it see what you see. This is why we re moving Cluing from research management toward context management. Collaborating with People - and AI We originally built Cluing so people could collect findings

... same issue or suggest the same improvement, it almost always becomes a top priority in the roadmap. What makes feedback relevant to you? The number of testers? The mix of respondents? Or the way the feedback is collected? Finding: Developers were able to give me detailed and more accurate feedback than users itself. Developers spotted many things, recommended things while user said: It's okay. :D Comment from Evert M(@evertmar): I'm a UX designer, would like to test ... ... only one using your own product daily, that's actually not nothing! It means YOU have the problem you're solving. The question is whether others share that same problem. What's been your experience with early user feedback vs

... navigate and organize long conversations in ChatGPT, Claude, and Gemini. Who it's for: Anyone who relies on AI daily for coding, research, writing, studying, or work and finds themselves scrolling through lengthy chat histories to locate previous conversations. The hardest part I'm solving: As AI conversations get longer, finding

... changes to Search in over 25 years. The shift is conceptual as much as it is technical: traditional search waits for you to show up with a question. Information agents operate continuously in the background, reasoning across information to find what you need at the right moment Here is what changed and why it matters for your brand. 1. Search now works while you sleep Google introduced information agents , persistent background processes that monitor the web on your behalf ... ... surface relevant findings

... 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

... 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 of voice, content structure, launch day performance Sample Top 500 + random 500 from leaderboard Finding #1: The Discovery Gap Is Real Academic research confirms 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 ... ... best AI tools launched this year?" the success rates collapse to 3.32% and 8.29% respectively. That's a 30-to-1 gap. What this means: Your Product Hunt launch gets you known . But it doesn't get you found . Finding

... Yesterday I showed you how to audit your AI visibility. Today I'm going to show you exactly what to do with those findings. After analyzing 50,000+ AI answers at 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

... LinkedIn posts in your voice. It's web-based, and I used Next.js, I used Better-Auth for authentication, and I added 3 strategies: email/password, Google OAuth and LinkedIn OAuth. I used PostgreSQL as the database and Polar (Stripe alternative) for billing. Other tools I used include: Resend for email, Tailwind CSS for styling, and Antigravity IDE. I hope this helps. Let me know if you need more help. Comment from Adam Pichardo(@adam_pichardo): CS student doing real research ... ... Solo builder here, won't say much about the product yet but happy to talk shop on the plumbing. Quick version of what I'd tell my past self: Auth - Supabase, Clerk, Auth0, pick one. An afternoon of setup vs

... accurate we went from 5% activation in June to now being over 50% (chart below generated in Basedash, of course ). Activation for us involves a company connecting their data to our platform, which can be quite a difficult task (finding database credentials, checking with their security team, and often self-hosting Basedash on their own infrastructure), and then actually building a dashboard or asking our AI a question. So, how does the AI work? First, we gave it access ... ... from our product database, to Stripe, to Segment, to our internal Slack, and everything in-between. The agent can explore all of this data incredibly efficiently ( cough unlike MCP), look up public info online, and reason through anything it finds