rohanrecommends · Product Hunt

Nautis The AI-native Operating System for founders.

@hannah24_app · X

u/UndefinedCurve · Reddit

I spent a lot of time researching problems that I could solve. This is what I built. Hey everyone, I'm a 17-year-old CS student. Instead of building another AI wrapper, I spent the last few weeks reading Reddit threads, App Store rejection posts, and developer forums to find a problem worth solving. I found out that numerous apps are rejected every year due to constant changes in the policies of the App Store and Play Store. So I built Veravo. You tell it about your app, and it tracks

@gorniv · X

Best client for App Store Connect and Google Play Console =) - .

@collinsvidzro · X

@iamdeepak89 · X

u/youex-ai · Reddit

Built a CRM w/ MCP -- Biggest misunderstanding on first look? We launched an MCP connector a few weeks ago that connects our CRM data. We're still working through our product market fit, so I asked Claude What is the biggest thing people misunderstand about YouEx.ai when they first see it? I was actually not even thinking about researching our actual CRM data, but that's what it did. The answer was actually really insightful: Your own data points at it: a large sha

@Bim_evaporates · X

Check out DMs open

@habeeb_tech · X

How does your team currently manage visitor check-ins at reception? Try Entrifi, a digital visitor management built for Nigerian businesses. Works on any device, no hardware needed. Get started free: Worth sharing with whoever manages your front desk.

@harryjwang · X

I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age

@Adam_Rhinehart_ · X