@AmeerHaio · X

your content everywhere!

@DonatasJab · X

Not long ago finished my first SaaS product - social media scheduling tool. Tomorrow will launch on Product Hunt. I hope it goes well.

@troyaunderwood · X

@BayofDomain · X

mjkl7896 · HN

Velprium - Time Workspace

@MM_AFRAZ · X

u/sweetnessssss · Reddit

Nine months ago I quit my data science job to build an app. The app is still hungry. Nine months ago I left a comfortable data science job at a travel company to build a sports betting app. Comfortable is and odd word for a job - it means the money arrives on schedule and nobody in your family asks if you're okay. The app is called THE LINEUP ( https://thelineup.pro/ ) -- it does the unromantic parts of betting: comparing prices, working with projections and tracking what happened afterwar

@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

@imjawith · X

@ranjitpata · X

@JespernissenSEO · X

@dailytaskproai · X