@MarcusNilsson83 · X

Check out what I just built with Lovable!

@AiShivam · X

AgentStacKPro is an OS for AI agents designed to make them production-ready, and it holds incredible potential. You can also check out the project I currently developed

footballaipredictionking · Product Hunt

Football AI Prediction King Free AI football predictions and match analysis

@FasScale26 · X

We've created a platform for entrepreneurs to evaluate business ideas and to set up and run their businesses. We've launched it in the UK and are planning to roll out to other countries over the coming year.

barretlee · GitHub

agent-pulse Evidence-backed AI industry intelligence — trends, source updates, daily data refreshes, and weekly decision briefs.

@AjayGB4 · X

@SaikatKuma87126 · X

Just shipped AI CRM 🚀 A lightweight CRM with AI-powered sales insights. Would love your feedback: Building with Next.js + Prisma + Gemini AI. #buildinpublic #NextJS #AI

@jozef_x1 · X

6 x designs, which one did I go with? vibe coded and designed in base44 by for

@holdbru · X

We make fundraising really easy for startup founders - check us out at

AI Todo — 像很多程序员一样，我日常要兼顾工作任务、生活琐事、项目计划，时常感到事情多但漏掉的也不少。想用智能方式管理这些待办，但市面上的工具要么设计臃肿，要么 AI 能力很弱。于是我花了几周开发了 AI Todo，目标是:

@AIWMCX1 · X

Consultants don’t need more noise. AIWMC Quantis helps turn messy business ideas into clear scenarios, runway views, and risk signals — so you can spend less time rebuilding analysis and more time giving the client a real answer.

@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