@LeoSparr · X

@yedil_bek · X

Building RedRun - security testing that proves what it finds. Runs the real exploit + hands you the exact request/response: SQLi, IDOR, SSRF, XSS, JWT + AI prompt-injection. Zero false positives. Free scan → Active engine invite-only - DM for access code

john_fike · Product Hunt

FitForge AI AI builds your workout & meal plan in 60 seconds

u/Hunter_79 · Reddit

LiftWell Side Project I recently created an app called LiftWell for building workout plans or single workouts. I found myself using AI to get ideas for home workouts with my cousins, but I couldn't track my weights, save workouts, or see if I was actually getting stronger. I decided why not create an app that I would use? So, I did. What makes it different is that it doesn't just generate a unique workout, it tracks your weights and PRs over time so you can see your progress. Plus, I have som

@_sunbo · X

I built ProductIdeaScout to help founders validate product ideas before they start building:

@Ebrahim_Rio · X

Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.

@discorev · X

I've been working on - Using gpt-5.6-sol and Fable in Claude Code via CLIProxyAPI. I've found using these two together with Claude Code Workflows is a killer combination

@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

@buildwithmaya · X

Hi Rashka I am building

@joffreyio · X

douvy · HN

Educational tool for understanding AI agents. Code: https://github.com/douvy/how-agents-think

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