@vitverb · X

hi :) One tap back to the work you meant to finish. No tasks to manage. Nothing to set up. Local-first

@trackowork · X

@alizreim · X

Documentation that lives in your own git repository

mistakevin · HN

I've been working on https://notebooker.ai if anyone is interested in giving some feedback. I tried to post a Show HN yesterday with details about how I built it, but was auto-flagged and not sure what rules I broke. Everything that I've built on top of open-notebook, like the plugin system for your own "creators" (aka, podcasts, infographics, etc) is at https://github.com/Notebooker-ai plus a Cloudflare Worker AI deployer to play with different models. Been working on it for about six months.

@AadyaAI · X

Aadya AI turns a client conversation into a branded, ready-to-send proposal or invoice PDF in 60 seconds — no templates, no formatting, no chasing.

@qayyumabro · X

GrokTask — 基于 Grok AI 的智能任务自动化平台，支持定时任务、AI 自动执行、信息收集与分析

@FileProInfo1 · X

🚀 has successfully launched! helps users convert, view, process, and manage files online — including documents, PDFs, OCR, archives, email formats, and more. We’re excited to share it with the community. Check it out here:

Doc2Md — 将 PDF、Word、Excel 等多种格式文件转换成 Agent 友好的 Markdown 格式 - [更多介绍](https://doc2md.cn)

@Tedlee92 · X

Let's connect. I'm building - tasks, events, and notes in one minimalist calendar

@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

@hannah24_app · X