
The social media scheduler for people who'd rather be building. | SocialRobot
A week of posts scheduled in one sitting. Your feed stays alive while you work. Schedule across Instagram, X, LinkedIn, TikTok, and more.
@ntorresdev · X
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A week of posts scheduled in one sitting. Your feed stays alive while you work. Schedule across Instagram, X, LinkedIn, TikTok, and more.
@ntorresdev · X

bundle.social 用统一 API 在多个社交媒体平台发布和安排内容。
u/bundlesocial · Reddit
A Fiverr animator ghosted us, so we made this product animation with 3 prompts and one HTML file We originally hired someone on Fiverr to create a product animation for bundle.social , the social media API we’ve been building for the past few years. Then they stopped replying. So instead of waiting, we tried building the first version ourselves. The video attached to this post was created from a self-contained HTML file in roughly 40 minutes, including a tea break. FYI, R

为AI代理提供托管服务,支持持久工作空间、记忆和自动备份。
@hijak01 · X
check out the demo

从单一仪表板向多个社交网络发布和调度帖子。
@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.

使用AI自动化社交媒体监控、内容生成和数据分析任务。
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

在 TikTok、Instagram、LinkedIn 等平台安排社交媒体,$5/月起。
@troyaunderwood · X

用 Onbookr 管理预约、同步日历、自动发送提醒。
@helloukey · X

根据目标、日程和设备生成个性化12周训练计划。
@levelupAItrain · X

管理学术会议:提交和审阅论文、处理日程安排和注册。
@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