
Schedulala - Schedule Social Media Posts to 11+ Platforms | Schedulala
在统一仪表板中为 11+ 个社交平台排期和发布帖子。
@AmeerHaio · X
your content everywhere!
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在统一仪表板中为 11+ 个社交平台排期和发布帖子。
@AmeerHaio · X
your content everywhere!

MessageFuture让你为亲人创建并安排消息、视频或语音,在特定日期发送。
@SidathRashen · X

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

用于跟踪时间、管理项目和组织客户的AI驱动工作区。
@baptistedulac · X
time tracking for freelancers and agencies


追踪您期待的活动和发布的倒计时。
u/_xtr0_ · Reddit
I built a hype almanac — a countdown calendar for everything you're waiting for Twenty-ish years ago I built a little tool just for myself to track release dates of video games I was hyped about with a funny js hype graph. I love to be able to look fw to things in my calendar. So I rebuilt it properly this time (vibe-coded the whole thing) and figured I'd share it: hypanac.com (hype + almanac -- back to the future) What it does: One calendar for everything you're looking forward to — ga

沙龙日程管理及Google日历同步、财务追踪集成应用。
@pedromusculini · X

Chat with Nylo to plan your time, manage your calendar, and review AI-proposed schedule changes before they happen.
@FinnFreema69220 · X
Nylo - Learning plans shaped to your fixed Schedule


AI首席助理监控你的邮件、日历和消息,自动分类、优先排序并执行任务。
@AriaInbox · X
Aria is an AI chief of staff that reads your inbox, drafts replies in your voice, and books meetings while you sleep.

AI驱动的每日摘要,整合Gmail、日历、GitHub和Figma。
@CodewithDivine · X
check this out

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