
Sendio – Book 5x more demos on LinkedIn, on autopilot
通过追踪购买信号和评分潜在客户,在LinkedIn上自动预订演示会议的AI代理。
@nickventuri · X
building to book high intent demos on autopilot founders just show up to meetings
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通过追踪购买信号和评分潜在客户,在LinkedIn上自动预订演示会议的AI代理。
@nickventuri · X
building to book high intent demos on autopilot founders just show up to meetings

自动化 LinkedIn 前景开发和销售外展,用 AI 帮助预订会议。
rohanrecommends · Product Hunt
Fuzzy AI We warm your prospects before reaching out

Fold 在一个工作空间中管理团队的任务、文件和聊天。
@foldtaskapp · X

将演讲、文章、演示稿或论文提交给AI评审小组获取真实反馈意见。
u/reezoras · Reddit
How to reach people? First, I want to say that I'm respecting this community by not posting ai sloppish text. I'm just trying to tell my story and ask for advice. I recently finished my master's degree. While preparing my presentation and sitting in on other people's defenses, I noticed that the there were two committees. One consisted more of older professors. And they were more laid-back and more interested in approaches and ideas than in final results. The other was made up of younger fa

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