
Cobrai
为订阅业务提供AI驱动的留存智能。
@cobraisystem · X
Manual retention can’t keep up with your acquisition - sees the slip, forecasts the churn, and triggers the save that keeps your growth compounding.
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为订阅业务提供AI驱动的留存智能。
@cobraisystem · X
Manual retention can’t keep up with your acquisition - sees the slip, forecasts the churn, and triggers the save that keeps your growth compounding.

为应用生成通用链接,支持 App Store、Google Play 等平台。
@adriendecoster_ · X
为Shopify商户监控Meta像素追踪,检测到中断时提醒
@danmercede · X
A standing watchdog for Shopify merchants: we verify your pixel code generates tracking requests and that Meta actually receives usable signal, and alert you only on confirmed breakage, with the evidence.

用AI实时追踪竞争对手的定价、定位和营销信息变化。
mhhabib · Product Hunt
IntelDif AI-driven competitive intelligence without the alert fatigue

用 AI 生成应用商店优化截图,编辑后同步到 App Store 和 Play Store。
@radomir_3 · X
Let’s connect. I’m building App screenshot studio

粘贴应用URL即可秒速生成App Store截图,配有AI生成的标题和设备框架。
@buildrstudio · X

为创始人和销售领导者提供的 AI 驱动 GTM 策略和智能工具。
@ourideaai

为D2C品牌提供库存智能分析,帮助检测缺货风险和滞销品。
@heydevottam · X
I'm building EVE — an inventory intelligence platform that helps D2C brands detect stockout risks, dead stock, and profit leakage before they become costly.

从 prompts 构建 web apps,将其部署上线,并生成营销内容。
@promptui · X

用 Kitbase 跟踪事件、了解用户,快速发布产品。
@kitbasedev · X
We just launched, check us out at

分析Reddit和GitHub讨论为产品决策生成客户洞察的AI平台。
@Tweet2Tusharr · X
hey everyone 👋 we've been working on something over the last few weeks around customer research. we'd genuinely love if you could spend 5 minutes trying it and tell us what we're getting wrong.

YouEx.ai 是面向 B2B 销售团队的 AI 原生 CRM 平台,自动捕获、丰富、评分和分配线索。
u/youex-ai · Reddit
Built a CRM w/ MCP -- Biggest misunderstanding on first look? We launched an MCP connector a few weeks ago that connects our CRM data. We're still working through our product market fit, so I asked Claude What is the biggest thing people misunderstand about YouEx.ai when they first see it? I was actually not even thinking about researching our actual CRM data, but that's what it did. The answer was actually really insightful: Your own data points at it: a large sha