金融商业2 weeks ago
DeserveOS: AI-Powered CRM for Service Businesses
AI驱动的CRM，用于潜在客户发现、电子邮件验证和冷联系。
@ensaktas · X
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61 projects
AI驱动的CRM，用于潜在客户发现、电子邮件验证和冷联系。
@ensaktas · X
用 AI 代理自动改进代码库并生成合并就绪的 PR。
@javimosch · X
Manage your entire business with AI agents that automate operations and replace 15+ tools.
@Ramona053764691 · X
Hey Adam! Bouwen aan Zynthoro AI-native ERP met 4 AI agents die samen een heel bedrijf runnen. XPRIZE genomineerd, gelanceerd op 30 juni. Jouw verhaal spreekt me aan zou graag connecten! 🚀
免费的网站安全扫描器，检查 SSL、headers 和合规性，提供即时的信任分数和 AI 指导的修复。
@MrPenetratorTP · X
MrPenetrator helps businesses monitor their website’s trust, security and performance before problems affect their visitors.
Supercurve 是一个学习你的业务、自动优化营销活动的 AI 系统。
@elanao · X
Marketing is typically the least systematized function in a startup. Supercurve is the system: it decides what's worth doing, does it, and gets sharper with every result. Backed by Antler.
为您的行业量身定制的通用业务操作系统。
@BObalabi1 · X
Product designer working at the intersection of AI, UX, and product strategy. Portfolio: • • Most client work is under NDA, so sharing a few personal projects that showcase my design and product thinking. Happy to connect 👋
AEE 是一个开源控制平面，对 AI 代理的每个操作进行授权、监控和验证。
eli-labz · GitHub
Agent-Execution-Partnership Agent Execution Partnership AEE is an open-source control plane that ensures every AI agent action is authorized before it runs, observable while it runs, and verifiable after it completes.
将生活困境分解为简单可行的行动计划，适应你的精力状态。
@_AshleeMyLovee · X
Check out what I just built with Lovable!
与AI代理聊天自动分类开发工作。
@suhaaspk · X
Hi! building Jungle ( Its a way for small teams to automatically triage bugs and feature requests to keep development moving while they sleep
seus.ai 自动设计、构建和部署软件系统。
@Bim_evaporates · X
Check out DMs open
使用在微虚拟机中运行代码的AI代码审查来捕获更多漏洞。
u/dumbfoundded · Reddit
Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db
