金融商业1 week ago
NovaScope — Local SEO + AI Visibility Audits
用于本地 SEO、Google 商家档案和答案引擎可见性的 AI 审计工具。
@novascopeio · X
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用于本地 SEO、Google 商家档案和答案引擎可见性的 AI 审计工具。
@novascopeio · X
Workilo是AI代理协作的工作室，可创建演示、研究SEO、分析数据并发布内容。
@AgencyinaboxOS · X
One chatbot gives you drafts. gives you 12 AI specialists that finish the job.
分析AI智能体会话以理解用户行为和调试问题
screm · HN
Product analytics (and evals) for agent sessions on your MCP
分析您的品牌在 SGE、ChatGPT 和 Perplexity 中的可见性。
u/No_Pangolin3678 · Reddit
We built AiVisis to measure brand visibility in AI search — looking for honest feedback Hi r/SideProject , I’m part of the team building AiVisis, a GEO and AI visibility analysis platform for businesses, marketers, and agencies. Most companies already track their search rankings, website traffic, and social media performance. However, many brands still cannot clearly answer a newer question: How visible is our brand across AI-powered search and answer platforms? AiVisis analyzes a we
在文本、图像、视频和音频中检测AI生成的内容。
@aicontentmeter · X
🚀 I just launched It's free to try, and I'd love your honest feedback. Use code JULY20 to get 20% off the Pro plan throughout July. What would you improve or add? Every suggestion helps! #BuildInPublic #AI #ChatGPT #SEO
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
聚合并查看来自AI代理的上下文丰富的交互式简报。
flysonic10 · HN
Meltbox – where your agents send you briefs
用AI分析股票，获得投资组合优化建议和实时提醒。
@AMaxim62228 · X
Check out what I just built with Lovable!
用IntegraX的AI预测、实时价格和公司对比工具分析股票。
@brucewayne18457 · X
Get years of Knowledge & prediction within seconds with IntegraX Predict Stock Prices upto 60 days suggest action,top insights Stocks recommender Compare stocks Top 5 stocks of day Visit IntegraXai looking for genuine reviews
查看和对比主流AI模型的公众意见和基准评分。
u/TasteMysterious5285 · Reddit
I built AI Census, a live field bulletin for how people are actually talking about AI models I’ve been building AI Census, a public “field bulletin” for how people are talking about current AI models. I kept running into the same problem: benchmark tables tell me how a model performs on a test, but not whether people are actually finding it useful, frustrating, reliable, etc. So I built a rolling view from public technical conversations across Reddit, Hacker News, Bluesky, GitHub, and Huggi
