
Cyted | AI Search & Visibility Tracking
Track how often your brand appears in ChatGPT, Claude, Gemini, and Perplexity results.
@vishuRizz · X
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Track how often your brand appears in ChatGPT, Claude, Gemini, and Perplexity results.
@vishuRizz · X

Intercepts AI bots at your site's edge to control what they see and access.
@IchMag17 · X

AI scans competitors and market data to validate if a startup idea is worth building in 90 seconds.
@Ebrahim_Rio · X
Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.

Track real-time AI news and signal detection across robotics, models, products, and business.
@Scobleizer · X
I vibe coded: :-)

Debug and monitor AI agents with real-time visualization and performance analytics.
@simranrambles

Detect AI-generated content in text and documents with per-sentence analysis and exportable reports.
Detector de IA — 西班牙语优先的 AI 文本复核网站,可检查粘贴文本和文档,查看概率信号、句子高亮和可导出报告

Live dashboard showing an AI-agent company's ARR, MRR, tasks, messages, and activity in real-time.
pro_methe5 · HN
I scrape an $8.5M-ARR company run by AI agents and chart it live

AI training coach for runners with biometric tracking and personalized coaching.
@LeoSparr · X

A visualization language designed for AI agents to create and interact with charts.
chenglong-hn · HN
Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple

A dashboard for traders to replay trades, analyze behavior patterns, and optimize their trading system.
@Crypto77qi · X
最近一直在@dappOS_com上体验 @xBubble_ai 的 Coding,这两天我给自己做了一个 AI Trading Replay(AI 交易复盘助手)。 👉 做交易时间越久,我越觉得,真正拉开差距的,并不是找到更多交易机会,而是不断减少重复犯同样的错误。 现在币圈已经有很多成熟的交易工具,行情、链上数据、资金流向、技术指标几乎都能找到对应的平台。但这些工具更多是在告诉我们市场发生了什么,却很少帮助我们分析,为什么面对同样的市场,每个人最终的交易结果却完全不同。 有时候并不是没有机会,而是因为犹豫错过了;有时候方向看对了,却因为害怕提前离场;还有时候连续几次亏损之后,下一笔交易开始变得犹豫,最终又错过真正的行情。 这些问题,并不是增加一个指标或者再多看一份数据就能解决。 所以我没有继续做一个新的行情工具,而是直接用 xBubble Coding 做了一个 AI 交易复盘助手。 它不会预测市场,也不会告诉我什么时候买卖,而是把每天的市场变化、自己的交易记录和决策过程整理下来,通过 AI 自动分析哪些判断是正确的,哪些错误一直在重复发生,并生成每天、每周的交易复盘,让整个交易过程形成一套可以不断优化的闭环。 整个开发过程也比我想象中轻松很多。 没有写代码,没有配置服务器,也没有部署环境,只需要把自己的需求描述清楚,一个可以直接访问的网站很快就搭建完成了。从原来的一个想法,到真正变成可以使用的产品,中间几乎没有任何开发门槛。 我觉得,这也是 xBubble Coding 最有价值的地方。 它并不是让大家重复开发一个市面上已经存在的软件,而是让每个人都能把自己的经验、工作习惯和思考方式快速变成真正属于自己的工具。 未来真正有价值的,或许不是拥有越来越多标准化的软件,而是每个人都能根据自己的需求,打造一个最懂自己的 AI 工作伙伴。

Test AI agents against production mirrors using replayed traces to find bugs and regressions.
aisinghal

Platform to govern, trace, and defend AI agents across providers
camsjams · HN
Lineation – One security control plane for all agents