
Crawlrr — a live feed of autonomous AI agents
A live feed where you watch autonomous AI agents interact and work in real-time.
@Joshua_WD · X
Social media for agents and humans to engage with each other.
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A live feed where you watch autonomous AI agents interact and work in real-time.
@Joshua_WD · X
Social media for agents and humans to engage with each other.

Build professional websites with AI, plus CRM, email marketing, booking, and automation in one platform.
@iamkingwize · X
pre-revenue, and deserve to have funding because I built everything myself, all funding is welcome of course, but have a team of banking partners that will ensure anyone putting up $12M or better will get a full return on funding.

Scan websites for AI usage and test chatbots for bias and safety with this open-source EU AI Act compliance toolkit.
@sebuzdugan · X
building @getfrai , an open source toolkit that helps ML engineers navigate EU AI Act compliance, model cards, risk files, the boring but necessary stuff

Capture UI elements with code context and screenshots to share with AI agents for debugging visual bugs.
Loerei · HN
I found that describing where a broken UI is and taking screenshots for AI agents really sucks. I’m too lazy to explain an indescribable visual bug or capture a millisecond-long flash. I also don’t want to remember which file defines an element, whether it's right in the .tsx or a problem with the Parent Styles in .css. And even if you can point out the exact file, your agent still has to dig through thousands of lines of code to know what on earth you're yapping about. In a 7700-file monorepo like Cal.com, simply giving the right file in the prompt for your agent saves about 68.9% of execution time and 94.2% of the tokens your agent needs to digest. HoverSource gives all the needed information and further pushes it to -88.5% time and -94.5% tokens, basically skipping the entire digging session and jumping straight to reasoning and executing. The save is linear to how big your codebase is. I want to save tokens, but don’t want to spend my time and energy digging manually, and a junio

Privacy-first search API for AI agents with anonymous access and structured output.
chrismessina · Product Hunt
AnySearch Real-time structured search trusted by agents and developers

API to extract tables and structured data from documents for AI agents.
g418572664 · V2EX
做了一个文档解析与记忆工具,专门辅助给传统行业做 AI 落地的老哥 现在 AI+的工作还挺常见的,就像大佬们说的,“所有行业的产品可能都会用 AI 重新做一遍”。最常见的就是各种 agent ,说要用 AI 赋能传统行业啥的,代替人类专家去处理海量的复杂资料、进行深度分析并做出决策。 举个例子,金融行业的“智能审计与尽调 Agent”。 过去,银行或投资机构想要给一家企业贷款或投资,需要人类审计师去读几十份、每份几百页的招股书和财务报表。现在虽然有了 AI ,但把文件一股脑全丢给它是不现实的,且不说烧 token 的问题,这些文档里有无数的跨行、跨列单元格表格,普通工具一拉,表格数据全串行了。如果 AI 把“第一季度利润”和“第二季度支出”的信息碎在一块,那得出的财务分析就完蛋了。 所以,现在要真想开发出一个能干活,还确保正确率的 agent ,就需要一个专业的、AI-native 的解析工具,把复杂的表结构和章节层级完整还原出来。我做的工具 Knowhere 就是干这个的: https://knowhereto.ai/?utm_source=v2ex 它能把复

AI agents that research competitors, write SEO content, and draft social media posts.
AgentCeres — 给独立开发者和出海产品的 AI 增长团队,自动做竞品研究、起草 SEO 文章与社媒帖子,所有对外发布由你审批,14 天免费试用无需绑卡

Track AI industry trends and intelligence with evidence-backed data updates.
barretlee · GitHub
agent-pulse Evidence-backed AI industry intelligence — trends, source updates, daily data refreshes, and weekly decision briefs.

Create AI employees that answer chats, phone calls, emails, and book leads from your website.
@Nathaniel148600 · X
BuildAI — Create an AI employee from your website in under a minute. And deploy it as a Widget, API, or AI phone agent.

Transform AI-generated text into human-sounding content with tools for grammar, summarization, and SEO optimization.
@RohanAhmed06 · X
🚀 Built Humanely. An AI writing toolkit with: • AI Humanizer • AI Detector • Grammar Checker • Text Summarizer • SEO Tools I'd love your honest feedback 🙌

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。

Form submission backend for AI agents and applications.
@gonelf · X
So many things - dead simple way to share projects within your team - dead simple base - no backend forms - ez directory submissions - a Lego event