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Audaxic — Memory for AI Agents
Affordable memory storage layer for AI agents.
@AudaxicTech · X
Memory for AI agents, most affordable in the industry
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32 projects
Affordable memory storage layer for AI agents.
@AudaxicTech · X
Memory for AI agents, most affordable in the industry
Design and deploy AI agents that automate tasks across software without requiring APIs.
@gabebenchimol · X
Deploy autonomous agents to run tasks for $8 a month with a simple interface.
u/liltechnomancer · Reddit
I forked Pi and tried to make it accessible to normies. I love all the open source coding agent harnesses. Pi chief among them. But I was bummed I couldn't really get my non-technical friends on them. They also would not be eager to drop a bunch of money on a mac mini to host an OpenClaw instance. This made me feel there may be a gap in the market. This made me wonder if people would like a web app based agent that could update itself. If you want to build a custom media pipeline for
Give AI agents a structured way to request human approval with safe retries and verification.
@GetAgentHail · X
AgentHail — a control layer for AI agents to request approval, execute work, and return verifiable results. Would you sign up or leave?
Run nine AI security agents to examine GitHub repositories, review findings, and export reports.
murderszn · HN
GPT 6 Astra gave me a reason to put the finishing touches on an old project, and I'm very content with where it landed. Back in June, I built Cerberus Agent as an AI "watchdog" for vibe-coded apps. The goal was to catch security issues before they turn into hacked apps, leaked credentials, or stolen data. Now its in production! Easier than I though it would be. It uses Pollinations as the AI backend to review GitHub repos, explain findings, and propose fixes. You can discuss the findings, inspec
Manage AI agent teams with a human-friendly interface.
jackcollinshq · Product Hunt
YAGNI Proactive agent teams you manage like humans
AI agent that generates comparable product concepts from ideas, sketches, images, and constraints.
@hedge8og · X
4月23日，我开始用 Codex 搭自己的产品。到现在，满3个月了。 截至7月19日，Codex 页面显示247.1亿个 Token，连续使用54天。标题里的“250亿”是取整，准确数字以截图为准。 这三个月，我把 做上线了，产品介绍视频改了很多版，也借助 Codex 做了不少以前一个人很难完成的工作。 具体一点：我用它整理过 BP、导出过 PDF、做过 PPT 工作稿，也把调研、文档、图片、浏览器操作、网站、视频和邮件这些常见 Skills 跑过一圈。有些后来真的进入了工作，有些试完就放下了。 现在回头看，最大的收获不是“我做过这么多种东西”，而是我越来越能分清：哪些能力能进入真实工作，哪些只是演示起来很厉害。 这些数字确实挺夸张。说实话，我自己看到也会觉得：是不是做了很多事？ 但如果现在让我总结，我不会把它们当成绩。它们只能证明我很投入，蹬得快冒烟了，不能证明产品已经有价值，更不能证明商业已经成立。 我大学辍学，最高学历是高中，之前一直在制造业工作，也不会写代码。写这些不是为了卖惨，更不是想证明学历没用，只是先把我的真实起点说清楚。 2月 OpenClaw 火起来以后，我开始认真试各种 AI 工具，想先解决工作里那些重复、又不得不做的事情。 这期间还有一次，客户马上要我提案，但公司的设计师手上也有急活。我用 ChatGPT 做了产品效果图，最后拿到了订单。 老板后来跟我说：“早知道你可以帮我做这些，那些外包设计早就可以不用买了。” 到了4月，我又用扣子搭了发货模板和记账系统。原来需要手工对比核验的条形码、外箱码，我也开始想办法交给工具处理。 这张订单图只能证明订单记录是真实存在的，不能单靠它证明订单一定是 AI 带来的，更不是 Ohwait 的客户案例。AI 参与了产品效果图制作，之后拿到订单，是我对那次真实工作过程的记录。 那一刻让我兴奋的，其实不是发现了什么创业机会，而是我第一次觉得，模型真的可以成为人的“数字外骨骼”。 回头看这三个月，我现在有5个比较确定的结论。 ### 1. AI最好先从一个真问题开始 发货、记账、核验、临时要效果图，这些都不是什么宏大命题，但它们有一个共同点：问题原本就存在，而且有人正在为它花时间、花钱或者承担压力。 我现在判断一个 AI 工具有没有价值，还是看两件事：它有没有少让我重复
AI sales representative that automatically captures, qualifies, and books leads for small businesses.
agentforgeai_cmd · Product Hunt
AgentForgeAI Your AI Sales Rep That Never Sleeps
Ultra-specialized secure AI agents to propel your revenues.
@TharejaRushil · X
Register agents, explore the research dataset, and watch a live AI economy on Base.
@CRYPtoKong32 · X
also Join our $MUSKOX Ai Ecosystem built on Base Coin. 🪙 $AGWC Websites:
Marketplace platform where AI operators discover, build, and deploy agents with community projects.
VektorGeist · HN
Vektorgeist- A platform for AI operators and their agent's
Share your team's workflows as reusable AI agents and skills inside your existing tools.
kristohb · HN
Rafter – an MCP server that shares one team's memory, skills and agents