Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.
人工智能企业MiniMax旗下关联公司上海稀宇极智科技有限公司全资持股成立珠海稀宇极智科技有限公司，注册资本500万元，聚焦人工智能全栈软硬件开发，旨在强化在华南地区的研发与业务协同能力。
A seven-layer composable reference architecture for taking enterprise agentic AI from prototype to production on Amazon Bedrock AgentCore. Each layer has a stable contract, so you can swap technologies at any layer without redesigning the rest of the stack.
迪士尼与Adobe宣布达成多年期战略合作，将共同开发面向迪士尼业务的定制化生成式AI模型，应用于主题公园、影视制作及体育媒体等领域。合作初期重点支持迪士尼体验部门项目开发，并利用AI技术助力华特迪士尼影业StudioLAB的创意探索。Adobe将协助提升ESPN与体育迷的互动，并推动体育档案内容的观众触达。双方基于Adobe Firefly Foundry平台，使用迪士尼IP训练专属私有AI模型，实现内容直接用于实际制作。目前，迪士尼幻想工程已部署定制图像生成模型及手绘草图转概念设计的AI工具。此次合作标志着生成式AI从创意辅助向生产环节延伸，同时强化知识产权控制。
Article URL: https://sergeykuhne.com/blog/fighting-ai-brain-fry Comments URL: https://news.ycombinator.com/item?id=50416363 Points: 83 # Comments: 56
Our most capable models for daunting technical work, Claude Fable 5.2 and Claude Mythos 5.2 set a new bar in coding, finance, and the sciences.
苹果新 CEO 最近对自家 AI 的进度有点不满意。 据彭博社最新爆料，苹果 CEO 约翰·特努斯已经要求软件主管费德里吉加快 AI 开发节奏，拿出更大胆的方案，不能只满足于给 Siri ....
Letting coding agents (such as Claude Code) work autonomously on repos on a daily-basis, brings a high level of uncertainty. For those who won't let the agent decide alone whether it can create/modify or delete any folder on their repos; I wanted to be able to trace actions performed and being able to let the agent continue his work autonomously, or ask him to request a human approval according to some criteria defined. After investigations, the cleanest way to do it was according to me to develop a deterministic control layer between the LLM and the work of the agent that would track and catch potential non-respect of pre-defined rules. How? by first setting up approval rules (Pass/requires approval/refused) before the work of the agent, and afterwards controlling cryptographic SHA 256 verifications, etc. This allows to compare the before-after work of the agent. I have put in my public GitHub repo a demo that you can launch easily and observe different scenarios behaviors like applied authorized modification, protected file waiting for Human approval, or outside perimeter authorization modification refused. The demo is offline, does not involve the official runtime, in native node.js and without dependencies. It should still allow you to understand the core principles. I would like to know: how do you implement and handle safety boundaries? Especially if you let agents work on critical data/sensitive codebases? With which guardrails? Comments URL: https://news.ycombinator.com/item?id=50422833 Points: 1 # Comments: 0
Article URL: https://jadmadi.net/posts/mounting-remote-directories-over-http/ Comments URL: https://news.ycombinator.com/item?id=50423261 Points: 1 # Comments: 0
Hi, Dmitri from Radioso here, an open-source platform to design AI agents for customers and visitors in any channel, in the style of Fin/Ada/Decagon. The repo is https://github.com/radioso-ai/radioso and our website is https://radioso.aiToday, your choices are:1. Buy a very expensive service for your vertical (usually starting with $$ five figures monthly) 2. Get a cheap off-the-shelf chatbot that mostly answers FAQs 3. Build your own, and then also build the inbox, human handoff, audit trail and integrations around itAt Radioso we wanted an open-source option that combines the best of all 3. You can integrate it with your own software via API/SDK or your CRM, or run it standalone, either self-hosted or in our cloud.The engine runs multi-step interactions and takes actions: calling your APIs, updating records, booking things. Maybe you’ve tried Parlant; Radioso is the alternative “with batteries included”: RAG, audit trail, customer service inbox, and integrations like Slack and email.Under the hood it’s plain TypeScript + PostgreSQL, works with OpenAI/Anthropic/other models, and is licensed under Apache 2.0 which includes all features (The only thing we keep under EE license is the multi-tenant layer we use to run our own cloud).We have existing customers running it for technical Q&A, course booking, online product recommendations and goods returns, among others, some reaching 92% of conversations automated. We’re currently working on voice support so we can offer a call center.Our ambition is to be “the WordPress of customer-facing AI” :), where people can contribute their favorite plugins and benefit from the ecosystem.Longer term, we think browsing the web will mostly mean talking to your own agent, and when you need a service or a purchase, your agent will talk to the vendor’s agent to get things done. For this, Radioso already publishes an A2A Agent Card and we’re working on the rest. Your Radioso services are also callable over MCP, for customers who already use it.Thanks for your attention. We’d especially love to hear which integrations you’d want, whether you’d self-host it, and what’s stopped you from deploying a customer-facing AI agent so far. Tell us what you think. Comments URL: https://news.ycombinator.com/item?id=50423820 Points: 1 # Comments: 0
