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CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.
LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.
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.
Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside agentic retrieval. This post explains how the maxAgentIteration parameter matches retrieval effort to each request, using an illustrative internal banking policy assistant.
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.
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
Article URL: https://github.com/SafeScript/safescript Comments URL: https://news.ycombinator.com/item?id=50417478 Points: 1 # Comments: 0
Article URL: https://github.com/aryamantepal/tabvault Comments URL: https://news.ycombinator.com/item?id=50419299 Points: 1 # Comments: 0
Article URL: https://github.com/tryaida/aida Comments URL: https://news.ycombinator.com/item?id=50412923 Points: 2 # Comments: 1
I've been beavering away at this since July, and its finally getting to a point where its stabilising.It started Windows only, but I recently converted over to Linux, so I've been ironing out the kinks in the last week. You can install it via apt or rpm, or .exe or .msi on Windows.Many more features to come, including native agent support and more preview features (images, PDF, document formats). Claude works pretty well in it, but I've not tried many other agents yet.I rarely leave throng now - 99% of my work happens inside it. I stopped using Visual Studio around 10 months ago (in favour of VSCode), and now I don't even use VSCode. Comments URL: https://news.ycombinator.com/item?id=50412226 Points: 1 # Comments: 0
Article URL: https://github.com/volcengine/OpenViking Comments URL: https://news.ycombinator.com/item?id=50410477 Points: 1 # Comments: 1
The Agent adoption chart in the Copilot impact dashboard now includes Copilot Chat and the VS Code Agents window, so you can see engagement across nine Copilot experiences. The adoption… The post Copilot impact dashboard adds chat and VS Code agent usage appeared first on The GitHub Blog.
Run Claude Code as a remote agent on Amazon Bedrock AgentCore. Each coding task runs in its own isolated session in your AWS account, every model call goes through Amazon Bedrock, and the result comes back as a GitHub pull request you review and merge.
A laser scientist reviews the laser light cannon from the Steven Universe cartoon, including its highly unusual beam shape.
Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.
Learn how to build a serverless Model Context Protocol (MCP) server on Amazon ECS with AWS Fargate that gives AI agents secure, metadata-driven access to an enterprise data lake. Exposing a new dataset requires only a JSON file, with no per-dataset integration code.
Article URL: https://github.com/Creepybits/trellis2-blackwell-wsl Comments URL: https://news.ycombinator.com/item?id=50403233 Points: 1 # Comments: 0
