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At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower […]
NVIDIA and Google infrastructure cuts AI inference costs Read More »
You don’t lose sleep over dashboards. You lose sleep over decisions. You’re standing on the factory floor, surrounded by the hum of million-dollar assets, and you know something is off. An alert flashes on a screen: “Anomaly Detected.” But what now? In that split second, the gap between a minor […] The post Your factory
Your factory floor is asking for better decisions. Are you listening? Read More »
Most AI agents today have a fundamental amnesia problem. Deploy one to browse the web, resolve GitHub issues, or navigate a shopping platform, and it approaches every single task as if it has never seen anything like it before. No matter how many times it has stumbled on the same type of problem, it repeats
Xiaomi MiMo team publicly released two new models: MiMo-V2.5-Pro and MiMo-V2.5. The benchmarks, combined with some genuinely striking real-world task demos, make a compelling case that open agentic AI is catching up to the frontier faster than most expected. Both models are available immediately via API, and priced competitively. What is an Agentic Model, and
In this tutorial, we implement an advanced agentic AI system using the CAMEL framework, orchestrating multiple specialized agents to collaboratively solve a complex task. We design a structured multi-agent pipeline consisting of a planner, researcher, writer, critic, and rewriter, each with clearly defined responsibilities and schema-constrained outputs. We integrate tool usage, self-consistency sampling, structured validation
Agentic AI in insurance has potential to transform insurance. Read four use cases. The post 4 core insurance functions primed to benefit from agentic AI appeared first on SAS Blogs.
4 core insurance functions primed to benefit from agentic AI Read More »
Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for coding agents. It brings substantial improvements in agentic coding, a novel Thinking Preservation mechanism, and a hybrid architecture that blends Gated DeltaNet linear attention with traditional self-attention — all
How to make multiple agents work together like an elite team — autonomously dividing tasks, communicating efficiently, and collaborating seamlessly? The openJiuwen community released the latest version of JiuwenClaw, which adds support for AgentTeam — a multi-agent collaborative capability. It proposes that the next leap beyond Harness Engineering is Coordination Engineering. In in-depth tests, this team
Next Leap to Harness Engineering: JiuwenClaw Pioneers ‘Coordination Engineering’ Read More »
Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was
Hugging Face has released ml-intern, an open-source AI agent designed to automate end-to-end post-training workflows for large language models (LLMs). Built on the company’s smolagents framework, the tool can autonomously perform literature review, dataset discovery, training script execution, and iterative evaluation — tasks that typically require significant manual effort from ML researchers and engineers. What
