At Kubecon Europe recently, Linux kernel maintainer Greg Kroah-Hartman said something that surprised me. After more than a year of AI-based pull requests and security reports that were worthless, living up to their nickname of “slop,” suddenly in the last month or so Kroah-Hartman discovered that those reports had become useful. At the time he didn’t know why, but guessed it was the result of improved tools and a deeper understanding of how to use them.
Since then, of course, we’ve learned about Anthropic’s Claude Mythos and seen the resulting scramble across closed-source and open-source projects to patch the significant bugs and issues Mythos has unveiled. The fixes and updates needed by large projects can be managed by their equally large teams, with corporate input as well as volunteers from around the world. But how do smaller projects deal with the rise in reported critical vulnerabilities, when they’re usually run by one or two people, often working in their spare time?
It’s a c
At first glance, Microsoft Foundry looks like a big grab bag of every AI-adjacent service that Microsoft has offered in the last decade, plus some new ones. In Microsoft’s own words, “Foundry consolidates several previous Azure AI services and tools into a unified platform” and “unifies agents, models, and tools under a single management grouping.”
Microsoft Foundry helps application developers to build and deploy agents, which may use models and tools. It also helps machine learning (ML) engineers and data scientists to fine-tune models, run evaluations, and manage model deployments. Finally, it helps IT administrators and platform engineers to govern AI resources, enforce policies, and manage access across teams. It isn’t quite a floor wax and a dessert topping, but it does try to serve three distinct audiences.
Key capabilities of Microsoft Foundry for building agents include multi-agent orchestration, workflows, a tool catalog, memory, knowledge integration, and publishing. Key cap
At Kubecon Europe recently, Linux kernel maintainer Greg Kroah-Hartman said something that surprised me. After more than a year of AI-based pull requests and security reports that were worthless, living up to their nickname of “slop,” suddenly in the last month or so Kroah-Hartman discovered that those reports had become useful. At the time he didn’t know why, but guessed it was the result of improved tools and a deeper understanding of how to use them.
Since then, of course, we’ve learned about Anthropic’s Claude Mythos and seen the resulting scramble across closed-source and open-source projects to patch the significant bugs and issues Mythos has unveiled. The fixes and updates needed by large projects can be managed by their equally large teams, with corporate input as well as volunteers from around the world. But how do smaller projects deal with the rise in reported critical vulnerabilities, when they’re usually run by one or two people, often working in their spare time?
It’s a c
Agentic artificial intelligence (AI) is set to fundamentally reshape the structure of enterprise work and commerce. Rather than simply responding to instructions, these agents actively participate in workflows by planning tasks, creating and using tools, correcting their own errors, and pursuing multistep goals autonomously. The result is faster, more adaptive...
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The post Why Agentic AI Requires More Than Better Models appeared first on Big Data Analytics News.
Sometimes, the hardest part about getting stuff done is simply remembering what you have to do — and when.
And ironically, lots of the tools that exist to help us juggle our endless array of incoming tasks only seem to make it even more overwhelming. Truly, it doesn’t take much for the very act of managing your tasks — or maybe even just figuring out the best way to do it — to become a chore in and of itself.
Like many perpetually perplexed plebeians, I’ve exerted far too much energy on the impossible-seeming task of finding a system for tracking tasks that (a) actually works — and (b) doesn’t feel like a burden of its own. I’ve gone through more tasks and reminders systems than any sane person should ever encounter in a lifetime.
And lemme tell ya: At long last, I’ve encountered one that’s the perfect blend of simplicity and power.
It’s a brand new, off-the-beaten-path Android app you probably haven’t heard of but that absolutely should be on your radar. It’s both easier and more effe
For decades, supply chain software has been defined by tools -- forecasting tools, optimization tools, scenario tools, dashboards, and planning applications. Each is designed to perform a specific task well. But as supply chains become more volatile and tightly coupled, the biggest challenge is no longer analytical sophistication. It's coordination. [...]
The post From Tools to Agents: How orchestrated decision intelligence is reshaping S&OP appeared first on SAS Blogs.
OpenAI just announced its new GPT-5.5 model, which the company calls its "smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer." OpenAI just released GPT-5.4 last month, but says that the new GPT-5.5 "excels" at tasks like writing and debugging code, doing research online, making spreadsheets and documents, and doing that work across different tools.
"Instead of carefully managing every step, you can give GPT-5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going," according to OpenAI. The company also notes that …
Read the full story at The Verge.
