Top 10 uses for Codex at work

Users will be able to create a podcast from Codex or Claude Code and import it to Spotify

The recent move by Anaconda to acquire Outerbounds is aimed directly at a gap between experimentation and production, where workflows often fail to run consistently across environments. Instead of replacing […] The post Anaconda Moves to Control AI Workflows with Outerbounds Buy appeared first on AIwire.

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

OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.

Singular Bank built Singularity, an internal assistant using ChatGPT and Codex to help bankers save 60–90 minutes daily on meeting prep, portfolio analysis, and follow-up.

OpenAI is rolling out Advanced Account Security for people concerned that their ChatGPT or Codex accounts could be potential targets of phishing attacks.

Compare 5 Turnitin alternatives with stronger AI detection, fairer pricing, and the same core tools, for individuals and institutions alike.

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