editor
3 stories filed under editor on Beyond Market Intelligence. The newest of them: “InfoQ Explores High-Performing Teams with New Certification Program”, “Navigating peer review timelines: what to do when a third review is delayed”, and “Bring AI coding agents to your machine learning projects in the cloud.”. InfoQ's new five-week certification program tackles the real challenge of building high-performing teams, and it's opening enrollment now. A month of silence after two reviews would test anyone's patience, especially when you've already acted on the feedback. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every editor story on Beyond Market Intelligence, newest first.
InfoQ Explores High-Performing Teams with New Certification Program
InfoQ's new five-week certification program tackles the real challenge of building high-performing teams, and it's opening enrollment now. Led by editor Olimpiu Pop, it focuses on practical areas like delivery flow and AI-enabled work, which feels timely. We're glad to see metrics included, as that's where many teams struggle. For a deeper look at related AI topics, explore our coverage in "Explore the Future of AI Deployment: Key Topics at QCon AI New York." It's a solid starting point for teams ready to improve.
Navigating peer review timelines: what to do when a third review is delayed
A month of silence after two reviews would test anyone's patience, especially when you've already acted on the feedback. It's a fair question whether the third review is still coming, and the lack of a response from the Action Editor only adds to the uncertainty. This isn't a critique of the process, your experience has been positive, and the submission surge is real. But waiting without a timeline is hard to navigate.
Bring AI coding agents to your machine learning projects in the cloud.
The question of running AI coding agents against a remote GPU is the right one to ask. You want the flexibility of your local editor with the horsepower of the cloud, and that setup absolutely exists. The key is using a platform that treats the remote machine as a seamless extension of your workspace. Docker Cloud Sandboxes offer a consistent environment for this, and exploring that approach is a solid starting point for building your ML workflow without leaving your preferred tools behind.