Canonical vs The Linux Foundation

Migrate Cloud TPU API Workloads to Compute Engine

This technical migration guide explains moving TPU workloads from Google Cloud's deprecated Cloud TPU API to Compute Engine instances. The Cloud TPU API is no longer under active development and future TPU hardware generations (starting with TPU7x) are supported only through Compute Engine or Google Kubernetes Engine. Migration requires flag and command mapping (e.g., accelerator-type -> machine-type, tpu-vm ssh -> compute ssh), checking different quota metrics (preemptible vs family quota) and provisioning models (FLEX_START, SPOT, STANDARD, RESERVATION_BOUND), and adjusting startup scripts and images (some Compute Engine accelerator images lack tools like docker). The guide documents practical troubleshooting: using SPOT to probe capacity, checking both quota metrics via the Cloud Quotas API, handling silent failures where RUNNING != ready, and other pitfalls encountered during real migrations.

• Google's Cloud TPU API is no longer under active development; new hardware generations starting with TPU7x are supported only via Compute Engine or GKE.
• Compute Engine uses different flags and flows (e.g., --machine-type=ct6e-standard-1t, --image-family, --request-valid-for-duration, --provisioning-model=FLEX_START) compared with the Cloud TPU API.
• Flex-start provisioning on Compute Engine consumes preemptible quota (PREEMPTIBLE-TPU-V6E-per-project-region) and falls back to the family quota; quota and capacity are separate and reported by different APIs.

Open Secure AI Alliance Joins the Linux Foundation to Build a Shared, Open Defense Stack for the AI Era

Originally founded by dozens of enterprise leaders and NVIDIA, the Alliance moves to neutral governance to expand industry collaboration on open AI security tools, research and shared defenses.

Open Secure AI Alliance Joins Linux Foundation for Open Defense Stack

The Open Secure AI Alliance, originally founded by NVIDIA and other enterprise leaders, has officially joined the Linux Foundation to establish a neutral governance home for developing open, shared defenses for AI systems. The Alliance aims to provide security leaders with transparent, adaptable, and controllable security tools across the AI stack, including models, agents, and infrastructure. A key initiative is the Shared AI Findings Exchange (SAFE), which will confidentially collect and analyze AI security incidents to enable collective learning and evidence-based controls. The move is expected to foster collaboration between AI, cybersecurity, and open source communities, enhancing the security posture of AI adoption across industries.

• The Open Secure AI Alliance has joined the Linux Foundation, gaining neutral governance.
• The Alliance was originally founded by NVIDIA in collaboration with members including the Linux Foundation.
• A core initiative, the Shared AI Findings Exchange (SAFE), is open for comments until September 21.

Context Passing in Multi-Agent AI Systems

Engineering teams face new challenges when capabilities are split across multiple independently deployed AI agents owned by different teams. Microsoft’s Industry Solutions Engineering (ISE) team published a case study describing three evaluated approaches for sharing conversational context across agents: (1) domain agents reading shared storage, (2) making domain agents stateful, and (3) embedding summarized conversation history in each message payload. Microsoft adopted the third approach, sending summarised history inside messages and applying a 10-turn summarisation threshold to balance fidelity and performance. The post contrasts the Model Context Protocol (MCP), which standardises agent-tool connections, with Agent2Agent (A2A), an open peer-to-peer agent communication protocol originally developed by Google and now stewarded via the Linux Foundation. The article highlights governance, security, auditability, and operational benefits of keeping domain agents stateless.

• Microsoft Industry Solutions Engineering (ISE) published a detailed account of how it addressed context passing in a multi-agent engagement.
• Agent2Agent (A2A) is an open agent communication protocol originally developed by Google and now maintained by a cross-vendor technical steering committee at the Linux Foundation.
• Microsoft evaluated three approaches for sharing conversational context (shared storage, stateful domain agents, and embedding summarised history in message payloads) and adopted payload summarisation.