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Showing 41–48 of 48 dossiers
The interesting change is architectural rather than another storage feature: migration becomes a server-to-server transfer initiated through an S3-compatible PutObject or UploadPart call, with range and multipart support for large objects.
Agent Identity is moving from a standalone credential boundary into a mainstream serverless runtime. Cloud Run can now assign agent identities and register agents/MCP servers automatically, reducing custom discovery and identity plumbing while keeping the runtime integration itself in Preview.
Cloud Storage project deletion no longer necessarily destroys every soft-deleted bucket immediately. Google’s August 17 change makes bucket retention part of project-recovery behavior, affecting disaster recovery, teardown assumptions and ongoing storage cost.
Connection Allowlists turn outbound browser networking into an explicit allowlist for Fetch and other web-platform APIs. The control is opt-in and currently Chromium-only, and strict policies can break legitimate dependencies such as FedCM identity-provider requests if teams omit required endpoints.
Cloud Run sandboxes now cover all resource types. The August 5 expansion matters for builders whose agents or automation run in batch jobs or continuously pulling workers rather than HTTP services, while the feature remains pre-GA and shares CPU and memory with the host container.
Supabase’s self-hosted stack now routes through Envoy by default, bringing new API-key support and hardened gateway defaults while breaking some Kong-specific assumptions.
Railway Cloud Agents are managed, persistent development machines rather than a new model or harness. They reuse developers’ existing agent credentials, sleep when disconnected by default, retain disk state, and live inside Railway project environments—blurring the boundary between remote coding workspace and deployment platform.
Meta has made the privacy-versus-price trade explicit in its Model API: developers can choose standard pricing or a contributor model ID with steeply discounted inference in exchange for training-data permission. The choice matters for proprietary code, customer data and AI SaaS workloads.