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Amazon Aurora Serverless gets faster burst scaling

Aurora Serverless can now add roughly 12 ACUs in the first second of a scale-up event on platform versions 3 and 4. The change is automatic and is most useful for bursty SaaS, API, batch and agent workloads, but it does not remove the separate resume delay when a database has scaled all the way to zero.

OpenAI says its Jalapeño chip reached tape-out in nine months with AI-assisted design

Jalapeño is working first-party silicon rather than a roadmap item, and OpenAI now says AI itself materially accelerated the design process. The distinction still matters: tape-out means the design was finalized for manufacturing; it does not mean fleet-scale production qualification or API deployment is complete.

AWS Lambda can now reference deployment packages directly from your S3 bucket

AWS has added a `REFERENCE` mode for Lambda deployment packages. It eliminates duplicate managed copies, raises the default managed-storage quota to 300GB, and gives teams direct control over encryption, lifecycle and audit policy—but a deleted or inaccessible source object can now make a function inactive.

Railway Cloud Agents turn coding agents into persistent deployment-adjacent VMs

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.

Cloudflare Workers replaces 3MB and 10MB compressed bundle caps with one 64MiB uncompressed limit

The change makes heavier frameworks and dependency trees deployable to Workers without plan-specific compressed-size ceilings, but it also changes what builders need to measure: the operative limit is now uncompressed Total Upload rather than the gzip number they may have optimized around.

Cloud Run adds singleton instances for long-lived, individually addressable workloads

Cloud Run instances sit between autoscaling serverless services and a small VM. They run one individually addressable container continuously, can be stopped and restarted, and use shared CPU economics; Google’s launch example prices 1 vCPU plus 1 GiB running for 30 days at $5.70.

Google Cloud extends Agent Identity into Cloud Run with automatic Agent Registry registration

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.

Google Cloud is making every Application Integration run execute under an explicit identity

The migration turns integration identity from an implicit platform detail into an operational dependency. Teams may need new run-as accounts and `Service Account User` grants not only for runtimes but also for editors, publishers, approvers and deployment automation.

GLM-5.3-Flash turns the anonymous Ox Alpha trial into an open-weight multimodal coding model

GLM-5.3-Flash combines open weights, multimodal coding/agent capability and an 18B-active sparse architecture with a large anonymous pre-launch trial. Z.ai has already issued a chat-template correction for early downloads, showing that day-one self-hosted deployments need artifact-level validation as well as model benchmarking.

Vercel Agent is bringing production investigations and approved actions into Slack

Vercel Agent now works in Slack as well as the Vercel dashboard, combining logs, metrics, deployments and repository context with team conversation before proposing approved actions such as pull requests, rollbacks, configuration changes and cache purges.

Abacus.AI’s Smaug Agentic fine-tune targets the failure tail in long-running coding agents

The useful part of Smaug Agentic is not another frontier-style benchmark claim. Abacus.AI is publishing a drop-in Kimi K3 derivative that targets a specific production failure mode in coding agents: long runs that burn the reasoning budget without converging. The weights and model card are public, but the training data is not disclosed and the benchmark gains remain vendor-produced.