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Updates and recent posts about Vertex AI..

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@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

The Pulse: AI load breaks GitHub – why not other vendors?

GitHub leaders created the reliability problems through weak capacity planning. As AI-agent users drove heavier traffic, GitHub engineers found migration risk and engineering debt that teams had allowed to build up... read more

The Pulse: AI load breaks GitHub – why not other vendors?

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@kaptain shared a link, 2 weeks, 3 days ago

FAUN.dev()

Mirantis has entered into an agreement to be acquired by IREN

Mirantis has agreed to an acquisition by IREN. The companies have announced no customer-facing product changes... read more

Mirantis has entered into an agreement to be acquired by IREN

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@kaptain shared a link, 2 weeks, 3 days ago

FAUN.dev()

What kubectl debug doesn’t tell you: The silent evidence gap

kubectl debugsessions leave almost no forensic trace: by design,EphemeralContainerStatushas nolastStateorrestartCount, so the exit code, session duration, target container, and debugger logs disappear from the Kubernetes API the moment anything else updates the pod. That breaks incident handoffs (th.. read more

What kubectl debug doesn’t tell you: The silent evidence gap

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@kaptain shared a link, 2 weeks, 3 days ago

FAUN.dev()

Extending AI gateways with Rust

Every gateway ships with a set of built-in policies. Authentication. Rate limiting. Request routing. Prompt guards. These cover most use cases. But what about the ones they don’t cover? What if you need to add a custom header based on a database lookup? What if you need to transform a request body i.. read more

Extending AI gateways with Rust

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@kaptain shared a link, 2 weeks, 3 days ago

FAUN.dev()

v1.36: Deprecation and removal of Service ExternalIPs

Kubernetes v1.36 deprecatesService.spec.externalIPsand starts the removal path, finally closing CVE-2020-8554, the trust-everyone hole the field has carried since the early days. The project has recommended disabling it via theDenyServiceExternalIPsadmission controller since v1.21, but SIG Network h.. read more

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@kaptain shared a link, 2 weeks, 3 days ago

FAUN.dev()

When AI agents become contributors: How KubeStellar reached 81% PR acceptance

The KubeStellar Console team learned that AI coding agents improve after engineers build deterministic feedback loops into the codebase. Engineers who grant more autonomy give agents more room to guess, with no new correction signal... read more

When AI agents become contributors: How KubeStellar reached 81% PR acceptance

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@kala shared a link, 2 weeks, 3 days ago

FAUN.dev()

How Code works in large codebases: Best practices and where to start

Anthropic breaks down the patterns behind successful Claude Code rollouts in monorepos, legacy systems, and codebases spanning thousands of developers, arguing that Claude Code performs agentic search over a live filesystem instead of relying on a RAG index that drifts out of sync with active engine.. read more

How Code works in large codebases: Best practices and where to start

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@kala shared a link, 2 weeks, 3 days ago

FAUN.dev()

Create Custom MCP Catalogs and Profiles

Docker made Custom Catalogs and Profiles available for MCP servers. Admins can distribute server catalogs they approve, and teams can package per-developer configurations as OCI artifacts... read more

Create Custom MCP Catalogs and Profiles

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@kala shared a link, 2 weeks, 3 days ago

FAUN.dev()

AI Is Doing the Testing Now

Brijesh Deb's third "comfortable lie" of software testing is that AI is now doing the testing: coverage dashboards hit 80%+, regression suites maintain themselves, and leadership concludes that risk is handled, while the experienced testers who knew the domain quietly get redeployed or made redundan.. read more

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@kala shared a link, 2 weeks, 3 days ago

FAUN.dev()

Claude’s next enterprise battle is not models: it’s the agent control plane

New data shows Microsoft and OpenAI leading agent orchestration, but Anthropic's rising stake signals a shift in control of AI infrastructure. Anthropic's move from model to orchestration layer hints at a strategic battle over agent runtime platforms where operational AI work happens... read more

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.