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@work4bots started using tool Spring , 1 day, 13 hours ago.

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@work4bots started using tool Helm , 1 day, 13 hours ago.

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@work4bots started using tool Azure Pipelines , 1 day, 13 hours ago.

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@work4bots started using tool Azure Kubernetes Service (AKS) , 1 day, 13 hours ago.

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@work4bots started using tool Azure , 1 day, 13 hours ago.

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@work4bots added a new tool Bicep , 1 day, 13 hours ago.

Story FAUN.dev() Team

@eon01 shared a post, 1 day, 17 hours ago

Founder, FAUN.dev

AWX in Action is out, and there's a course

#red hat... #ansible #Tower #AWX

"AWX in Action: Ansible Orchestration at Scale" is now available in print and ebook. It covers running AWX on Kubernetes for real, not a sandbox demo that falls over the moment you add a second execution node.

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@varbear shared a link, 1 day, 17 hours ago

FAUN.dev()

When Code Becomes Cheap, What's Left?

Teams that use Claude Opus 4.6 for spec-driven development generate code at low cost, so they spend scarce developer time on review and QA. Developers create more value by judging code than by typing it... read more

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@varbear shared a link, 1 day, 17 hours ago

FAUN.dev()

I Did 11 Technical Interviews in 60 Days. Here Is the Pattern Nobody Tells You.

The key insight from the article is that at mid-to-senior backend levels, coding rounds matter least while judgment, communication, structure, and ability to defend decisions are critical. Focus on rehearsing key design, incident, and behavioral answer structures to succeed, not just LeetCode... read more

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@varbear shared a link, 1 day, 17 hours ago

FAUN.dev()

Design Patterns Are Dead. Long Live Design Patterns.

Design patterns were created for human comprehension, not machines, serving as a shared vocabulary to communicate complex ideas quickly, manage working memory, and standardize solutions. Even in the era of AI-generated code, design patterns are crucial for containing the limitations of AI models and.. 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.