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@devopslinks shared a link, 4 months, 2 weeks ago
FAUN.dev()

From Static Rate Limiting to Adaptive Traffic Management in Airbnb’s Key-Value Store

Airbnb just rewired Mussel, its key-value store, with a smarter, layered QoS system. Out go the rigid QPS caps. In comeresource-aware rate control,criticality-based load shedding, andreal-time hot-key mitigation. Dispatchers now speak the language of backend cost -rows, bytes, latency - not just raw.. read more  

From Static Rate Limiting to Adaptive Traffic Management in Airbnb’s Key-Value Store
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@devopslinks shared a link, 4 months, 2 weeks ago
FAUN.dev()

Agent-Driven SRE Investigations: A Practical Deep Dive into Multi-Agent Incident Response

A sandboxed setup dropped multiple Claude-powered agents into Docker containers to run a full incident response drill. Each agent played a role: probing Kubernetes clusters, sniffing out root causes, and shipping remediation PRs straight to GitHub. Out of 7 test incidents, they nailed the diagnoses .. read more  

Agent-Driven SRE Investigations: A Practical Deep Dive into Multi-Agent Incident Response
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@devopslinks shared a link, 4 months, 2 weeks ago
FAUN.dev()

async dns

A developer went digging for safer async DNS incurlafterpthread_cancelstarted breaking things. Threadless, callback-free options took the spotlight.OpenBSD’sasrquickly stood out, clean event loop integration, no threads, no drama. Beat outc-areson portability and design clarity... read more  

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@devopslinks shared a link, 4 months, 2 weeks ago
FAUN.dev()

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.

Refinery 3.0 cuts CPU by 70% and slashes RAM by 60%. The trick: selective field extraction from serialized spans. No full deserialization. Fewer heap allocations. Way less waste. It also recycles buffers, handles metrics smarter, and is gearing up to parallelize its core decision loop... read more  

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.
News FAUN.dev() Team
@kaptain shared an update, 4 months, 2 weeks ago
FAUN.dev()

Docker Brings Production-Grade Hardened Images to Developers at No Cost

Docker

Docker has launched Docker Hardened Images, a secure and minimal set of production-ready images. These images are now freely available to developers.

Docker Brings Production-Grade Hardened Images to Developers at No Cost
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@anjali shared a link, 4 months, 2 weeks ago
Customer Marketing Manager, Last9

OTel Updates: OpenTelemetry Deprecates Zipkin Exporters

OpenTelemetry deprecates Zipkin exporters in favor of native OTLP support. Migration paths and timeline through December 2026.

depreciating_zipkin
News FAUN.dev() Team
@kaptain shared an update, 4 months, 2 weeks ago
FAUN.dev()

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks

Kubernetes Argo CD

ArgoCD v3.2.2 has been released, featuring a new addition, two enhancements, and a bug fix. This update aims to improve the overall functionality and reliability of the platform.

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks
News FAUN.dev() Team
@devopslinks shared an update, 4 months, 2 weeks ago
FAUN.dev()

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully

Rust GNU/Linux The Linux Kernel UNIX

The Rust experiment in the Linux kernel concludes, confirming its suitability and permanence in kernel development, with Rust now used in production and supported by major Linux distributions.

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully
Course
@eon01 published a course, 4 months, 3 weeks ago
Founder, FAUN.dev

Generative AI For The Rest Of US

ChatGPT GPT

Your Future, Decoded

Generative AI For The Rest Of US
News FAUN.dev() Team
@kaptain shared an update, 4 months, 3 weeks ago
FAUN.dev()

Kubernetes v1.35 Timbernetes Release: 60 Enhancements

Kubernetes Gateway API Kubernetes

Kubernetes v1.35, the Timbernetes Release, debuts with 60 enhancements, including stable in-place Pod updates and beta features for workload identity and certificate rotation.

Kubernetes v1.35 Timbernetes Release: 60 Enhancements
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.