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Kubernetes GPU Management Just Got a Major Upgrade

Kubernetes 1.34 droppedDynamic Resource Allocation (DRA)- think persistent volumes, but for GPUs and custom hardware. Vendors can now plug in drivers and schedulers for their devices, and workloads can pick exactly what they need. Coming in 1.35: a newworkload abstractionthat speaks the language of .. read more  

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Implementing assurance pipeline for Amazon EKS Platform

AWS released a full-stack CI/CD validation pipeline forAmazon EKS. It pulls in six layers of testing,Terraform,Helm,Locustload testing, and evenAWS Fault Injectionfor pushing resilience to the edge. The goal: bake policy checks, functional tests, and brutal load tests right into pre-deployment. Fewe.. read more  

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From Deterministic to Agentic: Creating Durable AI Workflows with Dapr

Dapr droppedDurable Agents- a mashup of classic workflows and LLM-driven agents that can actually get things done and survive rough edges. They track reasoning steps, tool calls, and chat states like a champ. If things crash, no problem: Dapr Workflows and Diagrid Catalyst bring it all back... read more  

From Deterministic to Agentic: Creating Durable AI Workflows with Dapr
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v1.35: Watch Based Route Reconciliation in the Cloud Controller Manager

Kubernetes v1.35 sneaks in an alphafeature gatethat flips the CCM route controller from "check every X minutes" to "watch and react." It now usesinformersto trigger syncs when nodes change - plus a light periodic check every 12–24 hours... read more  

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v1.35: New level of efficiency with in-place Pod restart

Kubernetes 1.35, as you may know, introducedin-place Pod restarts(alpha). It's a real reset: all containers, init and sidecars included - without killing the Pod or kicking off a reschedule. Think restart without the cloud drama. Big win for workloads with heavy inter-container dependencies or massi.. read more  

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1.35: Enhanced Debugging with Versioned z-pages APIs

Kubernetes 1.35 makes a quiet-but-crucial upgrade: z-pages debugging endpoints now returnstructured, machine-readable JSON. That means tools- not just tired humans - can parse control plane state directly. The responses areversioned, backward-compatible, and tucked behind feature flags for now... read more  

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@kala shared a link, 1 month, 3 weeks ago
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The 2026 Data Engineering Roadmap: Building Data Systems for the Agentic AI Era

Data engineering’s getting flipped.AI agentsandLLMsaren’t just tagging along anymore - they’re the main users now. That means engineers need to buildcontext-aware, machine-readable data systemsthat don’t just store info but actually make sense of it. Think:vector databases,knowledge graphs,semantic .. read more  

The 2026 Data Engineering Roadmap: Building Data Systems for the Agentic AI Era
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@kala shared a link, 1 month, 3 weeks ago
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Streamlining Security Investigations with Agents

Slack broke down how it's threading AI into its product without torching user trust.Slack AIleans hard ontenant-specific data isolationandzero data retention- no leftover crumbs from LLM interactions. Instead of piping user data through someone else’s APIs, Slack runs LLMs onits own infrawhere it ca.. read more  

Streamlining Security Investigations with Agents
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2025: The year in LLMs

2025 was the year LLMs stopped just answering questions and started building things.Reasoning modelslike OpenAI’s o-series and Claude Code took over tool-driven workflows. Asynchronous coding agentsbroke out. These models didn’t just write code - they ran it, debugged it, then did it again. That loo.. read more  

2025: The year in LLMs
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Meet the ‘Mad Max’-Loving CEO Challenging Nvidia With a Renegade Chip

June Paik spurned a takeover offer from Meta Platforms last year. Now his South Korean company, FuriosaAI, has an AI chip entering mass production... read more  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).