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@kaptain shared a link, 4 months ago
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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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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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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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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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My LLM coding workflow going into 2026

Anthropic saysClaude Code writes about 90% of its own code now. Why? Because devs are getting smart with AI. They're slicing problems into tight, testable chunks and running structured workflows that keep LLMs on a short leash. It's not just prompts anymore. Think context packaging, multi-agent setu.. read more  

My LLM coding workflow going into 2026
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The Architects of AI Are TIME's 2025 Person of the Year

The Architects of AI drove the economy, shaped geopolitics, and changed the way we interact with the world... read more  

The Architects of AI Are TIME's 2025 Person of the Year
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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  

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Race Condition in DynamoDB DNS System: Analyzing the AWS US-EAST-1 Outage

A long AWS smackdown in US-EAST-1 traced back to a ticking time bomb inDynamoDB’s automated DNS system. The flaw torpedoed EC2 networking, hobbled Lambda and Fargate, and dragged down theNetwork Load Balancer. Endpoints ghosted. Configs stalled. Everything snowballed. AWS says they’ll upgrade EC2 th.. read more  

Race Condition in DynamoDB DNS System: Analyzing the AWS US-EAST-1 Outage
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You don’t need NAT gateway to deploy Lambda into VPC

AWS just made a big dent in NAT gateway bills. You can now runLambda in VPCs with IPv6 and an egress-only Internet gateway- no more always-on NAT draining your wallet. Keep the private subnets locked down. Still get outbound Internet access. IPv6 handles the traffic, slicing out the NAT middleman... read more  

You don’t need NAT gateway to deploy Lambda into VPC
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ArgoCD diffs at scale

Monday.com ditched ArgoCD's built-in manifest diffing. Instead, they wired up a custom CI renderer that pre-renders Helm charts using real cluster data. Then it compares the desired states across Git branches. The kicker: diffs go to a UI with custom grouping support. Reviews get easier. New devs ge.. read more  

ArgoCD diffs at scale
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.