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Building Reproducible ML Systems with Apache Iceberg and SparkSQL

Apache Iceberg +SparkSQLbringsACID transactions,schema evolution, andtime travelto data lakes. That means ML pipelines finally get reproducibility and consistency without the hacks. Iceberg’s snapshot-based guts track every version, handle parallel writes without stepping on toes, and keep training .. read more  

Building Reproducible ML Systems with Apache Iceberg and SparkSQL
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Introducing the Amazon Bedrock AgentCore Code Interpreter

AWS just droppedAgentCore Code Interpreter—a managed box where AI agents can run Python, JavaScript, and TypeScript in isolation. Think of it as a secure playground with autoscaling, controlled file access, and deep hooks into frameworks likeLangChain,LangGraph,Strands, andCrewAI. Big picture: This.. read more  

Introducing the Amazon Bedrock AgentCore Code Interpreter
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Using generative AI for building AWS networks

Amazon Q Developer CLI and Bedrock just leveled up. You can now spin up AWS Cloud WANs and VPCs using plain English. Type what you need—get full deployments, phased migrations, and IaC for both CloudFormation and Terraform. Agents handle the whole stack: network discovery, rollout, and config. No m.. read more  

Using generative AI for building AWS networks
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AWS AgentCore: The Overlooked Privilege Escalation Path in Bedrock’s AI Tooling

AWS Bedrock AgentCore just got a new trick: agents (and anyone IAM-blessed) can now runCode Interpreters. Think arbitrary code execution—with custom or predefined IAM roles. But here’s the kicker: these interpreters skipresource policies, lean on control plane APIs, and don’t log squat—unlessyou fl.. read more  

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How to Build an Agent

A new framework lays out six sharp steps for building agents that actually ship. It kicks off with a grounded task, locks in SOPs, then tunes high-leverage prompts. The real choke point? LLM reasoning. Everything else—architecture, data flow, testing—is scoped to chase tight, measurable gains there... read more  

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Building AIOps with Amazon Q Developer CLI and MCP Server

Amazon Q Developer CLI now hooks into Model Context Protocol (MCP) servers, unlocking AIOps tasks—incident detection, remediation, security fixes—through plain English. Natural language in, real-time control out. It fetches data and talks to your AWS stack via a low-code UI. Tinkerable, scriptable,.. read more  

Building AIOps with Amazon Q Developer CLI and MCP Server
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Typed languages are better suited for vibecoding

Claude’s making typed, compiled languages feel like cheating. Rust, Go, TypeScript—rising fast where Python used to reign. Why? AI coding tools now catch bugs early, validate sprawling diffs, and help devs grok unfamiliar codebases without breaking a sweat. Compiler guarantees + AI pair = fast, safe.. read more  

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Azure AI Speech Service Configuration

Azure AI Speech now splits config paths forTTS(text-to-speech) andSTT(speech-to-text) when usingmanaged identity—and yes, they're different enough to matter. Roles, env vars, and auth flows don’t line up. Private endpoints? They nuke regional fallbacks, so you’ll need to pass full URLs. A shared ut.. read more  

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Browser-Based LLMs: WebGPU Enables AI in Your Browser

Browser-based LLMs likeBrowser-LLMnow run models likeLlama 2entirely in the browser—no server round-trips, no cloud bill. Just you, WebGPU, and up to7B parametershumming along on your machine. System shift:WebGPU cracks open real AI horsepower in the browser. Local inference gets faster, more priva.. read more  

Browser-Based LLMs: WebGPU Enables AI in Your Browser
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OpenAI prepares to launch GPT-5, but big leaps are unlikely

Internal testing showsGPT-5edges ahead of GPT-4—better code, cleaner math, sharper step-by-step thinking. But no breakthrough. No leap. OpenAI even scrapped “Orion,” the original GPT-5 push, and settled on GPT-4.5 instead. Translation: scaling Transformers is hitting a wall. System pivot:OpenAI’s n.. read more  

OpenAI prepares to launch GPT-5, but big leaps are unlikely
k0rdent is dedicated to establishing a standardized and efficient approach to deploying and managing Kubernetes clusters at scale. It functions as a "super control plane," orchestrating and managing multiple Kubernetes control planes seamlessly. Alternatively, k0rdent can be viewed as a powerful open-source platform tailored for Platform Engineering, offering tools to streamline workflows and enhance operational efficiency.

If you are building an Internal Developer Platform (IDP), require centralized management of Kubernetes clusters, or wish to create Golden Paths for consistent development practices, k0rdent is an ideal solution. It simplifies complex operations, enabling platform engineers to focus on delivering value.

Whether managing Kubernetes clusters on-premises, in the cloud, or across hybrid environments, k0rdent ensures consistency and reliability. With comprehensive lifecycle management capabilities—including provisioning, configuration, and maintenance—k0rdent provides a repeatable, secure, and centralized method to oversee Kubernetes clusters effectively.