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@kaptain shared a link, 6 months ago
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Building a Kubernetes Platform — Think Big, Think in Planes

Thinking in planes, as introduced by the Platform Engineering reference model, helps teams describe their platform in a simple, shared language, turning a collection of tools into a platform. It forces you to think horizontally, connecting teams and technologies instead of adding more layers, creati.. read more  

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@kaptain shared a link, 6 months ago
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Helm 4 Overview

Helm 4 ditches the old plugin model for a sharper, plugin-first architecture powered by WebAssembly. That means isolation/control, and deeper customization - if you're ready to adapt! Post-renderers are now plugins. That breaks compatibility with earlier exec-based setups, so expect some rewiring. .. read more  

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@kaptain shared a link, 6 months ago
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Unlocking next-generation AI performance with Dynamic Resource Allocation on Amazon EKS and Amazon EC2 P6e-GB200

Amazon just droppedEC2 P6e-GB200 UltraServers, packingNVIDIA GB200 Grace Blackwellchips. Built for running trillion-parameter AI models onAmazon EKSwithout losing sleep over scaling. Under the hood:NVLink 5.0,IMEX, andEFAv4stitch up to 72 Blackwell GPUs into one memory-coherent cluster per UltraServ.. read more  

Unlocking next-generation AI performance with Dynamic Resource Allocation on Amazon EKS and Amazon EC2 P6e-GB200
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@kaptain shared a link, 6 months ago
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The State of OCI Artifacts for AI/ML

OCI artifacts quietly leveled up. Over the last 18 months, they’ve gone from a niche hack to production muscle for AI/ML workloads on Kubernetes. The signs? Clear enough:KitOpsandModelPacklanded in the CNCF Sandbox. Kubernetes 1.31 got native support forImage Volume Source. Docker pushedModel Runner.. read more  

The State of OCI Artifacts for AI/ML
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@kala shared a link, 6 months ago
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Build AI Agents Worth Keeping: The Canvas Framework

MIT and McKinsey found a gap the size of the Grand Canyon: 80% of companies claim they’re using generative AI, but fewer than 1 in 10 use cases actually ship. Blame it on scattered data, fuzzy goals, and governance that's still MIA. A new stack is stepping in:product → agent → data → model. It flips.. read more  

Build AI Agents Worth Keeping: The Canvas Framework
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@kala shared a link, 6 months ago
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Detect inappropriate images in S3 with AWS Rekognition + Terraform

A serverless AWS pipeline runs image moderation on autopilot - withS3,Lambda,Rekognition,SNS, andEventBridgeall wired up throughTerraform. When a photo gets flagged, it’s tagged, maybe quarantined, and triggers an email alert. Daily scan? Handled... read more  

Detect inappropriate images in S3 with AWS Rekognition + Terraform
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@kala shared a link, 6 months ago
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Grokipedia

Grokipedia just dropped - a Wikipedia remix built from LLM output, pitched as an escape from "woke" bias. The pitch? Bold. The execution? Rough. Entries run long. Facts bend. Citations wander. And the tone? Cold, context-free, and unmistakably machine-made. The usual LLM suspects are here: hallucina.. read more  

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@kala shared a link, 6 months ago
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Agentic AI and Security

Agentic LLM apps come with a glaring security flaw: they can't tell the difference between data and code. That blind spot opens the door to prompt injection and similar attacks. The fix? Treat them like they're radioactive. Run sensitive tasks in containers. Break up agent workflows so they never ju.. read more  

Agentic AI and Security
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@kala shared a link, 6 months ago
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New trend: Programming by kicking off parallel AI agents

Senior engineers are starting to spin upparallel AI coding agents- think Claude Code, Cursor, and the like - to run tasks side by side. One agent sketches boilerplate. Another tackles tests. A third refactors old junk. All at once. Is it "multitasking on steroids"? Not just this as it messes with ho.. read more  

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@kala shared a link, 6 months ago
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Why GPUs accelerate AI learning: The power of parallel math

Modern AI eats GPUs for breakfast - training, inference, all of it. Matrix ops? Parallel everything. Models like LLaMA don’t blink without a gang of H100s working overtime... read more  

Why GPUs accelerate AI learning: The power of parallel math
GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.