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@kaptain shared a link, 5 months ago
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Kubernetes OptimizationInPlace Pod Resizing,ZoneAware Routin

Halodoc cut EC2 costs and shaved latency by leaning into two Kubernetes tricks: In-place pod resizing(v1.33) lets them dial pod resources up or down on the fly, especially handy during off-peak hours. Zone-aware routingviatopology-aware hintskeeps inter-service traffic close to home (same AZ), skipp.. read more  

Kubernetes OptimizationInPlace Pod Resizing,ZoneAware Routin
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@kaptain shared a link, 5 months ago
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Avoiding Zombie Cluster Members When Upgrading to etcd v3.6

etcd v3.5.26 patches a nasty upgrade bug. It now syncsv3storefromv2storeto stop zombie nodes from corrupting clusters during the jump to v3.6. The core issue: Older versions let stale store states bring removed members back from the dead... read more  

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@kala shared a link, 5 months ago
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Chinese AI in 2025, Wrapped

Chinese AI milestones in 2025: Big models from DeepSeek and others, AGI discussions at Alibaba, US-China chip war swings, Beijing's AI Action plan, and more. DeepSeek led the way with an open-source model, setting off a wave of Chinese companies going open-source. China's push for AGI and involvemen.. read more  

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@kala shared a link, 5 months ago
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Review of Deep Seek OCR

DeepSeek-OCRflips the OCR script. Instead of feeding full image tokens to the decoder, it leans on an encoder to compress them up front, trimming down input size and GPU strain in one move. That context diet? It opens the door for way bigger windows in LLMs. Why it matters:Shoving compression earlie.. read more  

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@kala shared a link, 5 months ago
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Evaluating AI Agents in Security Operations

Cotool threw frontier LLMs at real-world SecOps tasks using Splunk’s BOTSv3 dataset.GPT-5topped the chart in accuracy (62.7%) and gave the best results per dollar.Claude Haiku-4.5blazed through tasks fastest, just 240 seconds on average, maxing out tool integrations.Gemini-2.5-proflopped on both acc.. read more  

Evaluating AI Agents in Security Operations
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@kala shared a link, 5 months ago
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AI agents are starting to eat SaaS

AI coding agents are eating the lunch of low-complexity SaaS. Teams with a bit of dev muscle are skipping subscription logins and spinning up dashboards, pipelines, even decks, using Claude, Gemini, whoever’s fastest that day. Build vs. buy? Tilting back toward build. The kicker: build now takes min.. read more  

AI agents are starting to eat SaaS
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@kala shared a link, 5 months ago
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Everything to know about Google Gemini’s most recent AI updates

Google jammed a full no-code AI workshop into Gemini. The browser now bakes inOpal, a drag-and-drop app builder with a shiny newvisual editor. You can chain prompts, preview apps, and feed it text, voice, or images, without touching code. They also dropped theGemini 3 Flash model, built for dual rea.. read more  

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@devopslinks shared a link, 5 months ago
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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, 5 months ago
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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, 5 months ago
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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.
Pulumi is an open-source infrastructure-as-code platform that allows you to define, deploy, and manage cloud resources using familiar general-purpose programming languages like Python, JavaScript, Go, and TypeScript.

Pulumi represents a major shift in the Infrastructure-as-Code (IaC) landscape by moving away from proprietary domain-specific languages (DSLs) and static configuration files like YAML or JSON. Instead, it leverages the power of standard programming languages, allowing engineers to use loops, functions, classes, and existing package managers to define their cloud environments. This means you can apply software engineering best practices—such as unit testing, modularity, and CI/CD integration—directly to your infrastructure setups on providers like AWS, Azure, Google Cloud, and Kubernetes.

The platform works by utilizing a "State" mechanism similar to Terraform, where it tracks the current deployment against your desired code. When you run a Pulumi program, it builds a resource graph to determine the most efficient way to provision or update your services. Because it uses real code, it provides superior IDE support, including auto-completion and type-checking, which significantly reduces the syntax errors and "trial-and-error" deployments common with text-based configuration tools.

Furthermore, Pulumi excels in hybrid and multi-cloud environments by providing a unified workflow for both infrastructure and application delivery. It bridges the gap between developers and platform engineers, as both can now speak the same language—literally.