ContentPosts from @servsan..
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@kaptain shared a link, 1 month, 1 week ago
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Replaying massive data in a non-production environment using Pekko Streams and Kubernetes Pekko Cluster

DoubleVerify built a traffic replay tool that actually scales. It runs onPekko StreamsandPekko Cluster, pumping real production-like traffic into non-prod setups. Throttlenails the RPS with precision for functional tests.Distributed datasyncs stressful loads across cluster nodes without breaking a s.. read more  

Replaying massive data in a non-production environment using Pekko Streams and Kubernetes Pekko Cluster
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@kaptain shared a link, 1 month, 1 week ago
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Exposing Kubernetes Services Without Cloud LoadBalancers: A Practical Guide

Bare-metal Kubernetes just got a cloud-style glow-up. By wiring upMetalLBin layer2 mode with theNGINX ingress controller, the setup exposesLoadBalancer-typeservices—no cloud provider in sight. MetalLB dishes out static, LAN-routable IPs. NGINX funnels external traffic to internalClusterIPservices th.. read more  

Exposing Kubernetes Services Without Cloud LoadBalancers: A Practical Guide
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@kala shared a link, 1 month, 1 week ago
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Why open source may not survive the rise of generative AI

Generative AI is snapping the attribution chain thatcopyleft licenseslike theGNU GPLrely on. Without clear provenance, license terms get lost. Compliance? Forget it. The give-and-take that powersFOSSstops giving - or taking... read more  

Why open source may not survive the rise of generative AI
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@kala shared a link, 1 month, 1 week ago
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I regret building this $3000 Pi AI cluster

A 10-node Raspberry Pi 5 cluster built with16GB CM5 Lite modulestopped out at325 Gflops- then got lapped by an $8K x86 Framework PC cluster running4x faster. On the bright side? The Pi setup edged out in energy efficiency when pushed to thermal limits. It came with160 GB total RAM, but that didn’t h.. read more  

I regret building this $3000 Pi AI cluster
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@kala shared a link, 1 month, 1 week ago
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Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

Generative recommender systems need more than just observed user behavior to make accurate recommendations. Introducing A-SFT algorithm improves alignment between pre-trained models and reward models for more effective post-training... read more  

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@kala shared a link, 1 month, 1 week ago
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Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference

Amazon rolled out fine-tuning and distillation forVision LLMslike Nova Lite viaBedrockandSageMaker. Translation: better doc parsing—think messy tax forms, receipts, invoices. Developers get two tuning paths:PEFTor full fine-tune. Then choose how to ship:on-demand inference (ODI)orProvisioned Through.. read more  

Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference
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@kala shared a link, 1 month, 1 week ago
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What Significance Testing is, Why it matters, Various Types and Interpreting the p-Value

Significance testing determines if observed differences are meaningful by calculating the likelihood of results happening by chance. The p-value indicates this likelihood, with values below 0.05 suggesting statistical significance. Different tests, such as t-tests, ANOVA, and chi-square, help analyz.. read more  

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@devopslinks shared a link, 1 month, 1 week ago
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A FinOps Guide to Comparing Containers and Serverless Functions for Compute

AWS dropped a new cost-performance playbook pittingAmazon ECSagainstAWS Lambda. It's not just a tech choice - it’s a workload strategy. Go containers when you’ve got steady traffic, high CPU or memory needs, or sticky app state. Go serverless for spiky, event-driven bursts that don’t need a long lea.. read more  

A FinOps Guide to Comparing Containers and Serverless Functions for Compute
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@devopslinks shared a link, 1 month, 1 week ago
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Jump Starting Quantum Computing on Azure

Microsoft just pulled off full-stack quantum teleportation withAzure Quantum, wiring up Qiskit and Quantinuum’s simulator in the process. Entanglement? Check. Hadamard and CNOT gates set the stage. Classical control logic wrangles the flow. Validation lands cleanly on the backend... read more  

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@devopslinks shared a link, 1 month, 1 week ago
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How and Why Netflix Built a Real-Time Distributed Graph -  Ingesting and Processing Data Streams at Internet Scale

Netflix built a Real-Time Distributed Graph (RDG) to connect member interactions across different devices instantly. Using Apache Flink and Kafka, they process up to1 millionmessages per second for node and edge updates. Scaling Flink jobs individually reduced operational headaches and allowed for s.. read more Â