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@kaptain shared a link, 6 months, 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, 6 months, 1 week ago
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Spotlight on Policy Working Group

The Kubernetes Policy Working Group got busy turning good intentions into real specs. They rolled out thePolicy Reports API, dropped best-practice docs worth reading, and helped steerValidatingAdmissionPolicyandMutatingAdmissionPolicytoward GA. Their work pulled inSIG Auth,SIG Security, and anyone e.. read more  

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@kaptain shared a link, 6 months, 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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@kaptain shared a link, 6 months, 1 week ago
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7 Common Kubernetes Pitfalls (and How I Learned to Avoid Them)

Seven ways folks trip over Kubernetes - each more avoidable than the last. Top offenses: skippingresource requests/limits, forgettinghealth probes, trustingephemeral logsthat vanish when you need them. Reusing configs across dev and prod? Still a bad idea. Pushing off observability until it’s on fir.. read more  

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@kala shared a link, 6 months, 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, 6 months, 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, 6 months, 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, 6 months, 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, 6 months, 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, 6 months, 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
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.