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@kaptain shared a link, 2 months 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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@kala shared a link, 2 months 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, 2 months 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, 2 months 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, 2 months 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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@kala shared a link, 2 months 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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@devopslinks shared a link, 2 months 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, 2 months 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  

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@devopslinks shared a link, 2 months ago
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What is autonomous validation? The future of CI/CD in the AI era

CircleCI droppedautonomous validation, a smarter CI/CD that thinks on its feet. It scans your code, predicts breakage, runs only the tests that matter - and fixes the easy stuff on its own. If things get messy, it hands off full context so you’re not digging through logs. Bonus: it keeps learning fr.. read more  

What is autonomous validation? The future of CI/CD in the AI era
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@devopslinks shared a link, 2 months 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  

At its core, Argo CD treats Git as the single source of truth for application definitions. You declare the desired state of your Kubernetes applications in Git (manifests, Helm charts, Kustomize overlays), and Argo CD continuously compares that desired state with what is actually running in the cluster. When drift is detected, it can alert you or automatically reconcile the cluster back to the Git-defined state.

Argo CD runs inside Kubernetes and provides:

- Declarative application management
- Automated or manual sync from Git to cluster
- Continuous drift detection and health assessment
- Rollbacks by reverting Git commits
- Fine-grained RBAC and multi-cluster support

It integrates natively with common Kubernetes configuration formats:

- Plain YAML
- Helm
- Kustomize
- Jsonnet

Operationally, Argo CD exposes both a web UI and CLI, making it easy to visualize application state, deployment history, diffs, and sync status. It is commonly used in platform engineering and SRE teams to standardize deployments, reduce configuration drift, and enforce auditability.

Argo CD is part of the Argo Project, which is hosted by the Cloud Native Computing Foundation (CNCF), and is widely adopted in production Kubernetes environments ranging from startups to large enterprises.