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@kaptain shared a link, 1 month, 3 weeks ago
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How WebAssembly plugins simplify Kubernetes extensibility

Helm 4runsWebAssembly (Wasm)plugins to executeWASImodules insideOCIcontainers and VMs.Helmtemplates standardize module lifecycle. The Wasm plugin adds instruction-level sandboxing and Kubernetes segmentation.Helm 4preserves portability acrossx86/ARM. Compared withHelm 3plugins, it shows up to a 40% .. read more  

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@kaptain shared a link, 1 month, 3 weeks ago
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The great migration: Why every AI platform is converging on Kubernetes

The CNCF survey finds82%of container users runKubernetesin production.66%of GenAI hosts use it for inference. Kubernetes now stitches data processing, distributed training, LLM inference, and autonomous agents viaSpark,Kubeflow,Kueue,KServe, andArmada. GPU sharing and scheduling advanced withMIG, ti.. read more  

The great migration: Why every AI platform is converging on Kubernetes
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@kaptain shared a link, 1 month, 3 weeks ago
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pg_plan_alternatives: Tracing PostgreSQL’s Query Plan Alternatives using eBPF

The tracer hooks PostgreSQL's optimizer via eBPF. It captures every alternative plan path with cost estimates and flags the chosen plan. A kernel-space eBPF program reads planner structs using DWARF-derived offsets. A user-space collector gathers the data and a visualizer renders plan graphs. eBPF p.. read more  

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@kaptain shared a link, 1 month, 3 weeks ago
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How Does Kubernetes Self-Healing Work? Understand Self-Healing By Breaking a Real Cluster

KubeLab boots a three-nodeKubernetescluster and runs seven failure simulations. It deploysNode.js,Postgres,Prometheus, andGrafana. Then it deletes pods, forcesOOMKill, throttles CPU, drains nodes, and scales aStatefulSetto zero. Each scenario surfaces fixes:readiness probes,PodDisruptionBudget, anti.. read more  

How Does Kubernetes Self-Healing Work? Understand Self-Healing By Breaking a Real Cluster
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@kala shared a link, 1 month, 3 weeks ago
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The L in "LLM" Stands for Lying

The author arguesLLMschurn out fast, generic answers by remixing low-quality source material. They seed brittle, repetitive code viavibe-coding. The remedy: requiresource attributionand auditable inference to separate originals from forgeries and to reshape model training and deployment. Requiringso.. read more  

The L in "LLM" Stands for Lying
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@kala shared a link, 1 month, 3 weeks ago
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AI as tradecraft: How threat actors operationalize AI

Microsoft observes threat actors operationalizeAIandLLMsacross the cyberattack lifecycle. They accelerate reconnaissance, phishing, malware development, and post‑compromise triage. Actors abusejailbreakingtechniques andGANs. They craft personas, generate look‑alike domains, embed runtime‑adaptive pa.. read more  

AI as tradecraft: How threat actors operationalize AI
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@kala shared a link, 1 month, 3 weeks ago
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Reasoning models struggle to control their chains of thought, and that’s good

OpenAI's paper unveilsCoT-Control: an open-source suite of 13,000+ tasks fromGPQA, MMLU-Pro, HLE, BFCLthat measuresCoTcontrollability. Evaluations on 13 models show compliance at 0.1%-15.4%. Compliance is tiny. Controllability improves with model size. It drops as reasoning chains lengthen and after.. read more  

Reasoning models struggle to control their chains of thought, and that’s good
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@kala shared a link, 1 month, 3 weeks ago
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The reason big tech is giving away AI agent frameworks

A catalog of majoragent frameworks: LangGraph, CrewAI, Google ADK, AWS Strands, Microsoft Agent Framework, OpenAI Agents SDK, Mastra, Pydantic AI, Agno. Hyperscalers co-design free SDKs (e.g.,Strands,ADK). They tie those SDKs to metered runtimes -Bedrock,Vertex AI. Revenue shifts to inference and de.. read more  

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@kala shared a link, 1 month, 3 weeks ago
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LLMs are getting better at unmasking people online

Researchers at ETH Zurich show LLMs can stitch anonymous bios to public web data and reidentify users across platforms. Fine-tuned models and agent chains parse unstructured text and automate deanonymization in minutes at penny-level inference costs... read more  

LLMs are getting better at unmasking people online
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@devopslinks shared a link, 1 month, 3 weeks ago
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How I Dropped Our Production Database and Now Pay 10% More for AWS

Planned migration shifts the static site fromGitHub PagestoAWS S3. DNS moves toAWS.Djangostages on a subdomain before the main domain swaps. ATerraformauto-approve ran with no remote state. It destroyed productionRDS,VPC,ECS, and automated snapshots.AWSfound a hidden snapshot and recovered the DB in.. read more  

How I Dropped Our Production Database and Now Pay 10% More for AWS
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.