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@kaptain shared a link, 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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@kala shared a link, 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, 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, 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, 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, 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, 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
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@devopslinks shared a link, 3 weeks ago
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Amazon is back up after outage affecting tens of thousands of shoppers

Amazon faced an outage, affecting tens of thousands of shoppers globally on Thursday afternoon. Downdetector reported a surge in complaints, peaking at 20,000 by 3:49 p.m. ET. The outage involved checkout and pricing errors caused by a software code deployment... read more  

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@devopslinks shared a link, 3 weeks ago
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Why Serverless Compute Partners Are Now More Important Than Ever

The note saysAIworkloads are bursty. They spawn parallel tool calls, pull multi‑GB model weights into RAM, and endure long cold starts (e.g.,vLLM,SGLang). Companies wrestle with a fragmentedGPUmarket and poor peakGPU utilization. To hit latency, compliance, and cost targets they adoptmulti‑region/mu.. read more  

Why Serverless Compute Partners Are Now More Important Than Ever
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@devopslinks shared a link, 3 weeks ago
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AWS Cost Optimization Best Practices: A Maturity-Based Guide [2026]

The guide maps a five-stagematurity model— fromVisibilitytoFinOps Culture. It prescribes staged actions before commitment purchases. It recommends turning onCost ExplorerandAWS Budgets, enforcingtag policies, runningCompute Optimizer, testingGraviton, and usingCloudBurn/Amazon Qfor pre-deploy estima.. read more  

Magika is an open-source file type identification engine developed by Google that uses machine learning instead of traditional signature-based heuristics. Unlike classic tools such as file, which rely on magic bytes and handcrafted rules, Magika analyzes file content holistically using a trained model to infer the true file type.

It is designed to be both highly accurate and extremely fast, capable of classifying files in milliseconds. Magika excels at detecting edge cases where file extensions are incorrect, intentionally spoofed, or absent altogether. This makes it particularly valuable for security scanning, malware analysis, digital forensics, and large-scale content ingestion pipelines.

Magika supports hundreds of file formats, including programming languages, configuration files, documents, archives, executables, media formats, and data files. It is available as a Python library, a CLI, and integrates cleanly into automated workflows. The project is maintained by Google and released under an open-source license, making it suitable for both enterprise and research use.

Magika is commonly used in scenarios such as:

- Secure file uploads and content validation
- Malware detection and sandboxing pipelines
- Code repository scanning
- Data lake ingestion and classification
- Digital forensics and incident response