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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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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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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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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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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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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  

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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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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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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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Draw.io MCP for Diagram Generation: Why It’s Worth Using

Draw.io MCPlinks theModel Context Protocoltodraw.io. It ingests structured input (text,CSV,Mermaid) and emitsdraw.io XML, PNG/SVG, or hosted links. Draw.io MCPruns as anMCP Tool Server, CLI, or Copilot skill. It drafts small graphs (<50 nodes) in seconds and stores diagrams inGitfor diffs andCI/CDau.. read more  

Draw.io MCP for Diagram Generation: Why It’s Worth Using
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.