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GitHub MCP Registry: The fastest way to discover AI tools

GitHub rolled out theMCP Registry—a hub for findingModel Context Protocol (MCP) serverswithout hunting through scattered corners of the internet. No more siloed lists or mystery URLs. It's all in one place now. The goal? Cleaner access to AI agent tools, plus a path towardself-publishing, thanks to .. read more  

GitHub MCP Registry: The fastest way to discover AI tools
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Nine HTTP Edge Cases Every API Developer Should Understand

Last February, CVE-2024-26141 punched a nasty hole inRack's Range header parsing. All versions since 1.3.0 are exposed. The bug let attackers blow up memory usage and responses—classic denial-of-service—just by crafting bloated Range headers. The trick? Custom file download handlers. They skip the u.. read more  

Nine HTTP Edge Cases Every API Developer Should Understand
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A stateful browser agent using self-healing DOM maps

A stateful browser agent using self-healing DOM maps is now available. Users describe tasks, Agent4 performs them, creates reusable workflows from interactions, and executes instantly on subsequent requests. Under the hood, it checks for known maps in a vector DB, patches them if needed, and self-he.. read more  

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Foreign hackers breached a US nuclear weapons plant via SharePoint flaws

UnpatchedSharePoint flaws(CVE-2025-53770, CVE-2025-49704) cracked open theKansas City National Security Campusin July. IT systems tied to 80% of U.S. non-nuclear weapons parts got compromised. Attackers—likely state-backed, Russian or Chinese—moved fast, hitting the zero-day RCE and spoofing bugs ju.. read more  

Foreign hackers breached a US nuclear weapons plant via SharePoint flaws
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Python 3.14 Is Here. How Fast Is It?

Python 3.14 lands with a ~27% speed jump over 3.13, keeping the post-3.11 momentum alive. The big news: the newfree-threading interpreter—no GIL—now hits up to3.1x fasterthan regular CPython in multi-threaded, CPU-heavy benchmarks. That’s up from 2.2x in 3.13. Less shiny: theJIT interpreterstill can.. read more  

Python 3.14 Is Here. How Fast Is It?
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Supply Chain Risk in VSCode Extension Marketplaces

Wiz dug up 550+ leaked secrets buried in 500+ public VSCode extensions—including 130+ live access tokens forVSCode MarketplaceandOpenVSX. That’s a wide-open door to supply chain attacks through auto-updates. Microsoft reacted fast: dumped the breached tokens, rolled outpre-publish secret scanning, a.. read more  

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Sora 2 in Azure AI Foundry: Create videos with responsible AI

OpenAI’sSora 2just dropped intopublic previewvia theAzure AI FoundryAPI. It’s a multimodal video model aimed at serious use—enterprise safety, API-ready, built for scale. Azure didn’t stop there. It bundled inGPT-image-1,Flux 1.1, andKontext Pro, pulling together a full-gen stack under one roof... read more  

Sora 2 in Azure AI Foundry: Create videos with responsible AI
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How Microsoft Evaluates LLMs in Azure AI Foundry: A Practical, End-to-End Playbook

Microsoft’s Azure AI Foundry just released a proper workflow for putting LLMs through their paces. Thinkoffline/online tests,human-in-the-loop checks,automated scoring, and evencustom evaluators—all wired into one system. At the heart of it: the newAzure AI Evaluation SDK. You can run it locally whi.. read more  

How Microsoft Evaluates LLMs in Azure AI Foundry: A Practical, End-to-End Playbook
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Structured Vibe Coding: A Smarter Way to Build AI Agents with GitHub Copilot

A fresh approach calledstructured vibe codingblends human-style team habits with AI workflows. Specs, GitHub Issues, and Copilot now pull agents into the loop like actual teammates. Powered byGitHub Copilot Coding AgentsandAzure AI Foundry, devs can run full AI-driven sprints—spec to PR—right inside.. read more  

Structured Vibe Coding: A Smarter Way to Build AI Agents with GitHub Copilot
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Claude Skills are awesome, maybe a bigger deal than MCP

Anthropic releasedClaude Skills—a lean way to snap specialized instructions and scripts into Claude without bloating the prompt. Each “skill” lives in a folder with Markdown and optional code. Frontmatter tags tell Claude when to load what. No need to cram everything into the context window—Claude g.. read more  

Claude Skills are awesome, maybe a bigger deal than MCP
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.