Posts tagged with mlp
Why it matters: Cloudflare is scaling its abuse mitigation by integrating AI and real-time APIs. For engineers, this demonstrates how to handle high-volume legal and security compliance through automation and service-specific policies while maintaining network performance and reliability.
- •Cloudflare's H1 2025 transparency report highlights a significant increase in automated abuse detection and response capabilities.
- •The company is utilizing AI and machine learning to identify sophisticated patterns in unauthorized streaming and phishing campaigns.
- •A new API-driven reporting system for rightsholders has scaled DMCA processing, increasing actions from 1,000 to 54,000 in six months.
- •Cloudflare applies service-specific abuse policies, distinguishing between hosted content and CDN/security services.
- •Technical measures prevent the misconfiguration of free-tier plans for high-bandwidth video streaming to protect network resources.
- •Collaborative data sharing with rightsholders enables real-time identification and mitigation of domains involved in streaming abuse.
Why it matters: Building a scalable feature store is essential for real-time AI applications that require low-latency retrieval of complex user signals across hybrid environments. This approach enables engineers to move quickly from experimentation to production without managing underlying infrastructure.
- •Dropbox Dash utilizes a custom feature store to manage data signals for real-time machine learning ranking across fragmented company content.
- •The system bridges a hybrid infrastructure consisting of on-premises low-latency services and a Spark-native cloud environment for data processing.
- •Engineers selected Feast as the framework for its modular architecture and clear separation between feature definitions and infrastructure management.
- •To meet sub-100ms latency requirements, the store uses an in-house DynamoDB-compatible solution (Dynovault) for high-concurrency parallel reads.
- •The architecture supports both batch processing of historical data and real-time streaming ingestion to capture immediate user intent.
Why it matters: Microsoft's leadership in AI platforms highlights the transition from experimental LLM demos to production-grade agentic workflows. For engineers, this provides a unified framework for data grounding, multi-agent orchestration, and governance across cloud and edge environments.
- •Microsoft Foundry serves as a unified platform for building, deploying, and governing agentic AI applications at scale.
- •Foundry IQ and Tools provide a secure grounding API with over 1,400 connectors to integrate agents with real-world enterprise data.
- •Foundry Agent Service supports multi-agent orchestration, allowing autonomous agents to coordinate and drive complex business workflows.
- •The Foundry Control Plane offers enterprise-grade observability, audit trails, and policy enforcement for autonomous systems.
- •Deployment flexibility is enabled through Foundry Models for cloud-based GenAI Ops and Foundry Local for low-latency, on-device AI execution.
Why it matters: These updates provide engineers with a unified framework for building, governing, and scaling AI agents. By integrating advanced models like Claude and streamlining data retrieval via Foundry IQ, Microsoft is reducing the complexity of deploying enterprise-grade agentic workflows.
- •Azure Copilot introduces specialized agents to the portal and CLI to automate cloud migration, assessment, and governance tasks.
- •Foundry Control Plane enters public preview, offering centralized security, lifecycle management, and observability for AI agents.
- •Foundry IQ and Fabric IQ provide unified endpoints for RAG solutions and real-time analytics grounded in enterprise data.
- •The Microsoft Agent Pre-Purchase Plan (P3) simplifies AI procurement by providing a single fund for 32 Microsoft services.
- •Anthropic Claude models are now available in Microsoft Foundry, enabling advanced reasoning within a unified governance framework.
- •Azure HorizonDB for PostgreSQL has entered private preview to expand database options for cloud-native applications.
Why it matters: This report offers critical insights into evolving user behavior, platform dominance, and emerging tech trends like AI and digital finance. Engineers can leverage this data to inform product strategy, infrastructure planning, and understand the competitive landscape of internet services.
- •Cloudflare's 2025 report ranks top internet services based on anonymized DNS query data from its 1.1.1.1 resolver, highlighting shifts in popularity across nine categories.
- •Generative AI saw significant competition, with Claude, Gemini, and Perplexity challenging ChatGPT, and Gemini reaching the #2 spot by year-end.
- •The social media landscape shifted: Instagram rose to #5 overall, while TikTok and X declined, and Kwai gained traction in emerging markets.
- •Asian e-commerce platforms like Shopee and Temu joined Amazon in the global top 3, indicating a significant regional climb.
- •Google, Facebook, and Apple remained the top three overall internet services, with Microsoft and Instagram showing strong growth in their rankings.
- •Digital finance services like Stripe and neobank Nubank demonstrated continued dominance and growth, alongside a surge in cryptocurrency traffic for platforms like OKX.
Why it matters: This review offers critical insights into evolving Internet trends, including AI's impact on web traffic, the rise of post-quantum security, and network performance, essential for engineers building and securing online services.
- •Global Internet traffic grew 19% in 2025, with Starlink traffic doubling and Googlebot leading verified bot activity for search and AI training.
- •Post-quantum encrypted web traffic reached 52% of human-generated requests, highlighting a significant shift in security adoption.
- •AI-related crawling surged, with Googlebot's dual-purpose crawls dominating and "user action" crawling increasing 15x. AI bots were also frequently blocked via robots.txt.
- •Meta's llama-3-8b-instruct was the most popular model on Workers AI, primarily used for text generation tasks.
- •Mobile traffic saw iOS devices account for 35% globally, while HTTP/2 and HTTP/3 adoption continued to rise.
Why it matters: Scaling data virtualization across 100+ platforms requires handling diverse SQL semantics. By combining AI-driven configuration with massive automated validation, engineers can accelerate connector development by 4x while ensuring cross-engine query correctness and consistency.
- •Transitioned from manual C++ SQL transformations to a JSON-based configuration-driven dialect framework to scale connector development.
- •Leveraged AI agents to interpret remote SQL documentation and generate approximately 2,000 lines of JSON configuration per dialect.
- •Implemented a test-driven AI workflow that uses an ordered suite of tests to refine dialect sections and prevent regressions.
- •Developed an automated validation pipeline executing 25,000 queries to compare Hyper's local execution against remote engine results.
- •Created a closed-loop feedback system where remote error messages and result deviations are fed back into the AI model for iterative refinement.
- •Achieved a 4x reduction in engineering effort, cutting dialect construction time from 40 days to 10 days per engine.
Why it matters: This article introduces "Continuous Efficiency," an AI-driven method to embed sustainable and efficient coding practices directly into development workflows. It offers a practical path for engineers to improve code quality, performance, and reduce operational costs without manual effort.
- •"Continuous Efficiency" integrates AI-powered automation with green software principles to embed sustainability into development workflows.
- •This approach combines LLM-powered Continuous AI for CI/CD with Green Software practices, aiming for more performant, resilient, and cost-effective code.
- •It addresses the low priority of green software by enabling near-effortless, always-on optimization for efficiency and reduced environmental impact.
- •Implemented via Agentic Workflows in GitHub Actions, it allows defining engineering standards in natural language for scalable application.
- •Benefits include declarative rule authoring, semantic generalizability across languages, and intelligent remediation like automated pull requests.
- •Pilot projects demonstrate success in applying green software rules and Web Sustainability Guidelines, yielding measurable performance gains.
Why it matters: This article introduces GPT-5.2 in Microsoft Foundry, a new enterprise AI model designed for complex problem-solving and agentic execution. It offers advanced reasoning, context handling, and robust governance, setting a new standard for reliable and secure AI development in professional settings.
- •GPT-5.2 is generally available in Microsoft Foundry, designed for enterprise AI with advanced reasoning and agentic capabilities.
- •It offers deeper logical chains, richer context handling, and agentic execution to generate shippable artifacts like code and design docs.
- •Built on a new architecture, it delivers superior performance, efficiency, and reasoning depth, with enhanced safety and integrations.
- •Two versions are available: GPT-5.2 for complex problem-solving and GPT-5.2-Chat for efficient everyday tasks and learning.
- •Optimized for agent scenarios, it supports multi-step logical chains, context-aware planning, and end-to-end task coordination.
- •Includes enterprise-grade safety, governance, and managed identities for secure and compliant AI adoption.
- •Enables building AI agents for analytics, app modernization, data pipelines, and customer experiences across industries.
Why it matters: These Azure Storage innovations provide engineers with enhanced scalability, performance, and simplified management for AI workloads, from training to inference, enabling more efficient development and deployment of advanced AI solutions.
- •Azure Blob Storage is significantly enhanced for the entire AI lifecycle, offering exabyte scale, 10s of Tbps throughput, and millions of IOPS to power GPU-intensive AI model training and deployment.
- •Azure Managed Lustre (AMLFS) 2.0 (preview) provides a high-performance parallel file system for petabyte-scale AI training data, supporting 25 PiB namespaces and up to 512 GBps throughput, with Hierarchical Storage Management (HSM) integration for Azure Blob Storage.
- •AMLFS includes new auto-import and auto-export features to efficiently move data between Lustre and Blob Storage, optimizing GPU utilization and streamlining the AI data pipeline.
- •Premium Blob Storage delivers consistent low-latency and up to 3X faster retrieval performance, crucial for AI inferencing, including Retrieval-Augmented Generation (RAG) agents and enterprise data security.
- •The LangChain Azure Blob Loader is introduced, offering improved security, memory efficiency, and up to 5x faster performance for open-source AI frameworks.
- •New AI-driven tools like Storage Discovery and Copilot simplify exabyte-scale data management and analysis through intuitive dashboards and natural language queries.