Posts tagged with mlp
Why it matters: Engineers face increasing data fragmentation across SaaS silos. This post details how to build a unified context engine using knowledge graphs, multimodal processing, and prompt optimization (DSPy) to enable effective RAG and agentic workflows over proprietary enterprise data.
- •Dropbox Dash functions as a universal context engine, integrating disparate SaaS applications and proprietary content into a unified searchable index.
- •The system utilizes custom crawlers to navigate complex API rate limits, diverse authentication schemes, and granular permission systems (ACLs).
- •Content enrichment involves normalizing files into markdown and using multimodal models for scene extraction in video and transcription in audio.
- •Knowledge graphs are employed to map relationships between entities across platforms, providing deeper context for agentic queries.
- •The engineering team leverages DSPy for programmatic prompt optimization and 'LLM as a judge' frameworks for automated evaluation.
- •The architecture explores the Model Context Protocol (MCP) to standardize how LLMs interact with external data sources and tools.
Why it matters: Translating natural language to complex DSLs reduces friction for subject matter experts interacting with massive, federated datasets. This approach bridges the gap between intuitive human intent and rigid technical schemas, improving productivity across hundreds of enterprise applications.
- •Netflix is evolving its Graph Search platform to support natural language queries using Large Language Models (LLMs).
- •The system translates ambiguous user input into a structured Filter Domain Specific Language (DSL) for federated GraphQL data.
- •Accuracy is maintained by ensuring syntactic, semantic, and pragmatic correctness through schema validation and controlled vocabularies.
- •The architecture utilizes Retrieval-Augmented Generation (RAG) to provide domain-specific data processing without replacing existing UIs.
- •Pre-processing and context engineering are critical to prevent LLM hallucinations and ensure fields match the underlying index.
Why it matters: GitHub Copilot CLI brings agentic AI to the terminal, bridging the gap between IDEs and system-level tasks. By automating environment setup, debugging, and GitHub interactions via MCP, it significantly boosts developer velocity and reduces the cognitive load of manual CLI operations.
- •GitHub Copilot CLI enables agentic AI workflows directly within the terminal, reducing context switching between IDEs and command-line environments.
- •The tool automates complex terminal tasks such as repository cloning, dependency management, and process troubleshooting like identifying and killing PIDs.
- •It supports multimodal capabilities, allowing users to upload screenshots of UI bugs for automated analysis and suggested code fixes.
- •Integration with the Model Context Protocol (MCP) allows the CLI to interact with custom agents for specialized tasks like accessibility reviews or security audits.
- •Developers can query GitHub-specific data, such as open issues or PRs, and delegate multi-step tasks to coding agents without leaving the command line.
Why it matters: Maia 200 represents a shift toward custom first-party silicon optimized for LLM inference. It offers engineers high-performance FP4/FP8 compute and a flexible software stack, significantly reducing the cost and latency of deploying massive models like GPT-5.2 at scale.
- •Maia 200 is built on a TSMC 3nm process, featuring 140 billion transistors and delivering 10 petaFLOPS of FP4 and 5 petaFLOPS of FP8 performance.
- •The memory architecture utilizes 216GB of HBM3e at 7 TB/s alongside 272MB of on-chip SRAM to maximize token generation throughput.
- •It employs a custom Ethernet-based scale-up network providing 2.8 TB/s of bidirectional bandwidth for clusters of up to 6,144 accelerators.
- •The software ecosystem includes the Maia SDK with a Triton compiler, PyTorch integration, and a low-level programming language (NPL).
- •Engineered for efficiency, it achieves 30% better performance per dollar than existing hardware for models like GPT-5.2 and synthetic data generation.
Why it matters: This article details the architectural shift from fragmented point solutions to a unified AI stack. It provides a blueprint for solving data consistency and metadata scaling challenges, essential for engineers building reliable, real-time agentic systems at enterprise scale.
- •Salesforce unified its data, agent, and application layers into the Agentforce 360 stack to ensure consistent context and reasoning across all surfaces.
- •The platform uses Data 360 as a universal semantic model, harmonizing signals from streaming, batch, and zero-copy sources into a single plane of glass.
- •Engineers addressed metadata scaling by treating metadata as data, enabling efficient indexing and retrieval for massive entity volumes.
- •A harmonization metamodel defines mappings and transformations to generate canonical customer profiles from heterogeneous data sources.
- •The architecture centralizes freshness and ingest control to maintain identical answers across different AI agents and applications.
- •Real-time event correlation is optimized to update unified context immediately while balancing storage costs for large-scale personalization.
Why it matters: Azure Storage is shifting from passive storage to an active, AI-optimized platform. Engineers must understand these scale and performance improvements to architect systems capable of handling the high-concurrency, high-throughput demands of autonomous agents and LLM lifecycles.
- •Azure Storage is evolving into a unified platform supporting the full AI lifecycle, from frontier model training to large-scale inferencing and agentic applications.
- •Blob scaled accounts now support millions of objects across hundreds of scale units, enabling massive datasets for training and tuning.
- •Azure Managed Lustre (AMLFS) has expanded to support 25 PiB namespaces and 512 GBps throughput to maximize GPU utilization in high-performance computing.
- •Deep integration with frameworks like Microsoft Foundry, Ray, and LangChain facilitates seamless data grounding and low-latency context serving for RAG architectures.
- •Elastic SAN and Azure Container Storage (ACStor) are being optimized for 'agentic scale' to handle the high concurrency and query volume of autonomous agents.
- •New storage tiers and performance updates, such as Premium SSD v2 and Cold/Archive tiers for Azure Files, focus on reducing TCO for mission-critical workloads.
Why it matters: Building agentic workflows is difficult due to the complexity of context management and tool orchestration. This SDK abstracts those infrastructure hurdles, allowing engineers to focus on product logic while leveraging a production-tested agentic loop.
- •GitHub released the Copilot SDK in technical preview, enabling developers to embed the Copilot agentic core into custom applications.
- •The SDK provides programmatic access to the same execution loop used by Copilot CLI, including planning, tool orchestration, and multi-turn context management.
- •It supports major programming environments including Node.js, Python, Go, and .NET, with built-in support for GitHub authentication.
- •Key features include Model Context Protocol (MCP) server integration, custom tool definitions, and real-time streaming capabilities.
- •Developers can leverage existing Copilot subscriptions or provide their own API keys to power the agentic workflows.
Why it matters: Slash commands transform the Copilot CLI from a chat interface into a precise developer tool. By providing predictable, keyboard-driven shortcuts for context management and model selection, they minimize context switching and improve the reliability of AI-assisted terminal workflows.
- •Slash commands provide explicit, repeatable instructions in the GitHub Copilot CLI, reducing the need for complex natural language prompting.
- •Commands like /clear and /cwd allow developers to manage conversation history and directory scoping to prevent context bleed.
- •The /model command enables switching between different AI models to optimize for speed or reasoning depth based on the task.
- •Security and compliance are enhanced through commands like /add-dir and /list-dirs, which define clear boundaries for file access.
- •Advanced features include /mcp for connecting Model Context Protocol servers and /delegate for offloading tasks to specialized agents.
- •The CLI supports session management and usage tracking via /session and /usage commands to monitor resource consumption.
Why it matters: Triaging security alerts is often manual and repetitive. This framework allows engineers to automate human-like reasoning to filter false positives at scale, combining the precision of CodeQL with the pattern-matching flexibility of LLMs to find real vulnerabilities faster.
- •GitHub Security Lab introduced the Taskflow Agent, an open-source framework for automating security research and vulnerability triage using LLMs.
- •Taskflows are defined in YAML files, breaking complex audits into smaller, sequential tasks to overcome LLM context window limitations and improve accuracy.
- •The framework utilizes Model Context Protocol (MCP) servers to perform conventional programming tasks like file fetching and searching alongside AI reasoning.
- •It supports asynchronous batch processing, allowing engineers to apply templated audit logic across numerous CodeQL alerts simultaneously.
- •Real-world application of the tool successfully identified approximately 30 vulnerabilities by filtering out false positives that traditional static analysis tools struggle to detect.
Why it matters: Benchmarking AI systems against live providers is expensive and noisy. This mock service provides a deterministic, cost-effective way to validate performance and reliability at scale, allowing engineers to iterate faster without financial friction or external latency fluctuations.
- •Salesforce developed an internal LLM mock service to simulate AI provider behavior, supporting benchmarks of over 24,000 requests per minute.
- •The service reduced annual token-based costs by over $500,000 by replacing live LLM dependencies during performance and regression testing.
- •Deterministic latency controls allow engineers to isolate internal code performance from external provider variability, ensuring repeatable results.
- •The mock layer enables rapid scale and failover benchmarking by simulating high-volume traffic and controlled outages without external infrastructure.
- •By providing a shared platform capability, the service accelerates development loops and improves confidence in performance signals.