Nidhi Sharma's contributions

Build cross-system BI agents with MCP. Discover data, invoke tools, and ensure data provenance for reliable AI-driven insights.

Benchmark AgentCore Runtime isolation for multi-tenant AI. Evaluate latency, resource use, and security for robust AI systems.

Master MCP tool contracts with robust testing. Ensure API stability, prevent regressions, and build reliable AI integrations.

Securely integrate MCP traffic into enterprise gateways by treating headers as metadata, not authorization, and validating against the authoritative JSON-RPC body.

Master MCP Multi-Round Requests (MRTR) for reliable AI tool calls. Design stateless, interactive tools without long-lived sessions.

Master MCP observability in stateless production environments. Learn to leverage distributed tracing, request IDs, and explicit workflow identifiers for robust diagnostics.

Benchmark Zstandard compression in .NET 11 apps. Compare speed, size, CPU, and memory against GZip & Brotli for optimal data handling.

Master MCP load testing for stateless AI agent workloads. Optimize performance, identify bottlenecks, and ensure scalability with .NET.

C# 15 enhances memory safety by making unsafe code boundaries explicit, shifting focus from pointer syntax to actual memory access for better auditing.

Migrate .NET 8/9 apps before Nov 2026. Plan early for a smooth transition, covering dependencies, containers, and CI/CD.

Measure AI inference latency across CPU, GPU, and NPU. Understand factors, benchmark methodology, and choose the right compute platform.

Secure AI workloads in ASP.NET Core with policy-based authorization. Learn to implement fine-grained access control for AI capabilities.

Build scalable, event-driven AI processing pipelines with Azure Functions Durable Orchestrations. Manage complex, multi-step workflows reliably.

Build robust AI production pipelines with continuous regression testing. Ensure AI quality, detect regressions, and deploy with confidence.

Measure GitHub Copilot's AI code review quality with .NET. Track metrics, optimize pipelines, and ensure software quality at scale.

Optimize enterprise RAG systems by benchmarking chunking strategies. Maximize retrieval accuracy, minimize costs, and build reliable AI applications.

Build resilient enterprise AI with versioned knowledge bases. Ensure zero-downtime updates, instant rollbacks, and auditable RAG systems.

Secure AI apps with an Enterprise Prompt Firewall. Protect against injections, data leaks, and toxic output with inline middleware.

Secure AI tools with OAuth & MCP for fine-grained authorization, protecting enterprise data and operations.

Unlock AI agent insights beyond logs & metrics. Track decisions, tool calls, prompts, and context for robust debugging & optimization.

Build robust distributed background job systems using PostgreSQL's transactional features. Simplify infrastructure and enhance reliability without Redis.

Learn how to measure and reduce token usage in production LLM applications using prompt optimization, context management, caching, response limits, and monitoring.

Learn practical SQL Server indexing strategies for large OLTP systems, including clustered, nonclustered, composite, covering, and filtered indexes and execution plan analysis.

Build resilient .NET 11 HTTP clients with Polly v9. Master retry, timeout, and circuit breaker patterns for reliable service communication.

Master C# Channels for high-performance background processing. Improve API responsiveness, scalability, and reliability with async producer-consumer patterns.