productivity▌

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.

Deploy and manage MCP-based declarative agents across Microsoft 365 with admin center governance, role-based access, and organizational distribution. \n \n Supports five agent types: organization-published, creator-shared, Microsoft-native, external partner, and frontier agents, each with distinct deployment and approval workflows \n Provides admin controls for publishing, deploying to user groups, blocking, and removing agents; requires AI Admin role for full management \n Includes MCP-specific

You are a database administrator specializing in modern cloud database operations, automation, and reliability engineering.

Full-stack financial data and trading platform: CLI, Python/Rust SDK, MCP, and LLM integration.

Generate GitHub Copilot instruction files by analyzing actual codebase patterns and technology versions. \n \n Scans projects to detect exact language, framework, and library versions, then generates instructions that enforce compatibility with those specific versions \n Analyzes existing code patterns for naming conventions, architecture style, error handling, testing approaches, and documentation standards to ensure Copilot-generated code matches established practices \n Supports multiple tech

GraphQL schema design, federation architecture, and real-time subscription implementation for distributed graph systems. \n \n Covers schema-first design with Apollo Federation 2.5+, including entity resolution, subgraph composition, and federation directives \n Provides resolver patterns with DataLoader for N+1 prevention, batching, and caching across distributed architectures \n Includes query complexity analysis, depth limiting, and field-level security to prevent abuse before deployment \n S

Incremental migration strategies, dependency mapping, and facade designs for safely modernizing legacy systems. \n \n Guides five-step workflow: assess system, plan migration, build safety net with characterization tests, migrate incrementally via strangler fig pattern with feature flags, and validate before retiring legacy code \n Includes reference templates for strangler fig facades, feature flag wrappers, and characterization test patterns to capture existing behavior as a golden master \n E

$23

Fetch the transcript from a Loom video using Loom's GraphQL API.