patterns▌

tailwind-ui-patterns

Principles for building production-ready React applications.

Idiomatic Go patterns, best practices, and conventions for building robust applications. \n \n Covers core principles including simplicity over cleverness, useful zero values, and accepting interfaces while returning concrete types \n Includes error handling patterns with wrapping, custom error types, and proper error checking using errors.Is and errors.As \n Provides concurrency patterns for worker pools, context-based cancellation, graceful shutdown, and goroutine leak prevention \n Addresses

Comprehensive patterns for Next.js 14+ App Router, Server Components, and modern full-stack React development. \n \n Covers rendering modes (Server Components, Client Components, static, dynamic, streaming), file conventions, and core architectural patterns with practical TypeScript examples \n Includes eight key patterns: Server Components with data fetching, Client Components, Server Actions, parallel routes, intercepting routes for modals, streaming with Suspense, Route Handlers, and metadata

Implement common Redis patterns for high-performance applications.

Production-ready patterns for RAG pipelines, agent architectures, prompt management, and LLMOps monitoring. \n \n Covers five core RAG strategies: document chunking, embedding selection, retrieval methods (semantic, hybrid, multi-query, compression), and context-aware generation with citations \n Includes four agent patterns: ReAct (reasoning + acting), function calling, plan-and-execute, and multi-agent collaboration with specialized roles \n Provides prompt engineering practices: templating wi

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

Multi-agent architectures distribute work across multiple language model instances, each with its own context window. When designed well, this distribution enables capabilities beyond single-agent limits. When designed poorly, it introduces coordination overhead that negates benefits. The critical insight is that sub-agents exist primarily to isolate context, not to anthropomorphize role division.

Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research.