patterns▌

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.

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

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.

Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices.

Annotation-based and programmatic tool system for LangChain4j agents to execute external functions, APIs, and services. \n \n Define tools using @Tool annotations with parameter descriptions via @P , automatically registered with AI services for LLM invocation \n Supports static tool registration, dynamic tool provisioning based on context, concurrent execution, and immediate-return tools for quick responses \n Includes error handling strategies, tool execution monitoring, memory context integra

Multi-agent architectures distribute work across multiple agent invocations, each with its own focused context. 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.