Navigating Complexity: How Cartographer Maps Massive Codebases with Parallel AI
Discover how Cartographer uses parallel AI subagents and a deterministic graph to map massive codebases for coding agents.
Orchestrating Complex AI Agents: A Guide to Conductor’s Durable Execution
Discover how Conductor provides durable execution and orchestration for complex AI agent workflows and microservices using an event-driven architecture.
Scaling Production AI: A Deep Dive into the Bifrost AI Gateway
Discover how Bifrost provides an enterprise-grade, unified gateway for LLMs, featuring load balancing, semantic caching, and MCP integration.
Beyond Vector Search: Building High-Precision Hybrid RAG Pipelines
Learn how to build high-performance RAG pipelines using hybrid search, Reciprocal Rank Fusion (RRF), and LLM reranking to boost retrieval accuracy.
Revolutionizing Offensive Security: A Guide to HexStrike AI MCP
Discover how to deploy HexStrike AI, an advanced MCP server that bridges LLMs with 150+ security tools for autonomous penetration testing.
Streamlining Web Auditing with Caido and AI-Driven MCP Integration
Learn how to use Caido for efficient web application auditing and integrate the Caido MCP Server to empower your AI agents in security workflows.
PentestGPT: Revolutionizing Automated Penetration Testing with LLMs
Discover how PentestGPT uses a multi-module architecture to solve context drift in automated penetration testing and boost performance by 228%.
Autoresearch for Agents (hwchase17): Autonomous Optimization via Evals
Optimize AI agents autonomously using an experiment loop driven by LangSmith evals and automated code iteration.
Automate Your Agent Optimization (kayba-ai): An Introduction to Autoharness
Stop manual prompt tuning. Use autoharness to automate agent optimization through continuous evals and candidate proposals for production-ready agents.
Beyond Prompting (uditgoenka): Mastering Autonomous Iteration with Autoresearch
Learn how Autoresearch uses Karpathy's principles to automate code and content improvement through autonomous, metric-driven loops.