Practical articles, in-depth tutorials, and architectural insights on software engineering, AI, and systems.
Learn how to design and implement ultra-fast recommendation architectures using distributed vector databases to process billions of items in milliseconds.
Learn how to apply the Raft consensus protocol to ensure data consistency and fault recovery across multiple autonomous artificial intelligence agents operating in parallel.
Discover how to structure data workflows and apply memory-efficient fine-tuning in large models, optimizing computational costs and knowledge retention.
Learn how to run artificial intelligence directly on local edge devices using ONNX Runtime and dedicated neural processing units for maximum energy efficiency and speed.
Learn how to combine traditional vector search with structured knowledge graphs to eliminate hallucinations and deliver precise answers in generative artificial intelligence systems.
Learn how to anticipate traffic spikes and eliminate startup latency in serverless functions using predictive machine learning models.
Learn how to apply context-free grammars to force language models into strict type-safe outputs, eliminating parsing errors in production APIs.
Explore how knowledge graphs transform recommendation engines, bridging structured data and machine learning to predict user interests with high precision in real time.
Orchestrating systems with multiple autonomous agents, each powered by Large Language Models (LLMs), is key to solving complex production problems. We explore how to implement dynamic routing, manage shared context, and effectively use tool-calling to build robust and scalable applications.
Learn how to architect enterprise systems with multiple intelligent agents using dynamic routing, shared context, and resilient tool-calling in high-scale environments.
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