Go beyond the basics and understand how LLMs actually work. Master prompting techniques, compare models, and learn cost optimization strategies for production use.
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Event-Driven Architecture on GCP: Mastering Cloud Pub/Sub for Real-Time Systems
Introduction: Google Cloud Pub/Sub provides the foundation for event-driven architectures at any scale, offering globally distributed messaging with exactly-once delivery semantics and sub-second latency. This comprehensive guide explores Pub/Sub’s enterprise capabilities, from dead letter queues and message ordering to BigQuery subscriptions and schema enforcement. After building event-driven systems across multiple cloud platforms, I’ve found Pub/Sub […]
Read more →Building Multi-Agent Workflows: Advanced LangGraph Patterns
Building multi-agent workflows requires careful orchestration. After building 18+ multi-agent systems with LangGraph, I’ve learned what works. Here’s the complete guide to advanced LangGraph patterns for multi-agent workflows. Figure 1: Multi-Agent Architecture with LangGraph Why Multi-Agent Workflows Multi-agent systems offer significant advantages: Specialization: Each agent handles specific tasks Parallelism: Agents can work simultaneously Scalability: Add […]
Read more →Generative AI Fundamentals: A Practical Guide to the Technology Reshaping Software
Cut through the hype and understand what Generative AI actually is, how it works, and why it matters. A hands-on introduction for developers and architects ready to build with LLMs.
Read more →Enterprise PostgreSQL on Google Cloud: AlloyDB Architecture for Mission-Critical Workloads
Introduction: Google Cloud AlloyDB provides a fully managed, PostgreSQL-compatible database service designed for demanding enterprise workloads. This comprehensive guide explores AlloyDB’s enterprise capabilities, from its disaggregated storage architecture and columnar engine to high availability configurations, migration strategies, and cost optimization. After implementing AlloyDB for organizations requiring PostgreSQL compatibility with cloud-native performance, I’ve found it delivers […]
Read more →Mastering Google Cloud Dataflow: Building Unified Batch and Streaming Pipelines at Scale
Introduction: Google Cloud Dataflow provides a fully managed, serverless data processing service built on Apache Beam that unifies batch and streaming pipelines. This comprehensive guide explores Dataflow’s enterprise capabilities, from pipeline design patterns and windowing strategies to autoscaling, cost optimization, and production monitoring. After building data pipelines processing terabytes daily across multiple cloud providers, I’ve […]
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