Blog Posts
Exploring System Design, Architecture Patterns, and Emerging Technologies
23 articles found
Agentic AI: When Your AI Stops Asking and Starts Doing
What makes AI agents actually agentic, the architecture behind agent loops, and why the trust/control tradeoff is the real engineering problem.
Enterprise Architecture in the AI Era
How AI is changing the enterprise architect role -- from integration patterns to AI governance, model lifecycle management, and the new enterprise AI stack.
Event-Driven AI: Real-Time Intelligence at Scale
Processing real-time data streams for AI inference -- architecture patterns, feature consistency, backpressure, and graceful degradation.
Federated Learning: Training AI Without Seeing the Data
How federated learning trains models across decentralized devices without moving the data, and the practical problems that come with it.
Hybrid AI: When LLMs Meet Expert Systems
Why pure LLMs fail at rule-based reasoning, and how hybrid architectures combine language models with deterministic systems to get reliable results.
MCP in Practice: Building Real AI Integrations
A practical implementation guide for building MCP servers -- tool schemas, auth, streaming, testing, and deployment patterns.
AI Behind APIs: Microservices Patterns for ML Systems
How to expose AI capabilities through microservices -- model-as-a-service, async inference, batching, sidecar patterns, and when a monolith is the better call.
Modular AI: Building Systems You Can Actually Maintain
Designing AI systems with interchangeable components -- module taxonomy, interface contracts, hot-swapping models, and real tradeoffs vs monolithic deployments.
Multi-Agent Systems: When One AI Isn't Enough
Designing systems where multiple specialized AI agents collaborate -- orchestration patterns, communication protocols, and failure modes.
Synthetic Data: Training AI on Data That Doesn't Exist
How synthetic data solves data scarcity and privacy problems in AI training -- generation methods, quality metrics, real use cases, and where it falls apart.
Spec-Driven Development: The GSD Way
Why writing specs before code produces better software faster -- the Get Shit Done methodology for solo developers working with AI.
RAG Without Vector Search
PageIndex replaces vector search with LLM reasoning over a hierarchical document index. How it works, how to set it up, and when it beats traditional RAG.
Inside Claude's Agent System
How Claude Code's agent team works under the hood -- main agent orchestration, subagent spawning, parallel execution, and context window management.
When AI Agents Socialize
Moltbook is a social network where only AI agents can participate. 770,000+ agents have formed religions, governments, and encrypted communication channels. Here's what happened.
Spark vs Hadoop Showdown
Spark vs Hadoop -- architecture differences, performance tradeoffs, when to use each, and why Spark has mostly won for new projects.
Is AI a Bubble?
Is AI a bubble, a revolution, or both? Market data, historical parallels, and practical advice for engineers navigating the hype cycle.
LangChain vs LangGraph Compared
LangChain vs LangGraph -- when to use each, how they differ architecturally, and practical patterns for production LLM applications.
Kafka Meets Microservices
How Kafka fits into microservices architecture -- event-driven patterns, order processing flows, and production lessons from scaling an e-commerce platform.
Designing WhatsApp at Scale
Architecture breakdown of a WhatsApp-scale messaging platform -- microservices, message delivery flow, cost analysis of managed vs self-hosted infrastructure.
Local RAG with Ollama
Set up a fully local RAG server using Ollama and ChromaDB. Python implementation with vector embeddings and semantic search -- no API keys required.
When RAG Gets Smart
How autonomous agents improve RAG systems -- moving from static retrieval to dynamic, multi-step reasoning over documents.
How A2A Protocol Works
A practical look at Google's A2A Protocol -- how it works, what it enables for multi-agent systems, and where it fits alongside MCP.
MCP: USB for AI Tools
A practical guide to Anthropic's Model Context Protocol -- how it works, what problem it solves, and how to start using it.