Your guide to building
intelligent systems.
Deep-dive architecture, production patterns, and systems thinking for engineers who build with AI — not just about it.
Built for engineers, not tourists.
Architecture deep-dives
RAG pipelines, multi-agent patterns, vector databases, and production infrastructure.
Production engineering
SDK integration, API design, testing strategies, and real-world .NET + Azure patterns.
Honest experiments
Coding agents, AI code-review, knowledge-base prototypes — tested and documented.
Model evaluations
Claude vs GPT, fine-tune vs RAG, small models, token economics — beyond vibes.
Explore by domain.
AI Engineering
RAG, multi-agent systems, prompt engineering, token economics, evaluations and guardrails — the production discipline of building with LLMs.
7 articles →Backend (.NET / C#)
C# language features, Clean Architecture, CQRS, DDD, EF Core and Minimal APIs — the enterprise backend spine.
10 articles →Frontend (React)
Hooks, Fiber, Server Components, state management, React Query and Next.js App Router — the modern frontend runtime.
6 articles →Cloud (Azure)
Azure services, microservices, serverless, Service Bus, AKS, App Service — how real systems run in production on Microsoft cloud.
7 articles →Algorithms & DS
Sorting, graph traversal, dynamic programming, trees, heaps, recursion and Big-O analysis — the foundations every engineer needs.
7 articles →System Design
Caching, load balancing, messaging, scalability, consistency and distributed patterns — how to architect systems that scale.
6 articles →Prompt Engineering
System prompts, few-shot patterns, chain-of-thought, structured output, red-teaming and meta-prompting — the craft of communicating with LLMs.
8 articles →Agentic Systems
Tool use, planning loops, LangGraph, AutoGen, MCP, memory and multi-agent orchestration — building autonomous AI systems that act.
5 articles →DevOps & CI/CD
GitHub Actions, Docker, Kubernetes, Helm, monitoring and alerting — the engineering discipline of shipping reliably and continuously.
6 articles →LLM Model Landscape
GPT-4o, Claude, Llama, Gemini — model selection, benchmarks, cost-performance trade-offs, context windows and deployment options.
2 articles →Databases
PostgreSQL, Redis, Cosmos DB, pgvector, Pinecone — relational, cache, NoSQL and vector storage with production-grade patterns.
13 articles →TypeScript Deep Dive
Generics, utility types, conditional types, mapped types, Zod, type guards and module resolution — mastering the TypeScript type system.
6 articles →AI Observability
Tracing, evals, drift detection, cost telemetry, hallucination monitoring — keeping production AI systems honest.
1 articles →Infrastructure as Code
Bicep, Terraform, Pulumi — declarative infrastructure, modules, state and policy guardrails for cloud platforms.
10 articles →Security Engineering
Authn/Authz, secrets management, supply-chain hygiene, OWASP, prompt injection defense — security as a build-time concern.
8 articles →Testing Engineering
Unit, integration, snapshot, contract, fuzz and LLM evals — designing tests that catch real regressions without breaking the bank.
9 articles →API Design
REST, GraphQL, gRPC, OpenAPI, idempotency and versioning — designing APIs that survive scale and change.
8 articles →Four steps to get started.
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Choose AI Engineering, Agentic Systems, Prompt Engineering, and more.
Explore the knowledge tree
Each domain has an interactive map showing how topics connect.
Go deep
Read full articles with diagrams, code samples, and production notes.
Popular articles to start with.
Designing RAG Systems That Actually Scale
The most important AI architecture pattern in production today.
Integrating Azure OpenAI with ASP.NET Core
Connect LLMs to real enterprise systems safely.
Read →LLM Model LandscapeClaude vs GPT for Engineering Workflows
Practical model comparison beyond benchmarks.
Read →Agentic SystemsTesting Autonomous Coding Agents
See how AI tools perform in real engineering workflows.
Read →Agentic SystemsMulti-Agent Architecture Patterns
Orchestrator, supervisor, and swarm patterns with real trade-offs.
Read →Prompt EngineeringPrompt Engineering as Software Engineering
Treat prompts like code — version, test, and iterate.
Read →