Architect
intelligence.
Deep-dive architecture, production patterns, and systems thinking for engineers who build with AI — not just about it.
AI engineering from
first principles.
Most AI content is surface-level tutorials or dense academic papers. We fill the gap — structured deep-dives for engineers who want to understand the systems they build.
Start learning freeArchitecture
System design for RAG, multi-agent, and AI infrastructure at scale.
version: "3.9"
services:
vector-db:
image: qdrant/qdrant
rag-gateway:
build: ./gateway
depends_on:
- vector-dbEngineering
Production-grade SDK integration, testing, and deployment patterns.
// .NET Semantic Kernel
var builder = Kernel.CreateBuilder();
builder.AddOpenAIChatCompletion(
"gpt-4", endpoint
);
var kernel = builder.Build();Models
Evaluation, fine-tuning, cost optimization — beyond benchmark vibes.
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained("llm")
optimizer = torch.optim.AdamW()
loss.backward()Experiments
Frontier tools and agents tested in real engineering workflows.
agent = MultiAgentOrchestrator()
agent.add_tool(CodeExecutor)
result = await agent.run(
"Evaluate new framework"
)Four tracks. One engineering discipline.
Structured learning paths from foundations to expert-level AI systems engineering.
AI Architecture
System design patterns for AI-powered applications. RAG pipelines, orchestration layers, and infrastructure decisions.
Engineering
Practical engineering for AI systems. SDK integration, API design, testing strategies, and production-grade patterns.
Models
LLM evaluation, fine-tuning strategies, model selection, benchmarking, and cost-performance analysis.
Experiments
Hands-on explorations with AI tools, agent frameworks, coding assistants, and emerging techniques.
Latest from AI Architecture
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How to Build a Production-Ready AI System (Azure OpenAI + AI Search — Real Architecture)
Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.
Vector Database Selection for Production RAG
Cosmos DB, AI Search, Qdrant, Pinecone — benchmarks, cost, and operational complexity for production vector search.
Multi-Agent Architecture Patterns in Production
Orchestrator, supervisor, and swarm patterns for multi-agent systems with real trade-offs and failure modes.
Latest from Engineering
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Structured Outputs from LLMs: JSON Mode, Function Calling, and Schema Enforcement
Practical patterns for getting reliable structured data from LLMs — JSON mode, function calling, schema validation, and fallback strategies.
Prompt Engineering as Software Engineering
Version control, testing, parameterization, and CI pipelines for prompts — treating prompt engineering with the same rigor as application code.
Building Reliable AI Agents with Semantic Kernel
Plugin architecture, memory, planners, and error handling patterns for building production AI agents in .NET with Semantic Kernel.
Latest from Models
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Token Economics: Understanding and Optimizing LLM Costs
A practical guide to understanding token pricing, measuring real costs, and implementing optimization strategies — caching, prompt compression, model routing.
LLM Evaluation Beyond Vibes
Systematic approaches to evaluating LLM outputs — automated metrics, human evaluation frameworks, regression testing, and building evaluation pipelines.
Small Language Models in Production
When and how to use small language models like Phi, Gemma, and Mistral in production — quantization, deployment patterns, and latency-cost trade-offs.
Latest from Experiments
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From Chatbot to Agent: Adding Tools, Memory, and Planning to a Simple Chat Interface
A practical walkthrough of evolving a basic LLM chatbot into a capable agent — adding tool calling, persistent memory, and multi-step planning.
AI-Powered Code Review: Building a Review Bot That Actually Helps
How to build an AI code review system that catches real issues — architecture, prompt design, GitHub integration, and practical lessons.
MCP Servers: Building Tool-Using AI Agents with the Model Context Protocol
How MCP works, how to build MCP servers that expose tools to AI agents, and practical patterns for connecting LLMs to your systems.
Developer Playground.
See the systems in action.
Don't just read about it. Explore fully operational sandbox environments and modular tools to understand how the architecture handles real-world complexity.
Visual Flow Engines
Visual explainers dissecting how technology works under the hood.
PyAnimate Sandbox
Execution visualizer parsing Python logic in real-time motion.
Curriculum Paths
Structured progression graphs for evaluating technical mastery.
RAG Harness Explorer
Test vector chunking logic, semantic retrieval, and thresholds.
Agentic Orchestrator
Run multi-agent simulations to observe memory architectures.
Panchatantra
Algorithm storytelling for engineers.