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Practical AI engineering content on RAG, embeddings, LLM applications, model operations, agents, and real-time ML infrastructure.

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AI/ML

AI Token Usage: The Staff Engineer Guide to Context Optimization

The Economics of Intelligence Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. In the new world of AI Engineering, Tokens are the primary currency. Every interaction…

Apr 26, 20266 min readAdvancedDeep Dive

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AI Infrastructure Mastery

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AI/MLIntermediate

Claude Code Lesson 2: The Research -> Strategy -> Execution Cycle

Thinking Like an Agent Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. The biggest mistake senior engineers make when adopting Claude Code is treating it like a "Fas…

Apr 26, 20265 min read

Deep DiveClaude Code Mastery

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AI/MLIntermediate

Claude Code Masterclass: The Future of Agentic CLI Development

The Paradigm Shift: From Chat to Agent Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. For years, we have treated AI as a "Chatbot." We copy code, paste it into a we…

AI/ML Articles

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AI Token Usage: The Staff Engineer Guide to Context Optimization

More AI/ML Guides

Claude Code Lesson 2: The Research -> Strategy -> Execution Cycle

Claude Code Masterclass: The Future of Agentic CLI Development

Claude Code Lesson 4: Security & Safety Guardrails

Claude Code Lesson 1: Setting Up for Success (Context & Rules)

Claude Code Lesson 3: Advanced Tool Mastery (Grep, Replace, Shell)

Gemini CLI Lesson 2: The 2M Token Strategy (Analyzing Large Repos)

Gemini CLI Mastery: Harnessing the 2-Million Token Context

Gemini CLI Lesson 3: Multimodal Pipelines (Video-to-Code)

AI Masterclass: Vector Database Selection (Pinecone vs. Milvus vs. Weaviate)

Advanced RAG Architecture: Beyond Simple Vector Search

AI Infrastructure on AWS: SageMaker, EKS GPU Scheduling, and Cost-Efficient Inference

Kubernetes for AI Inference: GPUs, Autoscaling, Queues, and Cost Control

LLM Evaluation at Scale: LLM-as-Judge, RAGAS, and Building Automated Eval Pipelines

LLM Inference Optimization: Quantization, KV Cache, and High-Throughput Serving

LLM Observability in Production: Traces, Evals, Cost, Latency, and Failure Modes

MCP for Backend Engineers: Tools, Agents, and Production Guardrails

Retrieval Systems for RAG: BM25, Dense Retrieval, Hybrid Search, and Reranking

System Design: Building a Feature Store for Real-Time Machine Learning

Using Open Source Models with Claude Code: A Developer's Guide

Fine-Tuning LLMs: When to Fine-Tune, When to Prompt

Building AI Agents with Tool Use: From Chatbot to Autonomous Agent

Vector Embeddings: The Foundation of Modern AI Applications

Prompt Engineering: Advanced Techniques for Production LLMs

Building a Production RAG System: Embeddings, Vector DBs, and Retrieval

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