AI/ML Articles

Gemini CLI Lesson 5: Context Caching & Cost Control

Why teams abandon long-context workflows Most teams do not abandon Gemini CLI because the answers are bad. They abandon it because the workflow feels expensive, slow, and unpredictable. A 90-second wait is tolerable for…

May 20, 20266 min read

#ai-ml#performance#architecture

Case StudyGemini CLI Mastery

Gemini CLI Lesson 8: Monorepo Modernization Case Study

Decomposing and modernizing legacy structures within a massive commerce monorepo is one of the most high-risk tasks a platform team can face. In a repository comprising hundreds of services and shared dependencies, a sin…

May 20, 202612 min read

#ai-ml#system-design#devops

PlaybookGemini CLI Mastery

Gemini CLI Lesson 7: Security & Governance Playbook

What this playbook is for This playbook is for teams that want to use Gemini CLI against real repositories, real documents, and real internal tools without turning the workflow into a compliance exception. The core idea…

May 20, 20265 min read

#ai-ml#security#devops

Gemini CLI Lesson 6: Vertex AI, MCP, and Tool Integration

The real enterprise question is not “can Gemini do it?” The real question is: What should Gemini be allowed to touch, under which identity, with which logs, and through what interface? Once a Gemini CLI workflow leaves p…

May 20, 20265 min read

#ai-ml#devops#architecture

Claude Code Lesson 6: Multi-Agent Orchestration — Delegating Complex Tasks to Subagents

From Single Agent to an Engineering Team Mental Model > Running a single Claude Code session on a large task is like asking one developer to do everything solo. Multi-agent orchestration is like running a well-managed sp…

May 15, 20267 min read

#ai-ml#devops#architecture

Claude Code Lesson 5: The Project Rules Playbook (CLAUDE.md & .claudeignore)

From Ad Hoc Prompts to Project-Level Intelligence Mental Model > A well-configured Claude Code project is like onboarding a brilliant senior engineer who already knows your architecture, conventions, and non-negotiables…

May 15, 20266 min read

#ai-ml#devops#productivity

Case StudyClaude Code Mastery

Claude Code in Production: Migrating a Legacy Java Monolith to Spring Boot 3 — A Real-World Case Study

At a 200-person SaaS company, the backend team faced a mandatory migration: their Java 8 Spring MVC monolith (comprising approximately 50,000 lines of code) needed to move to Spring Boot 3.2, Java 21, and Project Loom vi…

May 15, 202613 min read

#ai-ml#java#devops

Gemini CLI Mastery: Prompt Engineering for Long-Context Reasoning — Getting Consistent, Expert-Level Outputs

Mental Model > Gemini's 2-million token window is like a conference room with every relevant document in your organization laid out on the table. Without a structured agenda (your prompt), the meeting produces chaos. Wit…

May 15, 20269 min read

#ai-ml#productivity#prompt-engineering

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 read

#ai-ml#performance

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…

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…

Apr 26, 20267 min read

Claude Code Lesson 4: Security & Safety Guardrails

The "God Mode" Risk Assessment Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Claude Code is incredibly powerful. Because it can read your local files, execute bash…

#ai-ml#security

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

The Paradigm Shift in Context Management Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. In the world of LLMs, Context is Gold. Every token you send into the context…

#ai-ml#devops#performance

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

Mastering the Instrument Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. To a beginner, Claude Code is a sophisticated text box. To a Staff Engineer, it is a Surgica…

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

The End of RAG for Codebase Analysis Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. For the past two years, the standard way to analyze a large codebase with AI was…

Apr 26, 20266 min read

#ai-ml#system-design

Gemini CLI Mastery: Harnessing the 2-Million Token Context

The Long-Context Revolution Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. While other AI tools focus on "Surgical Retrieval" (RAG), the Gemini CLI ecosystem change…

Apr 26, 20267 min read

#ai-ml#aws

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

The "Visual" Software Engineer Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Most AI tools are "Blind." They only see the text you provide. However, software engin…

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

Introduction to Vector Databases Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. In the first lesson of this series, we learned that Embeddings turn complex data int…

#ai-ml#system-design

Advanced RAG Architecture: Beyond Simple Vector Search

Advanced RAG: The Production Pipeline Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Simple Retrieval-Augmented Generation (RAG) is easy to build but hard to make a…

Apr 20, 20267 min read

#ai-ml#java#performance

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

Running AI workloads in production is a different discipline from running web services. The hardware is expensive, the failure modes are opaque, and the cost curve compounds fast. After operating inference infrastructure…

Apr 8, 202615 min read

#ai-ml#aws#performance

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

Kubernetes can run AI inference workloads well, but only if you treat GPU capacity as a scarce production resource instead of "just another Deployment." GPU nodes are expensive, images are large, cold starts are painful,…

Apr 8, 202612 min read

#ai-ml#aws#devops

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

Evaluating LLMs is the unsolved problem at the center of every production AI system. You can't ship a prompt change, a retrieval improvement, or a model upgrade without knowing whether it actually got better — or just be…

Apr 8, 202611 min read

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

The Real Cost of Running LLMs Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. A 70B-parameter model in fp16 requires roughly 140 GB of GPU VRAM just for weights. Tha…

Apr 8, 202614 min read

#ai-ml#aws

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

LLM systems fail differently from normal backend systems. A normal service usually fails with a timeout, an exception, or a bad status code. An LLM application can return a fluent answer that is wrong, unsupported by ret…

Apr 8, 202611 min read

#ai-ml#devops#performance

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

MCP, the Model Context Protocol, is useful because it gives AI applications a standard way to connect to tools and context. For backend engineers, the interesting part is not the hype around agents. The interesting part…

Apr 8, 202614 min read

#ai-ml#security#system-design

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

Retrieval is the component that determines whether your RAG system produces accurate answers or confident hallucinations. The LLM is constrained by what you hand it. If the retrieved chunks are wrong, the generation is w…

Apr 8, 202616 min read

Deep DiveAI/ML Mastery

#ai-ml#databases

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

Production machine learning systems fail when the model sees different data in training than it sees in production. A feature store helps solve this problem by providing a consistent way to define, compute, store, and se…

Apr 7, 202615 min read

#ai-ml#aws#system-design

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

Modern AI development doesn't have to mean sending all your code to proprietary APIs. With Claude Code and open source models, you can enjoy the power of AI-assisted development while keeping your data local and private.…

Mar 22, 20269 min read

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

Fine-tuning is often the wrong choice. Most problems that engineers reach for fine-tuning to solve are better solved with better prompt engineering, few-shot examples, or RAG. But when you genuinely need fine-tuning — fo…

Mar 27, 202510 min read

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

A chatbot answers questions. An agent takes actions. The difference is tool use: the ability to call functions, search databases, execute code, and interact with external systems. When a model can look up real informatio…

Mar 23, 202510 min read

Vector Embeddings: The Foundation of Modern AI Applications

Every modern AI application — semantic search, RAG, recommendations, duplicate detection — is built on vector embeddings. An embedding converts text, images, or audio into a point in high-dimensional space where semantic…

Mar 11, 202512 min read

PlaybookAI Infrastructure Mastery

Prompt Engineering: Advanced Techniques for Production LLMs

Most prompt engineering tutorials stop at "be specific and provide context." That's necessary but not sufficient for production systems. This article covers the advanced techniques that separate demos from production-gra…

Feb 26, 202511 min read

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

Retrieval-Augmented Generation (RAG) is the most practical technique for making LLMs useful on your private data. Instead of hoping the model memorizes your documents during training (it doesn't), RAG retrieves relevant…

Feb 12, 202512 min read