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#ai-ml

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58 articles

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

Deep DiveAI Infrastructure Mastery

#ai-ml#performance

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

#ai-ml

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…

Apr 26, 20267 min read

Deep DiveClaude Code Mastery

#ai-ml

AI/MLAdvanced

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…

Apr 26, 20265 min read

Deep DiveClaude Code Mastery

#ai-ml#security

AI/MLIntermediate

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…

Apr 26, 20265 min read

Deep DiveClaude Code Mastery

#ai-ml#devops#performance

AI/MLIntermediate

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…

Apr 26, 20265 min read

Deep DiveClaude Code Mastery

#ai-ml

DSAHard

DSA Masterclass: Dijkstra’s vs. Bellman-Ford (Shortest Path Algorithms)

Introduction to Shortest Path Algorithms Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. Finding the shortest path between two points is one of the most practical application…

Apr 26, 20266 min read

Deep DiveDSA Interview Mastery

#ai-ml#dsa#java

DSAHard

System Design Module 8: CAP Theorem & Consistency Models

What is the CAP Theorem? Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. The CAP Theorem states that in the event of a Network Partition, a distributed system can only provid…

Apr 26, 20266 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#dsa#system-design

AI/MLAdvanced

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

Deep DiveGemini CLI Mastery

#ai-ml#system-design

AI/MLIntermediate

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

Deep DiveGemini CLI Mastery

#ai-ml#aws

AI/MLAdvanced

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…

Apr 26, 20265 min read

Deep DiveGemini CLI Mastery

#ai-ml

System DesignAdvanced

Designing for 99.999% Availability: The Five Nines Blueprint

High Availability & Fault Tolerance Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. In high-scale systems, failure is a certainty. Designing for High Availability…

Apr 26, 20268 min read

PlaybookSystem Design Interview Mastery

#ai-ml#system-design

JavaIntermediate

LLD Lesson: The Chain of Responsibility Pattern

1. The Problem: The Large Validation Method Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Imagine a login request that needs to pass through: 1. Auth Check (Is the…

Apr 26, 20267 min read

Deep DiveLLD Interview Mastery

#ai-ml#java#system-design

AI/MLAdvanced

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…

Apr 26, 20265 min read

Deep DiveAI Infrastructure Mastery

#ai-ml#system-design

DatabasesAdvanced

Database Sharding Part 3: The Shard Key Blueprint

Once you have accepted the architectural complexity of sharding, you are faced with the single most critical decision in distributed database design: Choosing the Shard Key. 1. The Cardinality Trap Cardinality is the num…

Apr 20, 20267 min read

PlaybookSystem Design Interview Mastery

#ai-ml#databases#system-design

JavaIntermediate

Docker for Java Developers: A Production Guide to Containerization

Docker for Java Developers: Production Guide Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. A common mistake in Java containerization is copying a fat JAR into a si…

Apr 20, 20267 min read

Deep DiveBackend Systems Mastery

#ai-ml#devops#java

AWSAdvanced

S3 Express One Zone: When to Use it for Stateful Workloads

S3 Express One Zone Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Amazon S3 Express One Zone stores data in a single AZ, reducing network hops and latency. It's no…

Apr 20, 20265 min read

Deep DiveBackend Systems Mastery

#ai-ml#aws#data-engineering

MessagingAdvanced

Kafka Internals: Zero-Copy, Log Storage, and High Throughput

Kafka Internals: The Secret to 10M+ Messages/Sec Mental Model > An immutable, high-speed transaction log used to decouple producers from consumers. Apache Kafka is often described as a distributed streaming platform, but…

Apr 20, 20267 min read

Deep DiveMessaging & Event-Driven Mastery

#ai-ml#java#messaging

MessagingAdvanced

Kafka Internals: The Storage Layer, Zero-Copy, and High Throughput

Kafka Internals: The Quest for 10M msg/sec Mental Model > An immutable, high-speed transaction log used to decouple producers from consumers. Apache Kafka is often described as a distributed streaming platform, but its h…

Apr 20, 20267 min read

Deep DiveMessaging & Event-Driven Mastery

#ai-ml#java#messaging

DatabasesAdvanced

Inside the Linux Page Cache: The Invisible Database Accelerator

Inside the Linux Page Cache Mental Model > A low-latency memory buffer protecting your primary data source from read spikes. When your database (PostgreSQL, MongoDB, etc.) reads a row from disk, it doesn't just read the…

Apr 20, 20268 min read

Deep DiveBackend Systems Mastery

#ai-ml#databases#java

DatabasesAdvanced

LSM-Tree Compaction Strategies: Leveled vs. Size-Tiered

LSM-Tree Compaction Strategies Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. LSM-tree based databases (Cassandra, RocksDB, ScyllaDB) don't update data in place.…

Apr 20, 20267 min read

Deep DiveBackend Systems Mastery

#ai-ml#databases#dsa

DatabasesAdvanced

PostgreSQL Vacuum Tuning: Internals of Bloat Control

PostgreSQL Vacuum Tuning: Mastering Bloat Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. Postgres is an MVCC (Multi-Version Concurrency Control) database. When y…

Apr 20, 20266 min read

Deep DiveDatabase Internals

#ai-ml#databases#java

MessagingAdvanced

RabbitMQ Quorum Queues: Modern High Availability with Raft

RabbitMQ Quorum Queues: A New Era of Reliability Mental Model > An immutable, high-speed transaction log used to decouple producers from consumers. For years, RabbitMQ relied on Classic Mirrored Queues for high availabil…

Apr 20, 20267 min read

Deep DiveMessaging & Event-Driven Mastery

#ai-ml#messaging#system-design

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#java#performance

AWSAdvanced

S3 Express One Zone: When to use it

S3 Express One Zone Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. For stateful data processing (like Spark shuffle files), standard S3 latency is too high. S3 Expr…

Apr 20, 20265 min read

Deep DiveBackend Systems Mastery

#ai-ml#aws#java

DatabasesAdvanced

B-Trees vs. LSM-Trees: The Battle of Storage Engine Internals

B-Trees vs. LSM-Trees: Choosing Your Storage Engine Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. At the heart of every database is a storage engine that decide…

Apr 20, 20268 min read

Deep DiveDatabase Engineering Mastery

#ai-ml#databases#dsa

System DesignAdvanced

System Design: Designing Airbnb (Hotel/Home Booking)

Designing a platform like Airbnb or Booking.com involves two distinct technical challenges: Search (helping users find the perfect place) and Concurrency (ensuring that once found, the place can be booked without overlap…

Apr 20, 20268 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#java#system-design

System DesignAdvanced

System Design: Designing a Content Moderation System (Meta/TikTok Scale)

System Design: Designing a Content Moderation System Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. With billions of users uploading content every minute, platfo…

Apr 20, 20268 min read

Case StudySystem Design Interview Mastery

#ai-ml#system-design

System DesignAdvanced

System Design: Designing a Distributed BLOB Store (like S3/GCS)

Storage Systems (Blob, File, Object storage) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. Distributed storage systems like S3, GCS, or Azure Blob Storage are t…

Apr 20, 20267 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#system-design

System DesignAdvanced

System Design: Designing a Distributed File System (HDFS/GCS Style)

System Design: Designing a Distributed File System (HDFS) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. A Distributed File System (like HDFS or GFS) is designed…

Apr 20, 20267 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#data-engineering#system-design

System DesignAdvanced

System Design: Designing a Local-First Key-Value Store (LevelDB/RocksDB)

System Design: Designing a Local-First Key-Value Store Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. While distributed systems get all the glory, the foundation…

Apr 20, 20267 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#dsa#java

System DesignAdvanced

System Design: Designing Google Drive (Distributed File Storage)

Case Study: Design a File Storage System (Google Drive) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. Designing a file storage system like Google Drive or Dropb…

Apr 20, 20264 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#aws#system-design

System DesignAdvanced

System Design: Designing an Object Store (Amazon S3 Internals)

System Design: Designing an Object Store (Amazon S3) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. An Object Store is a distributed storage system designed to s…

Apr 20, 20268 min read

Case StudySystem Design Interview Mastery

#ai-ml#aws#system-design

System DesignAdvanced

System Design: Designing a Real-time Recommendation Engine (TikTok / Netflix)

Case Study: Design a News Feed Ranking System Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. How does TikTok or Facebook decide what you see next? It's a combina…

Apr 20, 20264 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#system-design

DatabasesAdvanced

Vector Search in NoSQL: Redis and MongoDB as Vector Databases

Vector Search in NoSQL: The AI Evolution Mental Model > A low-latency memory buffer protecting your primary data source from read spikes. With the rise of Large Language Models (LLMs), Vector Search has become a critical…

Apr 20, 20268 min read

Deep DiveDatabase Engineering Mastery

#ai-ml#databases

DSAMedium

Bellman-Ford Algorithm in Java: Shortest Paths with Negative Weights

Introduction to Bellman-Ford Algorithm Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. Bellman-Ford Algorithm is a single-source shortest path algorithm that works on weighte…

Apr 19, 202612 min read

Deep DiveDSA Interview Mastery

#ai-ml#dsa#java

DSAHard

N-Queens Problem in Java: Solving Constraints with Backtracking

Introduction to the N-Queens Problem Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. The N-Queens puzzle is the problem of placing N non-attacking queens on an N×N chessboard…

Apr 19, 202612 min read

Deep DiveMANG Interview Problem Bank

#ai-ml#dsa#java

DSAMedium

Sudoku Solver in Java: Efficient Backtracking and Pruning

Introduction to the Sudoku Solver Problem Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. The Sudoku puzzle is a classic number-placement puzzle. The objective is to fill a 9…

Apr 19, 202611 min read

Deep DiveDSA Interview Mastery

#ai-ml#dsa#java

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#aws#performance

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#aws#devops

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#aws

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#devops#performance

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml#security#system-design

AI/MLAdvanced

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 DesignAdvanced

System Design: Building a Data Export Platform

Data export is one of those features that sounds small until it takes down production. A product manager asks for "Export to CSV." The first implementation runs a SQL query in the web request, builds a file in memory, an…

Apr 8, 202612 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#system-design

System DesignAdvanced

System Design: Building an Email Delivery Platform

Sending one email is easy. Sending millions of emails reliably, legally, and without damaging your domain reputation is a system design problem. Email delivery touches product workflows, background jobs, third-party prov…

Apr 8, 202611 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#system-design

System DesignAdvanced

System Design: Building a File Upload Platform

File upload looks easy until production traffic arrives. Users upload huge files, mobile networks drop halfway through, browsers retry, malicious files appear, thumbnails fail, metadata gets out of sync, signed URLs expi…

Apr 8, 202611 min read

Deep DiveSystem Design Interview Mastery

#ai-ml#aws#security

AI/MLAdvanced

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 that problem. It provides a consistent way to define, compute, store, and se…

Apr 7, 20265 min read

Deep DiveAI Infrastructure Mastery

#ai-ml#aws#system-design

AI/MLIntermediate

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

Deep DiveClaude Code Mastery

#ai-ml

AWSAdvanced

Kubernetes in Production: Patterns Every Backend Engineer Must Know

Running a container in Kubernetes and running a production workload in Kubernetes are different disciplines. The gap between kubectl apply -f deployment.yaml and a service that survives node failures, deployment rollouts…

Jun 8, 20257 min read

PlaybookStaff Engineer Playbooks

#ai-ml#aws#devops

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml

AWSAdvanced

AWS ECS vs EKS: Choosing the Right Container Orchestration

Every team containerizing their workloads on AWS faces the same choice: ECS or EKS? ECS is simpler and tightly integrated with AWS. EKS is Kubernetes — portable, powerful, and complex. Getting this choice wrong means yea…

Mar 25, 202510 min read

ComparisonStaff Engineer Playbooks

#ai-ml#aws#devops

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml

AI/MLIntermediate

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

PlaybookAI Infrastructure Mastery

#ai-ml

AI/MLAdvanced

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

Deep DiveAI Infrastructure Mastery

#ai-ml

AWSAdvanced

AWS Architecture Patterns for High-Traffic Applications

Architecting on AWS is not about using every service in the catalog — it's about choosing the right services for your scale, stitching them together correctly, and understanding the failure modes of each. This article wa…

Jan 19, 202515 min read

Deep DiveStaff Engineer Playbooks

#ai-ml#aws#system-design