Archive · Nadeem Khan

Vector Compression and Quantisation: Fitting Billions of Vectors in Memory

A billion 768-dimensional float32 vectors take about 3 terabytes just for the raw numbers.

Vector Databases2026-08-22

HNSW buys speed and recall by keeping a graph, and every vector it touches, resident in RAM — IVF and IVF-PQ are what cluster-based systems reach for when that stops fitting.

Vector Databases2026-08-21

Indexing Deep Dive: HNSW, Under the Hood

How the multi-layer proximity graph is actually built and searched, and where the real trade-offs live.

Vector Databases2026-08-20

The Curse of Dimensionality: Why Exact Nearest-Neighbor Search Does Not Scale

As dimensionality increases, it becomes effectively impossible to tell the nearest point in a dataset from the farthest one.

Vector Databases2026-08-16

Inside the Encoder: Tokens, Attention, and How a Transformer Actually Works

Opening the sealed box: token embeddings, positional encoding, self-attention, multi-head, feedforward, and residuals.

Vector Databases2026-08-15

Distance Metrics and Similarity Search: Cosine, Euclidean, and Dot Product

Cosine, Euclidean, and dot product are not interchangeable dropdown options — pick the wrong one and every query still returns a confident, quietly wrong ranking.

Vector Databases2026-08-15

Scaling Contrastive Training: Batch Size, GPU Gathering, and Gradient Caching

Why bigger training batches make embedding models better, and how teams build batches too big for any single GPU to hold

Vector Databases2026-08-12

How Embedding Models Actually Learn: The Math Behind Contrastive Training

The InfoNCE formula, the pooling code, and the training loop behind every embedding model, worked through with real numbers.

Vector Databases2026-08-09

Embeddings 101: How Text, Images, and Audio Become Vectors

How embedding models learn to turn text, images, and audio into vectors, explained with everyday analogies and no jargon.

Vector Databases2026-08-09

What Is a Vector Database? Origins, Use Cases, and How It Differs from Relational and NoSQL Systems

Why nearest-neighbor search needed an entirely new kind of database, not just a new column type.

Vector Databases2026-08-02

LLM Serving Architectures: Batching, KV-Cache, Multi-Tenancy

Inside the batching, caching, and multi-tenancy decisions that determine your cost per token.

AI System Design2026-07-31

Orchestration & Memory: Context Management for Long-Running Agents

A system design guide to context windows, memory types, and multi-agent coordination for agents that run for hours or days.

AI System Design2026-07-30

Agentic Systems: Tool Use, Planning, Multi-Step Reasoning

A system design guide to how agents plan, call tools, and reason across multiple steps in production.

AI System Design2026-07-29

Fine-Tuning vs. RAG vs. Prompting: Choosing the Right Approach

A practical framework for deciding when to prompt, retrieve, or fine-tune, and how to combine all three.

AI System Design2026-07-27

Embeddings & Vector Databases: Architecture and Trade-offs

Inside the index: how embedding choices and ANN architecture decide your RAG system's speed, cost, and recall.

Vector Databases2026-07-25

Designing RAG Systems: Retrieval, Chunking, Re-ranking, Grounding

Chunking, hybrid retrieval, re-ranking, and grounding checks: designing RAG as a pipeline instead of a single embedding lookup

AI System Design2026-07-24

How AWS Lambda Runs 15 Trillion Invocations a Month: Firecracker, Snapshots, and Tiered Caches

Inside the microVM stack, snapshot restores, and three-tier caches that let one platform run trillions of tenant invocations.

System Design Case Studies2026-07-22

Pretraining vs. Post-Training: How a Text Predictor Becomes an Assistant

The two-stage training process that turns a raw next-token predictor into a model with a point of view.

J-Space Primer2026-07-20

Chain-of-Thought and Scratchpad Reasoning: The Model Thinking Out Loud

Why letting a model write out its reasoning improves accuracy, and why that visible channel isn't the whole story.

J-Space Primer2026-07-20

Attention Mechanism Basics: How Tokens Reach Across the Whole Context

How queries, keys, and values let any token pull information from anywhere earlier in a sequence, no matter how far back.

J-Space Primer2026-07-19

Procrastinate: Turning Postgres Into Your Task Queue

How PostgreSQL's LISTEN/NOTIFY and SKIP LOCKED quietly replace your message broker, and when they shouldn't.

Postgres Series2026-07-17

GPT-Live: OpenAI's Full-Duplex Voice Architecture Ends the Turn-Taking Era

Every voice assistant you have used, from Siri to the original ChatGPT Advanced Voice Mode, shares a hidden assumption: conversation…

AI Breakthroughs2026-07-16

Postgres Learns to Speak Graph: Inside SQL/PGQ and PostgreSQL 19

PostgreSQL 19 ships native graph pattern-matching with SQL/PGQ. Here's how CREATE PROPERTY GRAPH and GRAPH_TABLE work, and how they stack up

Postgres Series2026-07-15

How WhatsApp Moved 50 Billion Messages a Day With Just 32 Engineers

How 32 engineers ran a global messaging service on Erlang, Mnesia, and FreeBSD, and the ruthless simplicity that made it possible.

System Design Case Studies2026-07-12

Transformer Layers and the Residual Stream

The shared vector every transformer layer reads from and writes to, and why it's the concrete object interpretability tools actually probe.

J-Space Primer2026-07-08

Prompt Engineering as a System Design Discipline

Why the next evolution of prompt engineering looks like software architecture, not wordsmithing.

AI System Design2026-07-07

A Global Workspace in Language Models: Anthropic Finds a Silent "J-Space" Inside Claude

Anthropic's J-lens uncovers a compact internal workspace in Claude that mirrors global workspace theory of consciousness.

AI Breakthroughs2026-07-07

Why Framing Comes Before Architecture

Latency, cost, and quality are three dials that pull against each other. Good LLM design is choosing where each one sits, on purpose.

AI System Design2026-07-06

How YouTube Scaled MySQL to 2.49 Billion Users: The Vitess Story

A tour of Vitess, the layer that lets YouTube run 2.49 billion users on plain MySQL, plus the 9 patterns any team can steal from it.

System Design Case Studies2026-07-02

Why LLM-Era AI Systems Break Every Rule You Learned About ML in Production

The model is just one component. The system is everything around it: context, tools, orchestration, evals, and cost controls.

AI System Design2026-07-01

PostgreSQL Internals, CDC, Kafka, and Distributed Systems Engineering Series

PostgreSQL WAL, logical replication, Kafka, CDC pipelines, and distributed systems lessons from production-scale systems.

Postgres Series2026-05-18

Kafka-Based PostgreSQL → Salesforce Architecture (Part 2): How the System Actually Works

Reconciling streaming CDC, batch-oriented APIs, and lakehouse storage in a production-grade PostgreSQL to Salesforce architecture.

Postgres Series2026-04-24

PostgreSQL to Salesforce at Scale: Evolving a CDC Pipeline with Kafka

From table-based buffering to a distributed log architecture for multi-consumer, high-throughput systems

Postgres Series2026-04-10

Building a Reliable PostgreSQL → Salesforce CDC Pipeline: Lessons from WAL, Replication, and Failure Isolation

Designing a serverless data pipeline with Azure Functions while protecting PostgreSQL using deterministic control loops

Postgres Series2026-04-07

Protecting PostgreSQL Primaries from Replication Slot Failures

Exporting slot telemetry and building an automated WAL protection control loop for high-throughput CDC pipelines.

Postgres Series2026-03-19

PostgreSQL Logical Replication at Scale: Database-Side Guardrails for 60M+ Change Events

Configuring replication slots, WAL retention limits, failover strategy, and monitoring to protect the primary under sustained peak load.

Postgres Series2026-03-10

PostgreSQL Logical Replication Internals: restart_lsn vs confirmed_flush_lsn Explained

How LSN movement controls WAL retention and disk stability

Postgres Series2026-02-24

Understanding PostgreSQL Logical Replication: The Complete End-to-End Flow

Understand how PostgreSQL streams row-level changes using WAL, logical decoding, replication slots, and subscriber feedback.

Postgres Series2026-02-19

From Hot Partitions to Stable Throughput: Lessons From Kafka in Production

What partition skew, consumer lag, and downstream bottlenecks taught me about operating Kafka at scale

Backend & Infra2026-02-18

How to Choose the Right Messaging System in Distributed Systems

A practical guide to task queues, Log-Based Queues, and Pub/Sub

Backend & Infra2026-02-09

Partitioning vs. Sharding: A Practical Guide to Scaling Beyond One Machine

Confused by database scaling? Here is a plain-English breakdown of partitioning and sharding, and how they keep your app running fast.

Backend & Infra2026-02-09

How Kafka Really Works: Lessons from a 60M+ Events/Day Production Pipeline

Understanding Kafka's core architecture through real production experience at 60M+ events per day.

Backend & Infra2026-02-06

Beyond Accuracy: A Developer's Guide to Reliable LLM Evaluation

Accuracy is a vanity metric. To build production-grade AI, you need to master the tension between Precision, Recall, and the F1 Score

AI System Design2026-01-31

How to Stop Your NL2SQL Agents From Crashing in Production: The Worker-Pool Pattern

A guide to process boundary isolation, preventing system-wide failures in LLM-driven data applications

AI System Design2026-01-22

Beyond Schema: Why Your AI Can't Write Good SQL (and How to Fix It)

When we began building the NL2SQL platform, we thought, "If the AI knows the table schemas, it should be able to write good SQL, right?"…

AI System Design2026-01-12

Engineering Trust: A Defensive Architecture for NL2SQL Systems

The promise of Natural Language to SQL (NL2SQL) is transformative: democratising data access by enabling anyone to query complex databases…

AI System Design2026-01-12

Scaling Up RL: From Q-Tables to Deep Q-Networks (DQN)

Stop counting states and start approximating them. Here is how DQN scales Reinforcement Learning to the infinite.

LLM Architectures2026-01-12

From Q-Learning to LLMs: Mastering the Bedrock of Post-Training

Master the basics of Value-Based Learning before diving into complex policy-based methods like PPO and GRPO.

LLM Architectures2026-01-04

Scalable Inference with RDMA and Tiered KV Caching

How zero-copy networking and memory offloading resolve the GPU memory bottleneck

AI System Design2025-11-28

The Intuition Behind LoRA & QLoRA: Fine-Tuning LLMs Without Going Broke

Large Language Models (LLMs) have unlocked extraordinary capabilities, but all are trained on general-purpose data. You have to write long…

LLM Architectures2025-11-25

The 70B LLM Optimisation Playbook: From 57.5GB to 24.3GB Per GPU

A step-by-step guide to Weight, KV Cache, and Activation quantization (FP8 & 4-bit) to reclaim VRAM and unlock 2x performance.

AI System Design2025-11-21

How to Serve a 70B Model with a 128K Context on Just 8 H100s

Deploying a 70-billion-parameter model with a 128K context limit is a massive infrastructure challenge. A single H100 SXM GPU with 80GB of…

AI System Design2025-11-10

Decoding Real-Time LLM Inference: A Guide to the Latency vs. Throughput Bottleneck

A deep dive into the engineering bottlenecks (TTFT, batching, and the KV Cache) that define real-time LLM performance.

AI System Design2025-10-31

The Secret to the First Word: How LLMs Build Context with Prefill

A technical-but-simple guide to how LLMs process your prompt, build the KV Cache, and why it impacts response speed (TTFT)

LLM Architectures2025-10-29

How LLMs Understand Your Prompt: A Deep Dive into Prefill Attention

Go beyond Q, K, V. Learn how causal masks, parallel heads, and the KV cache work together to process your prompt instantly

LLM Architectures2025-10-27

From Prompt to Response: Unpacking the Magic of LLM Inference

Learn how Large Language Models (LLMs) work. This guide breaks down LLM inference into two simple phases: the prefill and the decode.

LLM Architectures2025-10-23

I Learned How Azure Functions Run My Code: A Deep Dive into the Python Worker and gRPC

How Azure Functions runs Python: Inside the out-of-process model, the Python Worker, and its gRPC connection.

Azure Functions Internals2025-06-30

I Learned How Azure Functions Run My Code: A Deep Dive into the Host and WebJobs SDK

A deep dive into the Azure Functions Host and the WebJobs SDK engine that powers your serverless code.

Azure Functions Internals2025-06-19

I Spent an Entire Weekend Demystifying the Azure Functions Runtime so that You Can Learn It in 5 Minutes.

Photo by Alexandre Debiève on Unsplash

Azure Functions Internals2025-06-16

Messy Logging Config? Here's the dictConfig Fix

Learn the intent behind dictConfig: using one clean dictionary to finally take control of your scattered Python logging configuration.

Python Logging2025-06-13

Python Logging Unveiled: What Happens When You Call .info()?

Ever wonder how Python logging decides what to show? We break down the journey of your log messages from creation to output.

Python Logging2025-06-11

How to Choose the Right Python Logging Setup: A Breakdown of the 4 Methods

Stop guessing which logging method to use. This guide breaks down the pros and cons of all four options to help you make the right decision

Python Logging2025-06-09

What Actually Happens When You Use logger.setLevel() in Python?

Demystifying logger.setLevel(): See How Python Instantly Manages Your Log Levels.

Python Logging2025-06-04

Get Your Python Logs Talking: A Clear Guide to Adapters & Filters

Add powerful context to your Python logs. A straightforward walkthrough of LoggerAdapters and Filters for truly better insights

Python Logging2025-06-03

Log Like a Pro: Understanding Python's Logging Essentials

Python logging deep dive: Grasp Loggers, Handlers, Filters, & Formatters for insightful, error-free code

Python Logging2025-05-29

From Data to Vectors: How Vector Databases Revolutionize Data Storage

Master vector databases and how they store text, images, and audio as vectors to enhance search and recommendation systems

Vector Databases2024-09-24

Discover the Magic Behind Your Searches: How Semantic and Vector Search Transform Your Online Experience

Discover how semantic and vector search technologies combine to deliver accurate, relevant, and efficient online search results.

AI System Design2024-09-20

Understanding Azure Synapse Link: Initial Sync, Incremental Changes, and In-Place Updates

In the world of data management, where efficiency and accuracy are everything, especially with the massive amounts of information stored in…

Azure & Cloud Fundamentals2024-09-16

The Mechanics of Query Expansion in RAG Systems: A Theoretical Exploration of PRF and LLM Techniques

Learn How Query Expansion, PRF & LLMs Power RAG Systems for Enhanced Search Accuracy and Relevant Results

AI System Design2024-09-16

Are You Combining Software Engineering and Data Engineering for Maximum Efficiency?

Optimize Data Engineering with Top Software Engineering Practices: Enhance Code Efficiency, Decoupling, Python SQL & More

Software Engineering2024-09-16

Rediscovering Query Expansion: The Classic Technique Powering Modern AI Searches

Boost Your Search with Query Expansion, PRF & LLMs: Enhance Accuracy Using Classic Techniques and Modern AI Strategies

AI System Design2024-09-16

Transitioning from Export to Data Lake to Azure Synapse Link: What You Need to Know?

As businesses increasingly rely on data-driven insights, choosing the right data storage and processing solutions is crucial. For those…

Azure & Cloud Fundamentals2024-09-08

Optimizing Data Synchronization for Downstream Systems in Azure Synapse Link

Keeping your source and downstream systems in sync is key to maintaining data accuracy and smooth operations. When using Azure Synapse…

Azure & Cloud Fundamentals2024-09-07

How Switching to Azure Synapse Link Delivers Cost Savings and Enhanced Performance

In this blog, we're diving into how switching to Azure Synapse Link can really save you money and boost your data performance. We'll walk…

Azure & Cloud Fundamentals2024-09-07

Demystifying D365 F&O Data Export: A Guide to Microsoft's Data Export Solutions

Over the past few years, Microsoft has rolled out three new ways to export data from D365 F&O. With one of these already set for…

Azure & Cloud Fundamentals2024-09-07

Managed Identities Explained

Managed Identities allow easy management of credentials lifecycle; access to any Azure Resource, API and SaaS that supports Azure AD Auth.

Azure & Cloud Fundamentals2024-08-18

Choose the proper subscription and management group strategy

In my previous blog, we learned about, how various component creates logical or geographical boundaries. Today we will be learning about…

Azure & Cloud Fundamentals2024-08-18

What is Azure Data Lake?

"Deep Dive into Azure Data Lake: Unleashing the Power of Big Data Storage and Analytics"

Azure & Cloud Fundamentals2023-08-30

What is a Data Lake?

Diving into Data Lakes: A Beginner's Guide to Understanding the Basics

Azure & Cloud Fundamentals2023-08-27

What is Data Lakehouse?

Exploring the Benefits of Data Lakehouse Architecture: A Scalable Solution for Storing and Analyzing Structured and Unstructured Data

Azure & Cloud Fundamentals2023-03-16

Azure AD Architecture Explained

Learn about the architecture of Azure Active Directory , and see how various design patterns are used to design Azure AD

Azure & Cloud Fundamentals2022-03-09

Azure Active Directory(Azure AD) Explained

Learn about Azure Active Directory, and how it helps protecting the user's identity and applications from security threats.

Azure & Cloud Fundamentals2022-03-09

Best Practices in Spring Boot Project Structure

Learn about different layers of Microservice, how to create request body, DTO, and models, best practices, and versioning in spring…

Java & Spring Boot2021-08-09

Consistency in Database

Learn data consistency in the databases, and when to use which type of consistency. Also, learn about different types of transactions.

Backend & Infra2021-07-20

Dependency Injection Explained

Learn about dependency, different ways of providing a dependency to a class. and , how does spring boot injection dependency.

Java & Spring Boot2021-07-12

How does Spring Boot Manage Dependency?

Learn about the management of dependencies; dependency scope; dependency versioning in Spring Boot. Also, learn about different classpath.

Java & Spring Boot2021-07-05

Know your pom.xml

Learn about different components of pom and how each part of pom plays a crucial role in better software development.

Java & Spring Boot2021-06-28

Do I need to scale my features?

In this blog, we will learn when we should scale the features. I usually divide Data Science projects into different pipelines: Exploratory…

Data Science2021-06-25

Multi-Module Spring Boot Project with Azure

This blog, we will learn about spring boot multi-nodule project This projects enables easy integration with azure.

Java & Spring Boot2021-02-01

Microsoft Azure Structure Explained

In my previous blog we discussed about Cloud Computing, and its type. From this blog, we will start diving deep in Microsoft Azure. We…

Azure & Cloud Fundamentals2020-08-12

OSI Model Demystified

In this blog, we have taken an example of Netflix to unveil the secret of the OSI model. Communication is explained as in humans are…

Backend & Infra2020-07-28

Is your Application Cloud-Ready

This blogs helps in understanding the why and how of cloud native apps.

Azure & Cloud Fundamentals2020-07-28

Kubernetes Architecture Demystified

In this blog, I have unveiled the underhood working of Kubernetes, so that developers can get a better understanding of Kubernetes.

Backend & Infra2020-07-28

Singleton Pattern Made Easy

In this blog, I have explained when, why and how of singleton pattern. I have also tired to relate singleton pattern with real life…

Software Engineering2020-06-09

Cloud Computing: Making Life easier for companies

Before diving into Microsoft Azure, we should familiarize ourselves with basics of cloud computing, this will help us building a strong…

Azure & Cloud Fundamentals2020-05-28

How to manage IoT devices at scale

In this blog, I have tried to simplified the explaination of Azure Device Provisioning Service

Azure & Cloud Fundamentals2020-05-13

Is Abstraction a solution for most of your complex problem

In this blog, I have focused on how abstraction is solved some complex problem, and how it can solve some complex issue of your company.

Software Engineering2020-05-05

What's new with Azure Backup

Microsoft is heavily investing in all possible security aspects. Recently they have launched a security update for Recovery Service Vault.

Azure & Cloud Fundamentals2020-02-08

All posts