In this blog, we will learn about OKF (Open Knowledge Format). We will also see why the knowledge about our data stays scattered across many places, how OKF writes that knowledge down as a folder of plain markdown files which any AI agent or any tool can read, and where it fits along with MCP and Agent Skills in the real world.

What is OKF (Open Knowledge Format)?

In this blog, we will learn about how Semantic Search works. We will also see why we need it, how it actually works step by step, and where it is used in real systems like search engines, AI assistants, and recommendations.

How does Semantic Search work?

In this blog, we will learn about how Cursor works. We will also see what Cursor is, why it is a code editor with an AI brain on top, how it indexes our codebase into embeddings and searches it by meaning, how Tab autocomplete, Chat, and Agent mode work, how Cursor applies changes through a diff, why it uses different models for different jobs, and how it tries to keep our code private.

How does Cursor work?

In this blog, we will learn about how context compaction works in Large Language Models. We will also see why long conversations overflow the context window, how summarization shrinks the older messages without losing the important points, and where compaction is used in real AI agents.

How does context compaction work?

In this blog, we will learn about how Claude Code works. We will also see what Claude Code is, why a normal AI chatbot is not enough, how the agent loop drives it, what tools give it its eyes and hands, how it searches a big project and verifies its own work, and how CLAUDE.md, permissions, plan mode, subagents, and hooks fit together.

How does Claude Code work?

In this blog, we will learn about how llama.cpp runs large language models on everyday hardware. We will also see what llama.cpp is, why it was created, how it shrinks huge models with quantization, how it loads them quickly, and how it shares work between the CPU and the GPU.

How does llama.cpp run LLMs on everyday hardware?

In this blog, we will learn about how Model Quantization works. We will also see why we need it, how it shrinks big AI models, and how it lets us run them on a laptop, a phone, or a single GPU.

How does Model Quantization work?

In this blog, we will learn about how Chain-of-Thought (CoT) Prompting works. We will also see why a model that jumps straight to the answer often gets it wrong, how making it reason step by step fixes this, the difference between zero-shot and few-shot CoT, and where this technique is truly useful.

How does Chain-of-Thought (CoT) Prompting work?

In this blog, we will learn about how Prompt Chaining works. We will also see why we need it, how it works step by step by passing the output of one prompt into the next, and where it is used in the real world to solve bigger tasks reliably.

How does Prompt Chaining work?

In this blog, we will learn about how PyTorch works. We will also see what a tensor is, how the computation graph and autograd work together to train a model, and why PyTorch uses the GPU to become one of the most popular tools in the real world.

How does PyTorch work?

In this blog, we will learn about how Semantic Caching works. We will also see why traditional caching falls short for AI apps, how Semantic Caching uses embeddings and similarity to reuse past answers, and how setting the right threshold makes it work in the real world.

How does Semantic Caching work?

In this blog, we will learn about how Hybrid Search works. We will also see why we need it, the two kinds of search it combines, how their results are merged together, and where it is used in real systems like RAG.

How does Hybrid Search work?

In this blog, we will learn about how HyDE works in RAG, which is the clever trick of searching with a fake answer. We will also see why searching with the plain question is weak, why a fake answer searches better, how HyDE works step by step with a worked example, and when to use it in the real world.

How does HyDE work in RAG?

In this blog, we will learn about Prefill vs Decode, the two phases of LLM inference, and how understanding them helps us optimize the speed of an LLM. We will also see how the prefill and decode phases work, how the KV cache connects them, how they differ and when to use which one based on our use case, and how we optimize each phase to make an LLM faster.

Prefill vs Decode: LLM Inference Optimization

In this blog, we will learn about how a GPU works for Deep Learning. We will also see why the GPU is perfect for deep learning, how they do so much math at the same time, and why companies like NVIDIA power almost all of modern AI.

How does a GPU work for Deep Learning?

In this blog, we will learn about how LangGraph works. We will also see why we need it, what graphs, state, nodes, and edges are, how tools work and who actually calls them, how memory and human-in-the-loop fit in through a complete example, and when to use it in the real world.

How does LangGraph work?

In this blog, we will learn about how LangChain works. We will also see why we need it, what chains, prompts, memory, and output parsers are, how retrieval and agents fit in, and how the full flow works together in the real world.

How does LangChain work?

In this blog, we will learn about how SGLang works. We will also see what problem it solves, how it makes serving large language models faster, and the clever ideas that make it special.

How does SGLang work?

In this blog, we will learn about Approximate Nearest Neighbor (ANN) Search, the idea that lets apps find "similar" things in a huge collection in the blink of an eye. It powers search engines, recommendation systems, face matching, and the memory behind modern AI chatbots. We will also see why the naive approach fails, how trees, hashing, clustering, and graphs make the search fast, and where ANN search is used in the real world.

How does Approximate Nearest Neighbor (ANN) search work?

In this blog, we will learn about how a Google TPU works. We will also see what a TPU is, why Google built it, how it is different from a CPU and a GPU, and how it makes machine learning fast.

How does a Google TPU work?

In this blog, we will learn about how image embeddings work. We will also see why we need image embeddings, how a computer turns a picture into numbers, how we measure the similarity between two of them, and where they are used in the real world.

How do Image Embeddings work?

In this blog, we will learn about how computer-use agents work.

How do Computer-Use Agents work?

In this blog, we are going to learn about Sakana Fugu, a family of AI models that work like a conductor for a team of other AI models.

Decoding Sakana Fugu Technical Report

In this blog, we will learn about Diffusion Language Models (DLMs), a new way to make models write text. They promise to generate words in a different way than the LLMs we use today, and that too much faster in many cases.

How do Diffusion Language Models (DLMs) work?

In this blog, we will learn about how an Embedding Cache works. We will also see what an embedding is, why an Embedding Cache saves us a lot of money and time, how the cache key is built, and where it is used in real systems like RAG and semantic search.

How does an Embedding Cache work?

In this blog, we will learn about how World Models work. We will also see why we need them, how they actually learn an internal picture of an environment, and where they are used in real systems like robotics, game-playing, and video generation.

How do World Models work?

In this blog, we will learn about how vLLM works. We will also see why we need it, how it manages memory so cleverly, and where it is used in the real world to serve large language models to many users at once.

How does vLLM work?

Techniques like KV Cache, Paged Attention, Flash Attention, Speculative Decoding, Continuous Batching, and Prompt Caching are what make LLMs fast and scalable in production.

LLM Inference Optimization

In this blog, we will learn about how Function Calling works in LLMs. We will see what it is, why we need it, the key insight behind it, and how it powers AI agents and assistants step by step.

How does Function Calling work in LLMs?

In this blog, we will learn about how GGUF works. We will also see what problem it solves, what is stored inside a GGUF file, how quantization makes big models fit on a normal laptop, and where it is used in real tools.

How does GGUF work?

In this blog, we will learn about how Knowledge Distillation works. We will also see why we need it, how a small model learns from a big model, and how this lets us run powerful AI on a phone, on an edge device, and at low cost.

How does Knowledge Distillation work?

In this blog, we will learn about how Token Streaming works. We will also see why we need it, how the server and the browser talk to each other to make it happen, and where it is used in real systems like ChatGPT and Claude.

How does Token Streaming work?

In this blog, we will learn about how Prompt Caching works. We will also see why we need it, how it actually works inside a large language model, and where it is used in real systems like AI assistants and agents.

How does Prompt Caching work?

In this blog, we will learn about how a Reranker works. We will also see where it sits in a search and RAG pipeline, why we need it, and how it makes our answers more accurate.

How does a Reranker work?

In this blog, we will learn about Joint Embedding Predictive Architecture (JEPA). This is one of the most exciting ideas in modern AI, and it comes from Yann LeCun, one of the most respected researchers in the field. Do not worry, we will learn about each part of it slowly, in very simple words. By the end, a complete beginner will understand every single word.

Joint Embedding Predictive Architecture (JEPA)

In this blog, we will learn about how a Vector Database works. This is one of the most important pieces behind modern AI search, recommendations, and tools like ChatGPT that answer questions from our own documents.

How does a Vector Database work?

In this blog, we will learn about Dropout in Neural Networks. We will understand what it is, the problem it solves, how it works step by step with a simple example, and where it is used.

Dropout in Neural Networks

In this blog, we will learn about Generative Adversarial Networks (GANs), one of the most fascinating ideas in Machine Learning that can create brand new images, faces, and art that never existed before.

Generative Adversarial Networks (GANs)

In this blog, we will learn about Diffusion Models. We will understand what they are, why we need them, how they work step by step, and how they generate amazing images like the ones we see in tools such as DALL-E, Stable Diffusion, and Midjourney.

Diffusion Models

In this blog, we will learn about Variational Autoencoders. We will understand what they are, why we need them, how they work step by step, and how they are able to generate brand new data like images that never existed before.

Variational Autoencoders

In this blog, we will learn about Continual Learning in LLMs. We will understand what it is, why we need it, the big problem of catastrophic forgetting, the approaches used to solve it, and where it is used in the real world.

Continual Learning in LLMs

In this blog, we will learn about Multi-Head Attention in Transformers. We will understand what it is, how it works step by step, and why it gives Transformers their power to understand language so well.

Multi-Head Attention in Transformers

In this blog, we will learn about Cross Attention in Transformers. We will understand what it is, how it works step by step, how it is different from Self Attention, and where it is used.

Cross Attention in Transformers

In this blog, we will learn about Self Attention in Transformers. We will understand what it is, how it works step by step, and why it is the heart of modern Large Language Models like BERT and GPT.

Self Attention in Transformers

In this blog, we will learn about the AI Agent Loop - what it is, why an AI Agent needs it, the think-act-observe cycle that powers it, how the loop knows when to stop, and the common ways the loop fails.

AI Agent Loop

In this blog, we will learn about AI Agent Observability. We will also see why we need it, how it is different from normal software monitoring, what we must observe inside an agent, the key concepts like traces and spans, the metrics we must track, the tools we can use, and the best practices to follow.

AI Agent Observability

In this blog, we will learn about how AI agents communicate. We will understand why agents need to communicate, the main ways they talk to each other, the message format, and the protocols that make agents work together to finish complex tasks.

How AI Agents Communicate

In this blog, we will learn about AI SubAgents. We will understand what they are, why we need them, how they work, and how to use them to build AI systems that can handle big and complex tasks.

AI SubAgents

In this blog, we will learn about LLM Evaluation. We will understand what it is, why we need it, the main types of evaluation, the automatic metrics and benchmarks we can use, human evaluation, LLM as a Judge, task-specific and safety evaluation, the common challenges, and the best practices to follow.

LLM Evaluation

In this blog, we will learn about AI Agent Evaluation. We will also see why it is different from LLM Evaluation, the types of evaluation we can do, the key metrics we must track, the methods we can use, and the best practices to follow.

AI Agent Evaluation

In this blog, we will learn about AI Orchestration. We will understand what it is, why we need it, how it is different from AI Agents, and the common patterns we use to coordinate multiple LLMs, tools, and steps together to build real AI products.

AI Orchestration

In this blog, we will learn about LLM as a Judge. We will also see how it works, why we need it, and how we can use it to evaluate the output of other LLMs.

LLM as a Judge

In this blog, we will learn about Contrastive Learning. We will also see how it works step-by-step and where it is used in the real world.

Contrastive Learning

In this blog, we will learn about Recursive Language Models (RLMs), a new way of using language models to handle very large inputs that do not fit in the model's context window.

Recursive Language Models (RLMs)

In this blog, we are going to learn about Group Relative Policy Optimization (GRPO). We will also see how GRPO works step-by-step and when to use it based on our use case.

Group Relative Policy Optimization (GRPO)

In this blog, we are going to learn about Direct Preference Optimization (DPO). We will also see how DPO works step-by-step and how it differs from RLHF (PPO).

Direct Preference Optimization (DPO)

In this blog, we are going to learn about Proximal Policy Optimization (PPO). We will also see how PPO works step-by-step and how it is used in training Large Language Models with RLHF.

Proximal Policy Optimization (PPO)

In this blog, we are going to learn about Batch Normalization vs Layer Normalization. We will also see how Batch Normalization and Layer Normalization differ from each other and when to use which one.

Batch Normalization vs Layer Normalization

In this blog, we will learn about Reinforcement Learning from Human Feedback (RLHF), the training technique that turns a raw pre-trained LLM into a helpful, honest, and safe assistant by teaching it from human preferences.

Reinforcement Learning from Human Feedback (RLHF)

In this blog, we will learn about Autoregressive Models, the family of models that generate one piece at a time by predicting the next step from the past.

Autoregressive Models

In this blog, we will learn about Large Reasoning Models (LRMs), how they are different from standard Large Language Models, how they think before they answer, how they are trained, and when we must use them.

Large Reasoning Models (LRMs)

In this blog, we will learn about Continuous Batching, a technique that lets LLM servers handle many more users at the same time by keeping the GPU busy at every single step of generation.

Continuous Batching in LLMs

In this blog, we will learn about Small Language Models (SLMs), what counts as small, why they matter, where they shine, and the trade-offs we must keep in mind.

Small Language Models (SLMs)

In this blog, we will learn about Multimodal AI, what it means, why it matters, how it works, and where we use it in the real world.

Multimodal AI

In this blog, we will learn about LLM Routing, why it matters, and how to send each user query to the right LLM based on cost, latency, and quality.

LLM Routing

In this blog, we will learn about Context Engineering - what it is, why it has become the most important skill for building reliable AI applications, how it differs from Prompt Engineering, the components that make up the context, common patterns like RAG, few-shot examples, tools, and memory, and the best practices and common mistakes to keep in mind.

Context Engineering

In this blog, we will learn about the Reflection Agent - what it is, how it is built, its anatomy, how it generates, critiques, and revises its own work, and how to handle its common failure modes.

Reflection Agent

In this blog, we will learn about Speculative Decoding - what it is, why LLM generation is slow without it, how a small draft model and a big target model work together to produce tokens faster, the rejection sampling math that guarantees no quality loss, real numbers showing the 2x to 3x speedup, where it is used in production, and the trade-offs to watch out for.

Speculative Decoding

In this blog, we will learn about GraphRAG and how it improves retrieval by using a knowledge graph along with vector search.

GraphRAG

In this blog, we will learn about the Plan-and-Execute Agent - what it is, its anatomy, how it plans and runs the steps, how it differs from a ReAct Agent, and how to handle its common failure modes.

Plan-and-Execute Agent

In this blog, we will learn about Agentic RAG - what it is, why standard RAG falls short, the agentic RAG loop, the three building blocks, the common patterns, when to use it, and the limitations to keep in mind.

Agentic RAG

In this blog, we will learn about the ReAct Agent - what it is, how it is built, its anatomy, how it thinks and acts, and how to handle its common failure modes.

ReAct Agent

In this blog, we will learn about Multi-Agent Systems - what they are, the three pillars that hold them together, the common agent roles, how agents communicate and coordinate, the trade-offs, and when to use them.

Multi-Agent Systems

In this blog, we will learn about AI Agent Memory - why agents need it, the memory stack, the four core operations (write, read, update, forget), how memory flows at runtime, and the common mistakes.

AI Agent Memory

In this blog, we will learn about the AI Agent - what it is, how it is different from a plain LLM, its five core parts, how it works end to end, the main types, and when to use one.

AI Agent Explained

In this blog, we will learn about RMSNorm, a faster and simpler alternative to Layer Normalization that powers most modern Large Language Models like Llama, Mistral, Gemma, Qwen, PaLM, and DeepSeek.

RMSNorm (Root Mean Square Layer Normalization)

In this blog, we will learn about DeepSeek-V4, the new family of open Mixture-of-Experts language models that natively supports a one-million-token context with dramatically lower inference cost.

Decoding DeepSeek-V4

In this blog, we will learn about LoRA - Low-Rank Adaptation of Large Language Models.

LoRA - Low-Rank Adaptation of LLMs

In this blog, we will learn about the math behind Rotary Position Embedding (RoPE) and why it is used in modern Large Language Models.

Math Behind RoPE (Rotary Position Embedding)

In this blog, we will learn about Grouped-Query Attention (GQA) and how it differs from Multi-Head Attention (MHA).

Grouped Query Attention

In this blog, we will learn about the math behind Cross-Entropy Loss with a step-by-step numeric example.

Math Behind Cross-Entropy Loss

In this blog, we will learn about the math behind gradient descent with a step-by-step numeric example.

Math Behind Gradient Descent

In this blog, we will learn about the Vision Transformer (ViT) by decoding how it splits an image into patches, turns those patches into tokens, and processes them with a transformer to classify the image.

Decoding Vision Transformer (ViT)

In this blog, we will learn about Feed-Forward Networks in LLMs - understanding what they are, how they work inside the Transformer architecture, why every Transformer layer needs one, and what role they play in making Large Language Models so powerful.

Feed-Forward Networks in LLMs

In this blog, we will learn about Flash Attention by decoding it piece by piece - understanding why standard attention is slow, what makes Flash Attention fast, how it uses GPU memory cleverly, and why it is used in almost every modern Large Language Model (LLM).

Decoding Flash Attention in LLMs

In this blog, we will learn about the Mixture of Experts (MoE) architecture - understanding what experts are, how the router picks them, why MoE makes large models faster and cheaper, and why it powers many of today''s most powerful Large Language Models (LLMs).

Mixture of Experts Explained

In this blog, we will learn about the Transformer architecture by decoding it piece by piece - understanding what each component does, how they work together, and why this architecture powers every modern Large Language Model (LLM)

Decoding Transformer Architecture

In this blog, we will learn about the math behind backpropagation in neural networks.

Math Behind Backpropagation

In this blog, we will learn about why we scale the dot product attention by √dₖ in the Transformer architecture with a step-by-step numeric example.

Math behind √dₖ Scaling Factor in Attention

In this blog, we will learn about the math behind Attention - Query(Q), Key(K), and Value(V) with a step-by-step numeric example.

Math behind Attention - Q, K, and V

In this blog, we will learn about Harness Engineering in AI.

Harness Engineering in AI

In this blog, we will learn about BPE (Byte Pair Encoding) - the tokenization algorithm used by most modern Large Language Models (LLMs) to break text into smaller pieces before processing it.

Byte Pair Encoding in LLMs

In this blog, we will learn about Paged Attention, a technique that solves the memory waste problem of KV Cache, allowing LLMs to serve many more users at the same time.

Paged Attention in LLMs

In this blog, we will learn about KV Cache - where K stands for Key and V stands for Value - and why it is used in Large Language Models (LLMs) to speed up text generation.

KV Cache in LLMs

In this blog, we will learn about the Causal Masking in Attention.

Causal Masking in Attention

In this blog, we will learn about Linear Regression vs Logistic Regression in Machine Learning.

Linear Regression vs Logistic Regression

In this blog, we will learn about Supervised vs Unsupervised Learning in Machine Learning.

Supervised vs Unsupervised Learning

In this blog, we will learn about the Android Push Notification Flow using FCM.

Android Push Notification Flow using FCM

Today, we are going to discuss everything about the Android System Design Interviews.

Android System Design Interviews

In this blog, we will learn about Write-Ahead Logging (WAL) and why it is used internally in databases.

Write-Ahead Logging (WAL)

In this blog, we will learn about the Composite Index in Database and why it offers better performance. We will also explore the impact of column order in a composite index.

All Blogs

Write-Ahead Logging (WAL)

Composite Index in Database

CoroutineContext in Kotlin

Parcelable vs Serializable

String vs StringBuffer vs StringBuilder

data class in Kotlin

Extension function in Kotlin

Database Normalization vs Denormalization

remember vs rememberSaveable