Engineering Blog

Read it. Then step through it.

289 long-form guides across algorithms, system design, and AI engineering — every one backed by an interactive visualizer or deep dive so the ideas stick.

DSA & Algorithms

Pattern-first breakdowns of classic coding interview problems: the intuition, the walkthrough, the code, and the complexity — each paired with an interactive visualizer you can step through.

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From scaling fundamentals to full interview designs like YouTube and Uber: architectures, trade-offs, capacity math, and the questions interviewers actually probe on.

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How modern AI actually works: neural networks, transformers, fine-tuning, RAG, reinforcement learning, and agent engineering — explained from first principles with real math and code.

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DSA & Algorithms

Two Pointers & Sliding Window

3Sum: Sort First, Then Let Two Pointers Do the Work

The sorted two-pointer solution to 3Sum explained step by step — sorting, duplicate skipping, JavaScript code, a worked walkthrough, and complexity analysis.

7 min read

Add Two Numbers: Pattern, Walkthrough, and JavaScript Solution

Add Two Numbers explained with the core pattern, a worked trace, JavaScript, complexity analysis, pitfalls, and interview-ready reasoning.

5 min read

Adding Nodes to a Linked List: The Traverse-to-index-1 Pattern

How to insert a node at a given index in a linked list — the traverse-to-index-1 pattern, JavaScript code, a worked walkthrough, and complexity analysis.

7 min read

Alien Dictionary: Deriving Order with Topological Sort

Solve Alien Dictionary with topological sort and Kahn's algorithm: derive the alien alphabet from a sorted word list, with JavaScript code and a walkthrough.

7 min read

All Paths From Source to Target: Backtracking Through a DAG

Enumerate every path from node 0 to node n-1 in a DAG with DFS backtracking — intuition, JavaScript code, a full walkthrough, and complexity, step by step.

7 min read

Greedy Algorithm

Assign Cookies: Pattern, Walkthrough, and JavaScript Solution

Assign Cookies explained with the core pattern, a worked trace, JavaScript, complexity analysis, pitfalls, and interview-ready reasoning.

5 min read

System Design

Scaling: Vertical vs Horizontal, and the Path to a Million Users

How vertical and horizontal scaling actually work, when to reach for each, and the real numbers behind replicas, sharding, and back-pressure at scale.

9 min read

SQL vs NoSQL: How to Actually Choose a Database

A practical SQL vs NoSQL guide — ACID, schemas, sharding, query-first modeling, and the interview trade-offs that decide which database fits your workload.

9 min read

CAP Theorem: What You Actually Give Up During a Partition

A precise guide to the CAP theorem — what consistency, availability, and partition tolerance mean, why you only ever choose C or A, and how CP and AP systems behave.

10 min read

Caching: Strategies, Eviction, and the Failure Modes That Bite

How caching actually works in a real system — cache-aside vs write-through, LRU vs LFU eviction, thundering herd, and the stale-data bugs that bite in production.

11 min read

Load Balancing: How One Address Fans Out to a Hundred Servers

How load balancers spread traffic across servers — L4 vs L7, round-robin vs power-of-two-choices, health checks, sticky sessions, and global routing.

10 min read

CDN: How Content Delivery Networks Actually Work

How a CDN cuts latency and origin load — edge caching, pull vs push, anycast routing, cache headers, invalidation, and the trade-offs interviewers probe.

10 min read

AI Engineering

How LLMs Actually Work: The Next-Token Loop

How LLMs actually work — every model is a next-token loop of logits, temperature, softmax, top-k, top-p, and sampling, traced one token at a time.

8 min read

From Hand-Written Rules to LLMs: How AI Learned to Learn

How AI moved from hand-written rules to learning from data — spam filters, word embeddings, attention, and the next-word loop behind ChatGPT, step by step.

9 min read

Neural Networks from Scratch: How Machines Actually Learn

How neural networks learn from scratch — the neuron, non-linearity, activation functions, MSE loss, gradient descent, and backprop traced on a real XOR network.

10 min read

Tensors and PyTorch: The Data Structure That Runs Deep Learning

How tensors, matrix multiplication, broadcasting, and PyTorch autograd power deep learning — with shapes, the dot product, and gradients traced by hand.

10 min read

Transformers & Architectures

Transformers, Part 1: Tokenization, Embeddings & Positional Encoding

How transformers turn text into meaningful numbers: tokenization, Byte-Pair Encoding, embeddings, and sinusoidal positional encoding, with worked examples.

9 min read

Transformers & Architectures

Transformers, Part 2: Self-Attention & Multi-Head Attention

How self-attention works — softmax(QKᵀ/√dₖ)V computed on a real sentence, why we scale by √dₖ, and how multi-head attention splits d_model across heads.

9 min read