Browse by topic
Every category, every subject — one page for search engines and humans to find their way in.
System Design
Scalable distributed systems architecture and design patterns
System Design Basics
Core concepts every engineer should know
Consistent Hashing
Distributing data across nodes with minimal reshuffling
Concurrency vs Parallelism
Threads, processes, and the event loop — what actually runs at the same time
The CAP Theorem
Consistency, Availability, and Partition Tolerance in distributed systems
Load Balancing
Distributing traffic reliably across servers
Reliability, Resilience & Observability Patterns
Design services that stay up when their dependencies don't — and prove it with the right signals
Rate Limiting
Choose the right algorithm, enforce it across a fleet, and design a rate limiter that survives its own failures
System Design Interview Framework & Capacity Estimation
Run any 45-minute system design interview with a repeatable structure and defensible numbers
Databases
Database internals, indexing, transactions, and SQL/NoSQL
Database Indexing
How indexes work and when to use them
Database Replication & Sharding
Scale reads with replicas, scale writes with shards, and reason about the failure modes of both
SQL vs NoSQL & Data Modeling for Scale
Choose the right storage model from access patterns, and design schemas that survive growth
Networking
TCP/IP, HTTP, DNS, CDNs, and network protocols
No subjects published yet.
Algorithms & Data Structures
Core algorithms, complexity analysis, and data structures
No subjects published yet.
Machine Learning
ML algorithms, model training, evaluation, and production ML systems
Decision Trees
How tree-based models split data and make decisions
LightGBM
Gradient boosting internals and hyperparameter mastery
LLM Application System Design
Design production LLM systems — RAG, evaluation, guardrails, cost and latency — the way interviewers expect
ML System Design Interview Framework
A repeatable 7-step method to turn any vague ML prompt into a scored, end-to-end design in 45 minutes
Case Study: Video Recommendation (YouTube / Netflix-style Homepage)
A complete model interview answer for designing a homepage video recommender, from objective to serving
Ranking & Recommendation System Architecture
Design the retrieval → ranking → re-ranking funnel that powers feeds, search and recommendations
Model Serving & Deployment
Choose an inference mode, meet a latency budget, and roll a model out without breaking production
Clustering & Dimensionality Reduction
Find structure in unlabelled data with k-means, DBSCAN, GMMs, PCA, t-SNE and UMAP — and know when each one lies to you
Data Science
Statistics, experimentation, regression, model evaluation, and the analytical toolkit of a data scientist
Time Series Fundamentals
Decompose, test for stationarity, read ACF/PACF, build ARIMA and boosted forecasters, and evaluate them without fooling yourself
Descriptive Statistics & Exploratory Data Analysis
Summarise, visualise, and interrogate a dataset before you model anything
Probability Fundamentals
The probability toolkit every data scientist is expected to reason with fluently
Logistic Regression & Linear Classifiers
Turn a linear score into a calibrated probability, choose a threshold on purpose, and know when a linear boundary is enough
Hypothesis Testing & Statistical Inference
Choose the right test, compute and read a p-value correctly, size a study, and avoid the traps that produce false discoveries
Causal Inference Basics
Estimate what a change caused when you could not run the experiment
Linear Regression
Fit, interpret, diagnose and defend an OLS model like a working data scientist
A/B Testing & Online Experimentation
Design, size, run and read online experiments that product decisions can actually rest on
Language Learning
English proficiency, IELTS preparation, and academic writing skills
No subjects published yet.
Finance & Trading
Market mechanics, technical analysis, and quantitative stock screening
No subjects published yet.
AI Engineering
Building with AI coding agents — Claude Code, tool use, and agent orchestration
Claude Code Fundamentals
How an agentic CLI coding tool actually works, turn by turn
Git Workflows with Claude Code
Commits, branches, PRs, reviews, and worktrees, driven by an agent instead of a diff editor
Plan Mode and Autonomous Workflows
Controlling the autonomy dial in Claude Code: plan mode, self-pacing loops, and goal-driven runs
MCP Servers and Subagent Orchestration
Connecting agents to external systems, and scaling one agent into many
Memory Systems for LLM Applications
Short-term, long-term, episodic and working memory — what each one is for, what to persist, when to retrieve, and how memory fails
Claude Code Best Practices: Reliable, Cheap, High-Leverage Sessions
The operating habits that separate a session you babysit from a session you can walk away from
CLAUDE.md and Context Configuration
Configuring durable memory, permissions, and guardrails for Claude Code
LLM Observability and Evaluation
Tracing agent steps, cost and latency dashboards, offline evals vs online monitoring, LLM-as-judge, CI regression suites, and canarying prompt changes
UI/UX Design
Design process, research, interaction and visual design, usability, accessibility, and portfolio/interview skills for UI/UX roles
Case Study: Redesign a Checkout Flow
A full worked answer to the classic 'this flow is broken, fix it' whiteboard prompt: clarifying questions, hypothesis generation, prioritization under time pressure, a sketched solution, and how you'd validate it
User Research Methods
The Nielsen Norman research-methods framework, the named methods interviewers expect you to place on it, and how to defend a method choice instead of just naming one
Usability Testing and Evaluation
Moderated vs. unmoderated testing, heuristic evaluation, task success metrics, the SUS scoring formula, think-aloud facilitation, and knowing usability testing from A/B testing when interviewers push on 'how would you validate this'
UX vs UI and the Design Process
What actually separates UX from UI, the process frameworks interviewers expect you to name and adapt, and the mistakes that expose a memorized answer
Interaction Design Principles
The named laws and heuristics interviewers expect you to cite by name and apply to a screenshot — Nielsen's 10, Gestalt, Fitts's and Hick's Laws, affordances, and the error-handling hierarchy
Design Metrics and Product Thinking
How to measure whether design work is actually working, tie it to business outcomes senior stakeholders care about, and defend it without either hiding behind numbers or ignoring them
Accessibility and Inclusive Design
WCAG's four principles and conformance levels, the exact contrast and keyboard rules interviewers fact-check, how screen readers actually consume a page, and the inclusive-design lens that goes beyond compliance
Wireframing and Prototyping
Why fidelity is a deliberate choice rather than a finish line, the cost-of-change curve that justifies it, and how to reason about it live in a whiteboard interview
Data Engineering
SQL depth, data modeling, warehouses and lakehouses, ETL/ELT, Airflow, dbt, Spark, and streaming pipelines
SQL Mental Model & Query Execution Order
Logical query processing order, JOIN semantics and row-count reasoning, set operations, three-valued NULL logic, and tracing a query step by step
ETL vs ELT & Pipeline Design
Why cheap warehouse compute flipped transform-then-load into load-then-transform, and the idempotency, incremental-load, CDC, backfill, and retry semantics that separate a pipeline that survives production from one that quietly corrupts a table
dbt & Analytics Engineering
The DAG, materializations, incremental models, and tests that make SQL transformations behave like software
Airflow & Workflow Orchestration
DAGs, the scheduling model, sensors vs deferrable operators, retries and SLAs, dynamic task mapping, XComs, and idempotent task design from the orchestrator's point of view
Data Warehouses & Lakehouses
OLTP vs OLAP, columnar storage, MPP architecture, and the lakehouse's bet on open table formats — the systems layer every data engineering interview eventually asks you to reason about
Data Modeling: Dimensional & Normalized
Normalization for OLTP, Kimball star schemas for analytics, Slowly Changing Dimensions, fact table grain, the One Big Table debate, and when Data Vault beats both
Data Engineering in Production
CI/CD for pipelines, environment strategy, secrets, cost control in cloud warehouses, on-call, and how the whole stack composes in a real company
Case Study: Design a Streaming Event Pipeline
A full data-engineering interview answer for a clickstream analytics pipeline at scale: event volume math, Kafka producer/partition strategy, windowed stream aggregation, the lambda-vs-kappa decision, end-to-end exactly-once semantics, watermarking for late data, schema evolution across years of events, real-time serving plus lakehouse cost math, and monitoring the pipeline itself
Wellbeing & Soft Skills
Psychology-backed guidance for anxiety, stress, communication, and the human side of work and life
No subjects published yet.