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
Load Balancing
Distributing traffic reliably across servers
The CAP Theorem
Consistency, Availability, and Partition Tolerance in distributed systems
Reliability, Resilience & Observability Patterns
Design services that stay up when their dependencies don't — and prove it with the right signals
System Design Interview Framework & Capacity Estimation
Run any 45-minute system design interview with a repeatable structure and defensible numbers
Rate Limiting
Choose the right algorithm, enforce it across a fleet, and design a rate limiter that survives its own failures
Databases
Database internals, indexing, transactions, and SQL/NoSQL
Database Indexing
How indexes work and when to use them
SQL vs NoSQL & Data Modeling for Scale
Choose the right storage model from access patterns, and design schemas that survive growth
Database Replication & Sharding
Scale reads with replicas, scale writes with shards, and reason about the failure modes of both
Networking
TCP/IP, HTTP, DNS, CDNs, and network protocols
No subjects published yet.
Algorithms & Data Structures
Core algorithms, complexity analysis, and data structures
Two Pointers
Solve array and string problems in linear time by walking two indices instead of one
Linked Lists
Master pointer manipulation, in-place rewiring, and the classic linked-list interview patterns
Backtracking
Explore every possibility, undo every mistake: the systematic way to search decision trees
Heaps
Keep the most important element one pop away with binary heaps and priority queues
Greedy
Make the best move now and never look back: when local choices add up to a global optimum
Hash Maps and Sets
Trade space for speed: turn O(n) and O(n^2) scans into O(1) lookups
Fast and Slow Pointers
Detect cycles and find midpoints in O(1) space with two pointers
Dynamic Programming
Turn exponential brute force into polynomial time by remembering what you've already solved
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
Bias–Variance Trade-off & Cross-Validation
Diagnose under- and overfitting, and estimate out-of-sample performance without fooling yourself
Multimodal LLMs and Vision
How GPT-4V/4o-style vision-language models see images — patches, attention, grounding, and why they're not object detectors
Case Study: Generative Fill — Inpainting, Outpainting and Object Removal
Design Photoshop-style Generative Fill — the mask-conditioned cascade behind Adobe Firefly and Canva's editing tools
MLOps Model Lifecycle & Governance
The full data-to-retrain lifecycle, versioning discipline, approval gates, and audit-ready model cards
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
Data Science
Statistics, experimentation, regression, model evaluation, and the analytical toolkit of a data scientist
Probability Fundamentals
The probability toolkit every data scientist is expected to reason with fluently
Descriptive Statistics & Exploratory Data Analysis
Summarise, visualise, and interrogate a dataset before you model anything
Time Series Fundamentals
Decompose, test for stationarity, read ACF/PACF, build ARIMA and boosted forecasters, and evaluate them without fooling yourself
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
SQL for Data Analysis
Write correct, fast analytical SQL — joins, windows, cohorts, funnels and the traps that silently corrupt numbers
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 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 Code Fundamentals
How an agentic CLI coding tool actually works, turn by turn
MCP Servers and Subagent Orchestration
Connecting agents to external systems, and scaling one agent into many
Plan Mode and Autonomous Workflows
Controlling the autonomy dial in Claude Code: plan mode, self-pacing loops, and goal-driven runs
CLAUDE.md and Context Configuration
Configuring durable memory, permissions, and guardrails for Claude Code
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
Git Workflows with Claude Code
Commits, branches, PRs, reviews, and worktrees, driven by an agent instead of a diff editor
Transformers for AI Engineers
Attention, KV caching and what parameter count really buys you — the internals an AI Engineer interview expects you to reason about, not just name
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
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
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
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
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'
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
Responsive and Mobile Design
Responsive vs. adaptive vs. mobile-first, breakpoints and fluid grids, touch target sizing, iOS HIG vs. Material Design, gestures, and progressive disclosure under real screen constraints
Data Engineering
SQL depth, data modeling, warehouses and lakehouses, ETL/ELT, Airflow, dbt, Spark, and streaming pipelines
dbt & Analytics Engineering
The DAG, materializations, incremental models, and tests that make SQL transformations behave like software
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
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
Spark Architecture & Execution Model
Driver, executors, and cluster managers; RDDs vs DataFrames; lazy evaluation and the transformation/action split; the Catalyst optimizer and Adaptive Query Execution; partitions and parallelism; and knowing when Spark is overkill
Case Study: The SQL Interview Gauntlet
Seven SQL problems that recur across almost every data engineering interview loop — funnels, retention, sessionization, deduplication, running metrics, Nth-highest-per-group, and year-over-year comparison — each fully worked with step-by-step reasoning, not just a final query
Streaming Fundamentals & Kafka
Batch vs streaming tradeoffs, Kafka's partition/replication model, delivery semantics and how exactly-once is actually achieved, event time vs processing time, watermarking, windowing, and where Kafka Streams, Flink, and Spark Structured Streaming each fit
Data Quality, Testing & Observability
The dimensions of data quality, where to enforce checks across source/pipeline/warehouse, schema contracts and breaking-change detection, freshness and anomaly monitoring, dbt tests vs. data-observability tooling, and incident response for a broken pipeline
Wellbeing & Soft Skills
Psychology-backed guidance for anxiety, stress, communication, and the human side of work and life
No subjects published yet.