Clear beginner-friendly explanations and rigorous technical definitions for essential computer science, web development, cloud computing, and AI terms.
A set of protocols and definitions that allows two software programs to communicate.
An architectural interface providing standardized endpoints (REST, GraphQL, gRPC) allowing client programs to access server resources and execute operations without direct database or internal engine access.
// Fetching data from a REST APIconst res = await fetch("https://api.kwasacademy.dev/courses");const data = await res.json();The underlying protocol used by the World Wide Web to transmit data.
An application-layer protocol for distributed, collaborative, hypermedia information systems. HTTPS layers Transport Layer Security (TLS/SSL) encryption on top of TCP to protect payload confidentiality and integrity.
// HTTP Request HeadersGET /api/v1/user HTTP/1.1Host: api.kwasacademy.devAuthorization: Bearer <jwt_token>Accept: application/jsonThe tree-like data representation of the HTML document rendered by the browser.
A language-independent object-oriented representation of the web page where each HTML tag becomes a node object exposing properties and methods for programmatic manipulation.
// Manipulating the DOMconst heading = document.querySelector("h1");heading.textContent = "Master Modern Web Development";heading.classList.add("highlight");The four basic operations of persistent storage.
The foundational actions performed on database entities, mapping directly to SQL operations (INSERT, SELECT, UPDATE, DELETE) and standard HTTP methods (POST, GET, PUT/PATCH, DELETE).
-- CRUD in SQL:-- Create: INSERT INTO users (name) VALUES ('Alex');-- Read: SELECT * FROM users WHERE id = 1;-- Update: UPDATE users SET name = 'Kenneth' WHERE id = 1;-- Delete: DELETE FROM users WHERE id = 1;A compact, URL-safe means of representing claims securely between two parties.
An open standard (RFC 7519) consisting of Header, Payload, and Signature separated by dots. Used for stateless user authentication and authorization across distributed microservices.
// JWT Structure: header.payload.signatureconst token = jwt.sign( { userId: "usr_123", role: "admin" }, process.env.JWT_SECRET, { expiresIn: "2h" });A set of properties of database transactions intended to guarantee validity even in the event of errors.
A transaction standard in RDBMS ensuring: Atomicity (all or nothing), Consistency (preserves valid states), Isolation (concurrent operations do not interfere), and Durability (committed data survives crashes).
BEGIN TRANSACTION;UPDATE accounts SET balance = balance - 100 WHERE id = 1;UPDATE accounts SET balance = balance + 100 WHERE id = 2;COMMIT;A distributed system can deliver at most two of the three properties simultaneously.
In a network-partitioned distributed data store, the system must choose between guaranteeing Consistency (every read receives the most recent write or an error) vs Availability (every request receives a non-error response, without guaranteeing it contains the most recent write).
A function bundled together with references to its surrounding lexical state.
A feature in JavaScript where an inner function retains access to the outer enclosing function's variables even after the outer function has completed execution and returned.
function createMultiplier(multiplier) { return function(number) { return number * multiplier; // multiplier is closed over };}const double = createMultiplier(2);console.log(double(5)); // 10A platform for building, shipping, and running distributed applications in isolated containers.
An OS-level virtualization technology utilizing Linux namespaces and cgroups to package code, runtime, system tools, and libraries into portable container images that execute consistently across all environments.
FROM node:20-alpineWORKDIR /appCOPY package*.json ./RUN npm ci --only=productionCOPY . .CMD ["node", "dist/server.js"]Enhancing AI language models with external factual knowledge retrieved at query time.
An AI architecture that converts user queries into vector embeddings, performs semantic similarity search against a vector database (e.g. pgvector, Pinecone), and injects the retrieved contextual chunks into the LLM prompt window to produce accurate, hallucination-free answers.
// Retrieve relevant knowledge embeddingsconst queryEmbedding = await generateEmbedding(userQuestion);const contextChunks = await vectorDb.search(queryEmbedding, { limit: 3 }); const prompt = `Context: ${contextChunks.join("\n")}\n\nQuestion: ${userQuestion}`;const answer = await llm.complete(prompt);