Turn technical knowledge into working software. Build AI agents, design cloud systems and ship full-stack projects with guided practice that sharpens your skills for interviews and everyday engineering.
From learning to certified, on masterclassbuddy.com.
Follow step-by-step paths for Agentic AI, AWS, DevOps, Cloud Architecture, AI Automation, n8n, Claude Code and System Design, practise 100+ questions each with mock exams, and walk into your certification ready.
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Claude Code terminal agent ships for multi-file software engineering
Anthropic launched Claude Code, an autonomous CLI agent that operates directly inside developer repositories to plan, edit files, execute test suites, and resolve Git diffs.
Key facts
Runs locally in your terminal with direct access to file trees and command-line tools.
Plans architectural changes across dozens of files before applying code edits.
Integrates prompt caching and compact token context to reduce execution costs by 80%.
Why it matters: Engineers spend less time on repetitive refactoring and boilerplate, focusing on core system architecture.
Learn: Agent loop — A model that evaluates outputs, executes tools iteratively, and corrects its own errors until a task succeeds.
OOpenAI NewsOpenAI expands reasoning models with advanced function-calling pipelines
OpenAI introduced updated reasoning capabilities with low-latency structured outputs, automated tool verification, and sandboxed execution environments.
Reasoning models now support strict JSON schemas with 100% schema compliance guarantee.
Chained function calls execute in parallel to minimize latency for complex analytical workflows.
Developers can reliably build production automation without brittle custom regex parsers or retry loops.
Learn: Structured outputs — Constraining model generation to guaranteed JSON schema types so APIs can consume answers without parsing errors.
GGoogle AI BlogGoogle Gemini 2.5 expands multimodal context and real-time audio streaming
Google AI rolled out Gemini 2.5 with native multimodal streaming, bidirectional audio processing, and expanded 2-million token context windows.
Processes full multi-hour video streams and complete application codebases in a single context window.
Native voice-to-voice communication achieves sub-250ms latency for conversational agents.
Enables interactive pair programming and instantaneous voice assistance over complex documentation.
Learn: Context window — The maximum amount of prompt text and media a model can inspect at one time without losing earlier conversation details.
MMicrosoft AI BlogMicrosoft Azure AI Studio adds automated evaluation & guardrails
Microsoft launched enterprise safety evaluators and automated agent benchmarking tools within Azure AI Studio for production LLM deployments.
Automated metrics score responses for factual groundedness, relevance, and safety compliance.
Real-time interceptors block prompt injections and toxic inputs before reaching backend models.
Enterprises can audit and safeguard mission-critical generative AI applications against regressions.
Learn: Model evaluations (Evals) — Automated test suites comparing LLM outputs against ground-truth benchmarks to detect regressions and hallucinations.
Prompt caching reuses static system instructions and documentation across calls, dropping both time-to-first-token and pricing.
Prompt caching: Storing token KV-pairs on server memory so repetitive prompts do not re-compute from scratch.
OMistral Blog · Mistral releases high-efficiency open multimodal models
Mistral AI unveiled open-weight vision models capable of reading dense engineering diagrams and architectural schematics.
Open weights: AI models whose neural network parameters are published publicly for local hosting and inspection.
Try this week
1
Run the same prompt on two models
Ask two current models the same coding question and compare accuracy, speed and token cost.
2
Cache a long system prompt
Send one long system prompt twice with prompt caching on, and note how latency and cost change.
3
Read one release note end to end
Pick this week's biggest launch above and list what changed for developers: APIs, limits and pricing.
Quick check
Why does a longer prompt cost more?
Models bill per token read and written, so every extra page of context adds input tokens to each call.
When should you pick a small, fast model?
For high-volume, simple tasks such as classification or extraction, where it passes your tests at lower cost and latency.
What does prompt caching save?
Repeated identical prefixes, such as a long system prompt, are processed once and reused, cutting cost and latency.
Why can a model give two different answers to the same question?
Sampling picks among likely next tokens; lower the temperature and fix the format to make answers more consistent.
Is a higher benchmark score always better for your app?
No. Benchmarks test general skills; only an evaluation on your own tasks shows which model fits your product.
What is a reasoning model good at?
Multi-step problems in maths, code and planning, at the cost of more tokens and slower answers.
How do you keep API keys safe in an AI app?
Keep them on the server in environment variables or a secrets manager; never ship them to the browser.
What should you check in a model release note?
New capabilities, price per token, context size, rate limits and anything deprecated that your code still uses.
Loading this week's stories…
Plan your route
Say where you want to get to. AI turns it into three stages, foundation, practice and a project, using the notes, quizzes and interview questions already in MasterClass.
Example: data engineer in 4 weeks
Stage 1: FoundationWeeks 1–2
SQL and Python for data
SQL
Python
Write joins and window functions on a sample dataset
Load a CSV with Python and clean it
By the endYou can query, join and clean real data
Stage 2: PracticeWeek 3
Pipelines and scheduling
PostgreSQL
Build a daily job that loads new rows
Add checks that stop bad data
By the endA pipeline that runs on its own and fails loudly
Stage 3: ProjectWeek 4
A small analytics warehouse
AWS
Model a fact table and two dimensions
Practice ten data engineering interview questions
By the endA project that shows data from source to dashboard
Subjective and objective questions for every technology on this platform, from LangChain and Next.js to Git and AWS, each rated by how often it comes up. Open one to see a model answer, or the right option and why.
01/03
JavaScript puzzles·Topic 1 of 3
Interview prep01
JavaScript puzzles
“What does this print?”: hoisting, coercion, closures and the event loop.
Subjective
–
Objective
–
Total
–
★★★★★most asked · ★★★★★deep cut
Loading questions…
Interview prep02
NestJS
Modules, providers, DI, guards, pipes, interceptors and microservices.
Subjective
–
Objective
–
Total
–
★★★★★most asked · ★★★★★deep cut
Loading questions…
Interview prep03
Jest
Matchers, mocks, spies, timers, snapshots and coverage.
Today's learning across AI, agents & the modern stack
A fresh set every day across 33 technologies: agentic AI and its terms, LangChain, LangGraph, LangSmith, LangFlow, Next.js, React, JavaScript, TypeScript, Node.js, Express, NestJS, GraphQL, the TanStack libraries, Redux, Zustand, testing with Jest, Vitest, RTL, Playwright and Cypress, AWS, Git, GitHub Actions, system design, Redis and more, each card linked to its official source.
01/01
Agentic AI01
Pattern
Workflows vs. Agents
Definition
Start with the simplest pattern that works; use an autonomous agent only when the steps can't be predicted.
Each one, explained
A
Workflows
Predefined code paths
Models and tools orchestrated through steps you write in code.
Use when The steps are known ahead of time.
B
Agents
Model-directed process
The model chooses its own steps and tools, looping on feedback from the environment.
Use when Open-ended problems where the steps can't be predicted.
Use cases and industry problems are in the full card on your dashboard.
Source: Anthropic: Building effective agents
Agentic AI02
Practice
Context engineering
Definition
The successor to prompt engineering: deciding which tokens an agent sees at every step of a long task.
Core features & key points
Context is finite: more tokens can mean worse recall, not better answers.
Load data just in time with tools instead of stuffing it in up front.
Use cases and industry problems are in the full card on your dashboard.
Source: Anthropic: Effective context engineering
Agentic AI03
Tooling
Coding agents in the terminal
Definition
Agents such as Claude Code read your repo, edit files, run commands and tests, then show you the diff.
Core features & key points
Permission modes decide what runs without asking you first.
Hooks, skills, sub-agents and MCP servers extend what it can do.
Use cases and industry problems are in the full card on your dashboard.
118 step-by-step blueprints: the system design behind apps you use every day, and LangChain, LangGraph, LangFlow and LangSmith agents solving real problems across 93 industries, from e-commerce and banking to hospitals. Each comes with its tech stack, design decisions, common mistakes and the interview questions most asked about it.
System Design·Food delivery
01/03
System Design01/03
Food delivery
Food delivery: order to doorstep
One order crosses payments, the restaurant, rider dispatch and live tracking, and must never be lost, charged twice or left without a rider.
Real-life problem
At dinner peak, thousands of orders a minute must be paid, accepted by restaurants and matched to nearby riders, while customers watch every step live.
Outcome
Orders confirm in about a second, riders are assigned within a minute, and a failed payment or a rejecting restaurant refunds automatically.
Person / client
Data store
Service
Queue
01The customer places the order with cart, address and payment method; the app sends an idempotency key so a retry never creates a second order.
02The API gateway authenticates the user, rate-limits per account and routes to the order service.
03The order service re-checks prices and that the restaurant is open, then writes the order (PENDING) and an outbox event in one database transaction.
04Payment is authorised through the gateway with the same idempotency key; the gateway's webhook confirms it, and a failure cancels the order (a saga compensation).
05A relay publishes OrderPaid from the outbox to Kafka, partitioned by order ID, so each order's events stay in order.
06The restaurant accepts and sets a preparation time; a rejection or a timeout triggers an automatic refund.
07Dispatch finds nearby free riders with a Redis geo query, scores them by distance, ETA and current load, and offers the order with a timeout before re-offering.
08Riders' GPS pings update the geo index; the tracking service pushes live position and ETA to the customer over WebSockets, and notifications fire on each status change.
System Design02/03
Ride hailing
Cab booking end to end (Uber & Ola)
A ride-hailing flow that takes a rider from pickup and drop entry through surge-priced quote, geo-cell driver matching, OTP-verified pickup, live tracking, fare settlement over UPI, cards, wallet or cash, ratings and safety. It must never assign one driver to two trips, charge a fare different from the agreed quote rules, or lose an SOS event.
Real-life problem
Riders need a car, auto or bike quickly at a fair, predictable price, and drivers need steady nearby requests, all while location data changes every few seconds. Supply, demand, payment methods and safety needs differ sharply by city and vehicle type.
Outcome
Riders get a locked quote, a confirmed driver with a road-based ETA, OTP-secured pickup and live trip sharing. Drivers get well-ranked offers, and the business gets consistent trip state, reliable payments including cash reconciliation, and an auditable event log.
Person / client
Service
Data store
Decision
Tool / step
Queue
01The rider sets pickup and drop on the map (with saved places and pickup-point snapping) and picks a vehicle type such as bike, auto, mini or sedan.
02The fare service gets route distance and time from the routing engine, applies the city rate card and the current surge multiplier for the pickup cell, and returns a quote with an ID and expiry so the shown price is honoured.
03Booking creates a trip in REQUESTED state using an idempotency key, so a double tap or retry never creates two trips; every state change is written with a version number.
04The matching engine pulls available drivers from the rider's H3 cell and surrounding rings, filters by vehicle type and status, ranks them by road ETA rather than straight-line distance, and may batch requests in short windows for better global assignment.
05The top driver gets a timed offer over a persistent socket with push fallback; acceptance is a conditional update (only if the trip is still unassigned and the driver is free), otherwise the next driver is offered.
06At pickup the rider tells the driver a short OTP; the trip moves to STARTED only if the OTP matches, which prevents wrong-rider and fake-start trips.
07During the trip the driver app streams GPS pings that update the geo index and feed the rider's live map, trip-sharing links and route-deviation checks.
08On drop the fare is computed from the quote rules plus allowed adjustments (route change, waiting, tolls), then collected by UPI, card, wallet or cash; cash trips post the platform commission to the driver's ledger.
09Both sides rate each other; cancellations after a grace window or driver arrival attract a fee per policy, and SOS alerts, emergency contacts and trip sharing are handled on a priority path.
System Design03/03
Public APIs
API rate limiter
A token bucket in front of the API keeps one noisy client from starving everyone else.
Real-life problem
One buggy client or scraper can exhaust capacity for every other customer.
Outcome
Fair usage per key, with clear 429 errors instead of outages.
Person / client
Service
Data store
Decision
01Every request carries a key: a user ID, an API key or an IP address.
02The gateway runs the limiter before any business code.
03Token bucket: each key holds N tokens that refill at a fixed rate; the limits come from config per plan.
04Redis keeps the counters and updates them atomically (INCR or a Lua script), so every server sees one count.
05A request that gets a token goes through to the service.
06An empty bucket returns 429 with a Retry-After header.
Certifications
Earn the certificate, one step at a time.
Step-by-step paths to the certifications employers ask for: System Design, Claude Code, the popular AWS exams, DevOps, Cloud Architecture & Infrastructure, n8n, Agentic AI, Python agents and AI automation with LangChain, LangGraph, LangSmith and LangFlow. Each path goes from the first lesson to exam day, with a hands-on task and a checkpoint at every step.
Start to finish
Every path is broken into weekly steps: what to learn, what to build, how you know you are done.
The right certificates
Exam codes, question counts, time, fee and passing score for each certificate.
Hands-on at every step
A small project per step, so you finish with a portfolio and not only a badge.
Track your progress
Tick off steps as you go and pick up exactly where you left off.
Short engineering articles from the dashboard. Each takes one idea that trips people up, explains it with real code, and ends with the interview questions you will be asked about it. Members and trial users read every article in full.
One idea per article
A single concept, explained until it clicks, without the padding.
Real, runnable code
Examples you can copy into a project, with the mistake and the fix side by side.
Interview questions
Every article ends with the questions interviewers ask, and their answers.
Short reads
Most take under ten minutes: one coffee, one idea you keep.
Ask anything. Get an answer card, not a pile of links.
Type a question about any technology you are learning and Search answers it as a card: the answer first, its key points, and the related topics from the same area so you always know what to read next.
Answer first
The card opens with the answer itself, then the key points that make it stick.
Related topics
Every answer lists what else its area covers, so one question leads to the next.
Explore by technology
Browse topics technology by technology when you do not know what to ask yet.
Your history, kept
Earlier questions stay one click away, and asking again reuses the answer.
New lessons arrive with interview questions built in.
Fresh lessons appear across the dashboard as cards with a cover picture, a short description and the date they were published. Read more opens the whole lesson in a calm reading window, with everything you need to revise it for an interview.
Most-asked questions
The questions interviewers ask about the topic, with to-the-point answers.
Step-by-step workflow
How the idea works in practice, in the order you would use it.
A diagram
The parts of the topic and how they connect, drawn for you.
Illustrations
Pictures that explain the idea rather than decorate the page.
Topic notes are short, focused and ranked by how much they matter for interviews and certification exams. Bookmark anything as you go, and when it is time to revise, everything you saved is waiting in one list.
Focused topic notes
Each chapter and topic boiled down to what you need to remember.
Priority stars
Five-star topics come first, so a short evening still covers what matters most.
Bookmarks everywhere
Save lessons, questions and topics from anywhere on the dashboard.
Take notes with you
Download study notes to revise offline, on the commute or right before an interview.
Every row below is a real entry from the dashboard: technology handbooks, API references, System Design courses, vocabulary and Techy topics. Search for your subject and see exactly where it opens once you sign up.
The comparisons interviewers and teams ask about most, from LangChain, LangGraph and LangSmith to Redux, Zustand and Redux Toolkit. Each one defines every technology in a line, compares them at a glance and answers 40+ difference questions.
01/01
What is the difference between PyTorch and TensorFlow (with Keras)?
Both train deep-learning models on GPUs. PyTorch is Pythonic with eager execution and dominates research; TensorFlow with Keras offers a high-level API and strong production and mobile deployment tooling.
TanStack Start for type-safe full-stack React, Zustand for lightweight app state, and n8n for workflow automation and AI agents, plus DSA and OOPs. Each comes with its current release, the features worth learning first, a runnable quick start and hand-picked official guides.
01/03
Zustand
Zustand 5 · tiny, hook-based state
Currentv5.0.16
A small, fast state manager for React: a store is a hook, there are no providers, and components re-render only for the slice they select.
create() store as a hook
Selectors & useShallow
No providers or boilerplate
persist middleware
devtools & immer middleware
Slices pattern
Vanilla createStore
Typed create<T>()()
Learning materials7Showing 1–4
TutorialBeginner10 minIntroductionCreate a store, read state with a selector and update it with actions in a few lines.zustand.docs.pmnd.rs
GuideBeginner12 minUpdating stateFlat merges with set(), nested updates without mutation, and when to reach for immer.zustand.docs.pmnd.rs
GuideIntermediate15 minTypeScript guideWhy create<T>()() is curried, typing middleware, and inferring state from the store.zustand.docs.pmnd.rs
GuideIntermediate8 minPrevent re-renders with useShallowSelect several fields at once without re-rendering on every unrelated change.zustand.docs.pmnd.rs
DSA
Data structures & algorithms · interview core
The data structures and algorithms behind every coding interview: complexity analysis, arrays to graphs, sorting, searching, greedy and dynamic programming.
Big-O complexity
Arrays & strings
Linked lists
Stacks & queues
Hash tables
Recursion & backtracking
+13 more
OOPs
Object-oriented programming · core to advanced
Every object-oriented concept: classes and objects, the four pillars, interfaces, overloading and overriding, relationships, SOLID and design patterns.
Guides read from the official websites, last refreshed 18 Oct 2026. Versions from npm and nodejs.org.
Techy15 topics
Walk into the interview already fluent.
The frameworks, cloud services and testing tools teams hire for, each one explained, questioned and coded. Try a real topic below: read it, answer today's interview question, then check yourself against the model answer.
Practice questions
300
Code puzzles
8
Workplace words
20
~/techy/python
❯ techy open python
Languages · 20 questions
Python
Python 3 fundamentals, collections, generators, async code and practical automation.
Type hints document contracts; runtime validation and tests verify behavior.
Interview question 16 of 20
What does await do in asyncio?
It suspends the current coroutine until an awaitable completes, allowing other scheduled work to proceed.
Six ways to practise every topic
Key features
Four crisp ideas per topic before you go deeper.
Subjective test
Answer out loud, then compare with a short model answer.
Objective quiz
The same concepts as multiple choice, with explanations.
Code examples
Connected multi-file projects in an editor-style reader.
Code puzzles
Predict the output, then see why it prints what it does.
Corporate English
Workplace words with synonyms and real sentences.
Open TechyIncluded with your account. Sign in to start practising.
API Docs5,698 identifiers
Every identifier, one calm reference.
Hooks, classes, functions and CLI commands, from AI tooling to databases, testing and cloud, organised so you find the right API in seconds. Search the real index below.
Technologies
59
Identifier types
14
Status labels
4
🎭Playwright
103 identifiers
IBrowserInterface
IBrowserContextInterface
FdefineConfigFunction
KdevicesConst
Fexpect.softFunction
Ftest.beforeEachFunction
Ftest.describeFunction
Fpage.gotoFunction
Fpage.clickFunction
Fpage.fillFunction
Fpage.waitForResponseFunction
Fpage.screenshotFunction
Search across every stack
Filter by technology, identifier type and status in one place.
Signatures you can read
Parameters, return values, key points and focused usage code.
Linked to the source
Every entry points to its official docs for version-specific detail.
Twelve new words every day. Hear each one, see what it means, read it in a real sentence and learn the words around it: what to say instead, and what it isn't.
01/01
Change & movementWord of the day
Instill
/in-STIL/
Meaning
To establish an idea or quality gradually in someone.
मन में बैठाना; धीरे-धीरे कोई गुण या विचार भरना
In a sentence
Training instilled real discipline.
Synonyms and antonyms are in the full card on your dashboard.
Antonyms & contrast
Dichotomy
/dy-KOT-uh-mee/
Meaning
A division into two sharply contrasted parts.
द्विभाजन; दो परस्पर विरोधी भागों में बँटाव
In a sentence
The build-or-buy dichotomy is usually a false one.
Synonyms and antonyms are in the full card on your dashboard.
Workplace speaking
Articulate
/ar-TIK-yuh-luht/
Meaning
Able to express ideas fluently, clearly and convincingly in speech.
स्पष्टवक्ता; बात को साफ़ और प्रभावी ढंग से कहने वाला
In a sentence
She stays articulate even when a client interrupts her mid-demo.
Synonyms and antonyms are in the full card on your dashboard.
Student storiesSample reviews
Small steps. Growing confidence.
These sample stories show how each area of the dashboard can fit into a week of study. They are illustrations, not quotes from real students, and will be replaced with real feedback as it arrives.
Word power
Twelve words a day sounded small, but hearing each one and reading it in a real sentence made them stick. The Hindi meaning helps when an English definition alone is not enough.
AS
Aditi S.Final-year B.Tech · Preparing for campus placements
Workflow blueprints
The blueprints walk through a URL shortener or a rate limiter one decision at a time, then list the mistakes people make. Reading the most-asked questions right after is exactly how I revise.
RM
Rohan M.Backend developer · System design rounds
API Docs
I keep API Docs open while I work. Finding a hook, its parameters and a short usage example in one place is quicker than jumping between three sites, and the link to the official page is always there.
MK
Meera K.Frontend engineer · React and Next.js
Interview prep
I answer the subjective questions out loud first, then take the objective set on the same technology. Seeing where my spoken answer and the quiz disagree shows me what to study next.
AP
Arjun P.Career switcher · First developer role
AI & agentic pulse
The pulse is my morning read. One card on LangGraph, one on RAG, one comparing two tools, each linked to its source, so I keep up with agents without losing an hour to news.
SR
Sana R.Data analyst · Moving into AI engineering
01 / 05
Membership17 dashboard areas
Your goals. One learning membership.
Every course and category in one plan, exactly as the Learning Dashboard is laid out: the daily home, Learning (including every Coffee & Code article in full) and Professional Learn. No course to choose and nothing behind a second tier; it all opens in English and Hindi.
Diwali Festive Sale · 25% off · ends 15 Nov
₹299₹225-25% / month25% off every payment · including renewals
Access starts after payment verification. Cancel renewal in Membership; access continues through your paid period. Revision notes and bookmarks are saved in your browser.
Dashboard home, every day
AI & agentic pulseA fresh card set across 33 technologies
Word powerNew words every day with audio and Hindi
Tech TopicsTechnology comparisons with interview questions
Technology docsPlatform guides, projects, DSA and OOPs
Learning
Search with AI modeAnswers from the notes, or from a model
On The GoStep-by-step build guides with every command
NotesRevision notes for every technology
VocabularyWorkplace English words with Hindi meanings and examples
Coffee & CodeShort engineering articles: real code, then interview questions
BookmarkSave anything to revise later
Professional Learn
Official DocsVendor documentation, summarised
TechyTopics, code examples and puzzles
API DocsEvery identifier, linked to its source
WorkflowSystem design and AI agent blueprints, step by step
LessonsPublished lessons for every course
QuizTimed practice with explanations
InterviewSubjective and objective banks per technology
News
Interview prep
Interview prep
Interview prep
Agentic AI
Agentic AI
Agentic AI
System Design
System Design
System Design
Certification
Certification
Certification
Certification
Certification
Certification
Certification
Certification
Certification
React
Next.js
JavaScript
Python
Git
TypeScript
System design
Redis
AI Search
AI Search
AI Search
AI Search
AI Search
AI Search
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
Featured lesson
AI latest · 29 October 2026
Notes & bookmarks
Notes & bookmarks
Notes & bookmarks
Notes & bookmarks
Notes & bookmarks
Notes & bookmarks
Change & movementWord of the day
Antonyms & contrast
Workplace speaking