Master Modern Technology
Through Real
Examples.
Learn programming, cloud engineering, DevOps, machine learning, and software testing through practical, production-ready tutorials.
Core Curriculum
Learning Roadmaps.
Ordered courses built from runnable lessons. All roadmaps & courses →
AI Engineer Roadmap
Python, ML, LLMs, embeddings, RAG, agents, MCP and deployment.
Cloud Engineer Roadmap
AWS, Azure, GCP, networking, IaC and cost.
Data Engineer Roadmap
Python, Spark, Airflow, Kafka, dbt and Snowflake.
Data Engineering Tools Roadmap
Spark, Airflow, Kafka, dbt, Snowflake and Databricks.
Database Roadmap
MySQL, PostgreSQL, SQL Server, MongoDB, Redis and replication.
DevOps Engineer Roadmap
Linux, Docker, Kubernetes, Terraform, Ansible, CI/CD and monitoring.
Interview Preparation Roadmap
Behavioral, DSA in Java or Python, SQL, cloud, data and AI/ML.
Kubernetes Administration Roadmap
Controllers, RBAC, observability, networking, autoscaling and Helm.
Kubernetes Engineer Roadmap
Pods, Deployments, Services, config, Helm, autoscaling and security.
Python Developer Roadmap
Python basics, OOP, concurrency, decorators, testing and security.
System Design Roadmap
Caching, consistent hashing, rate limiting, capacity and consensus.
Software Testing Roadmap
Test strategy, pytest, Selenium, Playwright, API and contract tests.
Technology Catalog
All Languages & Tools.
Python
Automation, ML, AI & backend development
FastAPI
Async Postgres, measured sync vs async & deployment
JavaScript
Frontend, backend & full-stack web apps
TypeScript
Type-safe modern web & enterprise systems
Java
Enterprise systems, banking & backend APIs
Go
Cloud-native systems & DevOps tooling
C#
.NET backend, Azure & desktop apps
C
OS internals, networking & embedded systems
C++
Game engines, trading & high-performance software
Rust
Safe systems software & cloud infrastructure
SQL
Queries, joins, window functions & CTEs
Bash
Linux automation, CI/CD & DevOps scripting
PowerShell
Object pipeline, CIM/WMI & Windows automation
LLM Engineering
RAG, prompt engineering, fine-tuning & tool use
AI Agents
ReAct, memory, tool-calling & multi-agent systems
MCP
Model Context Protocol servers & clients
Machine Learning
Scikit-learn, PyTorch & MLOps
PyTorch
Autograd, nn.Module & distributed training
TensorFlow
Keras, training loops & TFX deployment
MLOps
Experiment tracking, model registry & CI/CD for ML
NumPy
Arrays, broadcasting & linear algebra
Pandas
DataFrames, groupby & time series
Scikit-Learn
Estimators, pipelines & model selection
Security Fundamentals
CIA triad, risk, attack surface & secure defaults
Cryptography
Hashes, AES-GCM, signatures, TLS & key rotation
Defensive Security
Auth logs, Sigma, Suricata, triage & YARA
Web Security
OWASP Top 10, fixed in code & pinned by tests
DevSecOps
SAST, secrets, SCA, SBOM, DAST & a CI gate
Cloud Security
IaC scanning, policy as code, CIS & Pod Security
Ethical Hacking
Scope, RoE, CVSS findings & the report, lab-only
Data Engineering
Pipelines, ingestion, orchestration & data quality
PySpark
Distributed data & Apache Spark DataFrames
Apache Spark
RDDs, DAG execution, Spark SQL & cluster tuning
Kafka
Event streaming, Kafka Streams & ksqlDB
Airflow
DAGs, operators, sensors & production scheduling
Databricks
Delta Lake, Unity Catalog & MLflow integration
Scala
Apache Spark & distributed computing
R
Statistics, analytics & research workflows
dbt
Models, refs, tests & incremental builds
Snowflake
Warehouses, time travel, streams & cost control
AWS
EC2, S3, Lambda, IAM & cloud architecture
Azure
VMs, AKS, Functions, Entra ID & Microsoft cloud
Google Cloud
GKE, BigQuery, Cloud Run, GCS & GCP services
Multi-Cloud
Cross-cloud comparisons, cost, and architecture decisions
DevOps
CI/CD, automation, infrastructure, monitoring & reliability
Docker
Containers, images & Compose environments
Kubernetes
Pods, deployments, Helm & autoscaling
Linux
Shell, file system, permissions & sysadmin
Networking
Subnets, TCP, DNS, HTTP, firewalls & nmap
Git & GitHub
Branching, workflows & GitHub Actions
GitHub Actions
Workflows, matrix builds & reusable actions
Terraform
Infrastructure as code across cloud providers
Ansible
Playbooks, roles & configuration management
Jenkins
Pipelines-as-code, Blue Ocean & plugins
Monitoring & Observability
Prometheus, Grafana & OpenTelemetry
System Design (HLD)
Scalability, caching, load balancing & consistency
Low-Level Design & Patterns
SOLID, GoF design patterns & class modelling
Microservices
API gateways, service mesh & saga pattern
Event-Driven Architecture
Event sourcing, CQRS & outbox pattern
CUDA
GPU kernels, memory hierarchy & deep learning acceleration
CUDA C++
Thread hierarchies, shared memory & warp primitives
CUDA Python
CuPy, Numba CUDA & PyCUDA for ML workloads
GPU Computing Fundamentals
SIMT execution, memory coalescing & occupancy
Parallel Programming
Task & data parallelism, synchronisation & parallel patterns
Databases
Schema design, indexing & transactions
PostgreSQL
JSONB, full-text search & performance tuning
MySQL
Indexing, replication & stored procedures
MongoDB
Aggregation pipelines, indexing & Atlas
Redis
Caching, pub/sub, streams & Lua scripting
SQL Server
T-SQL, SSMS & enterprise administration
Testing Strategy
Test pyramid, test doubles & flake control
Pytest
Fixtures, parametrize, plugins & coverage
Playwright
Auto-waits, traces & parallel execution
Selenium
WebDriver, POM & cross-browser testing
API Testing
requests, pytest, mocking & OpenAPI contracts
JUnit
JUnit 6, Maven, parameterized & parallel tests
Interview Prep
System design, testing & data scenarios
DSA with Java
Arrays, trees, graphs & DP in Java
DSA with Python
Pythonic DSA & LeetCode-style problems
SQL Interviews
Window functions & schema design questions
Cloud Interviews
AWS/Azure/GCP scenario & architecture Qs
Data Interviews
SQL & Python rounds, pipelines & Spark
AI Interviews
ML fundamentals, LLMs, RAG & ML system design
Product Philosophy
Why SkillByExample.
Real-world Examples
Skip the generic "Hello World" boilerplate. Learn syntax through concrete, production-ready code samples designed for real developers.
Project-based Learning
Build actual utilities, cloud infrastructure templates, automation architectures, and ML models as you progress.
100% Free Access
Free, no paywalls, no sign-up. A pure developer-focused curriculum created to empower engineers across the globe.
Quick Starters
Featured Tutorials.
Autonomous Agent Workflows
Make long agent runs safe to crash and rerun: checkpoints, idempotent side effects, timeouts on parallel calls, approval gates and spending budgets.
Agent Evaluation
Grade agents on outcome and process, measure pass rates over repeated runs, test safety under pressure, and audit logged trajectories.
Introduction to AI Agents
What makes an AI agent: tools, the ReAct loop of thought, action and observation, step caps that stop runaway loops, and memory that survives sessions.
An Agent Loop Over MCP Tools, With Human Approval
Build an agent loop on MCP: discover tools, validate a plan, run read-only tools freely, gate destructive ones on approval, and use several MCP servers.
AI Agent Memory Systems
Sliding windows that forget, fact summaries that don't, a SQLite store that persists across sessions, pinned memories, and deletion, all as runnable code.
Multi-Agent Systems
Orchestrators and subagents in runnable Python: parallel fan-out, partial failures, parsing a model's plan safely, and approval gates for risky tool calls.