Learn the skills modern engineering work actually demands.
A free, practical learning space for people who need to keep growing as AI changes software engineering, quality, automation and delivery. Read the concept, make the engineering decision, then do the hands-on challenge.
No paywall. No certificate theatre.
The goal is useful capability: AI, automation, APIs, data, CI/CD, observability, accessibility, performance and engineering judgement that scales with your career.
6
learning tracks
18
practical modules
90
guided lessons
54
hands-on challenges
Learn the concept
Short explanations focus on the engineering idea and why it matters in production.
Practise the decision
Interactive scenarios and practical challenges force you to choose, design or test something.
Apply it at work
Turn the lesson into evidence you can reuse in a real project, test strategy, codebase or architecture discussion.
Flagship free module
Enterprise Quality Engineering + Supply Chain + SAP
Follow an enterprise system end to end: understand the supply-chain business flow, learn where SAP fits, then build a quality strategy using risk-based testing, APIs, integration checks, automation and release evidence.
12
guided sections
40+
learning points
10
hands-on exercises
1
capstone blueprint
Interactive labs
Start with a decision-based exercise
These are the original scenario labs. They stay available as fast practice alongside the broader curriculum.
AI Workflow & Governance Lab
Make confidence, risk, grounding and human-approval decisions in realistic AI scenarios.
Open labPlaywright Test Automation Lab
Practise selectors, assertions, synchronization and maintainability decisions.
Open labAPI Testing Beyond Status Codes
Work through contracts, negative paths, state transitions and business invariants.
Open labFull curriculum
Build breadth without losing practical depth.
Use the tracks in order if you want structure, or search for the exact skill you need today. Every module includes learning outcomes, guided content and hands-on work.
Learning track
Applied AI Engineering
Build useful AI workflows with grounding, structured outputs, evaluation, confidence, governance and human control.
AI Foundations for Engineers
Understand models, prompts, tokens, context, temperature, structured outputs and where AI systems fail.
Prompt Engineering & Prompt Testing
Treat prompts like production assets: version them, test them, compare them and defend them against regressions.
RAG, Grounding & Hallucination Control
Learn how retrieval-augmented generation changes the quality problem and how to validate source-grounded answers.
AI Governance & Human-in-the-loop
Turn confidence, risk, approval, auditability and fallback into concrete workflow controls.
Learning track
Modern Test Automation
Build automation that is reliable, maintainable and useful in delivery pipelines rather than simply increasing script count.
Playwright from Scratch
Learn browser automation through locators, assertions, fixtures, test data and debugging patterns.
Automation Framework Architecture
Design maintainable test automation using separation of concerns, services, fixtures and reusable business actions.
Flaky Test Engineering
Diagnose instability using timing, state, environment and data evidence instead of hiding failures with retries.
Learning track
API & Integration Quality
Test contracts, business rules, state changes, resilience and dependencies across distributed systems.
API Testing Beyond HTTP 200
Validate schemas, business invariants, negative paths, idempotency and downstream state instead of stopping at status codes.
Consumer Contract Testing
Understand when consumer-driven contracts reduce integration risk and where they do not replace end-to-end testing.
Integration & Resilience Testing
Test timeouts, retries, duplicate messages, partial failure and eventual consistency across integrated systems.
Learning track
CI/CD, Observability & Release Engineering
Move from test execution to evidence-driven delivery using pipelines, telemetry, quality gates and production feedback.
CI/CD Quality Gates
Design fast feedback and meaningful release gates without turning pipelines into slow, noisy blockers.
Observability for Testers
Use logs, metrics, traces and correlation IDs to test systems more intelligently and debug production behaviour faster.
Learning track
Core Quality Engineering
Strengthen the judgement behind test design, risk analysis, requirements, data and non-functional quality.
Risk-Based Test Design
Prioritise testing using likelihood, consequence, change complexity, usage and detectability instead of treating every requirement equally.
SQL & Data Validation for Testers
Use SQL to verify data state, joins, transformations, duplicates, nulls and business reconciliation.
Accessibility Testing Essentials
Build accessible quality checks into normal delivery using keyboard, semantics, labels, contrast awareness and automated scanning.
Performance Testing Basics
Understand load, stress, soak, latency percentiles and the system evidence needed before performance testing becomes useful.
Learning track
Agents, MCP & Future-ready Engineering
Understand how AI moves from answering questions to invoking tools, and what engineering controls are needed when systems can act.
MCP for Engineers
Understand Model Context Protocol concepts, tool boundaries, schemas, permissions and testing considerations.
Testing Agentic AI
Test plans, tool selection, loops, permissions, side effects and recovery when AI can perform multi-step actions.
A useful rule
Do not collect modules. Build evidence.
After every module, create something tangible: a test matrix, Playwright test, API suite, SQL query, pipeline rule, risk model, prompt evaluation dataset, MCP tool schema or observability checklist. That is what turns learning into capability.