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Free learning path · 7 days · 40 topics

Become an AI-Native Engineer in a week.

In one week you can learn more than many engineers who haven’t upskilled. This path is your starting point. Work through the theory and practice exercises to learn the mindset and apply it from day one.

Start with day 1Read it on GitHub ↗
01 / How to work through it
01

One day, one area

Each day covers one macro area, broken into topics. Every topic is its own page: the theory first, then a practice exercise you run on your own machine.

02

The links are the course

Every topic points at curated videos and articles. They are not extra reading. They carry most of the teaching, and the text around them is the map.

03

Two to three hours a day

That is the honest cost. You can stretch it over two weeks and lose nothing, but do not skip the practice half. Reading about agents does not make you fast with them.

04

Work in a real repo

Use a project you already know, not a toy. The whole point is to feel where the practices help and where they get in the way on code that has history.

02 / The seven days

Every topic has its own page with the theory, the practice exercise and the curated videos and articles it is built around. Open a day to see its topics, or jump straight to one.

Day 1

What is AI-Native Engineering

You'll learn what AI-Native Engineering means and why shifting from implementer to orchestrator matters. This foundation sets the mindset for the rest of the week.

5 topics·4 min
  1. 01What Is AI-Native Engineering?Definition and scope: AI as a first-class capability across the full development lifecycle, not an add-on.
  2. 02From Implementer to OrchestratorHow the role is evolving from writing every line to directing intent, constraints, and verification.
  3. 03What AI-Native Engineers Actually DoThe new skill stack: spec literacy, context engineering, orchestration, and quality ownership.
  4. 04AI-Native TeamsHow coding agents change the software development lifecycle at the team level: delegate, review, and own.
  5. 05Why This Matters Now: Career and RelevanceStaying relevant as the role evolves: why AI-Native Engineering becomes a leadership and multiplier role.
Day 1 overview
Day 2

The Basics

You'll learn how LLMs, agents, and tools work together: the building blocks of every AI coding assistant. Understanding these basics helps you use and evaluate tools with confidence.

7 topics·18 min
  1. 01How Large Language Models WorkTokens, context windows, attention mechanisms, and why LLMs are surprisingly good at code, and where they still fail.
  2. 02Prompt Engineering for EngineersHow to craft prompts that produce consistent, reliable results, and how the same techniques are used offensively to break AI systems.
  3. 03From Autocomplete to AgentsThe evolution from single-turn code completion to autonomous multi-step agents: what defines an agent, how the agentic loop works, and when to use agents versus simpler approaches.
  4. 04Building an Agent in 50 Lines of CodeA ground-up look at what an agent really is under the hood: an inference client, a set of tools, and a while loop, using Hugging Face's Tiny Agents as a concrete reference.
  5. 05Memory and State in Agent WorkflowsHow agents retain and retrieve information across steps and sessions: short-term context, long-term memory, and practical strategies to prevent context rot.
  6. 06AI Failure Modes Every Engineer Must KnowHallucination, confident wrongness, context drift, and the foundational security risks that arise when AI agents can take actions in the world.
  7. 07Human in the LoopWhy keeping humans in the decision loop is essential when working with AI agents, and how to design workflows that stay under control.
Day 2 overview
Day 3

The AI Agents Landscape

You'll tour the ecosystem of AI coding assistants and agent runners. Knowing the landscape helps you choose tools with criteria, not hype.

6 topics·14 min
  1. 01What AI Coding Agents Are Out ThereA practical map of the AI coding tools available today, from IDE extensions to cloud-based agents, and the criteria that actually matter when choosing one.
  2. 02Getting Started With an AI Coding AssistantA practical introduction to using an AI coding assistant day to day, using GitHub Copilot in VS Code as the reference example.
  3. 03From Chat to Plans: A First Step Into Spec-Driven DevelopmentHow plan mode works, why it produces better results than jumping straight to code, and how to use it as your first structured step toward Spec-Driven Development.
  4. 04Best Practices for Coding With AgentsThe patterns that make the difference between a frustrating agent session and one that actually ships something useful.
  5. 05Cloud Agents: Delegating Work in the BackgroundHow cloud-based coding agents work, when they make sense, and how to use them to clear your backlog without losing control of your codebase.
  6. 06Automated Code Review With AIHow AI-powered code review tools work, what they catch, and how to integrate them into your PR workflow without replacing human judgment.
Day 3 overview
Day 4

Context Engineering and MCP

You'll learn how to give AI the right context at the right time via rules, skills, and MCP. Strong context engineering is what makes AI outputs consistent and production-ready.

8 topics·30 min
  1. 01From Prompt Engineering to Context EngineeringWhy the craft has shifted from writing clever individual prompts to curating the full state an agent receives: tools, history, files, rules, and constraints. How that shift changes what good engineering looks like.
  2. 02Rules and Instructions: Persistent Context for Your AgentHow to use rules files, repository instructions, and AGENTS.md to give agents consistent, project-level context without repeating yourself in every prompt. The difference between global rules, project rules, and task-specific instructions.
  3. 03Skills and Commands: Reusable PatternsHow to package reusable capabilities as skills and reusable prompt workflows as commands, so agents can load exactly what they need for a task without bloating the default context.
  4. 04Custom Agents and PersonasWhen and how to define specialized agents configured for a specific role or task. How persona definitions, scoped instructions, and the AGENTS.md pattern let you build agents that stay focused and don't overstep.
  5. 05Rules, Skills, Commands, and Custom Agents: Knowing What to Use WhenA clear map of the four context management techniques available in modern AI coding tools, what each one is for, who triggers it, and how to combine them without creating a mess.
  6. 06Model Context Protocol (MCP): What It Is and Why It MattersWhat MCP is, why it emerged as a standard, and how the client-server architecture connects AI assistants to external data sources, tools, and APIs in a consistent and composable way.
  7. 07MCP Security: What Can Go WrongThe real security risks that come with connecting agents to external systems via MCP: prompt injection through tool outputs, data exfiltration, confused deputy attacks, and the practices that mitigate them.
  8. 08Harness Engineering: Everything Around the ModelWhy the systems wrapped around an LLM — the prompts, tools, sensors, and guardrails — matter as much as the model itself, and how to think about them as a discipline rather than a bag of tricks.
Day 4 overview
Day 5

Spec-Driven Development

You'll learn why writing specs before code matters and how the Specify → Plan → Execute loop works. SDD is the methodology that ties AI-Native Engineering together and keeps outputs aligned with intent.

4 topics·7 min
  1. 01Why Spec-Driven Development?The root problem SDD solves: drift, hallucination, and the limits of vibe coding. Why specs are durable artifacts that survive tool changes.
  2. 02SDD as a Pillar of AI-Native EngineeringHow Spec-Driven Development fits into the broader AI-Native Engineering discipline and why it's the methodology that ties everything together.
  3. 03Spec-kit: The Open ToolkitA hands-on look at GitHub's Spec Kit how it structures the Specify → Plan → Tasks workflow and integrates with PR review gates.
  4. 04Spec-kit Under the HoodA 20-30 minute deep dive into Spec-kit's source code. Once you know how it works in practice, reading its templates and commands shows you exactly what prompts drive the workflow and gives you a model for building your own.
Day 5 overview
Day 6

Spec-Driven Development

You'll compare BMAD and Spec-kit and build a full-stack app with BMAD. Hands-on practice with SDD frameworks helps you choose the right one for your team and project.

3 topics·6 min
  1. 01BMAD: The AI-Driven Delivery FrameworkWhat BMAD is, how its persona-based workflow guides you from ideation to agentic implementation, and where it fits in the SDLC.
  2. 02The BMAD Workflow MapHow BMAD structures the full delivery lifecycle across four phases, and how the Quick Flow track lets you skip phases for smaller, well-understood work.
  3. 03BMAD vs Spec-kit: Choosing the Right FrameworkA direct comparison of the two main SDD frameworks when to use each, their trade-offs, and how to decide what fits your team and project.
Day 6 overview
Day 7

AI in the SDLC

You'll see how AI-Native practices apply across the full lifecycle: from requirements and architecture through implementation, testing, review, and maintenance. This ties the week together and shows where to apply what you've learned.

7 topics·13 min
  1. 01AI in Requirements and Product CollaborationHow engineers can use AI to help gather requirements, turn them into structured user stories, and validate acceptance criteria before a single line of code is written. The SDLC starts before implementation, and AI can make the engineering-to-product handoff much more structured and less lossy.
  2. 02AI as the Bridge Between Design and DevelopmentHow AI is closing the gap between what designers create and what engineers implement. Tools like Figma MCP, v0, and Builder.io Visual Copilot let engineers turn design files, screenshots, or prompts into production-ready component code, reducing the manual translation work that traditionally consumed hours every week.
  3. 03Testing with AIHow to use AI agents to write, run, and maintain tests, from unit tests to end-to-end browser flows. Covers AI-generated test cases, Playwright MCP for browser automation, and the BMAD TEA module for risk-based test strategy and release gates.
  4. 04AI for DocumentationUsing AI to generate and maintain documentation: code comments, API references, architecture docs, and user-facing guides. Documentation rot is one of the most persistent problems in software teams; AI tools are starting to solve it by coupling docs directly to code and updating them automatically.
  5. 05Continuous AI: AI in Your CI/CD PipelineHow AI fits into CI/CD pipelines beyond running tests. GitHub Agentic Workflows, using AI models inside GitHub Actions to auto-triage build failures, fix broken tests, synchronize docs with code changes, and handle repetitive repo tasks that previously required manual effort.
  6. 06AI for Debugging and Incident ResponseUsing AI to speed up root cause analysis, correlate logs with code changes, and investigate production incidents. Engineers spend a significant chunk of their time debugging; AI tools are already cutting that time by a meaningful amount in real production systems.
  7. 07AI for Your Custom Workflows in the SDLCFrom automating performance audits on a frontend codebase to generating security reports, triaging support tickets or keeping runbooks up to date, almost every task you do in the SDLC can benefit from AI in some way. The key is knowing how to identify those opportunities and structure your workflow to take advantage of them.
Day 7 overview

40 topics, about 20,000 words of theory and practice, free to read and nothing to sign up for.

After day 7

See where your team actually stands.

The path teaches you the practices. The assessment scores how many of them your team really has: 25 questions, a radar, and three things to fix first. Around six minutes, no email.

AI-Native Software Engineering — book cover
Want to go deeper

A week gets you started.The book gets you to production.

This path is the shortest route to the ideas. The book is where each one is worked out in full, with the patterns, the trade-offs, the failure modes, and what changes once a whole team works this way.

  • Context engineering
  • Spec-driven development
  • Agent orchestration
  • Verification & quality gates
  • Scaling AI in teams
Read the bookRead the Early Release free ↗