←shelf// the path · 01 / 01
LIVE · CURRICULUM·AI ENGINEERING·2026·04 STAGES·19 LESSONS

if I were starting over,this is the pathI'd actually take.

Most roadmaps online are someone's wishlist. This is the order I'd do it in — boring fundamentals first, because shipping AI in production is 80% software, 20% prompts.

// 01 · the orderclick to open

Order matters.

Skip a stage and you'll spend months patching what should have been right at week one. Read it top to bottom.

Foundations.

stage 00·06 lessons
  1. 01

    Basics Frontend

    HTMLCSSJavascript

    The web's three primitives. Skip them and you're guessing for the next ten years.

  2. 02

    ReactJS

    JSXhooksstate

    The default for component-driven UI. Learn hooks, learn why your app re-renders too much, learn the lifecycle.

  3. 03

    NextJS

    App RouterRSCserver actions

    The default fullstack-React shape in 2026. App Router and server components are the part everyone gets wrong first.

  4. 04

    Backend

    RESTauthschemas

    One language, one framework. Take it past hello-world — auth, errors, schemas, a real deploy. Even if you're the only one hitting it.

  5. 05

    Python

    FastAPIFlask

    FastAPI is the right pick for AI work — async, typed, plays nicely with every LLM library you'll touch.

  6. 06

    NodeJS

    ExpressNestJS

    If your stack is JS-first, pick this. NestJS if you like structure, Express if you want raw.

Ship muscles.

stage 01·03 lessons
  1. 01

    Cloud

    AWSGCPAzure

    Pick one. AWS still wins on services, GCP on AI primitives, Azure on enterprise. You only need one.

  2. 02

    Docker

    Containers are the unit of shipping. Learn this before Kubernetes lies to you about needing a cluster.

  3. 03

    Terraform

    Infrastructure as code. Click-ops doesn't scale, and neither does your sanity at 3am.

The AI layer.

stage 02·07 lessons
  1. 01

    Pre-trained Models

    GPTSonnetGemini

    Three families. Pick one default, know each one's strengths. Don't ship a switching layer until a real customer asks for it.

  2. 02

    Vector RAG

    Embed, retrieve, augment. The first thing every "AI app" actually is. Master this before chasing fancier patterns.

  3. 03

    Graph RAG

    When relationships between things matter more than text similarity. Underused. Increasingly relevant.

  4. 04

    SQL RAG

    Query against structured data. The boring, reliable RAG that ships in actual products.

  5. 05

    AI Agents

    LLMs in a loop with tools. Most aren't worth it yet — learn enough to know when they're not.

  6. 06

    LangGraph

    State machines for agents. Less magic, more control. Currently the right shape for production agents.

  7. 07

    Crew AI

    Multi-agent orchestration. Useful for specific workflows, oversold for everything else.

Frontier.

stage 03·03 lessons
  1. 01

    MCP

    Anthropic's Model Context Protocol. The integration shape that matters in 2026 — adopt early or rebuild later.

  2. 02

    Multi-Modal AI

    Vision, voice, structured outputs from one model. Where the next batch of actually-useful products gets built.

  3. 03

    Harness building

    contextmemoryevalstool routing

    The infra layer between the model and your product — context management, memory, tool routing, retries, evals. Where the next two years of competitive advantage live, until one Anthropic release vaporizes the category. Worth learning to find out which.

// 02 · the takestrong opinions

Pick one,
finish it.

The trap is breadth. You'll be tempted to learn React and Vue, FastAPI and Express, AWS and GCP, LangGraph and Crew AI at the same time. Don't.

Pick one of each. Take it past hello-world — deploy it, hit the bugs that only show up in prod, fix them, watch your own logs at 2am. Even if you're the only user. You'll learn more from one deployed thing than from ten weekend tutorials.

“The next five years are either production-harness companies, or one Anthropic release that makes them all look like bloated wrappers. I'm betting on what I can control.”