Become an AI engineer by building.
Structured tracks from beginner to AI engineer. Coding labs that run in your browser. An AI mentor that sees your code and guides you to the fix. A verified Passport that proves what you can do.
Sign in with GitHub. No setup, no credit card.
Requirements
- cosine(a, b)
- normalize(v)
- VectorIndex.add(id, vector, metadata) stores items; adding an existing id replaces it
- VectorIndex.search(query, k, where=None)
def cosine(a, b): dot = sum(x * y for x, y in zip(a, b)) na = math.sqrt(sum(x * x for x in a)) nb = math.sqrt(sum(y * y for y in b)) return dot / (na * nb) # ✗ zero vector?
AI mentor: Your test cosine handles zero vector fails with ZeroDivisionError. What is the length of [0, 0] — and what should similarity mean when a vector has no direction?
Try a real lab — no sign-in
Write your first Python. Run the tests.
A real beginner lab, exactly as learners see it. Fill in the functions and press Run.
Make these pass
- average(scores) returns the mean of a list of numbers, rounded to 1 decimal place; an empty list returns 0.0
- letter_grade(score) returns A for 90+, B for 75+, C for 60+, D for 40+ and F below 40
- top_student(marks) takes a dict of name → score and returns the name with the highest score (alphabetically first on a tie)
- passed(marks, cutoff=40) returns the names that scored at least cutoff, sorted alphabetically
- count_words(text) returns a dict of lowercase word → how many times it appears, ignoring . , ! ?
Runs in your browser. In the Academy, hidden tests grade it in a sandbox and the AI mentor helps when you're stuck.
Save your progressAn AI mentor for every learner
It doesn't hand you the answer. It makes you the engineer who finds it.
The mentor sees your course, your previous attempts, your code, your quiz results and your project — and switches roles as you need.
Teacher
Explains the concept behind your bug with a tiny example — not your answer.
Debugger
Reads your code and failing tests, asks diagnostic questions, points to the line.
Reviewer
Reviews your repo like a senior engineer: correctness, evals, security.
Interviewer
Runs a mock interview on the topic you just learned, then scores you.
No setup. Just build.
AI engineering has a huge environment problem. Labs run Python right in your browser, and graded submissions run in an isolated sandbox.
4 tracks · one clear path each
See every module before you start.
Pick one track. Its modules unlock in order, so you always know the next step — and projects appear exactly when you're ready to build them.
AI for University Students
From your first Python script to a portfolio of shipped AI projects.
Best for: College students and fresh graduates in any branch
23
modules
15
labs
3
projects
- Write clean, tested Python and ship it through GitHub
- Explain how LLMs, embeddings and transformers actually work
- Build and evaluate a RAG application on your own documents
- Ship a tool-using agent and a portfolio that proves it
AI Engineer Intern · Junior AI Engineer · GenAI Developer · AI Solutions Associate
Inside a lesson
Plain English first. Then code.
Every module is a short, focused lesson written for builders, followed by a knowledge check, a graded lab and interview questions on the same topic.
- Store and change values using numbers, strings, lists and dictionaries
- Make decisions with if, elif and else
- Repeat work with for loops
- Write your own functions and read Python error messages without panic
Python from zero · 90 min
Why start here
Every AI system you will build later in this track is, underneath, an ordinary Python program: it reads some data, makes decisions, repeats steps and returns a result. If you are comfortable with the ideas in this module, everything after it is about what to build, not about fighting the language.
No setup is needed. The lab at the end runs Python right in your browser.
Values and variables
A variable is a name for a value:
name = "Asha" # a string (text) — always in quotes
marks = 82 # an integer (whole number)
attendance = 0.93 # a float (decimal number)
is_topper = True # a boolean: True or False
You can change a variable at any time, and combine values:
marks = marks + 5 # now 87
greeting = "Hello, " + name # "Hello, Asha"
print(greeting)
print(...) shows a value on the screen. It is the simplest way to see what your program is doing.
Lists: many values in order
A list holds several values in order, inside square brackets:
scores = [82, 67, 91, 45]
scores[0] # 82 — positions start at 0
scores[-1] # 45 — negative positions count from the end
len(scores) # 4
scores.append(73) # add to the end
sum(scores) # 358
Portfolio projects
Build things you can show an interviewer.
Each project lives in your own GitHub repo. An AI reviewer checks it against the acceptance criteria at a pinned commit — pass, and it's verified on your Passport.
Project: a production-style AI API
A small web service that sends the same task to several AI models, checks the answers are in the right format, and records how good, fast and expensive each one was.
- docker compose up starts everything with only an .env file
- Invalid LLM JSON is caught and retried; failure returns a clear 4xx/5xx
- Benchmark table covers 50 examples x 3 models with accuracy, p50/p95 latency and cost
University
Project: an evaluated RAG application
An assistant that answers questions from a set of documents, shows exactly where each answer came from, and has a scorecard proving how often it is right.
- Golden set of 100 questions including 15 unanswerable ones
- Experiment table with at least 4 configurations and recall@5, MRR, faithfulness, latency, cost
- Every answer shows citations that open the right source
University · FDE · Generalist
Project: a tool-using agent
An AI assistant that can take actions using real tools (search, database, calendar, tickets), asks for permission before risky steps, and can be tested repeatedly to see how reliable it is.
- Design doc explains why workflow steps vs agent steps
- MCP server usable from at least one other MCP client
- Benchmark of 20+ tasks run 5 times each with pass^1 and pass^5
University · FDE · Generalist
Capstone: a serious domain-specific AI system
One large project that looks like something a real company would build and pay for, solving a clear problem for a clear group of users, with everything you learned combined.
- Passes the capstone rubric at 80% or higher
- Real or realistic data, not toy examples
- Deployed demo, 3-minute video and full documentation
FDE
Capstone: AI architecture dossier
A complete architecture package for an enterprise AI system — the documents an architecture review board expects, plus a small working slice that proves the riskiest decision.
- Requirements state measurable quality, latency, scale, cost and compliance targets
- The architecture shows data flows, trust boundaries, identity propagation and where policy is enforced
- Estimates are computed with stated assumptions and checked against the latency budget and cost targets
Architect
Project: a multimodal system
An AI system that works with more than text: it reads scanned documents, understands images, or talks with users by voice.
- A: field-level precision/recall per field on 50+ documents; low-confidence routing to human review
- C: end-to-end response latency p50 and p95 with per-stage breakdown; barge-in works
- Cost per document or per minute of conversation reported
Generalist
Learn → build → prove
Not a certificate. A Passport of evidence.
Sandbox-graded labs
Every lab submission runs with hidden tests in an isolated microVM. No copy-pasted screenshots.
Verified projects
Projects live in your own GitHub repos. AI code review checks them against real acceptance criteria at a pinned commit.
AI Anytime Passport
A public profile where every skill links to its evidence — plus certificates anyone can verify.
How a skill gets verified
Every skill on a Passport shows how far it has been proven — and links to the evidence.
- Checked — passed the module's knowledge checke.g. Python (functions, classes, typing, packaging)
- Lab-verified — passed a sandbox-graded lab with hidden testse.g. Python (functions, classes, typing, packaging)
- Project-verified — used in a reviewed project the learner ownse.g. Python (functions, classes, typing, packaging)
Top learners earn community credits.
Weekly challenges and a monthly leaderboard. Each month the top learners earn AI Anytime community credits to spend on perks.
For hiring teams
Hire on what they've built.
Search learners who are open to work by verified skill, project scores, sandbox-graded labs and challenge results. Shortlist them and request an intro — the learner decides whether to share contact details.
- Search by verified skill, track and project
- See the evidence: repos, reviews, scores
- Shortlist and request intros in one place
Results rank learners by evidence: verified projects first, then sandbox-graded labs, then knowledge checks — with links to every repo and review.
Questions
Before you start.
Is it free?
Yes — sign in with GitHub and start any track. Lessons, checks, browser labs, projects and the Passport are included. Heavy AI usage has a daily limit; you can add your own model key in Settings to go beyond it.
Do I need to install anything?
No. Labs run Python in your browser and graded submissions run in an isolated cloud sandbox. For projects you'll use your own GitHub repo and whatever editor you like.
Which track should I pick?
Every track above says who it's best for and lists all its modules. Choose the one that matches the job you want next. Your dashboard then focuses on that track only; you can switch later in Settings.
Why do modules unlock in order?
Each module builds on the last, and your dashboard always shows exactly one next step. Projects are milestones — they open once you've finished the lessons before them.
What is XP?
XP measures verified work: reading a lesson, passing a check, passing a lab in the sandbox, getting a project verified. It drives the monthly leaderboard, and top learners earn community credits for perks.
How do hiring teams use it?
They search learners who are open to work by verified skills and projects, review the evidence, and request an intro. You decide whether to share your contact details.
Your first lab is ten minutes away.
Sign in with GitHub, pick a track, and write your first line of AI engineering code — in the browser.
Start learning