Catalog
Every lesson in the curriculum, in teaching order. 3 of 48 are ready today; the rest are clearly marked as coming soon.
48 lessons: 3 available now, 45 coming soon.
Coding foundations
Read, write, and debug real programs.
1 available, 10 coming soon in these modules
First steps: values and decisions
Beginner branchStore values, name them well, and let your program choose between paths.
- Values, variables, and types
Name values, change them, and learn why types matter.
Coming soonBeginner8 min - Making decisions with conditionals
Let your program choose between paths with if, else, and comparisons.
Coming soonBeginner8 min
Iteration and functions
Repeat work with loops, package it in functions, and organize data in collections.
- Loops, step by step
Watch a for loop run one step at a time, see exactly when the condition is checked, then fix a real off-by-one bug.
AvailableBeginner10 min - Functions that transform data
Package logic into reusable functions with parameters and return values.
Coming soonBeginner9 min - Arrays, objects, and maps
Organize data so your code can find it quickly.
Coming soonBeginner10 min
Debugging, testing, and Git
Find bugs methodically, prove code works with tests, and keep a history of your changes.
- Debugging with a plan
Replace guesswork with hypotheses, experiments, and evidence.
Coming soonBeginner9 min - Writing your first tests
Prove your code works, and keep it working.
Coming soonBeginner10 min - Git: saving and sharing work
Commit, branch, and undo safely.
Coming soonBeginner10 min
APIs, data, and async
Talk to services over HTTP, store data in databases, and write code that waits well.
- How HTTP APIs work
Requests, responses, status codes, and JSON.
Coming soonIntermediate11 min - Databases and queries
Store data durably and ask it questions.
Coming soonIntermediate11 min - Asynchronous code and the event loop
See how tasks move through a simplified event-loop model.
Coming soonIntermediate12 min
AI foundations
What machine learning actually does.
7 coming soon in these modules
What models learn
What machine learning does, how training differs from inference, and why data matters.
- What AI and machine learning do
Pattern-finding from examples, and what that means in practice.
Coming soonBeginner8 min - Training versus inference
Why learning a model and using a model are different jobs.
Coming soonBeginner8 min - Datasets and labels
Features, labels, splits, and the leaks that ruin evaluations.
Coming soonBeginner10 min
Measuring models
Evaluate models honestly and recognize when they memorize instead of generalize.
- Evaluating a model
Accuracy, precision, recall, and choosing what to measure.
Coming soonBeginner10 min - Overfitting and generalization
When a model memorizes the training set instead of learning.
Coming soonIntermediate9 min
Representations and responsibility
How embeddings turn meaning into coordinates, and how to use AI responsibly.
- Embeddings: meaning as coordinates
How text becomes vectors you can compare.
Coming soonIntermediate10 min - Responsible use: bias, privacy, and limits
Name the risks before you ship.
Coming soonBeginner9 min
LLM engineering
Build dependable features on large language models.
9 coming soon in these modules
How LLMs read and write
Tokens, context windows, and a conceptual model of attention.
- Tokens and tokenization
Why models read pieces of words, and what that costs.
Coming soonIntermediate9 min - Context windows
What fits, what doesn't, and what to do about it.
Coming soonIntermediate9 min - Attention, conceptually
A simplified, clearly labeled model of how tokens weigh each other.
Coming soonIntermediate11 min
Prompting, structure, and tools
Prompts that hold up, outputs your code can parse, and models that call functions.
- Prompting that holds up
Instructions, constraints, and examples that survive real inputs.
Coming soonIntermediate10 min - Structured outputs
Get JSON your code can trust, and validate it.
Coming soonIntermediate10 min - Tool calling
Let a model request functions your code runs.
Coming soonIntermediate12 min
LLMs in production
Evaluate, speed up, budget, and secure LLM features.
- Evaluating LLM features
Evaluation sets, graders, and regression tracking.
Coming soonAdvanced12 min - Latency and cost
Where time and money go, and how to spend less of both.
Coming soonAdvanced10 min - Safety and misuse
Prompt injection, data exposure, and unsafe outputs.
Coming soonAdvanced11 min
RAG engineering
Answer questions from your own documents, with evidence.
1 available, 8 coming soon in these modules
Retrieval-augmented generation
Follow documents from ingestion to grounded, cited answers.
- RAG, end to end
Follow a question through chunking, embedding, retrieval, and grounded generation, then tune chunk size and top-k until the evidence is right.
AvailableIntermediate12 min - Ingestion and parsing
Turn PDFs, HTML, and docs into clean, traceable text.
Coming soonIntermediate10 min - Chunking strategies
Fixed, recursive, and structure-aware chunking compared.
Coming soonIntermediate10 min
Better retrieval
Vector indexes, keyword search, hybrid retrieval, and reranking.
- Vector search and indexes
Nearest neighbors, approximate search, and recall.
Coming soonIntermediate11 min - Keyword and hybrid retrieval
When exact words beat meaning, and how to combine both.
Coming soonIntermediate11 min - Reranking
A second, slower pass that orders candidates better.
Coming soonAdvanced10 min
Grounded answers and evaluation
Generate answers that cite evidence, evaluate them, and analyze failures.
- Grounded generation and citations
Answer only from evidence, cite it, and abstain otherwise.
Coming soonAdvanced11 min - Evaluating RAG
Measure retrieval and answers separately.
Coming soonAdvanced12 min - Failure analysis
Trace a bad answer back to the stage that caused it.
Coming soonAdvanced11 min
System design
Reason about systems that serve real traffic.
1 available, 11 coming soon in these modules
Requirements and estimates
Turn vague asks into requirements, numbers, and API contracts.
- Requirements and constraints
Turn a vague request into requirements you can design against.
Coming soonIntermediate9 min - Capacity estimation
Back-of-the-envelope math with written assumptions.
Coming soonIntermediate10 min - Designing APIs
Resources, pagination, idempotency, and errors.
Coming soonIntermediate11 min
Performance and scale
Caching, queues, and load balancing: the tools for serving more with less.
- Caching: hits, misses, and freshness
Follow requests through a cache, estimate how hit rate changes average latency, and decide how stale is too stale.
AvailableIntermediate11 min - Queues and backpressure
Adjust arrival and processing rates and watch a backlog grow or shrink.
Coming soonIntermediate10 min - Load balancing
Spread traffic across servers and survive failures.
Coming soonIntermediate10 min
Data at scale
Choose storage, replicate it, and decide what consistency you need.
- Choosing storage
Relational, key-value, document, and object storage by access pattern.
Coming soonAdvanced11 min - Replication
Copies for durability and read scale, and the lag they bring.
Coming soonAdvanced10 min - Consistency models
Strong, eventual, and read-your-writes, with examples.
Coming soonAdvanced11 min
Operating in production
See what your system is doing, keep it up, and make tradeoffs explicit.
- Observability
Metrics, logs, traces, and SLOs.
Coming soonAdvanced10 min - Reliability and failure
Timeouts, retries, circuit breakers, and graceful degradation.
Coming soonAdvanced11 min - Making tradeoffs explicit
Write down assumptions and what you're giving up.
Coming soonAdvanced9 min