Programming lessons & courses
127 lessons · 29 learning paths · free, quiz-checked, no signup required
Compilers, concurrency, databases, reverse engineering, and the other load-bearing layers under everyday code. Each lesson explains what the machinery actually does (parsers, locks, B-trees, loaders) with worked examples and realistic code.
Learning paths
Performance Profiling: Finding the Bottleneck That Matters
Most optimisation effort goes into code that was never the problem, because intuition about where time goes is reliably wrong. This path builds the discipline instead: the arithmetic that caps what any optimisation can buy, how profilers work and how to read a flame graph, the six causes behind almost all real slowness, and how to benchmark honestly and recognise the point where optimising stops paying.
Deployment Strategies: Shipping Without Breaking Things
A deploy replaces a working system with a different one while people are using it. This path covers how teams make that routine: the four strategies as answers to how many users meet a bad version, canary releases as controlled experiments, feature flags separating deploy from release, and the compatibility discipline that decides whether the rollback you are counting on actually works.
The AWS Ecosystem: A Map of the Platform
AWS lists hundreds of services, which makes it look like a catalogue to memorise. It is not. This path gives you the scaffolding everything else hangs from: the geography of regions and availability zones, accounts and IAM as your blast radius, the compute spectrum from machines to functions, the three shapes of storage and how to choose a database, and the network layout that quietly decides your bill.
Testing Strategy: What Is Worth Testing
Tests are a purchase, not a virtue: you spend writing time, run time and maintenance time to buy confidence about specific breakages. This path works out how to spend that budget. What each level of test can and cannot catch, how test doubles buy speed by trading away fidelity, how property-based testing finds the inputs you would never have imagined, and why flakiness destroys a suite faster than any missing test.
Observability: Knowing Why Production Is Slow
Monitoring answers the questions you wrote down in advance; production fails in ways you did not. This path builds observability from its raw materials: the three telemetry signals and their very different bills, the percentile and cardinality arithmetic that decides what your data can honestly say, tracing and the craft of throwing spans away, and the SLO machinery that turns reliability into a number a team can actually spend.
Kubernetes: Orchestration as a Set of Control Loops
Kubernetes looks like a pile of object types until you notice it is one idea repeated. Store a description of the desired state, and run loops that compare it to reality and act on the difference. Because those loops read the current state rather than react to events, missed and duplicated messages are harmless and an interrupted controller simply resumes. This path builds the loop, shows every object as a controller joined by label matching, covers the two resource numbers that drive most cost and latency bugs, and ends with the failures the design itself creates.
Containers From First Principles
There is no container object in the Linux kernel and no container system call. A container is an ordinary process with several isolation features switched on, all of which predate the technology by years. So the thing that actually changed practice was not the kernel: it was an image format that is immutable, content-addressed and shippable, built on the same idea as Git's object store. This path covers the primitives, the format and its consequences, where data and traffic have to go, and the honest answer on how much the isolation is worth.
Git Internals: The Model Under the Commands
Git's command line has a deserved reputation, and the data model underneath is small and regular. It is a content-addressed store of four object types, plus a set of files holding hashes. This path derives the object hash by hand, shows that a branch is a forty-character file, explains what a three-way merge actually computes and why rebase creates new commits rather than moving them, and ends with recovery. Every command turns out to add objects or move a pointer.
Go: Goroutines, Channels, and What Was Left Out
A thread costs enough that programs are architected around not having many. A goroutine costs little enough that the constraint disappears, and the designs that become available are the point. This path builds the scheduler that makes them cheap, the channels that let them coordinate without shared memory, and the leak that is the characteristic Go bug because nothing stops a goroutine but its own return. It ends on the omissions: no exceptions, no inheritance, no formatting choices, and what each one trades.
Rust: Ownership, Borrowing, and What They Buy
Every language decides who frees memory and when, and Rust decides it at compile time from one rule: each value has exactly one owner. This path builds that rule, then the borrowing discipline that lets values be lent without transferring them. The payoff arrives in the third lesson, where data races turn out to be compile errors for free, because a race is aliasing plus mutation and the checker already forbids that. It ends on the escape hatches and where the guarantee genuinely stops.
No-Code Automation: Building Workflows That Keep Working
These platforms remove the cost of writing integration code, which was real, and leave the cost of operating an integration, which is larger and arrives later. This cursus covers why automations break once they run unattended, why partial failure is worse than total failure, and the idempotency problem behind most real damage. Then the patterns that survive production: validation, safe retries, rate limits, testing what you cannot test, and credentials. It ends on ownership, shadow automation, and when a flow has outgrown the tool.
Deploying Open-Source LLMs Privately
Self-hosting is usually justified on privacy grounds, and the argument is weaker than assumed: it changes who owns the controls, not whether controls are needed. This cursus examines the decision honestly, the reasons that hold up better than privacy, and the middle options the either-or framing hides. Then the serving stack: why generation is memory-bandwidth-bound, how continuous batching and KV cache management decide throughput, and sizing hardware. Then operating it, including the safety layer you inherited and must now rebuild.
Building an AI QA Function
Software QA assumes a specification, deterministic behaviour, and a system that changes only when you change it. An AI feature violates all three, and a provider updating a model shifts behaviour with no commit for your pipeline to catch. This cursus covers what replaces the old practice: characterising a distribution rather than asserting correctness, the evaluation set as the function's real asset, a release gate with thresholds set before results are seen, and how to stand the function up in ninety days without it quietly dying.
Subversion (SVN): The Centralized Version Control System
Git won, but Subversion never left, and plenty of long-lived, high-value codebases still run on it. Knowing SVN makes you useful where the code actually lives, and it sharpens your sense of what Git chose and why. This short cursus covers SVN on its own terms: the centralized model with its global revision numbers and atomic commits, the everyday update-edit-commit workflow with conflicts and locking, and branches, tags, and merging built from cheap directory copies, plus how to bridge to Git with git svn.
Healthcare Interoperability: HL7, FHIR, and Integration Engines
Clinical systems do not speak the same language by default, and someone has to make them. Healthcare interoperability is a durable, in-demand engineering niche with a low barrier to practice. This cursus builds it from the ground up: the interoperability problem and the HL7 v2 message model that still carries most hospital traffic, then FHIR's modern resource and REST API model, and finally the integration engine, Mirth Connect, that routes and transforms clinical data between systems, plus the concrete skills to break in.
How Unity works: the engine under your game
Stop fighting the engine and start using it. Learn why Unity is built on composition instead of class hierarchies, exactly when your code runs across Awake, Update, and the physics loop, how prefabs and serialization decide what survives to disk, and why physics runs on its own 50Hz clock. Finish able to explain the bugs that stump most Unity developers: the field that keeps resetting, the object that tunnels through walls, and the script that mysteriously never runs.
How Python really works: CPython internals
Go under the hood of the interpreter that runs most Python. Start with the object and data model, where every value is a heap object and behavior lives in dunder methods. See how source becomes bytecode and runs on a stack machine with an adaptive interpreter. Learn how reference counting and the cyclic collector reclaim memory. Finish with the GIL and the free-threading build that makes it optional. Come out able to reason about performance, memory, and concurrency from mechanism, not folklore.
Reverse engineering fundamentals
How to read a compiled binary back into something a human can reason about. The file format and loader, disassembly and decompilation of x86-64, and dynamic analysis with debuggers and instrumentation. Three lessons of mechanism — what the toolchain on each side actually does, and what no amount of effort can recover.
Advanced Python for backend
The advanced syntax and runtime patterns that production Python backends rely on. Async and asyncio fundamentals, the type system as both static contract and runtime spec, FastAPI's dependency-injection model, and SQLAlchemy 2.0's async ORM — four lessons of mechanism over framework hagiography.
Scalable System Design
Design systems that handle millions of users without falling over. You will size and scale app tiers with load balancers and autoscaling, apply caching strategies that cut DB load by 95%, partition databases with sharding and replication, and wire services together with message queues and resilience patterns — leaving you ready to lead a real system design review.
How Compilers Work
Follow source code on its full journey from raw characters to machine instructions. You will build intuition for each compiler phase — scanning, parsing, semantic analysis, and code generation — and understand why each exists, what can go wrong inside it, and how the phases connect. By the end you will be able to read compiler error messages with precision, write a toy expression evaluator, and reason about what your compiler is doing when you flip on -O2.
Distributed Systems Fundamentals
After finishing this cursus you will be able to design, evaluate, and reason about distributed systems with engineering precision: model failures and time correctly, choose the right consistency level for a workload, explain why consensus is hard and how Raft solves it safely, and architect replication and partitioning strategies that scale without creating hot spots or correctness bugs.
Database Internals
Go beneath the SQL surface and understand how databases actually work. After this track you will be able to explain how data is laid out on disk, why B-trees dominate relational indexes, when to reach for an LSM-tree instead, and how MVCC lets Postgres serve thousands of concurrent readers and writers without blocking. You will read query plans, tune buffer pools and checkpoints, design indexes that avoid heap fetches, and reason precisely about isolation levels and concurrency anomalies.
Concurrency and Parallel Programming
Master the full concurrency stack: from race conditions and lost updates, through mutexes and deadlock prevention, to CPU memory models and lock-free atomics, and finally to async event loops and coroutines. After this cursus you can reason about any concurrent system, pick the right synchronization primitive, and debug races and deadlocks methodically.
Applied Modern Cryptography
Build the cryptographic judgment to evaluate any system's security posture. You will be able to choose the right primitive for each job (AES-GCM, HMAC, Argon2, ECDH, Ed25519), explain why common constructions fail (ECB, textbook RSA, nonce reuse, bare CTR), read a TLS 1.3 handshake trace, and audit real code for the most dangerous cryptographic misuses.
Full-Stack with Node.js, React, and Next.js
An eight-lesson path that builds a full-stack engineer from the runtime up. Start with the Node.js event loop, build a REST API with Express, learn React fundamentals and hooks, master modern data fetching, then move into Next.js App Router, Server Components, Server Actions, and a Prisma + Postgres deployment.
Parallel Programming with CUDA
A six-lesson path from why GPUs exist to writing your own performant CUDA kernels. Learn the programming model, memory hierarchy, and optimization techniques that turn a 100x speedup from theoretical into practical.
API Design & Reliability
Six lessons on shipping an HTTP API that other engineers actually enjoy using. Start with REST principles and status codes, secure it with OAuth 2.0, then make it survive real traffic with rate limiting, idempotency, and load balancing.
Data Engineering Foundations
An eight-lesson path from SQL fundamentals to a modern data stack. You'll learn how to query and tune relational data, cache hot reads, model with dbt, choose between lakehouse and warehouse architectures, and wire pipelines together with Kafka and Airflow. By the end you can reason about every layer a production data platform actually runs on.
All Programming lessons
Benchmarks That Hold Up, and Knowing When to Stop
A benchmark is an experiment, and most are badly designed enough to produce confident wrong answers. This lesson covers what a measurement must control to mean anything, the ways microbenchmarks lie including code the compiler deletes, how to catch regressions in continuous integration despite noisy machines, and how to recognise the point where optimising stops paying.
Where Time Actually Goes: The Six Usual Suspects
Slow software is slow for a short list of reasons, and each one has a signature you can recognise before you find the code. This lesson covers the six recurring bottleneck classes, waiting on I/O, chatty queries, allocation pressure, lock contention, memory access patterns and serialisation, with the symptom that identifies each and the fix that actually works.
How Profilers Work, and How to Read a Flame Graph
A profiler is not a neutral observer: sampling and instrumentation see different things, distort the program in different ways, and answer different questions. This lesson covers how each works, why CPU time and wall-clock time give opposite answers, and how to read a flame graph correctly, including the axis that means nothing and is misread constantly.
Measure First: The Arithmetic That Decides What to Optimise
Most optimisation effort is spent on code that was never the problem, and the reason is that intuition about where time goes is reliably wrong. This lesson covers why guessing fails, the arithmetic that caps what any optimisation can buy, the difference between latency and throughput, and how to set a target that tells you when to stop.
Canary Releases: Deciding With Evidence Instead of Nerve
A canary release sends a slice of real traffic to a new version and asks whether it is healthy. This lesson covers what to measure, why comparing the canary against the current version beats comparing against history, the statistics problem that makes small canaries weak evidence, and how automated promotion and rollback turn a judgement call into a rule.
Why Deploys Break Things, and the Strategies That Answer It
Deploying is the moment a working system is replaced by a different one while people are using it. This lesson covers what actually goes wrong at that moment, the research finding that shipping fast and shipping safely are not opposites, and the four deployment strategies as answers to one question: how many users meet a bad version before you find out.
Making Rollback Possible: The Changes That Cannot Be Undone
Every deployment strategy assumes you can go back, and that assumption is the one most often false when it matters. This lesson covers what actually makes a rollback work, the database migration pattern that keeps schema changes reversible, the one-way doors that no amount of tooling can undo, and how to tell which kind of change you are about to ship.
Feature Flags: Separating Deploy From Release
A feature flag turns shipping code and exposing behaviour into two independent decisions, which is what lets a team deploy continuously while releasing on someone else's schedule. This lesson covers the four kinds of flag and their very different lifespans, the debt they accumulate, and the discipline that keeps a flag system from becoming untestable.
Networking and the Bill: Where the Surprises Live
Two things reliably surprise teams new to AWS: the network they must build before anything can talk, and an invoice driven by charges nobody chose deliberately. The two are connected, because moving data is where much of the cost hides. This lesson covers the virtual network primitives, the traffic charges that follow from them, and how to read a bill.
Storage and Data: Three Shapes, and Choosing a Database
Cloud storage looks like a long product list and is really three physical shapes, object, block and file, each with an access pattern it is built for and one it is bad at. This lesson covers those three, the difference between durability and availability that people conflate, what eleven nines actually means at scale, and how to choose a database by access pattern.
The Compute Spectrum: Machines, Containers, Functions
AWS offers several ways to run code, and they are not competitors so much as points on one spectrum trading control for operational relief. This lesson walks that spectrum from virtual machines to serverless functions, what you hand over at each step, the cold-start and state constraints that decide fit, and the honest cases where serverless is the wrong answer.
The Shape of AWS: Regions, Accounts, and Who Secures What
AWS offers hundreds of services, which makes it look like a catalogue to memorise. It is not. This lesson gives the four structures everything else hangs from: the physical geography of regions and availability zones, the account as a blast-radius boundary, IAM as the one gatekeeper every call passes, and the responsibility line between you and the provider.
Flakiness, Coverage, and Suites That Survive
A test suite is a product with a maintenance cost, and two forces decide whether it stays useful: flakiness, which destroys the signal, and the metrics teams use to judge it, which mostly measure the wrong thing. This lesson covers where flakiness comes from, the arithmetic that makes it fatal at scale, what coverage research actually found, and what to measure instead.
Property-Based Testing: Assert the Law, Not the Example
An example-based test checks the cases you thought of, which is exactly the set that excludes your bugs. Property-based testing inverts it: state a law the code must obey for all inputs, let the machine hunt for a counterexample, and let it shrink that counterexample to something you can read. This lesson covers the property patterns, generators, shrinking, and where the technique stops fitting.
Test Doubles: Isolation and What It Costs
Replacing a real dependency with a stand-in is what makes a unit test fast, deterministic, and able to reach states you cannot otherwise produce. It is also how a suite ends up green while the system is broken. This lesson covers the five kinds of double, when each is right, and the two failure modes that follow every team who reaches for mocks by reflex.
What Is Worth Testing, and the Shape That Follows
Every test costs time to write, time to run, and time to maintain when the code moves. This lesson works out what that budget should buy: what each level of test can and cannot catch, why the pyramid has the shape it does, why some teams invert it, and the arithmetic that decides both questions before anyone argues about it.
SLOs and Error Budgets: Turning Reliability Into a Number
How reliable should the service be? Wrong question: the right one is how much unreliability you can afford, spent deliberately. This lesson builds the SLI, SLO and error budget machinery from Google's SRE practice, does the arithmetic of nines, explains burn-rate alerting, and shows why 100 percent is the wrong target.
Distributed Tracing and the Art of Throwing Data Away
Tracing every request through every service produces the most useful telemetry you have and more of it than anyone can afford. This lesson covers how context propagation actually stitches a trace together, the head versus tail sampling decision and why it determines which incidents you can debug, and the collector pipeline where all of it is enforced.
Percentiles and Cardinality: The Two Numbers That Run Your Bill
Two pieces of arithmetic decide whether your telemetry is useful and affordable. Percentiles, because averages hide exactly the users who are suffering, and you cannot average a p99. Cardinality, because metric cost is not per event but per label combination, and one careless label can multiply your bill by the size of your user base. This lesson does both mechanisms by hand.
Metrics, Logs, Traces: Three Signals, Three Cost Models
Observability is not a product you buy but a property your system has: can you explain a behaviour you did not predict? This lesson defines the three telemetry signals, what question each answers, why their costs grow along completely different axes, and why the difference between monitoring and observability is the difference between known and unknown failure modes.
The Failure Modes the Design Creates
Reconciliation buys robustness and charges for it in a specific currency: nothing is ever definitely finished, commands succeed without meaning anything worked, and manual fixes are silently undone. This lesson covers the failures that come from the model rather than from bugs, how to debug them, and when the whole thing is the wrong tool.
Scheduling and Resources: Requests Are Not Limits
Two numbers govern where a pod lands and how it behaves under pressure, and they do completely different jobs. Requests are used for placement and are a promise; limits are enforced at run time and are a ceiling. This lesson separates them, covers the asymmetry between CPU and memory enforcement, and explains what actually happens when a node runs out.
Pods, Services, and the Label That Joins Them
The object types look like an arbitrary vocabulary until you notice they are layers of controllers, each reconciling the one below. This lesson works up from the pod, explains why a Service can point at something whose address changes constantly, and shows that the whole system is joined by one mechanism: a label match.
Reconciliation: Declare the End State, Not the Steps
Kubernetes is often taught as a pile of object types. Underneath it is one idea repeated: store a description of the desired state, and run loops that continuously compare it to reality and act on the difference. This lesson builds that loop, explains why it is level-triggered rather than event-driven, and shows what the design buys and costs.