Updated June 2026

Advanced Zig

Class Duration

21 hours of live training delivered over 3 days.

Student Prerequisites

Completion of Zig Essentials or equivalent working experience with Zig
Comfort with the Zig toolchain: building, testing, and running projects with zig build
Working knowledge of pointers, slices, structs, error unions, and basic allocator usage
General systems programming background is helpful but not required

Target Audience

This course is for developers who already write Zig and want to master the parts of the language that make it distinctive: comptime metaprogramming, explicit allocator design, and the new std.Io interface introduced in the Zig 0.16 standard library redesign. It is well suited to teams building libraries, services, games, or embedded systems in Zig who need to write code that is fast, testable, and idiomatic on Zig 0.16 and beyond.

Description

Advanced Zig takes working Zig programmers deep into the language and standard library as they exist in 2026. Zig 0.16, released in April 2026, shipped the largest standard library redesign in the language's history, centered on the new std.Io interface: I/O capability is now passed as a parameter — just like an Allocator — and async/await returned to the language via std.Io.Evented, with stackful coroutines, no function coloring, and high-performance backends built on io_uring on Linux and Grand Central Dispatch on macOS. This course covers that new world in depth: writing Io-generic libraries, structuring concurrent code around idempotent await and cancel with the defer-cancel pattern, and understanding the lock-free, thread-safe allocator design underneath.

Beyond I/O, the course is a tour of advanced Zig craftsmanship: comptime metaprogramming with types as first-class values, designing and composing allocators, error handling strategies that scale beyond a single file, data-oriented design with MultiArrayList, explicit SIMD with @Vector, and the testing, fuzzing, and coverage tooling highlighted on the 2026 Zig roadmap. Students leave able to make informed performance and architecture decisions and to write Zig that other Zig programmers recognize as idiomatic.

Learning Outcomes

Use comptime to write generic, reflective, and zero-cost abstractions, and know where comptime evaluation reaches its limits
Choose, compose, and implement allocators — arena, fixed-buffer, general-purpose, and custom — and detect leaks in tests
Write libraries and applications against the Zig 0.16 std.Io interface, accepting Io as a parameter rather than hard-coding an I/O implementation
Build concurrent programs with std.Io.Evented async/await, tasks, and stackful coroutines, backed by io_uring or Grand Central Dispatch
Apply the defer-cancel pattern and reason about idempotent await and cancel semantics
Design error sets, errdefer cleanup, and diagnostics patterns that scale across large codebases
Restructure data with MultiArrayList and struct-of-arrays layouts for cache-friendly performance
Vectorize hot loops explicitly with @Vector SIMD types
Test, fuzz, and measure coverage of Zig code in line with the 2026 roadmap tooling, and profile real workloads
Select build modes deliberately and explain the safety semantics of each

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Students will need a free, personal GitHub account to access the courseware. Students will need permission to install Zig 0.16, a C toolchain, and Visual Studio Code (with the Zig language extension) on their computers. A Linux environment is recommended for the io_uring portions of the course; macOS students will use the Grand Central Dispatch backend. If students are unable to configure a local environment, a cloud-based environment can be provided.

Training Topics

Zig in 2026

Zig 0.16: the largest stdlib redesign in the language's history
What changed and why: std.Io, async/await reintroduced
The road to 1.0 and the 2026 roadmap: compiler speedups, fuzzing, coverage
Tracking the 0.17 development cycle
Reading and navigating the standard library source

Comptime Metaprogramming Deep Dive

Types as first-class values
Generic functions and generic data structures
@TypeOf, @typeInfo, and reflection
comptime blocks, parameters, and variables
Generating code and tables at compile time
Comptime evaluation limits: branch quotas and what cannot run at comptime
Comparing comptime to macros, templates, and codegen

Allocator Design

The Allocator interface and the philosophy of explicit allocation
Arena allocators and lifetime-scoped allocation
Fixed-buffer allocators for embedded and hot paths
The general-purpose allocator and its lock-free, thread-safe design
Writing custom allocators: wrapping, instrumenting, and failing on purpose
Leak detection in tests with std.testing.allocator
Failure-injection testing of allocation paths

The New std.Io In Depth

Io as a parameter: the Allocator pattern applied to I/O
std.Io.Evented and the async/await model
Stackful coroutines and fibers: no function coloring
Backends: io_uring on Linux, Grand Central Dispatch on macOS
Spawning and managing tasks
Idempotent await and cancel semantics
The defer-cancel pattern for structured cleanup
Writing Io-generic libraries that callers can drive with any implementation
Blocking, threaded, and evented implementations side by side

Error Handling at Scale

Error sets: inferred, explicit, and merged
Designing error taxonomies for libraries vs. applications
errdefer and partial-initialization cleanup
Diagnostics patterns: returning rich error context without exceptions
Error return traces and debugging strategies

Data-Oriented Design

Why struct-of-arrays beats array-of-structs on modern hardware
std.MultiArrayList in practice
Handles and indices instead of pointers
Memory layout, alignment, and cache behavior
Case study: refactoring an entity store to SoA

SIMD with @Vector

@Vector types and vector operations
@splat, @reduce, @shuffle, and @select
Letting the compiler choose vector width vs. fixing it
Vectorizing a real hot loop and measuring the gain
When the auto-vectorizer already has you covered

Testing, Fuzzing, and Coverage

Structuring test blocks across a large codebase
Built-in fuzzing on the 2026 roadmap: zig build test --fuzz
Coverage tooling and interpreting results
Testing Io-generic code with test implementations
Property-style testing patterns in Zig

Performance, Build Modes, and Safety

Build modes: Debug, ReleaseSafe, ReleaseFast, ReleaseSmall
Safety semantics: which checks exist in which mode
Illegal behavior vs. undefined behavior
Profiling Zig programs: perf, Tracy, and benchmark harnesses
Reading optimized output and verifying assumptions