CppCon 2026

For a long time, programming was constrained by our ability to produce code. That constraint is eroding rapidly. AI systems can now generate substantial amounts of code with little effort, while inspection, validation, and integration remain difficult.

This transition alters the economics of software engineering. Code is becoming easier to generate than to reason about, and sound judgment grows scarcer relative to specialized intelligence. The emphasis shifts from authoring programs to understanding systems: their structure, invariants, interactions, and long-term evolution.

The talk examines this transition through the lenses of history, language design, and recent developments in AI and metaprogramming. It argues that the central challenges ahead concern not code generation, but the management of complexity at scale.

Topics include: - historical parallels and failed predictions in computing - AI-assisted software development - language design and metaprogramming - C++ reflection and compile-time programming - abstraction as context compression - complexity management in large systems

AI will undoubtedly transform how we build software. As before, the decisive factor will be our capacity to adapt.

The C++ standard is moving more and more in the direction of safety. In the process, the committee is providing us with tools to help make our own code safer.

Many of these tools are specifically around numerics and none of them are enabled by default! To make matters worse, most C++ programmers don't even know these library features exist! Even if they did know, they wouldn't use them, because they are far too wordy.

We're going to do a survey of the relatively recently added new numeric safety related language features and see how they might come together in a real-ish project.

To write the best C++ application code, you must think like a library author. This talk reframes application development as a form of library design, where every component is a potential asset for future maintenance and extension.

We'll survey the C++ language features that empower library authors to create powerful, flexible, and safe abstractions. From there, we'll distill the 'library author mindset.’ You will leave with concrete principles and techniques to make your application code more modular, maintainable, composable, reusable, testable, and ultimately, more valuable.

How far can C++26 reflection take us? What are some techniques, gotchas, limitations and workarounds? How would reflection help write code that interacts with bespoke binary data formats? Come along as we explore a practical example together.

This talk will review advice gleaned from applying C++26 reflection to binary (structs on a wire) protocol handling. This type of protocol exists all around the world from financial market data formats to custom hardware interaction. Is the proposed reflection support enough to take a protocol definition written in the native terms of C++ and create flexible components to interact with that protocol?

To navigate safely, a robot must simultaneously ingest high-frequency sensor data, compute complex spatial algorithms, and publish control commands. Historically, frameworks like ROS 2 have handled this via asynchronous callbacks. However, as autonomous stacks scale, relying heavily on unstructured callbacks can obscure control flow, making state synchronization difficult and robust task cancellation notoriously complex.

This talk explores how to transform unstructured, callback-based architectures in a robotics navigation stack into declarative pipelines using structured concurrency (specifically, C++ Senders and Receivers via stdexec). Attendees will dive into two real-world architectural examples: a collision monitor and a waypoint follower, showcasing readable logic pipelines, thread-hopping between an event loop and a static thread pool, and clean task cancellation.

As legacy C++ codebases evolve to meet new requirements, fix architectural issues, and address performance bottlenecks, refactoring becomes inevitable — but full rewrites are risky, costly, and rarely practical. This talk explores how modern C++ features — especially functional programming techniques and type erasure — can be used to refactor and extend legacy systems. We will present a concrete, low-risk, and measurable four-step technical framework for incremental modernization, by leveraging pure functions, discriminated union types, and type-erased callables to achieve safer, more flexible code with compile-time guarantees, even in traditionally object-oriented codebases.

The core of the talk will walk through the entire process on a concrete code example, illustrating (1) how to isolate existing logic, (2) define safer data models using immutable types, (3) deploy type erasure to create a robust, non-disruptive adapter boundary between old and new code, and (4) measurably execute the migration with safe testing practices. We will also share lessons learned from real-world experience and conclude by showing how these foundational patterns can be extended into future-thinking designs, leveraging examples with accepted C++26 features. Every step is grounded in real production constraints and trade-offs, and the techniques transfer directly to any domain where full rewrites are not an option. Attendees will walk away equipped with practical strategies to incrementally refactor your legacy systems without sacrificing stability or velocity.

In many languages, you can mock almost anything. Python has unittest.mock, Java has Mockito and PowerMock, and C# has Moq. In C++, you can usually mock only what was designed to be mockable — and little else. Want to mock a free function? Wrap it in an interface. A static method? Refactor to a template. A non-virtual member? Redesign the class hierarchy. The cost is real: developers either shape production code around testing-tool constraints instead of domain needs, or they simply leave hard-to-mock code untested.

This talk introduces BonoboMock, a new C++ mocking library we developed and published as open source that can mock almost any C++ callable — free functions, static methods, non-virtual members, private members, templates, lambdas, and even extern "C" routines — without modifying the code under test. Developed and battle-tested at Bloomberg for three years across a very large C++/C codebase, it aims to deliver a first-class testing experience for C++. Under the hood it uses runtime binary patching — replacing function prologues with redirections at test time, restoring them on scope exit — all surfaced through familiar Google Mock syntax.

We'll show BonoboMock in action, walk through the patching mechanism, and share how to get started -- you'll walk away ready to test almost any function, regardless of how it was designed. Strong test coverage has always been essential, and AI-assisted code generation only increases the need for reliable testing backpressure. C++ deserves mocking tooling that matches.

Tooling Track sessions are sponsored by Optiver.

First day on a new project. You set up your workstation and then download a repository of over 1,000,000 lines of code. Even more intimidating, the code has been around for 20 years. Parts are in legacy C++, parts use the latest C++26 features, and increasingly, AI coding assistants are weaving new code through all of it, meaning some parts of the codebase may have never been fully understood by a developer. You feel overwhelmed. Don't Panic! In this talk, we share techniques and tools that real engineers use to find their bearings in code they didn't write, from the humble grep through debuggers and profilers to time-travel debugging and AI-assisted exploration. The audience will leave this talk with a mix of simple and advanced tricks for navigating large and complicated codebases.

An overview of contemporary hardware platforms and how software engineering and design practices and methodologies could help in building performant systems, with a particular focus on low level optimizations. Contrary to attempting direct low level software engineering for addressing performance specifics, this talk would focus on how higher level abstractions could and should be used, and how a software engineer could help the compiler to “do the right thing”. The trend of moving software engineering focus upward to constructs that have a tendency of hiding the specific of the underlying platform is quite clear – and there certainly are very good reasons for such a paradigm change.

Performance matters. Premature optimization is evil. Is there anything in between those two extremes? How feasible is it to rely on the abstraction of the platform as hidden by the compiler and the corresponding libraries and language constructs, expecting that it will be able to realize the intended lower level optimizations? How much of hints and specifics would one need to expose for the compiler to be able to get the equivalent of direct low level approach? What is the right balance between expressing application logic via higher level construct yet still being able to gain the performance benefits compared relying on the low level specifics?

Looking from the practical applicability of lambdas, iterators, ranges, coroutines, error handling, and safe(r) memory access, the talk would attempt to cover a set of use cases with a focus on analysis of what could be done for focusing on performance.

The overall goal of the talk is not to go deep into low level aspects; the goal is to explain that one needs to be aware of such low level details while operating on the higher level constructs.

Air-gapped systems used to be protected by what they could not reach. That assumption breaks when telemetry, sensor data, and diagnostic output leave the system and AI model-influenced behavior comes back in, even without a network connection.

This talk examines the C++ engineering decisions that determine whether that data exchange is safe or silently dangerous. The problems are C++-specific and subtle: struct layout and padding that corrupt data silently across serialization boundaries; undefined behavior in type-punned casts that passes tests but breaks under a different compiler or optimization level; ownership and lifetime assumptions that hold inside the system but become dangling references once data crosses a trust boundary; allocator and exception constraints in no-exception embedded environments that make standard validation patterns unavailable.

We will look at how C++ systems in industrial, embedded, and safety-critical environments can export logs, telemetry, and traces into AI pipelines without creating feedback loops that affect production behavior in unverified ways. The engineering controls are concrete: typed serialization boundaries that make layout assumptions explicit, schema validation that survives ABI differences between host and edge, provenance tracking built into ownership types, deterministic guardrails around probabilistic inference outputs, and fail-safe fallbacks that don't rely on the model being correct.

Attendees will leave with a design approach for integrating AI-adjacent workflows into high-integrity C++ systems. One that treats the data boundary as a first-class security surface, not an afterthought.

C++ lifetime management is fundamentally built around synchronous scope exit. Constructors establish invariants, destructors release resources, and RAII permits ownership and cleanup to compose naturally with ordinary control flow. Asynchronous systems disrupt this model. Destruction may itself require asynchronous work, and “just launch another task in the destructor” quickly turns deterministic lifetime management into unstructured background activity.

This talk explores the implementation of async lifetime management in std::execution, based on the enter/exit scope sender framework proposed in P3955. Rather than treating async construction and destruction as special cases, the model reframes them as composable asynchronous protocols built around explicit async scope entry and exit operations. The talk follows the process of turning these ideas into working code, beginning from the low-level enter/exit sender abstractions and progressively assembling higher-level lifetime facilities on top. Along the way, the implementation uncovers an important self-similarity in the problem domain: Higher-level async lifetime facilities can themselves be expressed in terms of the same lower-level async lifetime primitives.

The implementation discussion focuses on the machinery required to make these guarantees real: Coordinating async teardown within structured concurrency, managing partially-entered scopes, and preserving deterministic cleanup semantics even when destruction itself becomes asynchronous. The resulting design serves both as a practical exploration of async lifetime management and as a case study in how implementing an abstraction can reveal deeper structural properties hiding inside the model itself.

In this talk we will explore "Concepts," a powerful feature for expressing template requirements added in C++20. Participants will learn how concepts improve code readability, provide clearer compiler diagnostics, and help enforce correct usage of templates and function overloading. Through simple, hands-on examples, we will explore the syntax, common use cases, and best practices for integrating concepts into modern C++ code. By the end of the session, attendees will understand how to write safer, more maintainable template code and leverage concepts to communicate intent clearly.

Many of the problems you encounter in C++ have already been solved, just not by anyone you talk to regularly. Idioms encode best practices into recognizable patterns that help reduce cognitive load, convey intent, and eliminate entire categories of bugs. However, C++ isn’t a single community. It is a language spoken across many industries, including, but not limited to: scientific computing, games, embedded systems, and finance. Each of these domains has developed its own idioms, patterns that are shaped by the specific constraints they face.

While our vernacular evolves independently, the problems they solve often overlap. Consider the convergence around the Curiously Recurring Template Pattern (CRTP) and variant-based dispatch. Embedded systems engineers, working without a heap, paired CRTP with tagged unions to achieve polymorphic behavior over a closed set of types in static storage. Independent of this, high frequency trading firms, driven by latency rather than memory constraints, adopted the same combination to avoid indirect branches and allocator contention. Two communities, two different constraints, but the same pattern. Unfortunately, since each community was solving the problem in isolation, it had to be discovered twice.

This is not a new phenomenon. C++ idioms have been crossing boundaries since at least Coplien’s 1992 work on advanced programming styles. Data-oriented design is making a similar journey today, moving from game engines, where cache-friendly layouts are basically required, to financial systems, where the same CPU architecture imposes the same penalties.

Like a spoken language, C++ evolves not just through formal specification but through usage. New idioms emerge as communities discover better ways to express intent with new or existing features. They grow and spread via conferences, open source libraries, and cross-industry hiring. The C++ Core Guidelines capture some of this evolution, but much of it lives within codebases and communities that you may never find if you don’t look.

This is a call to look for them. We will trace how specific idioms have traveled between industries, examine what made them transferable, and argue that the next improvement to your codebase may already exist, written in a usage of C++ you haven’t learned about yet. While you are at this conference, attend the talks from industries other than your own. You may find a solution in a session that you may otherwise skip.

The software in most embedded projects written in 2026 looks like the software in embedded projects written in 1996. This isn't because 1996 was the peak of innovation and quality but rather has more to do with biases and FUD. Unfortunately, much of the world suffers from these choices: users, developers, managers, and shareholders.

There are better ways to write firmware for embedded systems; proven techniques and implementation strategies that I will describe in this talk. We will explore techniques based on abstraction and composition that maximize understanding while reducing the codegen footprint. We will apply these strategies at all levels of the firmware stack: interrupts, register manipulation, peripheral communications, and the application glue.

Join me and leave with open-source libraries, an open-source starter project, and techniques that you can apply to your greenfield and existing projects.

How far can modern C++ push an emulator before performance stops being a C++ problem and becomes a systems problem? This talk is the final part of a CppCon trilogy about building an extremely fast Game Boy emulator in modern C++, moving from high-performance emulation techniques to the limits imposed by hardware, compilers, and CPU architecture.

The target is intentionally absurd but not arbitrary: a Game Boy emulator running Tetris at roughly 100 times original speed, about 6,000 frames per second. However, the goal is not to support every Game Boy cartridge ever made. This project deliberately specializes for one iconic game first, using the code paths Tetris actually exercises as a guide, while keeping enough design discipline to test other games later. That means questioning the architecture of the emulator itself: instruction dispatch, memory access, generated code size, cache behavior, branch prediction, compile-time computation, benchmarking methodology, and the tension between accuracy, maintainability, flexibility, and raw throughput.

This is not a victory-lap performance talk. It is a measurement-driven engineering investigation. Rather than treating 6,000 FPS as just a headline number, this talk treats it as a stress test: a way to force difficult design decisions into the open. Attendees will leave with concrete lessons about performance-oriented C++: how to measure methodically, how to specialize without losing control, how to reason about modern CPU behavior, and how to decide when an optimization is worth its cost.