Deep Engineering #26: Pushing More Work into the Compiler with Ivo Balbaert
Mojo, metaprogramming, and squeezing more from your existing compute.
Quantum Computing for Leaders: 3-Part Masterclass
Build your team’s quantum readiness in just three sessions. Join Robert Loredo and IBM guest experts Travis Scholten and John Buselli to learn how to evaluate quantum bets, design quantum-ready architecture, and secure your org against future quantum threats. Use code QUANTUM30 for 30% off.
✍️From the editor’s desk,
Anthropic just announced it plans to spend $50 billion on custom AI data centers in Texas and New York, aiming to bring them online from 2026 onwards. That sits on top of a Q3 report from TD Cowen showing US hyperscalers leased 7.4 GW of data center capacity in a single quarter—more than in all of 2024, with another ~10.2 GW in the pipeline. But, that much steel and silicon only pays off if the software running on it is fast, predictable, and safe to operate at scale.
So, what does it look like in practice to push more work into the compiler so our systems run faster and fail less often in production? Our feature, Compile-Time Code: Writing Faster, Safer Software Before It Runs, looks at how C++26, Rust, Swift, Zig, and Mojo are each moving computation, code generation, and safety checks into the compile step—using metaprogramming, static analysis, and hardware-aware specialization to trade a bit more build time for significantly better runtime behavior.
We then turn to Ivo Balbaert’s fourth article in our ongoing Building with Mojo series, which walks you through Mojo’s compile-time metaprogramming toolkit—aliases, @parameter loops and conditionals, constrained functions, parametric closures, and parameterized structs—and shows how to use these features.
Balbaert is a lecturer in web programming and databases at CVO Antwerpen and a long-time Packt author of introductions to new languages—including Dart, Julia, Rust, and Red.
Rounding out today’s theme, our Tool of the Week, Pyrefly, shows the same “do more before it runs” philosophy applied to large Python codebases, and this issue’s Tech Briefs track the surrounding ecosystem shifts—from VS Code’s new Agent HQ, LLVM 21.1.5, and NVIDIA’s GTC DC keynote on AI factories and extreme co-design, to Kubernetes 1.35’s release cycle and a recent Pragmatic Engineer conversation with Chris Lattner on Swift, Mojo, and high-performance AI engineering.
Let’s get started!
C++ Memory Management Masterclass (Cohort 2)
Stop chasing C++ memory leaks. In this 2-day live masterclass, Patrice Roy (ISO C++ committee member and author of C++ Memory Management) walks you through RAII, smart pointers, and modern techniques to write fast, safe, leak-free C++ code.
Compile-Time Code: Writing Faster, Safer Software Before It Runs with Ivo Balbaert
In the last year, mainstream and emerging languages have pushed more work into the compiler, letting us “write code that writes code” before programs ever run. C++26 has frozen its feature set with static reflection, so C++ code can inspect types at compile time and generate helpers such as enum-to-string mappers or bindings to other languages.
Swift’s macro system lets engineers attach tags to existing code that generate new code at compile time by operating on the abstract syntax tree, and the generated code is fully integrated into the compiler so it is treated exactly like manually written Swift and benefits from the same optimizations. Mojo, a new language for AI systems, goes further by allowing many functions and loops to run at compile time via aliases and @parameter decorators, in a style similar to Zig’s comptime. Across these ecosystems, the goal is the same: shift work into the compiler so the resulting binaries are faster, safer, and more predictable.
Performance Wins: Code Generation Ahead of Time
The most visible payoff is raw speed. In Mojo, any expression bound to an alias is evaluated at compile time, so the final binary just reads a constant instead of recomputing a value. For example, the following Mojo snippet from Balbaert’s article shows how an alias precomputes a sum at compile time, while the same function can still be called at runtime:
alias SUM = sum(10, 20, 2)
fn sum(lb: Int, ub: Int, step: Int) -> Int:
var total = 0
for i in range(lb, ub, step):
total += i
return total
fn main():
print(SUM) # => 70
print(sum(10, 20, 2)) # => 70In his recent “impossible optimization,” Evan Ovadia who is part of the Mojo Compiler Team shows a regex engine that parses a pattern and unrolls it entirely during compilation, generating a specialized matcher for each regex node. The compiler emits roughly twenty-eight times more inlined code, but the resulting email-matching regex runs about ten times faster than a conventional implementation and within roughly three percent of a hand-tuned C version . Metaprogramming effectively turns a generic algorithm into bespoke machine code.
Rust and Swift are converging on similar patterns. Rust and Swift are converging on similar patterns. Recent overviews describe Rust’s macro system as “structural” metaprogramming that operates on the abstract syntax tree rather than on raw text, integrating closely with the compiler’s hygiene and type-checking so that macros transform code safely at compile time. Swift macros, meanwhile, are described by Duolingo’s iOS architecture team as tags attached to declarations that operate on the Swift AST and generate additional code during compilation, with the expanded code treated exactly like handwritten Swift and benefiting from the same type checking and optimizations. In all three languages, the compiler effectively becomes a code generator that specializes logic ahead of time instead of relying on runtime indirection.
The trade-off is build cost. In the regex case study, the Mojo compiler had to generate around five times more code and spent noticeably longer compiling to achieve the speed-up. Rust developers report similar pain: the 2025 Rust compiler performance survey highlights procedural macros, deep generic code, and large dependency graphs as key reasons builds feel slow, and the compiler team has prioritized changes to reduce this overhead. Practically, aggressive metaprogramming needs to be reserved for performance-critical hotspots where the runtime gains justify extra compile time.
Safety by Static Analysis: Fewer Bugs at Runtime
Compile-time mechanisms are also being used to strengthen correctness. Rust’s borrow checker uses static analysis to enforce ownership and borrowing rules before the program runs. As Sunil Kumar, Principle Solution Architect at ailoitte explains, each value has a single owner and, when the owner goes out of scope, Rust frees the memory automatically, eliminating classes of bugs such as dangling pointers and double frees. The compiler also enforces that code can have many readers or exactly one writer, but never both at the same time, which prevents data races at compile time. These checks are purely static, so once the code passes, it runs at native speed with no runtime overhead.
Mojo adopts a similar philosophy, combining high-level syntax with Rust-like safety and systems-level control, and leaning on compile-time analysis to drive memory management rather than relying on a garbage collector. Tooling follows the same pattern. Meta’s Pyrefly, a new open-source Python type checker written in Rust, is designed to provide fast, incremental analysis so very large Python codebases can catch errors before runtime while still giving near-instant feedback in the editor. Together, these trends show static analysis moving into the compilation and tooling pipeline, improving safety without sacrificing performance.
One Language, Many Targets
Modern systems increasingly span CPUs, GPUs, and accelerators, and compile-time metaprogramming is becoming a key technique for targeting all of them efficiently. C++26’s static reflection is explicitly described as a building block for generating bindings to other languages and synthesizing code that adapts to platform-specific details at build time. Zig’s comptime, as Aleksey Kladov shows, supports partial evaluation and specialisation: marking a parameter as comptime forces the compiler to evaluate that dimension up front, leaving only the truly dynamic parts for runtime and enabling patterns like specialised serializers without separate code-generation steps. Mojo, built on MLIR and LLVM, is designed so that the same high-level kernel can be compiled into different low-level representations depending on the target CPU or accelerator, with loop unrolling and layout choices made at compile time rather than in hand-written device code. In each case, compile-time facilities let one codebase stretch across architectures while still producing optimized binaries.
Metaprogramming as a Design Tool
Compile-time metaprogramming is moving from niche trick to first-class design tool. Teams no longer have to choose strictly between productivity and performance; with the right language support, they can write generic, high-level code and let the compiler specialize it.
The emerging best practice is to treat compile-time facilities as a scarce, high-leverage resource: use them to pre-compute expensive decisions, encode invariants, and adapt to hardware, while keeping a close eye on build-time cost and complexity. For teams evaluating Mojo alongside Rust, Swift, C++, and Zig: structured macros, strong static analysis, and disciplined compile-time execution can deliver faster, safer systems without degenerating into unmaintainable metaprogramming hacks.
🧠Expert Insight
If you want to move from the architectural view back down into concrete syntax, the following article by Ivo Balbaert walks you through Mojo’s compile-time model step by step—from aliases and comptime execution to parametric functions, decorators like @parameter for and @parameter if, constraints, and closures. It’s a focused tour of the language features behind the trends discussed above, illustrated with small, targeted examples.
Building with Mojo (Part 4): Compile-Time Metaprogramming in Mojo
This article is Part 4 of our ongoing series on the Mojo programming language. Part 1 introduced Mojo’s origins, design goals, and its promise to unify Pythonic ergonomics with systems-level performance.
🛠️Tool of the Week
Pyrefly: Meta’s high-performance Python type checker
Pyrefly is an open source, Rust-based type checker and language server for Python. It powers lightning-fast IDE feedback and static typing over very large codebases and is intended to replace their previous internal checker in production.
Highlights:
Scale + speed: Pyrefly can type-check over 1.85 million lines of code per second on Meta’s infra, and the entire Instagram codebase in 13.4 seconds vs 100+ seconds with the previous checker.
“Invisible” safety: Designed for instant IDE feedback, so teams can push more correctness checks earlier without slowing down developers.
Gradual adoption: It supports large, partially-typed Python codebases and can infer many types automatically, making it realistic for real-world migration rather than greenfield only.
📎Tech Briefs
VS Code 1.106—Agent HQ and MCP Security Controls for AI-Heavy Workflows: The October 2025 Visual Studio Code release doubles down on AI-assisted development with a new “Agent HQ” experience that centralizes Copilot and other agents into a single dashboard, plus improved Model Context Protocol (MCP) authentication and governance. Teams can define custom agents, control which MCP servers are allowed in a workspace, and require pre-/post-approval for tools, which matters if you’re letting agents run commands or touch internal systems.
LLVM 21.1.5—Toolchain Stability for Compilers, Runtimes, and Browsers:
This is a point update but relevant for anyone shipping code on top of LLVM (including Clang, lld, libc++, MLIR, and a long tail of language runtimes and tools). The release bundles targeted fixes across x86 codegen, sanitizers, linkers, and build infrastructure, and is already being packaged by major distros like Arch Linux.
NVIDIA GTC Washington, D.C. Keynote with CEO Jensen Huang: NVIDIA’s GTC DC keynote frames AI and accelerated computing as America’s “next Apollo moment,” arguing that GPUs, AI factories, and extreme co-design across chips, systems, software, and data centers are the new essential infrastructure for science, industry, and national competitiveness. Jensen Huang walks through how this model underpins everything from 6G networks and quantum error correction to humanoid robots, autonomous vehicles, and U.S. re-industrialization via NVIDIA’s expanding hardware, software, and partner ecosystem.
Kubernetes v1.35 Release Cycle—Alpha Builds Landing Ahead of December GA: Kubernetes v1.35 is now deep into its release cycle, with alpha.3 tagged in late October and code freeze scheduled for early November ahead of a planned GA on 17 December 2025. The release team’s schedule highlights ongoing work on enhancements, production-readiness reviews, and test freezes, while the alpha builds are already available via the main kubernetes/kubernetes GitHub releases page.
From Swift to Mojo and High-Performance AI Engineering (podcast): Gergely Orosz sits down with Chris Lattner (LLVM, Swift, Mojo) to unpack how language and compiler design can lower the barrier to AI development while still hitting systems-level performance. The episode covers why Lattner thinks readability matters more than “AI-native” languages in an LLM world, how Mojo layers Python-like ergonomics on top of features like compressed floating-point formats and compile-time metaprogramming.
That’s all for today. Thank you for reading this issue of Deep Engineering. We’re just getting started, and your feedback will help shape what comes next. Do take a moment to fill out this short survey we run monthly—as a thank-you, we’ll add one Packt credit to your account, redeemable for any book of your choice.
We’ll be back next week with more expert-led content.
Stay awesome,
Divya Anne Selvaraj
Editor-in-Chief, Deep Engineering
If your company is interested in reaching an audience of developers, software engineers, and tech decision makers, you may want to advertise with us.