Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
What if changing a single function in a million-line codebase took **50 milliseconds* to rebuild instead of 30 seconds?*
For decades, we've accepted a strange reality of software engineering.
You change one line of code, yet the compiler often behaves as though you've rewritten the world.
Build systems like Make, Ninja, Bazel, Cargo, and Gradle have become remarkably sophisticated at figuring out which files need rebuilding. But most of that intelligence lives outside the compiler. Once the compiler is invoked, it frequently has to redo far more work than seems necessary.
The Zig project takes a different approach.
Instead of asking:
Which files changed?
it asks:
Which declarations actually changed, and what truly depends on them?
That subtle shift in perspective opens the door to dramatically faster feedback loops.
Let's explore why incremental compilation matters, how Zig approaches the problem, and why compiler researchers have been pursuing this idea for decades.
The hidden tax of waiting
Every developer knows this loop.
Edit
↓
Build
↓
Wait...
↓
Run
↓
Repeat
On a toy project, the delay is barely noticeable.
On a large codebase, it becomes part of your day.
A few seconds here. Ten seconds there. Hundreds of builds every week.
The cost isn't simply time.
Long feedback loops discourage experimentation.
Instead of testing an idea immediately, developers begin batching unrelated changes together because "it's not worth waiting for another rebuild."
Kent Beck has often argued that software development is fundamentally governed by the speed of feedback. Faster feedback encourages experimentation, smaller commits, and more confident refactoring.
Compiler engineers have known this for decades as well. Improving compile times isn't just about benchmarks—it changes how programmers think.
Traditional compilation thinks in files
Imagine you're renovating a city.
One house changes.
A traditional compiler often behaves like a city inspector who says:
"Let's inspect every building on this street, just to be safe."
Modern build systems improve this considerably.
main.c
├── parser.c
├── lexer.c
└── util.c
If parser.c changes, the build system avoids recompiling completely unrelated files.
That's a huge improvement.
But files are still a fairly crude unit of change.
Suppose this file contains twenty functions.
Only one function changes.
The compiler still ends up reprocessing the entire translation unit because, historically, C and C++ compilers fundamentally operate on source files and headers rather than individual declarations.
This design made perfect sense in the 1970s.
Today's software is rather larger.
Zig thinks in declarations instead
Andrew Kelley, Zig's creator, has long argued that much of the compiler's work should be understood in terms of semantic dependencies rather than files. His article, :contentReference[oaicite:0]{index=0}, outlines many of the design goals that eventually led to Zig's modern compiler architecture.
Consider this Zig source file.
const PI = 3.14159;
fn area(r: f64) f64 {
return PI * r * r;
}
fn perimeter(r: f64) f64 {
return 2 * PI * r;
}
fn fibonacci(n: u32) u32 {
...
}
Suppose you modify only area().
Should the compiler revisit perimeter()?
Probably not.
Should it revisit fibonacci()?
Certainly not.
Instead of treating the entire file as one indivisible object, Zig tracks dependencies between individual declarations.
Only the declarations affected by your edit—and anything depending on them—need to be reconsidered.
That's much closer to how humans naturally reason about software.
The compiler becomes a dependency graph
The key insight is that software is really a graph.
Not a graph of files.
A graph of relationships.
Function A
│
▼
Type B
│
▼
Constant C
│
▼
Function D
Now imagine changing Constant C.
Only the reachable portion of the graph needs updating.
Everything else remains valid.
A useful analogy is Microsoft Excel.
When you change one spreadsheet cell, Excel doesn't recompute the entire workbook.
Only the formulas depending on that cell are recalculated.
Incremental compilation follows exactly the same philosophy.
The difference is that software dependency graphs are vastly more complicated than spreadsheets.
Functions call functions.
Types depend on other types.
Compile-time evaluation generates additional dependencies.
Generic instantiations create entirely new ones.
Keeping track of all this correctly is one of the hardest problems in compiler engineering.
The real challenge is invalidation
People often imagine incremental compilation as simply "saving previous work."
That's actually the easy part.
The difficult question is:
How does the compiler know exactly what became invalid?
Phil Karlton famously joked:
"There are only two hard things in Computer Science: cache invalidation and naming things."
Incremental compilation is essentially one enormous cache invalidation problem.
Imagine changing
const BufferSize = 4096;
Now what?
Should the compiler revisit functions using it?
Generic instantiations?
Compile-time evaluated expressions?
Type layouts?
Generated machine code?
Getting this wrong either produces incorrect binaries—or wastes time rebuilding things unnecessarily.
Much of Zig's recent compiler work has focused precisely on this problem.
Compiler engineer Matthew Lugg explains this journey in the excellent Zig engineering article :contentReference[oaicite:1]{index=1}, describing how the compiler's type resolution and semantic analysis were redesigned so dependency tracking becomes far more precise instead of repeatedly "over-analyzing" the program.
This isn't flashy work.
But it's exactly the kind of engineering that determines whether incremental compilation actually feels instantaneous.
Why compiler researchers have chased this for decades
Incremental compilation didn't begin with Zig.
Researchers have explored it since the 1980s, driven first by interactive programming environments and later by IDEs, language servers, and ever-larger software systems.
One particularly approachable modern paper is :contentReference[oaicite:2]{index=2}, which argues that dependency management should increasingly become the compiler's responsibility rather than being handled exclusively by external build systems.
Zig is interesting because it was designed with this direction in mind.
Rather than bolting incremental compilation onto an existing decades-old compiler architecture, the project has steadily evolved its semantic analysis, dependency tracking, and self-hosted compiler so that incremental rebuilding becomes a natural consequence of the design.
That's a much harder path.
It may also prove to be the more scalable one.
More than faster builds
It's tempting to think incremental compilation is simply about shaving seconds off compile times.
The deeper benefit is psychological.
Imagine your compiler remaining alive between edits.
It already knows your program.
It remembers previous analysis.
You save a file.
Only a tiny portion of the dependency graph changes.
Errors appear almost immediately.
That changes how you work.
Instead of thinking,
"I'll make five edits before rebuilding,"
you naturally start thinking,
"Let's verify every tiny change."
Smaller feedback loops encourage smaller experiments.
Smaller experiments usually lead to cleaner designs, easier debugging, and greater confidence while refactoring.
The fastest compiler isn't necessarily the one that produces binaries the quickest.
It's the one that tells you whether your last idea worked before you've lost the thread of your thinking.
Final thoughts
Programming languages often compete on syntax.
Or performance.
Or memory safety.
Or ecosystems.
Zig is making another bet.
That developer feedback latency deserves to be treated as a first-class design goal.
Instead of asking,
"How can we compile faster?"
the Zig compiler increasingly asks,
"How little work is actually necessary after this edit?"
Those aren't the same question.
One optimizes the compiler.
The other optimizes for the programmer.
As our codebases continue growing into millions of lines, that distinction may become one of the more important innovations in modern compiler design.
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
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git-lrc
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git-lrc
Free, Micro AI Code Reviews That Run on Commit
GenAI today is a race car without brakes. It accelerates fast -- you describe something, and large blocks of code appear instantly. But AI agents silently break things: they remove logic, relax constraints, introduce expensive cloud calls, leak credentials, and change behavior -- without telling you. You often find out in production.
git-lrc is your braking system. It hooks into git commit and runs an AI review on every diff before it lands. 60-second setup. Completely free.
In short, git-lrc helps Prevent Outages, Breaches, and Technical Debt Before They Happen
At a glance: 10 risk categories · 100+ failure patterns tracked · every commit…
Top comments (1)
I found the concept of tracking dependencies between individual declarations in Zig to be particularly intriguing, as it deviates from the traditional file-based approach used in most compilers. The example with the
area()function modification highlights the potential benefits of this approach, where only the affected declarations need to be recompiled. This got me thinking - how does Zig handle more complex scenarios, such as template metaprogramming or cross-module dependencies, and what implications might this have for build systems and optimization strategies?