PGO in Go: Optimizing AOT Code Like JIT
We often think of JIT-compiled languages like Java or JavaScript as the kings of runtime optimization, dynamically adapting to usage patterns. But what if I told you Go—a strictly AOT-compiled language—can achieve similar optimizations without runtime overhead? Thanks to Profile-Guided Optimization (PGO), introduced in Go 1.20, we can now give the compiler a 'cheat sheet' of how our code behaves in production. This isn’t just theoretical: real-world cases show measurable gains, especially for long-running services. Let’s explore how PGO works, when to use it, and why it might change how you think about AOT compilation.
Why Go Isn’t Always Ideal for Beginners
Go has earned its reputation as a robust, cloud-native language—fast, simple, and reliable. But as its adoption grows, so does the mismatch between its design and the expectations of newcomers. Many assume Go’s minimalism means ease of use, only to encounter its unique error handling, goroutines, and lack of familiar abstractions. This isn’t just about syntax; it’s about how the language shapes problem-solving. Whether you’re considering Go for a team or as a learner, understanding these nuances early can save frustration later.
Solving Problems Before Writing Code
In software development, we often celebrate elegant code or clever architectures—but these mean nothing if they solve the wrong problem. The pressure to deliver quickly tempts us to skip fundamental steps, yet the best solutions emerge from disciplined problem-solving first. This isn’t about methodology dogma; it’s about recognizing that code is just one tool in a larger process. Let’s explore how focusing on the problem space—through requirements, modular design, and continuous collaboration—creates software that actually delivers value.
Software That Matters:
Building for Value, Not Code
In software development, it’s easy to get lost in the weeds—endless debates about frameworks, obsessive unit testing, or over-engineered solutions. But the best systems aren’t measured by their technical elegance alone. What truly matters is whether they solve real problems for real users. This isn’t just about skipping TDD or cutting corners. It’s about aligning every decision—from testing strategies to architecture—with tangible outcomes. Let’s explore how focusing on value, not just code, leads to software that actually delivers.
Microservices vs Monoliths:
The Hidden Tradeoffs
Software architecture decisions often come down to invisible tradeoffs. While microservices have dominated recent conversations as the 'modern' solution, their benefits frequently come with hidden complexities that aren’t immediately apparent. Through this exploration, I want to unpack both sides of this architectural debate—not to prescribe one approach over another, but to highlight the considerations that truly matter when choosing between distributed and monolithic designs. The best solutions emerge not from following trends, but from understanding these fundamental tensions.