Coding experience often involves working with various programming languages – Python, Java, Rust, or perhaps even some obscure ones. Most developers have a favourite, the one they can write in their sleep, but they’re also well aware of the glitches, bugs, and failure modes that come with it.
Recently, Santiago Valdarrama, a computer scientist, recently took to X to declare his love for Python. “Python is the best language in the world. I don’t care if it’s slow because most of what I do doesn’t need to be faster than what I get.”
Nevertheless, for developers who “truly care about speed”, Valdarrama suggested checking out Perforator, an open-source tool to help them profile their code and identify errors.
“Try this on your codebase, and you’ll realise that the language has nothing to do with your application being slow. 99% of the time [it] is something you did, not the language’s fault.”
Meanwhile, some others prefer more rigid and high-performance alternatives. Pratham Patel, a member of Rocky Enterprise Software Foundation, told AIM, “For me, it is Rust.”
According to Patel, Rust stands out for its excellent first-party tooling, a strict yet helpful compiler that not only identifies errors but also suggests fixes, strong type enforcement to minimise bugs, and a solid standard library that covers most generic use cases.
However, it comes with its share of challenges. Its compiler is notoriously slow, and while it starts simple, it can quickly spiral into C++-like complexity if not carefully managed. Async programming in Rust is particularly challenging, and function colouring with traits and lifetimes adds another layer of frustration. Despite these hurdles, those who master Rust find it immensely rewarding.
In an exclusive conversation with AIM, Amlan Panigrahi, GenAI engineer at Deloitte, said, “I love both Python and C++. Both of them have their pros and cons. Python makes my job easier to prototype E2E AI solutions (finetuning LLMs, application backends, RAG).”
On the other hand, he mentioned that C++ is indispensable for his work with resource-constrained edge devices and AI PCs. The reason behind this is that it is highly performant, has its own compiler, and is the preferred approach for launching high-performance computing (HPC) kernels.
You are Smiling at the Programming Language
It turns out that your choice of programming language might say something about your happiness levels.
A study using Face API analysed developer profile pictures and found that some programming communities are, well, just happier. R developers are the happiest, followed by Go, C#, and Python programmers. However, Java developers? Not so much.
“I’m a happy C++ developer, but I am willing to admit it may just be Stockholm Syndrome,” one Reddit user jokingly acknowledged.
Another chimed in, “I need to update my GitHub profile picture. It’s currently a photo of my dog, and he seems to be smiling. I’d estimate a 0.7 dog smile.”
What’s Next?
Every year, new programming languages emerge in the tech world, each promising to eliminate old problems and push the boundaries of innovation. Developers thrive on reinvention, constantly seeking better, more efficient ways to build the future.
Developers are always looking for better and more efficient ways to build the future, and now, the next big shift is AI. As AI-driven solutions become the backbone of industries, mastering AI-focused programming languages has never been more crucial. According to research firm Gartner, 80% of organisations are expected to integrate AI into their operations by 2026 – a massive leap from just 5% in 2023.
This shift highlights the growing demand for languages that can support AI-driven solutions across sectors. Whether it’s Python’s simplicity and vast ecosystem, Java’s scalability for enterprise applications, C++’s performance-driven capabilities, or Julia’s efficiency in handling complex mathematical computations, the choice of programming language depends on the specific demands of AI applications.
As AI continues to advance, the languages that power it will evolve as well, shaping the next generation of innovation. For developers, staying ahead of this curve in the rapidly changing tech landscape isn’t just an advantage; it’s a necessity.
