🧭 Conclusion

The conclusions are based on the following results. Clearly, it's a personal opinion shaped by my education and experiences. I quickly started coding at 10 with BASIC (on DOS), then moved on to learn Visual Basic 6, and after that, I developed a long-lasting affection for Delphi from Borland and Embarcadero. Subsequently, I pursued studies in physics and chemistry at university. Following that, I learned Python, C++, and most recently, ASM, purely for enjoyment.

C# and .NET framework 📦

👍 Advantages

• Cross-platform: code can easily be ported to Linux with no changes required
• Great optimization: comparable in speed to C++
• Not verbose: language syntax is clear and straightforward
• Simplified parallel computing
• Abundant scientific functions included in .NET Core, eliminating the need to reinvent the wheel
• Built-in garbage collector
• Nice debugger, profiler…

👎 Disadvantages

• Requires installation of the .NET Core runtime
• Limited memory control
• Controlled by Microsoft; the product roadmap is unpredictable and subject to frequent changes. In my opinion, it's more suitable for developers rather than scientists.
• Steep learning curve
• Size of the runtime + “binaries”
• Limited support for GPU computing (perhaps through libraries like ILGPU?)
• Not ideal for prototyping due to strong typing

✅ Suitable Use Cases

• When integrating with the entire .NET ecosystem without needing to switch to other platforms
• For developers aiming to quickly build cross-platform desktop applications

❌ Less Suitable Use Cases

• In the field of scientific computing
• For machine learning applications

JavaScript 🌐

👍 Advantages

• Good performance compared to Python
• Compatible with all web browsers
• Numerous visualization libraries and frameworks
• Enables full-stack development with Node.js
• Easy to learn and use
• Suitable for rapid prototyping due to its dynamic typing
• Debugging is okay

👎 Disadvantages

• While generally faster than interpreted languages like Python, JavaScript's performance lags behind that of compiled languages.
• Limited precision in floating-point numbers due to IEEE 754 double precision, which can cause problems in scientific computations.
• Challenges with memory management
• Size of the runtime + script
• Scarcity of scientific libraries when compared to other languages
• Managing asynchrony can be complex for beginners, and it may not be intuitive for scientists.
• Overwhelming number of frameworks that are constantly being released

✅ Ideal Use Cases

• Excellent for creating web interfaces (front only)

❌ Less Ideal Use Cases

• Not the best choice for desktop applications, memory hungry
• Not recommended for applications where performance is a critical concern
• The scientific field, due to the lack of specialized libraries and precision issues
• Machine learning applications, which often require more specialized tools and libraries

ASM 🧮

👍 Advantages

• Encourages knowledge of computer architecture, which is beneficial for learning
• Results in a small program footprint
• Presents a healthy challenge to developers
• No abstraction layers: interaction with hardware is direct and explicit
• Opportunities for hardware-specific optimization
• Seamless interaction with C and C++ (a significant plus)

👎 Disadvantages

• Inherently high complexity
• Optimization is hard (my C++ program is faster)
• Debugging can be difficult and tedious
• Not inherently portable; requires adaptation for different platforms (different hardware, operating systems)
• Lengthy development time
• Lack of scientific libraries available
• Highly prone to errors due to the low-level nature of the language (totally unsafe)
• Often involves reinventing the wheel for many common functionalities

✅ Ideal Use Cases

• Suitable for embedded device development
• When high-performance is critical for specific sections of code that need to be directly integrated with C/C++ binaries

❌ Less Ideal Use Cases

• Not practical for developing a modern application
• Prototyping in assembly language (ASM) is highly impractical
• Ill-suited for applications requiring user interaction

C++ 🧩

👍 Advantages

• Great performance
• Good portability
• Mature ecosystem with a lot of scientific libraries
• Manual memory control
• Template metaprogramming and multi-paradigm language
• Can use CUDA for GPU calculations
• Small size

👎 Disadvantages

• A bit complex if you want to create complex code
• Not very intuitive, but simpler than JavaScript for me
• A bit verbose
• More error-prone because memory management is manual
• A bit unsafe

✅ Ideal Use Cases

• High-performance applications (executables and libraries)
• Small size of the executable
• Small portions of code to speed up sections in Python
• Embedded device development
• Large desktop applications

❌ Less Ideal Use Cases (for me)

• Prototyping
• Web apps

Python 🐍

👍 Advantages

• Extremely user-friendly
• Rapid prototyping capabilities
• Effortless direct conversion to native code with Cython
• A wealth of high-quality libraries (e.g., NumPy, Pandas, PyTorch)
• Straightforward integration with C and C++, offering extensive compatibility
• Strong community support
• Cross-platform compatibility
• Simplified data visualization
• Ease of performing GPU calculations
• Performance boost with PyPy without extra work

👎 Disadvantages

• Generally slower performance, with computationally intensive tasks often handled by packages written in C/C++
• The Global Interpreter Lock (GIL) can be a bottleneck; however, it can be circumvented using Cython or interfacing with C/C++
• Really slow (for loop) without native packages
• Higher memory consumption compared to some other languages
• Indentation-based syntax may be unfamiliar to those new to Python
• Not tailored for mobile computing
• Larger footprint due to the combined size of Python, scripts, and virtual environments
• Managing dependencies can be complicated when distributing applications

✅ Ideal Use Cases

• Quick and efficient prototyping
• Research in the scientific field, especially when utilizing extensions in C/C++ and Cython
• Machine learning projects, particularly with frameworks that have CUDA integration like PyTorch
• Backend development for web applications
• Creating small graphical user interface (GUI) applications

❌ Less Ideal Use Cases

• Building standalone heavyweight desktop applications