Choosing the Right Programming Language for Your Project
As an application developer, I need to decide which programming language best suits each project I take on. Sometimes I have to make compromises—if I’m not proficient in a particular language, I’ll pick my second choice. But it shouldn’t become a blocker for the work itself.
IRC-Coding.de already has plenty of articles covering JavaScript frameworks and Python frameworks like FastAPI. In this article, I want to give you a quick overview of which programming languages make sense for different projects, and most importantly, why.
The 15 Most Important Programming Languages in 2026 and What to Use Them For
1. Python – The Swiss Army Knife for AI, Data Science & Automation
I’m working heavily with Python right now. Currently, I’m using FastAPI to build an entire intranet using only Python. It feels great, and I don’t run into limits whether I’m processing documents or querying APIs. For public websites, I might approach things differently, but Python has become unavoidable in modern development. Especially for AI projects—whether that’s preparing data for your own or external LLMs, or integrating tools like Ollama, AnythingLLM, or WebUI with frameworks like Streamlit or FastAPI.
What projects is it suited for? Machine learning, AI prototypes, data analysis, data pipelines, backend APIs, automation scripts, MLOps.
Why Python? Python remains unchallenged as number one in 2026, commanding roughly 36 % of the global market according to the PYPL Index.1 Over 80 % of all AI and machine learning projects are built in Python.2 The reason: an enormous ecosystem. With libraries like TensorFlow, PyTorch, Pandas, FastAPI, and Scikit-learn, you can handle everything from data import to a finished API endpoint in a single language with one community behind it.
Add to that beginner-friendly syntax, an ecosystem growing roughly 47 % annually, and over 178,000 active packages available on PyPI.3 Bottom line: if you’re working with data, AI, or automation in 2026, there’s no way around Python.
Typical tools: FastAPI, Django, Flask, Pandas, NumPy, LangChain, Hugging Face Transformers
Python is genuinely rewarding to work with. At my job, I once built an AI interface in a morning and had a fully functional business card scanner using OpenCV and an LLM ready by the end of the same day.
Need to pull data from APIs? Document government IP ranges? Whatever—a 15–100 line Python script and your database is ready to go.
Book recommendation:
Python 3: Das umfassende Handbuch
Bei Amazon ansehenAffiliate-Link: Bei einem Kauf erhalten wir möglicherweise eine Provision.
2. JavaScript – The Backbone of the Web
What projects is it suited for? Web frontends, interactive UIs, browser applications, rapid prototypes.
Why JavaScript? JavaScript runs in every browser—no installation, no configuration needed. If you’re building a website with dynamic content, animations, or user interactions, you can’t avoid JS. More than 60 % of professional developers worldwide use JavaScript as their primary language.4
Frameworks like React, Vue, and Svelte let you build complex single-page applications that feel like native apps.
Typical tools: React, Vue, Svelte, Vite, Next.js, Remix
3. TypeScript – JavaScript with a Safety Net
Normally I’d rank TypeScript at number two, but developers will understand why it comes after JavaScript here. If you’ve worked with C# and similar languages, you’ve learned to love type safety. Testing becomes noticeably more reassuring. If you’re using JavaScript, you should know TypeScript.
What projects is it suited for? Large web projects, team development, enterprise frontends, full-stack applications.
Why TypeScript? TypeScript is a type-safe extension of JavaScript. Working alone on a weekend project, you might not need it. But once your team grows, codebases expand, and bugs become expensive, TypeScript is the de facto standard in 2026.5
The compiler catches errors before runtime, saving hours of debugging. And because TypeScript compiles to JavaScript, compatibility is preserved.
Typical tools: TypeScript + React, Angular, Next.js, tRPC, Zod
4. Node.js – JavaScript on the Server
What projects is it suited for? REST APIs, microservices, real-time applications (chat, notifications), backends for SPAs.
Why Node.js? Node.js lets you use JavaScript on the server side—a huge win for teams already using JS in the frontend. The npm ecosystem is the largest package ecosystem in the world. Thanks to its event-loop architecture, Node.js shines with I/O-heavy applications like real-time chat or streaming APIs.
IRC-Coding.de has several articles on Node.js frameworks already. A good starting point is our coverage of Fastify and Express.
Typical tools: Express, Fastify, NestJS, Prisma, Socket.io
5. Rust – Security and Performance Without Compromise
What projects is it suited for? System software, embedded systems, WebAssembly modules, security-critical infrastructure, CLI tools.
Why Rust? Rust was named the “most admired language” among developers for 2026.6 The reason is clear: Rust delivers C/C++-level performance but with an ownership system that eliminates memory errors at compile time, without needing a garbage collector. The US National Institute of Standards and Technology (NIST) explicitly rates Rust as a safer systems programming language.7
That sounds theoretical, but in practice it means fewer crashes, fewer security vulnerabilities, and greater confidence in critical software. If you’re building system services, CLI tools, or WebAssembly components, Rust is definitely worth serious consideration in 2026.
Typical tools: Cargo, Tokio (async), Axum, wasm-bindgen, ripgrep
6. Go – Simple, Fast, Cloud-Native
What projects is it suited for? Microservices, cloud backends, DevOps tools, API servers, Kubernetes operators.
Why Go? Go was created by Google and optimized for one thing: fast, maintainable server software. The language is deliberately simple—no generics overload, no complex inheritance hierarchies. What you write runs quickly and is easy to read.
In the cloud world, Go is established: Kubernetes, Docker, and Terraform are all written in Go. If you’re building microservices or DevOps tooling, Go is an excellent choice in 2026.8
Typical tools: Gin, Echo, Fiber, gRPC, Cobra
7. Java – Enterprise Veteran with a Second Wind
What projects is it suited for? Enterprise applications, banking backends, large systems, Android (legacy), Spring microservices.
Why Java? Java has lost some ground in recent years, but don’t count it out. With Java 21, Spring Boot 3, and GraalVM, the ecosystem is more modern and cloud-ready than ever.9 Especially in enterprise settings—insurance, banking, government—Java is deeply embedded and will remain so for years.
If you’re pursuing a career at large organizations or working on legacy systems, Java knowledge is essential.
Typical tools: Spring Boot, Quarkus, Micronaut, Hibernate, Maven/Gradle
8. Kotlin – The Modern Java for Android & Backend
When to use it? Android apps, JVM-based backend alternatives to Java, cross-platform development with Kotlin Multiplatform.
Why Kotlin? Kotlin is Google’s official language for Android development. It offers everything Java can do, but with far less boilerplate and modern syntax. Null-safety is built in, coroutines make asynchronous code readable, and JVM compatibility means you can keep using Java libraries.
Kotlin Multiplatform lets you share logic across Android, iOS, and the web – a rapidly growing area.
Typical tools: Jetpack Compose, Ktor, Kotlin Multiplatform, Android Studio
9. Swift – Native Development in the Apple Ecosystem
When to use it? iOS apps, macOS applications, watchOS, tvOS, SwiftUI interfaces.
Why Swift? The entire Apple ecosystem runs on Swift as of 2026. The language is expressive, safe (thanks to Optionals and strong typing), and considerably easier to learn than its predecessor Objective-C.10 If you’re building native Apple apps, you need Swift—there’s no real alternative when you need access to the latest Apple APIs.
Typical tools: SwiftUI, UIKit, Xcode, Combine, Swift Package Manager
10. PHP – The Veteran of Web Development
When to use it? Content management systems, WordPress themes and plugins, traditional websites, e-commerce platforms.
Why PHP? PHP runs on 72% of all web servers globally and powers WordPress, which drives approximately 43% of all websites.11 For new large-scale projects, PHP isn’t the first choice—but if you’re working with WordPress or existing PHP systems, or maintaining CMS-based sites for clients, PHP is unavoidable.
Modern PHP frameworks like Laravel have significantly elevated the language.
Typical tools: Laravel, Symfony, WordPress, Composer, Eloquent ORM
Laravel! The PHP Framework That Still Dominates the Market
Regardless of what people say about PHP, it has proven itself in the marketplace—and with Laravel, you can still land plenty of jobs and contracts today.
Why Laravel is so valuable as an application developer and in the market: Laravel is the most widely used PHP framework in 2026 and has become the standard for modern PHP development. As an application developer, Laravel’s value lies in the enormously productive workflow it enables. With Eloquent ORM, you interact with databases as objects; the Blade templating engine makes frontend integration intuitive; and the integrated Artisan CLI automates repetitive tasks. Laravel is in high demand among freelancers and agencies because projects ship faster than with plain PHP or competing frameworks. The Laravel community is massive, and its ecosystem—featuring Laravel Nova, Laravel Horizon, and Laravel Vapor—covers everything from admin panels to serverless deployment. If you’re building PHP projects and want to optimize time, costs, and code quality, Laravel is essential in 2026. The job market reflects this: Laravel developers are in higher demand than ever, and the framework continues to evolve to meet modern requirements.
11. Dart / Flutter – Cross-Platform from One Codebase
When to use it? Mobile apps (iOS and Android), progressive web apps, desktop applications—all from a single codebase.
Why Dart/Flutter? Google’s Flutter lets you build native-like apps for iOS, Android, web, and desktop with a single codebase. Dart is the accompanying language—quick to learn, well documented. If you don’t want to maintain separate design systems for each platform, Flutter saves you enormous time.12
Typical tools: Flutter, Dart, pub.dev, Riverpod, Hive
12. SQL – The Language of Data
When to use it? Any project that stores, queries, or manages data—which is nearly every project.
Why SQL? SQL isn’t a “new” language, but it’s as essential in 2026 as it’s always been. Databases are the backbone of every application.13 If you master SQL, you can efficiently query, optimize, and manage data from relational databases like PostgreSQL, MySQL, or SQLite.
No framework in the world replaces a solid understanding of SQL—ORM or not.
Typical tools: PostgreSQL, MySQL, SQLite, Supabase, Prisma, DuckDB
13. C++ – Performance Where It Matters
When to use it? Game engines, embedded systems, high-frequency trading, operating system components, 3D software.
Why C++? C++ remains in the top 5 of the TIOBE Index in 2026.14 Where maximum performance and direct memory access are critical—game engines like Unreal, real-time systems, trading software—C++ is still the first choice. It’s not beginner-friendly, but for systems where every millisecond counts, there are few alternatives.
Typical tools: Unreal Engine, CMake, Qt, LLVM, OpenCV
14. Ruby (on Rails) – Get to Product Fast
When to use it? Startups, MVPs, CRUD web applications, rapid prototypes.
Why Ruby? Ruby on Rails was the go-to framework for startups for years—and for good reason. With Rails, you can build CRUD applications incredibly fast because many conventions are built in. For startups looking to launch an MVP quickly, Rails remains a solid choice in 2026.
Typical tools: Ruby on Rails, Hotwire, Sidekiq, RSpec, Devise
15. WebAssembly (Wasm) – The Future of the Browser
When to use it? Performance-intensive browser applications, image and video editing in the browser, portable runtimes, serverless functions.
Why WebAssembly? WebAssembly isn’t a direct competitor to JavaScript—it’s a complement. With Wasm, you can run code written in Rust, C++, or Go directly in the browser with near-native performance.15 This opens the door to applications that were previously only conceivable as desktop software: video editing, CAD, emulators.
Typical tools: wasm-bindgen, Emscripten, Wasmtime, WASI, Spin (Fermyon)
🔀 Mixed Projects – When One Language Isn’t Enough
In practice, few projects stay neatly within a single language. Here are some typical combinations:
Full-Stack Web Application
TypeScript (React) + Node.js (Express/Fastify) + PostgreSQL (SQL)
Frontend, backend, and database—everything in one language (TypeScript). No context switching, shared types via tRPC or GraphQL, straightforward onboarding for new developers.
AI-Powered Web App
Python (FastAPI / LangChain) + TypeScript (React) + PostgreSQL
AI backend in Python, because libraries like Hugging Face and LangChain live there. The UI in React/TypeScript for the best DX in the frontend. Data in PostgreSQL with pgvector for embeddings.
Mobile App with Shared Logic
Kotlin Multiplatform + Swift (iOS-specific) + Ktor (Backend)
Write business logic once in Kotlin, use it directly on Android, integrate it on iOS—then implement the native UI layer separately in Swift (iOS) or Jetpack Compose (Android).
Cloud-native microservice system
Go (services) + Python (ML service) + TypeScript (API Gateway) + SQL + Kafka
Each service uses the language it’s optimized for. Go for fast, resource-efficient services. Python for ML inference. TypeScript for the API Gateway with a BFF pattern. This isn’t over-engineering—it’s the result of deliberate technological choices.
High-performance desktop and game applications
C++ (engine/core) + Python (scripting) + SQL (local data)
Many game engines (like Unreal) rely on C++ for performance-critical engine code while allowing Python or Lua scripting for game logic. It’s a battle-tested combination that’s been working for years.
There’s no universally “best” programming language—there’s the best language for your specific context. As a developer, it pays to have a broad perspective: master one primary language, build solid understanding in two or three others, and know when to reach for which tool.
Looking ahead to 2026, my view on the most strategically valuable investments:
- Python for everything around AI, data, and automation
- TypeScript/JavaScript for web and full-stack development
- Rust or Go as your second systems language
- SQL always and everywhere
On IRC-Coding.de, I’ll be publishing deeper dives into most of these languages and frameworks. Stop by regularly!
Sources
Frequently asked questions
Which programming language should I learn in 2026 as a beginner?
For web development, I’d recommend JavaScript/TypeScript—you can produce visible results in the browser right away. For data science and AI, Python is the ideal entry point: the syntax is clean, the community is huge, and it’s immediately applicable in real-world work. Both are absolutely future-proof choices for 2026.
Do I need to know multiple programming languages?
You don’t need to master multiple languages—but understanding more than one opens many doors. In practice, you’ll often work with stacks like TypeScript + SQL or Python + Bash. Someone who knows only one language often can’t combine tools meaningfully. The goal is depth in one language plus breadth across several.
Is Python really as popular as claimed?
Yes. According to the PYPL Index, Python held roughly 36% of the global market share in April 2026, making it the most searched programming language worldwide. On GitHub, Python has even overtaken JavaScript as the most-used language, driven by AI and data science projects.
Why learn Rust if C++ does the same thing?
Rust offers similar performance to C++ but eliminates an entire class of memory errors (dangling pointers, buffer overflows, data races) through its ownership system at compile time—without a garbage collector. The US NIST explicitly recommends Rust as a safer alternative for systems programming. For new projects in the systems space, Rust is often the smarter choice in 2026.
When does it make sense to combine multiple languages in one project?
Whenever different parts of your system have different requirements. The typical pattern: Python for the AI service, TypeScript for the frontend and API layer, Go for high-frequency microservices, SQL for data persistence. The art is in drawing clean boundaries between components—via REST or gRPC interfaces, for example—so each language stays in its area of strength.
Is PHP still relevant?
Yes, with caveats. PHP runs on roughly 72% of all web servers and powers WordPress. For new, standalone projects in 2026, I’d lean toward Node.js, Go, or Python instead. But if you work in the WordPress ecosystem or maintain existing PHP projects, PHP skills are essential.
What is WebAssembly and do I really need it?
WebAssembly (Wasm) lets you write code in languages like Rust or C++ and run it directly in the browser at nearly native speed. Most web apps don’t need it—JavaScript is fine. But if you’re building performance-intensive browser applications (video editing, CAD, emulators), Wasm is a genuine game-changer tool in 2026.
Go or Node.js for a new backend?
It depends on your team and requirements. Node.js is ideal if your team already knows JavaScript/TypeScript and you want to iterate quickly. Go is the better choice if performance, low resource usage, and simple deployments (single binary) are priorities—especially for microservices in cloud environments.
Recommended reading: Programming languages
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Should you only choose between these 15 programming languages?
Not at all. But let’s be honest: all the big projects are built on the usual suspects. If you’ve read more articles here, I’m also interested in TCL, C, Perl, and the like. I recently found an old dusty CURL (programming language) book and wrote an article about it: https://www.irc-coding.de/programmiersprache-multiparadigmen-curl-surge-rte-ide-
Some of these languages were unfamiliar to me, or I had little experience with them. For years I’ve wanted to explore Ada more deeply, but time is scarce…and sometimes motivation is too. ;)
25 More Programming and Scripting Languages Worth Knowing
16. TCL – Tool Command Language
Best for? Embedded systems, testing frameworks, rapid prototyping, Internet Relay Chat (IRC) bots. TCL excels at automation and as an embedding language in C applications.
17. Perl – The Swiss Army Knife of Languages
Best for? System administration, text processing, bioinformatics, legacy web applications, Internet Relay Chat (IRC) scripting. Perl shines with regular expressions and file handling.
18. C – The Systems Programming Classic
Best for? Operating systems, embedded systems, performance-critical applications, low-level hardware programming, compiler development. C remains the foundation of modern systems.
19. Bash – Unix Shell Scripting
Best for? System administration, DevOps automation, CI/CD pipelines, server management, backup scripts. Bash is indispensable for Linux/Unix systems.
Bash in detail: I’ve often wondered why I didn’t rank Bash in the top 10. As a Linux user and server administrator, I use a lot of Bash scripts. Bash is the standard shell on nearly all Linux systems and perfect for quick automation tasks. It really shines in system administration, where you can tap directly into Linux commands and tools. CI/CD platforms like GitHub Actions and GitLab CI rely heavily on Bash for build and deployment scripts.
Bash vs. Python: In recent years, though, I’ve increasingly reached for Python for scripting tasks—often it’s more enjoyable to write and the codebase is easier to maintain. Python offers better error handling, modern data structures, and works across platforms. For quick, direct system tasks, Bash is often still the right choice, especially when you’re already in the shell and want to automate something fast.
Typical use cases: backup scripts, log rotation, server monitoring, file processing, Docker container management, Git hooks.
20. Lua – Lightweight Embedding Language
Best for? Game scripting (Roblox, World of Warcraft), embedded systems, configuration files, prototyping. Lua is incredibly small and fast.
21. R – Statistics and Data Science
Best for? Statistical analysis, scientific research, data visualization, academic publications. R is the standard in statistics.
Why R’s dominance is fading: R once ruled statistics unchallenged, but Python has caught up significantly. The main reason: Python unifies data science with general-purpose programming in a single language. Today, when you build a machine learning model, you often want a web API and database integration at the same time—Python (FastAPI, Pandas, scikit-learn) handles this seamlessly, while R requires external tools. R remains the leader in academic statistics and specialized statistical analysis, particularly for complex statistical tests and visualizations where specialized packages exist.
22. Scala – Functional Programming on the JVM
Best for? Big data (Apache Spark), distributed systems, functional programming, enterprise applications. Scala combines object-oriented and functional paradigms.
What is Scala exactly: Scala is a modern programming language on the Java Virtual Machine that brings together object-oriented and functional programming. The name stands for “scalable language”—it grows with your project’s demands. Scala has expressive syntax with features like pattern matching, higher-order functions, and immutable collections. The biggest advantage: full access to the Java ecosystem while using a modern language. In the big data world, Scala leads because Apache Spark is written in Scala. Scala does have a steep learning curve, though, and its community is smaller than Java or Kotlin.
23. Elixir – Erlang Ecosystem
Best for? Real-time systems, chat applications, IoT, fault-tolerant systems. Elixir on the BEAM VM is extremely robust.
24. Haskell – Pure Functional Programming
Best for? Academic research, compiler development, formal verification, complex algorithms. Haskell promotes correct code.
25. F# – Functional .NET Language
Best for? Data science, financial analysis, web backends with ASP.NET, functional programming in the .NET ecosystem.
F# in the .NET ecosystem: F# remains an important topic in the .NET space, especially for functional programming and data science. Microsoft actively supports F#, and it’s part of the official .NET stack. The advantage: F# has full access to all .NET libraries while offering modern functional syntax. In the financial sector, F# is popular for quantitative analysis and risk modeling. It’s also used in web backends with ASP.NET Core, particularly for complex business logic that benefits from functional programming. The community is smaller than C#, but engaged and high quality.
26. Clojure – Lisp on the JVM
Best for? Data processing, web applications, REPL-based development, functional programming. Clojure offers dynamic typing with JVM performance.
27. Ada – Safety-Critical Systems
Best for? Aerospace, military systems, railway control, safety-critical software. Ada is designed for reliability.
Ada in detail: Ada is a programming language created specifically for safety-critical systems. It’s named after Ada Lovelace, the world’s first programmer. Ada was commissioned by the US Department of Defense and is known for its strict typing, built-in error checking, and support for concurrent programming. It’s particularly strong for real-time and embedded systems where failures can have catastrophic consequences. The language’s design enforces many good programming practices and makes it difficult to write buggy code. Despite these strengths, Ada sees little use in new projects because the learning curve is steep and modern alternatives like Rust offer similar safety guarantees with larger communities.
28. Fortran – Scientific Computing
Best for? Numerical simulations, weather forecasting, scientific calculations, high-performance computing. Fortran dominates in HPC.
29. COBOL – Legacy Systems
Best for? Banking and insurance systems, government applications, legacy migration. COBOL runs in many critical systems today.
COBOL today: COBOL (Common Business-Oriented Language) is one of the oldest programming languages, created specifically for business applications. Today, COBOL still runs in countless critical systems: banks process trillions of dollars daily through COBOL systems, insurers calculate policies, governments manage social benefits. The problem: there are barely any COBOL developers left—many have retired. This creates a “COBOL crisis”—companies desperately need developers who can maintain or modernize legacy systems. COBOL is incredibly stable and reliable, but development is cumbersome by modern standards. No one would choose COBOL for new projects, but for maintaining critical systems, it’s indispensable.
30. Lisp – The Oldest High-Level Language
Best for: AI research, Emacs extensions, symbolic computation, academic teaching. Lisp has influenced countless modern languages.
Lisp’s relevance today: Lisp is no longer a mainstream language in 2026, but its influence is enormous. Many concepts we take for granted today originated in Lisp: garbage collection, higher-order functions, recursion, and dynamic typing. Even the REPL (Read-Eval-Print Loop) comes from Lisp. In AI research, Lisp remains relevant—particularly for symbolic AI and expert systems. Emacs, one of the most powerful editors ever built, is written entirely in Lisp and fully extensible. Academia still uses Lisp to teach fundamental programming concepts. While new commercial projects rarely choose Lisp, its flexibility makes it unbeatable for prototyping and research.
31. Prolog – Logic Programming
Best for: Expert systems, AI research, natural language processing, rule-based systems. Prolog works with facts and rules.
32. Erlang – The Telecommunications Language
Best for: Telecommunications systems, chat servers, real-time applications, highly available systems. Erlang is built for concurrency.
33. Smalltalk – The OOP Pioneer
Best for: Education, rapid prototyping, live programming, object-oriented research. Smalltalk shaped modern OOP languages.
34. Objective-C – Apple’s Predecessor
Best for: Legacy iOS applications, macOS software, legacy systems, migration to Swift. Objective-C is still used in many Apple projects.
35. PowerShell – Windows Automation
Best for: Windows system administration, cloud management (Azure), DevOps, Windows-specific automation. PowerShell is object-oriented.
36. Raku – The Perl Successor
Best for: Text processing, linguistics, prototyping, modern scripting applications. Raku (formerly Perl 6) brings modern features to the table.
37. Zig – Rethinking Systems Programming
Best for: Systems programming, embedded systems, game development, performance-critical applications. Zig is simpler than C or Rust.
38. Nim – Python-Like Systems Language
Best for: Systems programming, game development, embedded systems, scientific computing. Nim combines Python syntax with C performance.
I’ll admit I hadn’t heard much about Nim before researching it, but what I found got me intrigued.
39. V – Fast Systems Language
Best for: Systems development, web backends, CLI tools, game development. V is simple, fast, and safe like Go or Rust.
40. Crystal – Ruby-Like Performance
Best for: Web applications, microservices, CLI tools, high-performance scripting. Crystal delivers Ruby syntax with C-level performance.
41. Deno – Secure JavaScript Runtime
Best for: Serverless functions, web backends, CLI tools, secure web applications. Deno is a modern alternative to Node.js with built-in security features.
Deno in detail: Ryan Dahl, who created the original Node.js, designed Deno to fix Node.js’s shortcomings. Deno has TypeScript support built in, is secure by default (no file access without explicit permission), and features a modern standard ecosystem. Deno shines for serverless functions and security-conscious web applications. The choice between Node.js and Deno often comes down to whether you need existing npm packages (Node.js) or prioritize security and modern features (Deno).
42. Bun – Ultrafast JavaScript Runtime
Best for: High-performance web backends, build tools, testing frameworks, fast CLI applications. Bun is an extremely fast JavaScript runtime.
Bun in detail: Bun is a relatively new JavaScript runtime optimized for speed. It’s up to 3x faster than Node.js at startup and up to 2x faster during execution. Bun particularly excels in build tools and testing frameworks where speed matters. It’s also a package manager and bundler rolled into one. The learning curve is gentle since Bun is largely Node.js-compatible. For new projects where performance is critical, Bun is a serious contender in 2026.
43. MATLAB – Engineering and Research
Best for: Engineering, scientific simulations, signal processing, academic research. MATLAB is the standard across many technical disciplines.
MATLAB today: MATLAB is a commercial programming language and development environment for numerical computing. In engineering, signal processing, and academic research, MATLAB remains a leader. The language includes built-in tools for plotting, simulations, and data analysis. Python with libraries like NumPy and SciPy has taken significant market share from MATLAB, but the language remains strong in industry and academia—especially for specialized toolboxes and Simulink integration.
44. Mojo – Python for AI and High Performance
Best for: AI/ML development, high-performance computing, Python extensions, scientific computing. Mojo is an emerging language for AI development.
Mojo in detail: Mojo is a relatively new language from Modular AI, purpose-built for AI/ML development. It combines Python’s approachable syntax with C/C++ performance. Mojo is particularly interesting for developers using Python for machine learning who need better performance. Though still in development, Mojo has already garnered significant attention in the AI community. For new AI projects built on Python that need more performance, Mojo is worth watching.
Missing any languages worth mentioning? Drop me a line…