Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set.

With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management.

In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights.

Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

8 minute read

A Jungle of Programming Languages: Evolution of Computer Programming Languages

Introduction

Why do computer scientist create different programming languages?

Think about this: there are nearly 8,000 human languages spoken around the world. Some, like English or Mandarin, are spoken by millions. Others are known only to small communities tucked away in remote regions. But it’s not just about numbers—languages are shaped by culture, geography, and history. How a civilization farms, trades, worships, or governs—these things all influence how its language sounds and works. And as times change, so do languages. New needs bring new words, new grammar, even entirely new ways of speaking. Language evolves because people evolve.

Now let’s switch to the world of programming. If you’ve ever looked at the long list of programming languages—Python, JavaScript, Rust, Go, Swift, and so many more—you’ve probably wondered: Why are there so many? Which one should I learn? Why doesn’t someone just create the ultimate language so we can all stop juggling five at once?

Well, just like human languages, programming languages are born out of need. Different times, different problems, and different goals lead to the creation of different tools. One language might be built for speed, another for safety, and yet another for simplicity or readability.

In this article, we’ll dive into what motivates people to create new programming languages and take a quick tour of some that have shaped the way we build software today.

Key Motivation Factors for creating Programming Languages

The key motivation factors that often drive the development of new programming languages:

🚀 1. Performance Improvements

  • Desire for faster execution or lower memory usage (e.g., Rust vs. C++).
  • Need for fine-grained control over system resources.

🧵 2. Better Concurrency and Parallelism

  • Support for multi-core CPUs and scalable applications.
  • Easier and safer concurrency models (e.g., Go’s goroutines, Erlang’s actor model).

🧠 3. Improved Developer Productivity

  • Reducing boilerplate code and making syntax more expressive or concise.
  • Simplifying tooling, debugging, and testing.

🔐 4. Safety and Reliability

  • Prevention of common bugs like null pointer exceptions, memory leaks, data races.
  • Use of strong typing, immutability, and ownership models (e.g., Rust).

🎯 5. Domain-Specific Needs

  • Tailored for specific fields like:

    • Data science (e.g., Julia),
    • Web development (e.g., Elm, ReScript),
    • Embedded systems (e.g., Zig).

📦 6. Simplified Compilation and Deployment

  • Easier compilation to a single binary without external dependencies (e.g., Go).
  • Smaller footprints for cloud and serverless environments.

🔁 7. Interoperability with Existing Ecosystems

  • Better integration with existing platforms (e.g., Kotlin with Java, Elixir with Erlang).
  • Bridge between scripting and systems programming (e.g., Nim).

🔧 8. Modern Language Features

  • Inclusion of functional programming, pattern matching, immutability, etc.
  • Cleaner, safer replacements for older languages.

🌱 9. Educational or Experimental Goals

  • To explore new language theories, syntax ideas, or type systems.
  • Academic or hobby projects that push language design boundaries.

🧩 10. Dissatisfaction with Existing Languages

  • Frustration with verbosity, complexity, or lack of specific features.
  • Desire to “fix” perceived flaws in popular languages (e.g., TypeScript vs. JavaScript).

How many programming languages are there?

There are hundreds of programming languages, each developed to address specific computing needs, paradigms, or technological advancements. These languages are tailored for various applications, including system programming, web development, scientific research, and more.

Below is a curated table highlighting some of the most influential programming languages, their year of creation, and their primary developers:

Language Year Developer(s) / Organization Purpose / Notes
Fortran 1957 John W. Backus, IBM First high-level language for scientific and engineering computations.
LISP 1958 John McCarthy Pioneered functional programming; widely used in AI research.
COBOL 1959 Grace Hopper & CODASYL Committee Designed for business data processing with English-like syntax.
BASIC 1964 John G. Kemeny & Thomas E. Kurtz Made programming accessible to beginners and students.
C 1972 Dennis Ritchie, Bell Labs General-purpose language; foundation for many modern languages.
Pascal 1970 Niklaus Wirth Emphasized structured programming and data structuring.
Smalltalk 1972 Alan Kay, Adele Goldberg, Dan Ingalls Introduced object-oriented programming concepts.
C++ 1983 Bjarne Stroustrup Extended C with object-oriented features.
Perl 1987 Larry Wall Text processing and scripting; known for flexibility and “There’s more than one way to do it” philosophy.
Python 1991 Guido van Rossum Emphasizes code readability and simplicity; widely used across various domains.
Java 1995 James Gosling, Sun Microsystems Platform-independent; “Write once, run anywhere” capability.
JavaScript 1995 Brendan Eich, Netscape Scripting language for web development; enables interactive web pages.
Ruby 1995 Yukihiro Matsumoto Focuses on simplicity and productivity; known for elegant syntax.
PHP 1995 Rasmus Lerdorf Server-side scripting; widely used for web development.
C# 2000 Microsoft Combines principles from C and Java; integral to .NET framework.
Go 2007 Robert Griesemer, Rob Pike, Ken Thompson (Google) Designed for simplicity and efficiency in system programming.
Rust 2010 Graydon Hoare, Mozilla Focuses on safety and performance; prevents memory errors.
Kotlin 2011 JetBrains Interoperable with Java; officially supported for Android development.
Swift 2014 Apple Inc. Modern language for iOS and macOS development; emphasizes safety and speed.
Julia 2012 Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman High-performance language for technical and scientific computing.

Programming Languages after 2006

Language Year Summary
Go (Golang) 2007 Developed by Google to provide fast compilation and simple concurrency with goroutines. Known for its clean syntax and efficient performance in system-level applications.
Clojure 2007 A modern Lisp dialect for the JVM, emphasizing immutability and functional programming. Excellent for concurrent and data-intensive applications.
Nim 2008 Combines Python-like readability with C-like performance. Offers metaprogramming and manual memory management capabilities.
Rust 2010 Designed by Mozilla to provide memory safety without a garbage collector. Loved for systems programming, especially where safety and performance are crucial.
Elixir 2011 A functional, concurrent language built on the Erlang VM (BEAM). Optimized for scalable and fault-tolerant applications, especially in web services.
Kotlin 2011 Developed by JetBrains as a modern alternative to Java on the JVM. Now officially supported for Android development.
Dart 2011 Created by Google for building frontend UIs, especially with Flutter. Designed to replace JavaScript in modern web and mobile apps.
Julia 2012 Created for high-performance scientific computing with easy syntax like Python. Balances speed with dynamic language flexibility.
Crystal 2014 A language with Ruby-like syntax but compiled and statically typed. Aims to provide high performance with developer-friendly syntax.
Swift 2014 Developed by Apple for iOS and macOS development. Emphasizes safety, speed, and modern language features.
ReasonML 2016 Developed by Facebook (now Meta) as a syntax layer for OCaml. Designed for writing fast and type-safe frontends (e.g., via ReScript).
Bosque 2019 Microsoft research language aiming for predictable and readable code. Avoids loops and mutable state to improve reliability.
ReScript 2020 A fork and rebranding of ReasonML focused purely on JavaScript output. Offers excellent performance and type safety for web development.

Here’s a curated list of popular programming languages along with their most powerful and popular frameworks—these frameworks often play a major role in making the languages so widely adopted:

🐍 Python

  • Django – High-level web framework for rapid development and clean design.
  • Flask – Lightweight web framework for APIs and microservices.
  • TensorFlow / PyTorch – Leading frameworks in machine learning and AI.
  • Pandas / NumPy / SciPy – Essential for data science, statistics, and scientific computing.
  • FastAPI – High-performance framework for building APIs with automatic docs.

☕ Java

  • Spring / Spring Boot – Comprehensive ecosystem for enterprise-grade applications.
  • Hibernate – Powerful ORM framework for database interactions.
  • Apache Struts – MVC web application framework.
  • Vaadin – Modern web UI framework for Java developers.

🌐 JavaScript / TypeScript

  • React.js – Declarative UI library maintained by Meta (Facebook).
  • Angular – Full-featured front-end framework by Google.
  • Vue.js – Progressive framework for building interactive UIs.
  • Node.js – Server-side runtime for building scalable network applications.
  • Express.js – Minimalist framework for web apps and REST APIs on Node.js.

🟡 TypeScript (superset of JavaScript)

  • NestJS – Scalable, enterprise-grade server-side framework built with TypeScript.
  • Next.js – Framework for server-rendered React apps and static sites.
  • Angular – Fully written in TypeScript with strong typing and tooling support.

🧱 C#

  • ASP.NET Core – Cross-platform, high-performance web and API framework.
  • Blazor – For building interactive web UIs with C# instead of JavaScript.
  • Unity – Game development engine with strong support for C#.

🐘 PHP

  • Laravel – Elegant, full-featured web application framework.
  • Symfony – Modular, reusable components for enterprise applications.
  • CodeIgniter – Lightweight MVC framework for fast development.

🦀 Rust

  • Rocket – Fast, type-safe web framework.
  • Actix Web – High-performance asynchronous web framework.
  • Bevy – Modern game engine built with Rust.

⚙️ Go (Golang)

  • Gin – Lightweight web framework with performance and productivity in mind.
  • Echo – High-performance, minimalist Go web framework.
  • Fiber – Express-inspired web framework built on Fasthttp.

🍎 Swift

  • SwiftUI – Declarative UI framework for Apple platforms.
  • Vapor – Web framework for server-side Swift applications.

🟣 Kotlin

  • Ktor – Asynchronous framework for building web apps and APIs.
  • Jetpack Compose (Android) – UI toolkit for building native Android UIs in Kotlin.
  • Spring (Kotlin DSL) – Kotlin-friendly version of the Spring framework.

📈 R

  • Shiny – Web app framework for building interactive dashboards and data visualizations.
  • tidyverse – Collection of R packages for data science and analysis.

Here’s a list of popular programming languages along with the most widely used:

  • Static Site Generators (SSG)
  • Dynamic Site Generators (DSG)
  • Content Management Systems (CMS)

These tools are often what attract developers to use a language for websites, blogs, documentation, or full-fledged web apps.

🐍 Python

Type Tools
Static Site Generators Pelican, MkDocs, Lektor
Dynamic Site Generators Wagtail (can render dynamically with Django), Plone
CMS Django CMS, Wagtail, Mezzanine

☕ Java

Type Tools
Static Site Generators JBake, Jekyll (via JRuby)
CMS Alfresco, Magnolia, Hippo CMS / Bloomreach

🟨 JavaScript / TypeScript

Type Tools
Static Site Generators Next.js (SSG + DSG), Gatsby, Nuxt, Eleventy (11ty)
Dynamic Site Generators Remix, SvelteKit, [Next.js (hybrid)]
CMS Strapi, KeystoneJS, Directus, Ghost

⚙️ Go (Golang)

Type Tools
Static Site Generators Hugo (one of the fastest SSGs)
CMS Caddy CMS, Decap CMS earlier name Netlify CMS (used with Hugo)

🟣 Ruby

Type Tools
Static Site Generators Jekyll (official SSG for GitHub Pages), Middleman
CMS Refinery CMS, ComfortableMexicanSofa

🧱 C#

Type Tools
Static Site Generators Statiq, Wyam (predecessor of Statiq)
CMS Orchard Core, Umbraco, DotNetNuke (DNN)

🍎 Swift

Type Tools
Static Site Generators Publish
CMS Swift is rarely used for traditional CMS, but can be paired with headless CMS like Contentful or Strapi in iOS apps.

🦀 Rust

Type Tools
Static Site Generators Zola, Cobalt
CMS Rust is typically used with headless CMS (e.g., GraphCMS, Strapi) or backend microservices, not traditional CMS.

📈 R

Type Tools
Static Site Generators Blogdown (uses Hugo), Distill
CMS Not commonly used for CMS, mostly used for academic publishing and dashboards.

🐘 PHP

Type Tools
Static Site Generators Sculpin, Jigsaw
CMS WordPress, Drupal, Joomla, Craft CMS, October CMS

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About the Author

Author avatar

Dr. Hari Thapliyal is a prolific blogger and seasoned professional with an extensive background in Data Science, Project Management, and Advait-Vedanta Philosophy. He holds a Doctorate in AI/NLP from SSBM, Geneva, along with Master’s degrees in Computers, Business Management, Data Science, and Economics. With over three decades of experience in management and leadership, Hari has extensive expertise in training, consulting, and coaching within the technology sector. His specializations include Data Science, AI, Computer Vision, NLP, and machine learning. Hari is also passionate about meditation and nature, often retreating to secluded places for reflection and peace.

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