RedMonk Language Rankings

RedMonk Language Rankings

Beyond Recursion

Recursion, a foundational concept in computer science, often captivates with its elegant, self-referential solutions to complex problems. Defined by a function calling itself to solve smaller instances of the same problem, it can lead to remarkably concise code, particularly for tasks involving tree structures or mathematical sequences. However, beneath this allure lies a set of significant problems that often render recursion problematic, inefficient, and even unsafe for production-grade software, especially when compared to alternative programming paradigms and approaches.

One of the most critical issues with recursion is the risk of stack overflow. Each recursive call adds a new frame to the program's call stack. For deep recursion, or when dealing with large datasets, this stack can quickly consume all available memory, leading to a StackOverflowError and program termination. This makes recursion inherently memory-inefficient for many practical scenarios, as the overhead of managing numerous stack frames can be substantial. Furthermore, the performance overhead associated with function calls (context switching, parameter passing, return address management) can make recursive solutions significantly slower than their iterative counterparts, even if a stack overflow is avoided.

Beyond resource consumption, recursion can introduce increased complexity and debugging challenges. While the initial recursive definition might appear simple, tracing the execution flow and understanding the state at each recursive step can be notoriously difficult. This complexity often makes identifying and fixing bugs more arduous compared to a clear, step-by-step iterative loop. The implicit state management through the call stack, though elegant, obscures the program's flow, making it harder to reason about, particularly for developers less familiar with the recursive pattern.

Given these drawbacks, several alternative paradigm shifts and approaches offer more compelling, efficient, simpler in complexity, and memory-safe ways to program.

The most direct alternative is iteration. Any problem solvable with recursion can also be solved with iteration, typically using loops (e.g., for, while). Iterative solutions manage state explicitly, often with a few variables, rather than relying on the call stack. This direct control over memory usage and execution flow makes iterative programs inherently more memory-safe by avoiding stack overflow issues and generally more performant due to reduced function call overhead. For instance, calculating factorials or traversing a list iteratively is clearer, more efficient, and less prone to errors than their recursive equivalents.

In the realm of functional programming, tail recursion optimization (TCO) offers a partial mitigation. A tail-recursive function is one where the recursive call is the very last operation performed. Compilers or interpreters capable of TCO can optimize these calls into iterative loops, effectively eliminating the stack overhead. While this addresses the stack overflow problem and improves performance, it requires specific language support and careful structuring of the recursive function, which might not always be intuitive or possible in all contexts. Languages like Scheme and Scala often support TCO, but it's less common or not guaranteed in others like Python or Java.

Beyond direct iterative translation, data structures and algorithms designed for specific problems can often provide more efficient solutions than general-purpose recursion. For graph traversal, explicit stack or queue data structures can be used to implement Depth-First Search (DFS) or Breadth-First Search (BFS) iteratively, offering fine-grained control over memory and execution. Similarly, dynamic programming, which often involves building up solutions from subproblems and storing intermediate results (memoization), avoids redundant computations inherent in naive recursive solutions, leading to significant performance gains and often being implemented iteratively.

Finally, adopting a problem-solving mindset that favors explicit state management and clear control flow over implicit recursive calls can lead to more robust and maintainable code. Object-oriented programming, for example, might encapsulate state within objects and use methods to manipulate that state iteratively. Event-driven programming or reactive programming paradigms focus on responding to events and managing asynchronous flows, often using callbacks or streams, which inherently steer away from deep recursive call chains.

While recursion holds a place for its conceptual elegance in specific scenarios, its practical limitations regarding stack overflow, performance overhead, and debugging complexity make it a problematic choice for many real-world applications. Embracing iterative approaches, leveraging tail recursion where supported, employing appropriate data structures and algorithms, and adopting paradigms that prioritize explicit state management and clear control flow are more compelling, efficient, simpler in complexity, and memory-safe alternatives for building robust and scalable software.

Proton Native

Proton Native

Fastify

Fastify

Go, Javascript, and Python

The world of application development is rapidly evolving, with demand for multiplatform experiences, generative AI (GenAI), and agentic AI at an all-time high. Choosing the right programming language and its associated ecosystem of frameworks and libraries is crucial for success. While Python and JavaScript have dominated these spaces for years, Go is emerging as a compelling alternative, particularly where performance, concurrency, and deployability are paramount.

Go's Approach: Go's strength lies in its ability to compile to a single, self-contained binary, making deployment straightforward across various operating systems. While Go doesn't have a direct equivalent to Flutter (Dart) or React Native (JavaScript) for native UI development from a single codebase, frameworks like Fyne and Gio offer cross-platform GUI capabilities, rendering native-looking interfaces for desktop and, increasingly, mobile platforms. Go's strong concurrency model (goroutines and channels) is also beneficial for building responsive applications that can handle multiple tasks without freezing the UI. This is particularly appealing for backend services that power multiplatform frontends.

Python's Landscape: Python's multiplatform GUI options include Kivy and BeeWare. Kivy is known for its custom UI rendering, while BeeWare aims for native-looking interfaces. However, neither has achieved the widespread adoption or seamless native integration seen in the JavaScript ecosystem. For web-based multiplatform apps, Python often relies on frameworks like Django or Flask for the backend, with frontends built using JavaScript frameworks.

JavaScript's Dominance: JavaScript, through frameworks like React Native and Ionic, is arguably the current king of multiplatform app development. React Native allows developers to build truly native-rendered mobile applications using JavaScript, leveraging a massive existing developer base. Ionic, on the other hand, focuses on hybrid apps using web technologies (HTML, CSS, JavaScript) wrapped in native containers, ideal for Progressive Web Apps (PWAs) and rapid development across web, mobile, and desktop. The sheer volume of libraries and community support makes JavaScript a compelling choice for many multiplatform projects.

Go's Niche in AI: While not its traditional stronghold, Go is making inroads in the AI space, especially for the deployment and serving of AI models, where its performance and concurrency are highly advantageous. Libraries like go-openai and generative-ai-go provide official and community-driven SDKs for interacting with large language models (LLMs) from providers like OpenAI and Google. Frameworks like Eino and Genkit are emerging, inspired by Python's LangChain, aiming to facilitate LLM application development, agentic workflows, and prompt management in Go. Go's ability to handle high concurrency makes it excellent for building scalable inference APIs for GenAI models. For agentic AI, which often involves coordinating multiple AI components and tools, Go's robust concurrency patterns can be a significant asset in designing efficient and reliable agent architectures.

Python's Reign in AI: Python remains the undisputed leader in GenAI and Agentic AI development. Libraries like TensorFlow, PyTorch, and Hugging Face Transformers form the backbone of modern machine learning, offering unparalleled tools for model training, fine-tuning, and deployment. For agentic AI, frameworks such as LangChain, LlamaIndex, CrewAI, and AutoGen provide high-level abstractions and comprehensive toolkits for building complex AI agents, managing conversations, and orchestrating multi-step reasoning. Python's rich scientific computing ecosystem (NumPy, Pandas, SciPy) further solidifies its position for data manipulation and analysis, which are integral to AI development. The vast academic and research community heavily relies on Python, leading to an abundance of pre-trained models, tutorials, and shared knowledge.

JavaScript's Growing AI Presence: JavaScript has also seen significant growth in AI, particularly for client-side inference and interactive AI experiences in the browser. TensorFlow.js and ML5.js enable developers to run and even train machine learning models directly in web browsers. For GenAI, JavaScript can interact with cloud-based LLM APIs. While dedicated agentic AI frameworks in JavaScript are not as mature or abundant as in Python, libraries like langchain.js are bridging the gap, allowing for similar agent orchestration patterns in the JavaScript ecosystem. JavaScript's strength lies in its ubiquitous presence on the web, enabling novel interactive AI applications that run directly in the user's browser.

For multiplatform app development, JavaScript with React Native or Ionic often provides the quickest path to native-like experiences across mobile and web. Go offers a compelling alternative for desktop-focused cross-platform GUIs and robust backend services. In the realm of GenAI and Agentic AI, Python maintains its dominant position due to its mature and expansive ecosystem of libraries and frameworks, making it the go-to for research, model training, and complex agentic workflows. However, Go is carving out a strong niche for high-performance AI inference and service deployment, where its concurrency and compilation benefits shine. JavaScript, meanwhile, excels at bringing AI directly to the browser for interactive frontends. The choice between these ecosystems ultimately depends on the specific project requirements, performance needs, deployment targets, and the existing expertise within the development team

Why Swift is so terrible

Swift, Apple's darling of a programming language, burst onto the scene in 2014 with promises of safety, speed, and modern syntax. For many, it delivered. Its adoption has been widespread, powering countless iOS, macOS, watchOS, and tvOS applications. Yet, beneath the polished surface and enthusiastic evangelism, a growing chorus of developers finds themselves frustrated, even exasperated, with Swift. While undeniably powerful, a closer examination reveals a language burdened by significant drawbacks that can make the development experience less than delightful, and at times, outright agonizing.

One of Swift's most frequently lauded features, and ironically a source of considerable pain, is its rapid evolution and API instability. While continuous improvement is generally a positive, Swift’s early years were characterized by a relentless pace of change that frequently broke existing codebases. Migrators from Swift 2 to 3, or even 3 to 4, remember the dread of opening a project only to be confronted with a cascade of errors, often requiring substantial refactoring due to fundamental API shifts. While the pace has somewhat slowed, the underlying architectural philosophy that permits such breaking changes remains a concern for long-term project stability. This constant chasing of the bleeding edge can translate into significant maintenance overhead and a deterrent for enterprises seeking rock-solid foundations.

Another substantial gripe centers around compiler performance and error messages. Even with modern hardware, compiling large Swift projects can be excruciatingly slow, transforming quick iterative changes into agonizing waiting games. This dramatically hinders developer productivity and discourages the rapid experimentation that is vital for efficient problem-solving. Compounding this issue are the infamous Swift error messages. Often cryptic, verbose, and pointing to seemingly unrelated lines of code, they can send developers down rabbit holes of debugging, wasting precious hours trying to decipher the compiler's enigmatic pronouncements. The frustration is palpable when a simple typo triggers a multi-line, unhelpful error, leaving even seasoned professionals scratching their heads.

Furthermore, Swift's complexity, particularly around generics and protocols, presents a significant barrier to entry and ongoing comprehension. While powerful constructs, they are often implemented with an academic rigor that can feel overly abstract and difficult to grasp, especially for those new to the language or coming from simpler paradigms. Debugging issues within complex generic code can be a nightmare, as the runtime behavior often deviates from intuitive expectations. This steep learning curve and the potential for obscure bugs make it challenging to write truly robust and maintainable code without deep expertise, which can be scarce and expensive.

Beyond the language itself, the tight coupling with Apple's ecosystem can also be seen as a double-edged sword. While providing a seamless experience for developing Apple-platform applications, it limits Swift's broader appeal and utility in more diverse environments. While server-side Swift and other ventures exist, the reality is that the vast majority of Swift development remains firmly within the Apple walled garden. This can feel restrictive for developers who prefer more platform-agnostic tools or for companies aiming for cross-platform solutions without relying on frameworks like React Native or Flutter.

While Swift undeniably offers many commendable features and has carved out a dominant niche in Apple’s development landscape, it is far from a universally lauded marvel. Its history of API instability, coupled with often-frustrating compiler performance and cryptic error messages, can severely dampen the development experience. The inherent complexity of some of its more powerful features, alongside its strong tether to the Apple ecosystem, further contributes to a picture of a language that, for many, is far from ideal. For every developer singing its praises, there's another silently wrestling with its frustrations, longing for a simpler, more stable, and less enigmatic path to app creation.

Downhill Spiral of Atlassian Tools

Once hailed as essential pillars in software development and project management, Atlassian's suite of tools—Jira, Confluence, Bitbucket, and others—appears, for many, to be on a perceived downhill spiral. What were once celebrated as robust solutions have, over time, increasingly transformed into sources of profound frustration, hindering productivity rather than enhancing it. The ubiquity of these platforms now seems less a testament to their inherent excellence and more a reflection of their entrenched market position, as users and administrators alike wrestle with a growing list of grievances that paint a picture of steady deterioration.

At the heart of this perceived decline is the issue of unrelenting complexity and feature bloat. What began as a flexible framework has metastasized into an unwieldy behemoth. Jira, in particular, has become infamous for its labyrinthine configuration options, intricate workflow schemes, and convoluted permission settings. While this extensive customizability is theoretically powerful for large, complex enterprises, it imposes an exorbitant administrative burden that often far outweighs its benefits. Smaller and medium-sized teams find themselves drowning in a sea of unnecessary features and arcane settings, requiring disproportionate time and dedicated personnel simply to maintain baseline functionality. This overhead not only saps resources but also creates a significant barrier to entry and adoption, pushing away potential users overwhelmed by the initial learning curve.

Compounding this complexity, performance bottlenecks have become a chronic and escalating problem. As Atlassian instances mature and accumulate more data, users, and projects, their responsiveness often plummets. Slow page loads, agonizingly long search times, and general sluggishness when navigating dense information are common complaints. This isn't merely an inconvenience; it directly translates into lost productivity and heightened user frustration. The constant need for costly hardware upgrades, intricate database optimizations, or a forced migration to their cloud services (which come with their own set of costs and data management concerns) highlights a fundamental scalability challenge that often feels inadequately addressed, turning daily operations into a test of endurance.

Furthermore, the user interface (UI) and user experience (UX) often feel like a relic, especially when compared to more modern, streamlined alternatives. Despite sporadic redesigns, the Atlassian ecosystem frequently presents a disjointed and unintuitive experience. Navigating between applications—from a Jira ticket to a related Confluence page or a Bitbucket repository—can feel like jumping between different software generations. Essential functionalities are often obscured or inconsistently placed, requiring users to memorize non-obvious pathways rather than instinctively engaging with the tools. This lack of a cohesive, intuitive design language significantly contributes to the perception that these tools are fighting against the user, rather than seamlessly supporting their work, demanding exhaustive training rather than fostering organic adoption.

Finally, the escalating cost factor represents a growing deterrent. What might initially appear as a manageable investment quickly balloons with the necessity of add-ons, integrations, and the tiered pricing models for growing user bases. Organizations often find themselves ensnared in a vendor lock-in, where the extensive data and deeply embedded workflows make migrating away from Atlassian an economically and operationally prohibitive endeavor. This creates a captive audience, forced to absorb rising costs for tools that, for many, are increasingly failing to deliver on their promise of efficient collaboration.

The journey of Atlassian tools, from industry darlings to objects of widespread exasperation, reflects a failure to balance comprehensive functionality with user-centric design and consistent performance. The very strengths that once defined them—extensibility and feature richness—have, for many, become liabilities, manifesting as overwhelming complexity, crippling performance issues, inconsistent user experiences, and unsustainable costs. For a significant segment of its user base, the dream of streamlined collaboration has devolved into a daily battle against cumbersome and frustrating systems, casting a long shadow over their continued dominance in the market.

Dart and Flutter

Google's Flutter framework, powered by the Dart programming language, has emerged as a formidable contender for building beautiful, natively compiled applications across mobile, web, and desktop from a single codebase. Its innovative approach to UI rendering and developer experience has garnered significant attention and a growing community.

Flutter empowers developers to create high-performance, visually consistent, and feature-rich applications for a diverse range of platforms. From intricate e-commerce platforms and dynamic social media applications to robust enterprise tools, utility apps, and even embedded systems, Flutter's versatility shines. Its ability to deliver a consistent user interface and experience across iOS, Android, web browsers, Windows, macOS, and Linux makes it an ideal choice for businesses aiming for broad reach with efficient resource allocation.

While React Native, another popular cross-platform framework, translates JavaScript code into native UI components, Flutter takes a different, often advantageous, approach. Flutter compiles Dart code directly to ARM machine code for mobile, JavaScript for the web, and native desktop binaries. This "ahead-of-time" (AOT) compilation eliminates the need for a JavaScript bridge to communicate with native components, often resulting in superior performance, smoother animations (at 60 or even 120 frames per second), and faster startup times. Furthermore, Flutter renders its own widgets using its high-performance Skia graphics engine, ensuring pixel-perfect control and visual consistency across all platforms, regardless of OEM (Original Equipment Manufacturer) UI differences. This "widget-tree" architecture, combined with Dart's strong typing and null safety, contributes to a more predictable and robust development environment. The renowned "hot reload" and "hot restart" features significantly accelerate the development cycle, allowing developers to see changes reflected almost instantly, a key advantage in rapid prototyping and iteration.

The Flutter ecosystem is vibrant and continually expanding, offering a wealth of libraries (known as packages) to streamline development. Noteworthy examples include state management solutions like provider and riverpod for simpler apps, or bloc and cubit for more complex architectural needs. For navigation, go_router provides a declarative routing solution. Integration with backend services is seamless with packages like firebase_core for Google's Firebase suite, while shared_preferences and sqflite offer robust options for local data storage.

Looking ahead, Flutter's future appears bright. Continued advancements in web and desktop support are making it an increasingly viable choice for full-stack application development. The framework is also exploring ambient computing, extending its reach to devices beyond traditional screens. The ecosystem is expected to see more official packages, enhanced tooling, and deeper platform integrations. Dart itself continues to evolve, with ongoing improvements in performance and language features.

For those eager to learn Flutter, the official documentation at flutter.dev is the definitive starting point, offering comprehensive guides, tutorials, and API references. The Flutter YouTube channel provides excellent video content, including official updates and coding examples. Platforms like Medium host a vast collection of articles and tutorials from the community, covering a wide range of topics and best practices. Additionally, online course platforms such as Udemy and Coursera offer structured learning paths from experienced instructors, while community forums like Stack Overflow and Reddit provide valuable support and problem-solving assistance.

Dart and Flutter present a powerful and efficient solution for building modern, high-quality, cross-platform applications. Its performance advantages, consistent UI rendering, and growing ecosystem position it as a leading choice for developers and businesses looking to innovate and scale their digital presence.

Swift

• Stanford University's CS193p
• Hacking with Swift (Paul Hudson)
• CodeWithChris
• Kodeco
• AppCoda
• Swift.org Documentation

Third-Party Licensing Services

• LicenseSpring
• 10Duke
• Cryptolens
• PACE
• Wibu
• Keygen
• LicenseOne
• SoftwareKey
• QuickLicense
• ProtectionMaster
• SafetNet Sentinel
• Trelica
• OpenLM
• Software Shield
• Zluri
• Flexera
• Ivanti
• Snow
• AssetSonar
• Reprise
• Torii
• AWS license manager
• ServiceNow

Generating Software License Keys

How to generate license keys

Haxe

Haxe

Rust Language

Rust Language

Is Unsafe an Achilles' Heel?

Is Unsafe an Achilles' Heel?

Rust Mixed-Methods Study

Rust Mixed-Methods Study

Rust Sucks

Rust is metaphorically marketed as a better systems programming language that focuses on performance-sensitive applications, especially for memory safety and concurrency. But, all of this is not very transparent, inaccessible, and hidden from the developer. Not to mention, it all comes with a steep learning curve. So, the question to ask, is it really worth it?

Ownership and Borrowing: This feature helps prevent memory leaks and data races. But, it is difficult to understand and profile especially if you are used to garbage collection.

Lifetimes: This helps ensure memory safety. But, again it is complex and difficult to reason about.

Complex Type System: It comes with a sophisticated type system to catch errors at compile time. But, again it can be difficult to understand.

Steep Learning Curve: It simply has a steep learning curve that requires time to learn. Time that is spent being less productive in actually delivering on work. This means it is more an academic language for people that have all the time in the world to learn a new language. If it takes so much time to learn than is it really worth it in the end. By the time you become competent at it there is likely a better programming language with a simpler approach to doing things. Complex languages are more difficult to test.

Compilation Time: It can be slow, very slow as a result of extensive checks to ensure memory safety and prevent data races, all happening under-the-hood. Should you trust it? This will lead to longer compilation times. Time that could be better spent like maybe getting a cup of coffee?

Verbosity: It is explicit that leads to more code. More code leads to more tests! This means more development time and larger codebases.

Ecosystem Maturity: Let's just say it is growing. This means fewer readily available libraries and tools for certain tasks. And, more than likely tons of undiscovered and unresolved bugs in the backlog.

Cognitive Overhead: Developers have to think more explicitly about memory management, even when it doesn't require manual memory allocation. This means a lot of cognitive overhead making the whole development process more challenging. You are surrounded by complexity. Defeating the whole premise of "Keep it simple, stupid". And, the often quoted in complexity circles: "Complexity is the root of all evil".

Not Suitable for All Tasks: This language is still very much domain-specific. Tasks that it can be good for are systems programming, embedded systems, and other performance-sensitive applications. Especially, if you are akin to making things more complex than they need to be. In most workplaces, agility matters in getting things done, where this programming language will not be useful for majority of development tasks.

Error Handling: Very verbose error handling that requires more code.

String Handling: Way too many string types.

IDE Support: Let's just say it is improving and not as feature-rich.

Debugging: Imagine a language that focuses on memory safety but is a challenge to debug. Most things in this language just go against the grain of being productive and focus on academic rituals of memory safety. It will make you pull your hair out of frustration.

State of Rust Survey 2024

ngrok

ngrok

Make

Make

Firebase vs DigitalOcean App Platform

Compare Firebase vs DigitalOcean App Platform

Monaco Editor

Monaco Editor

Firebase Alternatives

• AWS Amplify
• Parse
• Backendless
• Kuzzle
• Supabase
• appwrite
• Nhost
• Kinsta
• Hasura
• DigitalOcean App Platform