{
  "id": 3333505,
  "title": "What Actually Makes a Software Engineer Great (It's Not the Framework You Know)",
  "url": "https://urgent.news/2026/08/25/what-actually-makes-a-software-engineer-great-its-not-the-framework",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-25T18:50:15.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/allenarduino/what-actually-makes-a-software-engineer-great-its-not-the-framework-you-know-2m3l"
  },
  "original_language": "en",
  "account": "Many developers seeking to advance their skills receive similar guidance: master React, learn backend languages, create projects, apply for jobs. While this advice is sound, it omits the critical factor that distinguishes a senior engineer from a junior one. It isn't the framework listed on your resume. Rather, it's your ability to comprehend why a framework was developed and your capacity to reason about a problem sufficiently to choose the appropriate tool over the familiar one. Here are the three key elements that truly make a difference.\n\n1. Grasp the 'Why,' Not Just the 'How'\n\nWhen embarking on your coding journey, the prevailing recommendation is to learn React. React is indeed popular and in demand. However, if you don't understand why React came into existence, how frontend development existed before it, and what problem it aimed to solve, you'll soon reach a professional ceiling.\n\nConsider this scenario: prior to the advent of async/await, asynchronous JavaScript was managed using nested callbacks. As you accumulated more interconnected async operations, that nesting evolved into what was colloquially known as \"callback hell.\" Promises arrived to streamline this, and async/await followed to render asynchronous code akin to synchronous code. Learning async/await merely as the syntax for asynchronous operations would leave you stranded should you encounter an older codebase littered with .then() chains or raw callbacks. Understanding what problem async/await resolves enables you to truly engage with and work with code predating its introduction. This matters because tools are selected for reasons, and those reasons evolve based on the specific problem. A senior engineer does not default to utilizing React (or Rails, Django, or any trending technology). Instead, they assess what the project truly needs to address, both presently and as it scales, and select technology accordingly. A tangible illustration of this concept emerges when comparing Node.js and Python.\n\nThis understanding of the 'why' takes on greater substance when contrasting Node.js and Python, as these two languages approach concurrency in fundamentally distinct manners, shaping the optimal use case for each language. Node.js operates on a single thread, employing a non-blocking event loop. When a request necessitates file reading, database interaction, or API invocation, Node.js does not halt that thread while awaiting the response. Instead, it delegates the operation (handled by libuv behind the scenes), continues processing other requests, and returns to your request once the data is available. This characteristic renders Node.js highly efficient for I/O-bound tasks, such as REST APIs, real-time applications (chat, live notifications via WebSockets), streaming services, and any scenario where a request devotes most of its time to network or disk operations rather than substantial computation.\n\nConversely, Node.js encounters challenges with CPU-bound work. Since everything transpires on that solitary thread, a resource-intensive synchronous computation (image processing, video encoding, complex encryption, large-scale in-memory data processing) will block the event loop entirely. All other requests waiting on the same process will freeze until the computation concludes. Node.js compensates for this through worker_threads and child_process, which allow CPU-heavy tasks to be offloaded from the main thread; however, these are deliberate workarounds rather than the default execution model.\n\nPython, on the other hand, exhibits a contrasting profile. Python incorporates the Global Interpreter Lock (GIL), which restricts multiple threads within a single Python process from executing Python bytecode concurrently. Thus, naive multi-threading in Python does not yield true parallelism for CPU-bound workloads. Expanding the thread count for a CPU-intensive Python task typically does not enhance performance, as they all contend for the same lock. This disparity explains why Python is not typically the primary choice for constructing high-concurrency real-time API servers from scratch, unless augmented with additional mechanisms. Yet, Python's prominence in data science and machine learning presents a compelling counterpoint. Two factors contribute to Python's effectiveness: I/O-bound operations still benefit from Python's concurrency tools. The GIL is released while a thread awaits I/O (network calls, file reads), enabling Python's threading module or asyncio (mirroring Node.js's event loop model) to function effectively for I/O-heavy workloads, such as web scraping or concurrent API calls.\n\nIn contrast, CPU-intensive libraries execute their core tasks outside the GIL. Libraries like NumPy, pandas, and PyTorch leverage compiled code (C, C++, or CUDA) to perform heavy numerical computations, releasing the GIL during processing and potentially executing in parallel or on a GPU. Python functions as the orchestration layer, coordinating the interaction between the high-level Python code and the optimized C extensions. For genuine CPU-bound parallelism in pure Python (excluding delegation to C extensions), the multiprocessing module can be employed, which spawns separate processes, each with its own interpreter and GIL, to distribute the workload across CPU cores. However, this approach is heavier than threading due to each process possessing its own memory space.\n\nWhen to use each: Node.js excels at APIs and microservices with high concurrent I/O, real-time applications, streaming, and thin services with minimal CPU work. Heavy synchronous computations, video/image processing, CPU-intensive encryption, and machine learning model training are better suited to Python.",
  "summary": "Most advice for developers who want to level up sounds the same: learn React, learn a backend language, build projects, apply to jobs. That advice isn't wrong, but it misses the thing that actually separates a senior engineer from someone who's been writing code for six months. It's not the framework in your resume. It's whether you understand why that framework exists in the first place, and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}