C vs Python in 2026: AI-Powered Development

🐍 Shallow Copy vs Deep Copy in Python — Explained Simply When working with data structures in Python, especially lists, dictionaries, or nested objects, you might hear the terms Shallow Copy and Deep Copy. Understanding the difference between them helps avoid unexpected bugs when modifying data. 🔹 What is a Shallow Copy? A shallow copy creates a new object, but it does not copy the nested objects inside it. Instead, it just copies the references to those inner objects. This means: - The outer container is new. - The inner objects are still shared between the original and the copy. Example: import copy original = [[1, 2], [3, 4]] shallow = copy.copy(original) shallow[0][0] = 99 print(original) # [[99, 2], [3, 4]] print(shallow) # [[99, 2], [3, 4]] Here, changing the nested list in the shallow copy also changes the original list because both reference the same inner objects. 📌 When to use Shallow Copy - When the data structure contains immutable objects - When you only need a separate outer container - When performance matters (shallow copy is faster) --- 🔹 What is a Deep Copy? A deep copy creates a completely independent copy of the original object including all nested objects. Every level of the structure is duplicated. Example: import copy original = [[1, 2], [3, 4]] deep = copy.deepcopy(original) deep[0][0] = 99 print(original) # [[1, 2], [3, 4]] print(deep) # [[99, 2], [3, 4]] In this case, modifying the deep copy does not affect the original, because all nested objects were copied as well. 📌 When to use Deep Copy - When working with nested lists, dictionaries, or complex objects - When you want complete independence between copies - When modifying copied data without affecting the original --- ✅ Simple Way to Remember - Shallow Copy: Copies the container, shares inner objects. - Deep Copy: Copies everything, including nested objects. --- 💡 Real-World Use Cases - Data preprocessing in Machine Learning - Avoiding unintended mutations in large programs - Creating backup versions of complex data structures Understanding this small concept can prevent many hidden bugs and improve how you manage data in Python. #Python #Programming #SoftwareDevelopment #CodingTips #PythonLearning

🚀 Which Programming Language Should You Master in the AI Era (2026)? The "best" language isn't just about syntax anymore—it's about where you sit in the AI Stack. If you're a student looking to future-proof your career, here is your 2026 roadmap based on the latest market trends: 1. 🐍 The "Orchestrator": Python Python remains the undisputed king because it acts as the "glue" for the entire AI ecosystem. While it isn't the fastest, it is the control room where models are trained and complex multi-agent systems are designed. * Best for: AI Research, Data Science, and orchestrating autonomous workflows. 2. 🦀 The "Muscle": Rust & Mojo As AI moves from "cool experiments" to "heavy production," speed and safety are non-negotiable. * Rust is rapidly replacing C++ for building core AI engines because it prevents memory bugs at compile-time while matching C++ in raw speed. * Mojo is the rising star, designed to offer "Python syntax with C-speed," allowing you to write high-performance GPU kernels without leaving a Pythonic environment. * Best for: High-performance infrastructure and high-throughput inference systems. 3. 🛡️ The "Guardrails": TypeScript AI coding assistants like GitHub Copilot now write nearly half of all code. However, AI performs significantly better when it has "guardrails"—which is why it generates much more reliable code in typed languages like TypeScript. * Best for: Building AI-powered web interfaces and SaaS products. 4. ☁️ The "Foundation": Go (Golang) AI needs massive cloud power to run at scale. Go is the language behind the tools that make this possible, such as Kubernetes, Docker, and Terraform. * Best for: Cloud-native infrastructure and MLOps (Machine Learning Operations). 💰 Why It Matters (The Numbers) * Explosive Growth: AI and Machine Learning roles have grown approximately 143% year-over-year. * Top Salaries: In the US, mid-level AI Engineers are commanding base salaries between $150,000 and $250,000. Senior professionals can regularly clear $300,000+ when adding equity and bonuses. 💡 The Bottom Line for Students In 2026, the specific language matters less than your ability to understand system architecture. AI will help you write the code snippets, but you need to be the architect who connects the models, the data, and the user interface. Master one "orchestration" language (Python) and one "performance" language (Rust or Mojo) to stand out in the top 1%. #AI #Programming #CareerAdvice #TechTrends2026 #Coding #Python #Rust #TypeScript https://lnkd.in/d6E7xDx6

🚀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁: 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Learning Python becomes much easier when you understand the core concepts that form the foundation of the language. Python is widely appreciated for its simple syntax, readability, and versatility, which is why it is used in fields like data science, machine learning, automation, and web development. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 :-https://lnkd.in/dG25FCrF 𝗛𝗲𝗿𝗲 𝗶𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗰𝗵𝗲𝗮𝘁 𝘀𝗵𝗲𝗲𝘁: 🔹𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 — Variables are used to store data values such as numbers or text. Python does not require explicit type declaration, making it beginner-friendly and flexible. 🔹𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬 — Python supports multiple built-in data types including integers, floating-point numbers, strings, booleans, and lists. Understanding data types helps developers structure and process data efficiently. 🔹𝐁𝐚𝐬𝐢𝐜 𝐒𝐲𝐧𝐭𝐚𝐱 — Python’s syntax is designed to be clean and readable. It allows developers to write logical instructions with minimal complexity, making it ideal for beginners and professionals alike. 🔹𝐋𝐢𝐬𝐭𝐬 — Lists are ordered collections used to store multiple values in a single structure. They are commonly used for managing datasets and performing operations on groups of elements. 🔹𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 — Functions help organize code into reusable blocks that perform specific tasks. This improves code maintainability and reduces repetition in larger programs. 🔹𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 — Conditional logic allows programs to make decisions based on certain conditions, enabling dynamic and intelligent workflows. 🔹𝐋𝐨𝐨𝐩𝐬 — Loops allow repeated execution of tasks, which is essential for processing datasets, automating tasks, and building scalable applications. 🔹𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬 — Dictionaries store information in key-value pairs, making them ideal for representing structured or labeled data. 🔹𝐅𝐢𝐥𝐞 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 — Python provides built-in capabilities to read, write, and manage files, which is essential for working with datasets, logs, and external data sources. 🔹𝐌𝐨𝐝𝐮𝐥𝐞𝐬 𝐚𝐧𝐝 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 — One of Python’s biggest strengths is its ecosystem of modules and libraries that extend its functionality for tasks such as data analysis, automation, and scientific computing. 💡 𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝘀 𝗦𝗼 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 — Python has become one of the most in-demand programming languages because it powers many modern technologies including artificial intelligence, data analytics, and cloud applications. Its strong community support and vast library ecosystem make it a powerful tool for developers at every level.

🐍 𝗪𝗵𝘆 𝗶𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 #𝟭 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲, 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁𝘀 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗸𝗲𝗲𝗽 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴? Every year, new frameworks, languages, and “faster alternatives” enter the conversation… yet Python continues to dominate every major ranking. Over the past months, I’ve explored dozens of industry reports, surveys, and ecosystem trends, and it’s clear that Python’s rise isn’t a coincidence. It’s a reflection of how modern software is actually built in 2025. Here’s what the data shows:  • 📊 #𝟭 𝗶𝗻 𝘁𝗵𝗲 𝗧𝗜𝗢𝗕𝗘 𝗜𝗻𝗱𝗲𝘅 – 23.37% rating, the highest score recorded since 2001.  • 📈 𝟱𝟳.𝟵% 𝗼𝗳 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝘂𝘀𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 – Stack Overflow 2025 Survey.  • 🌍 𝗨𝘀𝗲𝗱 𝗯𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗶𝗻 𝟭𝟱𝟬+ 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀 – JetBrains Global Python Survey.  • 🚀 𝟴𝟱𝟬,𝟬𝟬𝟬+ 𝗻𝗲𝘄 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗼𝗿𝘀 joined Python projects in a single year – GitHub Octoverse 2025.  • 📦 𝟱𝟬𝟬,𝟬𝟬𝟬+ 𝗽𝗮𝗰𝗸𝗮𝗴𝗲𝘀 𝗼𝗻 𝗣𝘆𝗣𝗜, making it one of the richest ecosystems in the world.  • 🏆 #𝟭 𝗶𝗻 𝗜𝗘𝗘𝗘 𝗦𝗽𝗲𝗰𝘁𝗿𝘂𝗺’𝘀 𝗧𝗼𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 for engineering, AI, and research.  • 📚 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 #𝟭 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗶𝗻 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 globally – CS education reports.  • 🤖 𝗧𝗵𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗼𝗳 𝗔𝗜 – 70%+ of ML engineers rely on Python as their primary language. And beyond numbers, the story is even more compelling. Python sits at the intersection of AI, data engineering, automation, DevOps, backend development, and scientific computing – the fields driving the fastest innovation today. Companies like Google, Netflix, Instagram, J.P. Morgan, NASA - National Aeronautics and Space Administration, Meta, Spotify, Intel, and Dropbox rely on Python for everything from internal automation and ML workflows to massive distributed systems. At PLANEKS, we see this first-hand: Python consistently becomes the fastest, most flexible path from idea → prototype → production. Its clarity, extensibility, and scientific ecosystem allow teams to move quickly while still building scalable, maintainable systems. Whether it’s powering high-load backends, orchestrating data pipelines, automating infrastructure, or running complex AI models – Python continues to be the language teams trust when both speed and adaptability matter. If you want a deeper breakdown of Python’s global adoption and the statistics shaping its future, here’s the full article 👇 https://lnkd.in/e48Nt6F4 Want to explore how Python can power your next project? Reach out anytime – I’m happy to discuss architecture, cost, and the most efficient path forward for your product.

Day 3 of my Software Engineer → AI Engineer transition. I was wrong about Python learnings 2 times today. Here's exactly what I assumed vs what's true: ❌ WRONG: Dunder methods like `__len__` are for encapsulation/hiding variables ✅ ACTUAL: They are runtime hooks that let your custom objects plug into Python's built-in syntax. When Python sees `len(my_object)`, it doesn't check what type `my_object` is. It just looks for `__len__` and calls it. That's it. My custom PriceCart class: ``` class PriceCart:   def __init__(self, items):     self.items = items   def __len__(self):     return len(self.items) cart = PriceCart([100.0, 200.0]) len(cart) # works. No inheritance. No magic. ``` ❌ WRONG: Python uses inheritance to call the right dunder method ✅ ACTUAL: This is duck typing. Python doesn't ask "are you a list?" Python asks "do you have __len__?" If yes → call it. If no → raise TypeError. This is baked into CPython's source code. `len()` is literally hardcoded to look for `__len__`. Every built-in operation has a corresponding hardcoded dunder. No inheritance involved. Why does this matter? Here's what dunder methods actually give you: → Consistency: One syntax for everything. `len(my_list)`, `len(my_cart)`, `len(my_dataset)`. Same call, works everywhere. No memorizing `.size()` vs `.length()` vs `.count()` per class. → Free superpowers: Implement '__len__' + '__getitem__' on your class and you instantly get len(), [] indexing/slicing, for loops, and even 'random.choice()' without writing any of that yourself. Being wrong 2 times in one day and understanding why → that's the actual learning. All predictions, notes, and code experiments pushed to GitHub 👇 https://lnkd.in/gHg-vwbh #AIEngineering #Python #CareerTransition #BuildingInPublic

The Python Pivot: AI-Driven Growth Amidst Market Competition The Python ecosystem is at a fascinating crossroads in early 2026. While specialized languages like R and Perl are staging surprising comebacks in data science and scripting, Python is doubling down on its role as the primary "glue" for the Generative AI revolution. From node-based visual development to "off-label" compression tricks, the language is evolving to meet the demands of a post-LLM world. Key Highlights / Takeaways: 🔹 The TIOBE Market Shift — For the first time in years, Python’s market share has dipped (from 26.98% in July 2025 to 21.81% in February 2026). This is largely due to the resurgence of R (climbing to 8th) and Perl (surging to 11th), as developers return to specialized tools for statistical and legacy scripting tasks. 🔹 Visual AI with ComfyUI — Python is bridging the gap between design and code. Tools like ComfyUI allow developers to build complex AI image/video workflows visually and then export them directly into executable Python scripts, effectively turning nodes into production code. 🔹 Zstd: The Lightweight LLM Alternative — Python 3.14's new zstd module is being used for "compression-based text classification." This method achieves 91% accuracy on standard datasets in seconds, offering a massive, gradient-free efficiency boost over traditional LLMs for specific NLP tasks. 🔹 Zero-Setup PostgreSQL — The emergence of pgserver allows developers to run a full PostgreSQL instance (including pgvector) via a simple pip install. This removes the "infrastructure tax" of setting up local databases for AI and data projects. 🔹 The .pyc Security Debt — A major alert for 2026: thousands of GitHub repositories are leaking secrets via compiled .pyc bytecode files. Developers are urged to scrub their history with git-filter-repo and ensure **/*.pyc is explicitly blocked in .gitignore. 💡 Perspective: Python is transitioning from being a "general purpose" language to an "orchestration layer." While its raw popularity may fluctuate as developers rediscover specialized tools like R, its dominance in the AI-agent and local-first development space (supported by tools like pgserver and zstd) makes it the undisputed operating system for the AI era. https://lnkd.in/ghPGQ4zb #Python #GenerativeAI #DataScience #WebDev #CyberSecurity #TIOBE #ComfyUI #Azure #PostgreSQL

I read Martin Fowler's "Refactoring" 15 years ago and thought it was overkill. A section devoted to the refactoring “Rename Method”? But that precise vocabulary is becoming more and more valuable. It's exactly what you need to describe refactoring patterns to AI. Refactoring used to mean digging in manually and doing a bunch of work to fix code. Now it's different. AI searches, finds patterns, you review what it finds, then use your judgment to direct how to fix it. Having textbook vocabulary knowledge of patterns has never been more leveraged because that's what you're prompting with. You can even make up your own patterns. I use one I call “CLI Pushdown”. How it works for me: In Claude and other TUIs, I can write automations using markdown files. Natural language with embedded code snippets. The AI treats them as automation scripts. Very flexible, loosely coupled. The issue is sometimes the AI takes a ton of time doing something magical when a Python script would be a thousand times faster and cheaper. So I have a pattern now. First of all, I always have a CLI associated with every project that is just a flat list of commands. I do this instead of having a bunch of random scripts. Then I tell the AI to take a section markdown and make it a Python CLI command. Push down the markdown into Python. I borrowed the term from "predicate pushdown" from databases, but the AI can re-contextualize that because I documented it in a skill. What it does is remove the English and embedded scripts, then generates Python that implements the same thing much faster and more reliably, and then replaces that markdown section with a specific CLI invocation. So the workflow looks like: explore requirements using markdown where the AI figures out how to do stuff. "Go update the second comment in the GitHub issue." It cooks, researches, figures out the right CLI incantation. Once you see it doing the same thing repeatedly, you push it down to Python. Because it’s a documented pattern with a name I can one shot it nearly 100% of the time. You've taken requirements in English––magically executable because of the magic of LLMs––and effectively compiled them to Python. You can see it directly in your PRs. English is red; Python is green. You explored and iterated on requirements in a substrate that is slow and unreliable but highly flexible, extracted the deterministic bits, and pushed it down into something fast and cheap. It's fun to play with and make these AI-native refactoring techniques. And now my codebase understands it because it's just documented in a little markdown file.

Totally agree with this point. I think this is true in 'testing' and 'debugging' and other things now, too. LLMs speak English very, very well. They know the nuances. Knowing the right terminology to use matters. Example: do you know what the word 'ultimate' means? A friend asked the AI the other day to create the 'ultimate example' of something. It made this kind of dystopian, minimalist version. Why? Because 'ultimate' means 'last.' 'Ultimate' as a metaphor for best implies "you sorted from worst to best and took the last one." That's not necessarily what the LLM assumed from the word 'ultimate.'

This seems like a true AI unlock! I've been thinking lately about how to make agents perform deterministic tasks instead of "wandering" into a solution every time, and this method nails it!

If you're interested in the craftsmanship aspect of vibe coding, Nick's CLI-pushdown technique is a clever and insightful process for your workflow and worth a quick read.