Why Python Dominates in AI/ML/DL Despite Performance Concerns

Python is the world's number one language for AI. It's also how most teams accidentally build their worst technical debt. We've reviewed 50+ Python codebases. The same 4 mistakes appear every time. Swipe to see what to fix before your codebase becomes a liability. → Mistake 1: No type hints → Mistake 2: Notebooks in production → Mistake 3: Unpinned dependencies → Mistake 4: Sync where you need async The best Python codebases we've worked on share one thing: They were written as if the team expected it to still be running in 5 years. Type hints. Tested modules. Pinned deps. Async where it matters. That discipline is the difference between a Python product and a Python project. Bacancy builds Python systems that scale. DM us if you're inheriting one that doesn't. #Python #PythonDevelopment #CleanCode #TechnicalDebt #SoftwareEngineering #BackendDevelopment #EngineeringLeadership #HirePythonDevelopers

Which Python do you know in 2026? 🐍 Most people say they “know Python”…but in reality, they only know the basics. Today, Python is not just a programming language it’s a complete ecosystem. From data analysis (pandas, Polars) to machine learning (scikit-learn, PyTorch), from big data (PySpark) to AI & LLM apps (Hugging Face, LangChain, LlamaIndex) your growth depends on the tools you use with Python. Want to build dashboards? → Streamlit Want to scale systems? → Ray, Dask Want to manage pipelines? → Prefect Want clean projects? → Poetry 👉 The difference between an average developer and a high-value professional is tool awareness + real-world usage. Don’t just learn Python, Learn what to build with Python. 📌 Start small → Pick one tool → Build projects → Stay consistent. So tell me 👇 Which of these tools have you already used? And what are you learning next? #Python #DataAnalytics #DataScience #AI #MachineLearning #CareerGrowth

🚀 Python GIL vs No-GIL — Real FastAPI Benchmarks (Python 3.13) Free-threaded Python is no longer just an experiment — it’s starting to show real impact. I came across a benchmark comparing Python 3.12 (with GIL) vs Python 3.13t (No-GIL) using FastAPI, and the results are pretty interesting 👇 💡 Key Takeaways: 🔹 Massive CPU Boost (~8x) CPU-bound endpoints jumped from ~4 RPS to ~32 RPS — with ZERO code changes. This is what true parallelism across cores looks like. 🔹 Threading inside requests ≠ better performance Even without GIL, spawning threads inside a single request didn’t help. Why? Under load, request-level parallelism already saturates the CPU. Extra threads just add overhead. 🔹 I/O performance unchanged No surprise here — GIL was never the bottleneck for I/O-bound workloads. Async + I/O still behaves the same. 📊 What this means in practice: ✅ Use No-GIL Python when: - You have CPU-heavy APIs (ML inference, image processing, data pipelines) - High concurrency + CPU contention exists - You previously relied on multiprocessing to bypass GIL ❌ Don’t expect gains if: - Your app is mostly I/O (DB calls, HTTP requests) - You’re already using async effectively ⚠️ Things to keep in mind: - Free-threading is still evolving - Thread safety is now YOUR responsibility - Some C extensions may not be ready yet 🔥 The most exciting part? Same code. Same FastAPI app. Just a different Python runtime → 8x improvement. This could seriously change how we design backend systems in Python. Curious — would you switch to No-GIL Python for your APIs? #Python #FastAPI #BackendEngineering #Performance #Concurrency #AI #SoftwareEngineering

🐍 Why Python Continues to Dominate the Tech World Python isn’t just a programming language—it’s a gateway to innovation. From data science to web development, automation to AI, Python has become the go-to choice for developers across industries. 💡 What makes Python so powerful? • Simple and readable syntax — perfect for beginners and experts alike • Massive ecosystem of libraries like Pandas, NumPy, and TensorFlow • Strong community support and continuous evolution • Versatility across domains: AI, Machine Learning, Web Apps, Automation, and more 🚀 Whether you're building predictive models, automating workflows, or developing scalable applications, Python provides the flexibility and power to bring ideas to life. The real question isn’t why Python? — it’s what will you build with it? #Python #Programming #AI #MachineLearning #DataScience #SoftwareDevelopment #Tech

Ever wondered why so much of AI is built around Python? It feels a bit counterintuitive at first. Python isn’t the fastest language. There’s the GIL. And if you compare it with Java or Go, it shouldn’t really win on performance. But in most AI workloads, seems python isn’t where the heavy work happens. The actual computation runs underneath—inside optimized libraries, C/C++ code, and on GPUs. Take NumPy for example. When you do something like multiplying two large arrays, it looks like Python—but the computation is actually executed in highly optimised C code behind the scenes. Python ends up being the layer that ties everything together. And that trade-off seems to have worked well: faster to build, easier to experiment, huge ecosystem. I might be wrong here, but it feels like performance is handled in the right place. So it’s less about Python being fast, and more about where the speed actually comes from.

🚀 Mastering Recursion with Gray Code Generation I recently worked on an interesting problem—generating Gray Codes using recursion in Python. This problem is a great example of how powerful and elegant recursive thinking can be. 🔹 What is Gray Code? Gray Code is a binary sequence where two consecutive values differ in only one bit. It has applications in digital systems, error correction, and algorithms. 🔹 Approach Used: Instead of generating all binary numbers and converting them, I used a recursive pattern: Base case: For n = 1 → ["0", "1"] Recursively get Gray codes for n-1 Prefix "0" to the original list Prefix "1" to the reversed list Combine both 🔹 Python Implementation: class Solution: def graycode(self,n): if n ==1: return ["0","1"] prev_gray = self.graycode(n - 1) result = [] for code in prev_gray: result.append("0" + code) for code in reversed(prev_gray): result.append("1" + code) return result 💡 Key Learning: Sometimes the best solutions don’t require complex logic—just recognizing patterns and applying recursion smartly. 📈 This problem strengthened my understanding of: Recursion Pattern building Problem decomposition Would love to hear how others approached this problem or optimized it further! 😊 #Python #Recursion #Algorithms #CodingJourney #DataStructures #ProblemSolving

Do you actually understand what Python is… or do you just know its definition?🐍 Most people say: “Python is a high-level, interpreted language created by Guido van Rossum in 1991.” That’s not understanding. That’s memorization. Python is not just a language. Python is a layer of abstraction. ⚙️ When early languages like C were designed, they stayed very close to the machine. 💻 You had to think about memory, pointers, and low-level details. That’s why C is fast—because it sits close to hardware. But here’s the trade-off: Closer to hardware → more control, more complexity Higher abstraction → less control, more productivity Python was built to move you away from the machine and toward problem-solving. Someone already did the hard work: Memory management? Handled. Complex system interactions? Hidden. Syntax complexity? Reduced. So instead of thinking: “How does the computer execute this?” You think: “What logic solves this problem?” 🚀 That’s why Python is widely used in: Machine Learning Web Development Automation Data Analysis Not because it’s the fastest — it’s not. But, because it allows you to build faster and think more clearly. Final point: 🎯 Python didn’t become popular by accident. It became popular because it removes friction between your idea and implementation. #python #pythonprogramming #learnpython #coding #programming #machinelearning #deeplearning #datascience #artificialintelligence #ai #ml #softwareengineering #systemdesign #computerscience #codinglife #programminglogic

Happy to share that I’ve recently started exploring Python 🐍 — and I’m genuinely having a lot of fun with it! Coming from a frontend background, it’s interesting to see how expressive and concise Python can be. A few things that stood out to me so far: ✔ Clean and readable syntax (feels almost like writing plain English) ✔ Powerful built-in data structures like lists, sets, and dictionaries ✔ List comprehensions — simple yet very elegant ✔ Strong ecosystem for AI/ML, automation, and scripting ✔ Great for quickly prototyping ideas It’s refreshing to step outside your primary tech stack and look at problems from a different perspective. Still early in the journey, but definitely enjoying the process of learning something new. Would love to hear from the community: **What’s one Python feature or use case that you found most powerful?** #Python #Learning #DeveloperJourney #AI #Programming #SoftwareDevelopment

Python’s __slots__ — why it matters: By default, Python classes allow you to add attributes dynamically. That flexibility is powerful, but it comes at a memory cost, especially in large, object‑heavy systems. Using __slots__ restricts dynamic attribute creation, meaning your objects only hold the attributes you define. The result? Lower memory usage, faster access, and more efficient performance when scaling applications. Think of it as giving your class a blueprint that keeps things lean and optimized. Perfect for developers building systems with thousands (or millions) of objects. At IT Learning AI, we simplify these advanced concepts so you can write smarter, more efficient code without the overwhelm. Want to dive deeper into Python’s hidden gems? Explore tutorials, guides, and practical coding insights at https://itlearning.ai 🔗 Learn. Apply. Grow. With IT Learning AI. #itlearningai #pythonprogramming #learnpython #pythontips #pythonbasics #pythonforbeginners #codesmarter #codedaily #programmerslife #codingisfun #techcommunity #buildwithpython #growwithtech

Why Your Python Code is Slow (and How NumPy Fixes It) If you are still using for loops for mathematical operations in Python, you’re leaving massive performance gains on the table. 📉 I’ve been diving deep into the architecture of NumPy for my upcoming project, and it’s a game-changer for anyone in AI, DSP, or Geometry. 💡 The Secret Sauce: Vectorization Standard Python lists are flexible but slow. NumPy introduces ndarrays—byte-sized, contiguous memory blocks that talk directly to compiled C libraries. In the screenshots below, notice the power of Universal Functions (ufuncs): The "Slow" Way: Using a list comprehension to calculate sin(x) requires Python to iterate over every single item manually. The NumPy Way: np.sin(x) happens in the compiled layer. No explicit loops. Just pure speed. ⚡ 🔪 Precision Slicing Beyond speed, the syntax for multidimensional data is incredibly intuitive. Whether you’re reversing columns with x[:, ::-1] or grabbing specific axes, NumPy makes handling complex matrices feel like second nature. Visit my website at: https://lnkd.in/dZ4nF6Ey #Python #NumPy #MachineLearning #DataScience #ArtificialIntelligence #Mathematics #AppliedGeometry #Coding #DigitalSignalProcessing #PythonProgramming #TechCommunity #Bioinformatics #SoftwareEngineering #Vectorization #DataEngineering