Python C++ Java Assembly Evolution Compiler AI

the reason i started programming in python was for it's simplicity, but with maturity, it seems python has some big inherent flaws which is going nowhere soon. the biggest: GIL (global interprator lock) - this limits the actual true parllelism unlike Java or Golang or any other compiled languages for that instance. the fact that no matter how many threads you add, due to locking it's only going to increase overhead for cpu-bound tasks rather reducing it is baffling and complete wastage of resources. if someone is interested towards building high performance systems or atleast interested in dwelling with the idea of building one, Python as a language seems to be bottleneck. and i'm a firm believer of whatever being built these days in the name of AI is merely API calls, that can be replicated using rather high performance programming languages, unless and until things are not dependent on open source ecosystem in case you're dealing with core machine learning and deep learning. one can argue that, oh, GIL nowhere is effecting IO bound tasks, and in case we're building using tensorflow, pytorch or cuda the underlying hood is almost always c++ code that's being executed. but i would argue, that still limits our performant systems, and why have something inferior when you can have something superior. challenges with ecosystem is understandable to be honest, not everything is measured in terms of raw speed, rather business impact as well. i so wish the entire thing can be changed. it's too late for now i assume! cpython3.13 implementation has experimental version with no GIL, but best of luck using it in production, only god knows what bugs it comes with.

Forget Python Or Java: What You’re Speaking Is Code The most important programming language in the AI era is English! Not Python. Not Java. Not JavaScript. But there is a catch: Natural language only becomes “code” when it is precise enough to guide machines. A vague prompt is not engineering. A clear specification is. As AI coding agents become more capable, the developer’s role is shifting from writing every line of code to defining intent, constraints, architecture, tests, and quality. That is the idea behind my recent Forbes article: https://lnkd.in/dWsX2a-8 My view: the future is not less engineering. It is better engineering. What do you think will matter most for developers in the next few years: coding, prompting, architecture, or product judgment?

You don't need Python to experiment with LLMs. This weekend, I tried the following use case: receiving a joke from a local model about a specific topic. It sounds trivial, but the interesting part was delving into the stack behind it. How a request flows: 1. My Spring Boot app receives an HTTP call and builds a prompt with the ChatClient API. 2. Spring AI sends it to Ollama. 3. Ollama loads the model into RAM (or VRAM if you have a GPU), runs inference, and streams tokens back. 4. Spring AI assembles the response and returns it to the user. It runs offline and by only using these two tools. The Java ecosystem now provides useful tools for AI development. You can build LLM features while maintaining type safety and the Spring patterns you already use. It was interesting to test the inference of an open weight model on my machine and I'll try more in the coming weeks.

In Python and JavaScript, we are taught to fear functions like `eval()` and `exec()`. (And for good security reasons! 🛑) But taking a simple text string like `"print(2 + 2)"` and executing it as a running program isn't just a quirky language feature. It is the single greatest breakthrough in computer science history. 💡 Before 1936, machines were hardwired to do exactly one thing. A calculator added. An Enigma machine encrypted. If you wanted a machine to do a new task, you had to physically build a new machine. Then came Alan Turing. He conceptualized the Universal Turing Machine, a single device that could simulate any other machine, simply by reading a set of rules fed to it as data on a tape. This birthed the most profound paradigm shift in technology: Code is Data, and Data is Code. When you pass a string into `eval()`, you are watching Turing’s theory happen in real-time. The computer looks at a piece of passive data (a string of text), temporarily changes its own architecture to match the rules hidden in that text, and executes it. There is no fundamental difference between the data your program processes and the instructions that make up the program itself. This is the exact concept that gave us the stored-program computers we use today, the interpreters we run in our terminals, and the dynamic software that powers the web. So next time you see code dynamically executing a string, take a second to appreciate it. You aren't just looking at a script, you're looking at the 90-year-old magic trick that made modern computing possible. 🪄💻

Python vs Node.js is not a language debate. It is a debate over workload and execution models. Python - Interpreted, synchronous-first (with async support) - Strong for CPU-intensive and data-heavy workloads - Dominates in AI/ML, data engineering, and automation - Prioritises readability and developer productivity Node.js - Single-threaded event loop with async I/O - Strong for high-concurrency, I/O-heavy workloads - Ideal for real-time systems and lightweight APIs - Fast iteration, especially with JavaScript/TypeScript teams The real difference is not “which is better?” It is where each runtime performs best. Python often wins in data-driven systems, AI pipelines, and backend logic. Node.js shines in event-driven services, BFFs, and real-time applications. Good engineering is choosing the right model for the workload.

The most powerful programming language in 2026 isn't Python. It isn’t Java. It’s what you’re speaking right now. For decades, the "translation layer" between human intent and machine execution has been the biggest barrier to innovation. We moved from machine code to high-level languages like C++ and Python, each step bringing us closer to human logic. But the gap was always there. Today, that gap is closing entirely. According to a recent Forbes feature, natural language is becoming the ultimate programming interface. Large Language Models (LLMs) have turned words into instructions, allowing us to move directly from intent to results. This shift is fundamentally changing what it means to be a "developer": Syntax is secondary: AI can handle the boilerplate and the libraries. What it can’t replace is clear thinking. The Spec is the Code: The ability to write a structured, logical specification is now more valuable than writing 100 lines of error-free syntax. Human-Machine Collaboration: The most competitive professionals will be those who can provide precise context, manage constraints, and guide intelligent systems through language. The future of technology isn't just about who can code; it’s about who can communicate. We are entering an era of "spec-driven development" where your clarity is your greatest technical asset. Are we witnessing the end of traditional coding, or just its ultimate evolution? #GenerativeAI #FutureOfWork #SoftwareDevelopment #Coding #TechTrends #AI #PromptEngineering #Innovation #ForbesTechCouncil

🚨 “Python is slow.” If you’ve ever said this… There’s a 90% chance you don’t understand the GIL. And that misunderstanding is costing you performance. Big time. Let’s break your assumption: You spin up 10 threads. You expect 🚀 10x speed. Reality? 👉 Your CPU is still doing ONE task at a time. Welcome to the truth of Python. 🧠 The villain (or hero?): GIL — Global Interpreter Lock It ensures: 👉 Only ONE thread executes Python bytecode at a time 👉 Even on a multi-core machine So yes… ❌ Threads don’t give true parallelism for CPU-heavy work ❌ More threads ≠ more speed ❌ Sometimes performance actually DROPS 💥 Brutal example: You write multithreading for: Data processing Image transformations Heavy calculations And then… “Why is this still slow?” 😐 Because you solved the wrong problem with the wrong tool. 🧵 Where threads ACTUALLY shine: When your program is mostly waiting: ✅ API calls ✅ Database queries ✅ File I/O 👉 While one thread waits, another runs 👉 That’s where multithreading wins ⚙️ Want REAL power? Use Multiprocessing. ✔ Separate processes ✔ Separate memory ✔ Separate Python interpreters ✔ NO GIL bottleneck 👉 Finally… TRUE parallel execution across CPU cores ⚡ Shift your mindset: Multithreading ≠ speed booster Multiprocessing ≠ overkill 👉 They are tools. Use them correctly. 🔥 The rule elite developers follow: 👉 I/O-bound → Multithreading 👉 CPU-bound → Multiprocessing 💣 Hard truth: Most developers don’t have a performance problem… They have a mental model problem. 💬 Be honest: Did you ever assume threads = parallelism in Python? #Python #GIL #Performance #Multithreading #Multiprocessing #BackendDevelopment #Developers

🐍 Python Concurrency: Stop guessing, start choosing! Threading vs Async vs Multiprocessing - when to use what? I see devs pick these at random. Here's the mental model that changed how I write production Python. 👇 ━━━━━━━━━━━━━━━━━━━━ ⚡ MULTITHREADING - Best for I/O-bound tasks (file reads, DB queries, network calls) Due to the GIL, threads don't run in true parallel for CPU tasks - but they shine when your code is waiting on I/O. from concurrent.futures import ThreadPoolExecutor import requests urls = ["https://lnkd.in/gwfCxrVP", "https://lnkd.in/gEWYHnaM"] def fetch(url):   return requests.get(url).json() with ThreadPoolExecutor(max_workers=5) as ex:   results = list(ex.map(fetch, urls)) # Production use: scraping APIs, bulk DB inserts, reading files concurrently ━━━━━━━━━━━━━━━━━━━━ 🔄 ASYNC/AWAIT - Best for high-concurrency I/O (1000s of simultaneous connections, real-time apps) Single-threaded, event-loop driven. No thread overhead. Perfect when you have massive I/O concurrency but each task is lightweight. import asyncio import aiohttp async def fetch(session, url):   async with session.get(url) as r:     return await r.json() async def main(urls):   async with aiohttp.ClientSession() as session:     tasks = [fetch(session, u) for u in urls]     return await asyncio.gather(*tasks) # Production use: WebSocket servers, FastAPI, real-time pipelines ━━━━━━━━━━━━━━━━━━━━ 🚀 MULTIPROCESSING - Best for CPU-bound tasks (data crunching, ML training, image processing) Bypasses the GIL completely. Each process gets its own memory. True parallelism on multi-core machines. from multiprocessing import Pool def crunch(data_chunk):   return sum(x**2 for x in data_chunk) data = list(range(10_000_000)) chunks = [data[i::4] for i in range(4)] with Pool(processes=4) as pool:   results = pool.map(crunch, chunks) # Production use: ML preprocessing, image resizing, scientific computing ━━━━━━━━━━━━━━━━━━━━ 🎯 Quick decision guide: • Waiting on network/disk? → Threading or Async • 1000+ concurrent connections? → Async • Heavy CPU computation? → Multiprocessing • Mixing both? → Async + ProcessPoolExecutor 💡 Pro tip: FastAPI + asyncio + Celery workers (multiprocessing) is the production stack for 90% of data-heavy Python backends. The best engineers don't memorize syntax - they understand the trade-offs. 🔑 What's your go-to concurrency pattern? Drop it below 👇 #Python #SoftwareEngineering #Backend #Programming #AsyncPython #PythonDev

I heard a tip to use Rust instead of Python whenever you are coding with AI due to the speed and more importantly the validation. The code wont compile if there are errors. Unlike writing in the Python where you have to do the validation for AI and go back and forth with prompts to fix it. I'm finding it way faster to generate to code. Even though I dont know Rust that well it will be a great learning experience. Right now I'm using Claude Code but I might switch back to OpenCode's models again to see if that works. https://lnkd.in/gaDkaHXu