Pine Programming Language Achieves Modular Scalability

Async it’s just smart waiting. When people hear async and await, it often sounds complex. Threads? Parallelism? Event loops? But if you look at this simple analogy, the idea is actually clear 👇 Most developers use async and await. But ask this question: 👉 What exactly happens when a coroutine hits await? Does it stop the program? Who resumes it? Is it multithreading? To truly understand async, I built a small repo that explains everything step by step using a simple example: 🍕 Order Pizza (5 min) 🍔 Order Burger (3 min) Synchronous → 8 minutes Async → 5 minutes From there, I break down: • Coroutines • What await really pauses • How the Event Loop manages execution • asyncio.create_task() vs gather() • Blocking vs Non-blocking (with real timing comparison) Clean examples. No overcomplication. Just clarity. If you're learning backend development this might help. 🔗 https://lnkd.in/dPxiPXMA Feedback is always welcome 👨💻 #Python #AsyncProgramming #SoftwareEngineering #Backend #Developers

Island’s latest blog explores how teams can accelerate C# development by combining Rust with Island’s Cursor technology to improve runtime speed and productivity. This approach shows practical ways to get the best of both languages for modern systems programming. Read the full article to learn how this method boosts speed and safety in real-world C# projects: https://hubs.la/Q0455hfh0 #RustLang #SystemsProgramming #DevEfficiency

The paradigm matters more than the language. Sometimes, all that really matters is your ability to understand how a programming paradigm actually works. Whether it’s Imperative or Declarative, Object-Oriented or Functional, if you understand the intricacies of the paradigm, you can read or write almost any code regardless of the syntax. Now with AI, the "barrier to entry" for a new language is at an all-time low. This means you can finally choose the right tool for the right task: Stop forcing PHP into highly concurrent functions when Go handles them natively. Don’t fight Python for Windows desktop apps when C# / .NET are the native kings of that domain. Understand the paradigm first; the language is secondary. That said, having "language freedom" isn't an excuse to clutter a single repository with five different languages. Unless you are moving toward Microservices, keep it clean. And even then, seeing giants like Amazon pull back on microservices lately, it’s going to take a lot to convince me that they are always the "correct" way to go. Focus on the fundamentals. The rest is just syntax. #SoftwareEngineering #Programming #CleanCode #Microservices #TechTrends

Building Jhansi, one milestone at a time. Today, it reached its first real milestone: it can print back 42. That's it. I type an integer in, and Jhansi returns the same integer. A tiny result on the surface. But it felt bigger than it sounds. Because even that small interaction only works if three separate parts of the system agree perfectly. The lexer reads raw input character by character and decides what it is looking at. 42 becomes an integer token. The parser takes that token and gives it structure. That integer becomes a Number node in a tree. The evaluator walks that tree and produces a result. Number(42) becomes 42. Each stage is narrow. Each stage only does one job. And that separation is exactly what makes the whole thing work. What I'm starting to appreciate is that the essence of a programming language is already visible, even here. A language is not magic. It is a system for reading what you typed, deciding what it means, and refusing what does not fit. That last part feels more important to me now than it did before. A language is defined not just by what it can express, but by the rules it enforces. What counts. What fits. What gets rejected. So yes, "Jhansi can print 42" sounds trivial. But to me, it feels like the point where Jhansi stopped being just an idea and started becoming a real system. Next up: arithmetic. Teaching Jhansi why 3 + 4 is very different from 34. Follow along on GitHub: https://lnkd.in/e98utmvj

Day 13, Swift basics: Review & Foundations Matter Today wasn’t about learning something new It was about reviewing everything from the beginning And honestly, this might be more important than learning new topics Because fundamentals decide how strong your architecture will be later ⸻ 1. Variables vs Constants Swift gives us two choices: var → value can change let → value cannot change The real mindset shift: Use let by default Constants: - Prevent accidental changes - Improve readability - Allow compiler optimizations - Make code safer Small decision. Big impact 2. Strong Typing Swift is strongly typed That means: - Every value has a type - You can’t mix incompatible types - Errors are caught at compile time It may feel strict at first. But it prevents chaos later 3. Core Data Types String → text Int → whole numbers Double → decimals (preferred over Float) Bool → true/false Understanding types is understanding how data flows through your app 4. Operators & Logic Arithmetic Comparison Logical operators They’re simple, but they build the foundation for decision-making in code Even string interpolation: "Your name is \(name)" Cleaner. Safer. More expressive. 5. Collections: Arrays & Dictionaries Arrays: - Ordered - Indexed - Zero-based Dictionaries: - Key-value pairs - More descriptive access - No guaranteed order Use arrays when order matters Use dictionaries when meaning matters 6. Conditionals & Loops if, else, switch: decision control for, while: repetition control One key realization: Most programs are just: - Storing data - Making decisions - Repeating actions That’s it Everything more complex is built on top of this ⸻ 7. Why Review Matters When learning fast, it’s easy to move forward without reinforcing basics But: - Clean code starts with small habits - Architecture starts with understanding types - Safety starts with using constants Today wasn’t flashy It was foundational And strong foundations scale On to the next layer #Swift #iOSDevelopment #100DaysOfSwift #ProgrammingBasics #LearningInPublic

Most people think programming is just writing code. But in reality, it’s much deeper. → At the surface, we see tools like HTML, CSS, JavaScript, Python. These are the technologies most developers start with. → Go a little deeper and you reach languages like Java, Go, C#. This is where software engineering starts becoming more structured. → Further down, you find C, C++, SQL, Rust. These languages teach how systems actually work. → And at the deepest levels, programming becomes theory. Concepts like Lambda Calculus, Pi Calculus, and Set Theory shape the foundations of computing itself. The deeper you go, the more you realize: Programming isn’t just about coding. It’s about understanding how computers think. The iceberg keeps going. How deep have you explored programming so far?

It was just a matter of time: and here it is … the first programming language for AI specs. Interesting that it was build by one of the big IDE providers 😃 Shows IMO, that the direction of Software Engineering toward Spec Driven approaches is more than a hype.

A week ago I shared that I started building an HTTP server from scratch in Zig to relearn the fundamentals. Here's what happened since. I implemented epoll for non-blocking I/O, HTTP/1.1 keep-alive, and zero-copy request parsing. The result: from 6,600 req/sec with blocking I/O to 151,000 req/sec. But the real learning wasn't about performance numbers. It was about understanding what allocations actually cost. Every time I eliminated an allocation, I didn't just see faster code, I started thinking differently about how I approach problems. The hardest part of this journey so far? It's not Zig. It's unlearning years of OOP thinking. I caught myself designing absurdly complex solutions for tiny problems. When you've been writing Java and TypeScript for years, your brain defaults to abstractions and layers. Zig forces you to stop and ask: do I actually need this? One thing that helped me a lot was reading uWebSockets' HttpParser.h by Alex Hultman. That code is something else. Branch prediction hints with likely/unlikely, bloom filters for duplicate header detection, in-place lowercase with |=32, 8-byte parallel searching (SWAR). Optimization everywhere you look, but never random, every trick serves a purpose. Reading great code teaches faster than any tutorial. I'm planning to write a blog post diving deeper into what I learned from reading that code. For now, the repo is open if you want to follow how i struggle with low level programing. 🔗 https://lnkd.in/dAB256yK #ZigLang #Zig #LowLevel #BackendDevelopment #SystemDesign #OpenSource

💡 Revisiting the fundamentals of Object-Oriented Programming (OOP) Strong software isn’t just about writing code—it’s about designing it the right way. I recently reviewed the core OOP concepts that help build scalable, maintainable, and reusable applications: 🔹 Class & Object 🔹 Encapsulation & Abstraction 🔹 Inheritance & Polymorphism 🔹 Association, Aggregation & Composition 🔹 Dependency & Realization Understanding these principles is improving how I structure programs and think about real-world problem solving in code.

I built a small programming language and LLVM compiler to understand how compilers actually work. I could use programming languages, but the pipeline that turns text into something runnable still felt like black magic. So I decided to build the smallest “real” compiler I could. Glitchy includes: • a handwritten recursive-descent parser • semantic analysis with type inference • LLVM IR generation • an optional “glitch mode” that intentionally mutates programs so you can practice debugging them One lesson that surprised me: features that sound small (like dynamic typing) often explode into entire subsystems once you try to implement them. Writeup + repo: https://lnkd.in/g6aYaMJP https://lnkd.in/ggTkWHTy