Paradigm over Language: Mastering Code Fundamentals

Evolution of programming languages 👨💻 1940s → Machine Code 1970 → C 1995 → Java / JavaScript / PHP 2010 → Rust / Kotlin / TypeScript 2026 → English 😅 Developers in 1990: “Computers will never understand humans.” Developers in 2026: “Hey AI, build me a microservice with Spring Boot, Kafka and deploy it to Kubernetes.” Maybe the most important skill now is not writing code… but asking the right questions. What do you think? Are we moving toward natural language programming? #softwareengineering #programming #ai #developers #technology

Came across an open source project that genuinely impressed me this week: code-review-graph by @tirth8205 The problem it solves is real: every time you ask Claude Code to review a PR, it re-reads your entire codebase from scratch. No memory. No structure. Just brute-force context stuffing. → 200 files read. ~150k tokens burned. Every. Single. Time. code-review-graph fixes this by building a persistent knowledge graph of your codebase using Tree-sitter AST parsing. It stores function/class/file relationships incrementally in SQLite — no external DB needed — and on every review, computes the blast radius of what actually changed. Clause only sees the 8-12 files that matter, not all 200. The result: ✅ 5-10x fewer tokens per review (~25k vs ~150k) ✅ Incremental updates in <2 seconds ✅ Supports 12+ languages (Python, TS, Go, Rust, Java, C#, and more) ✅ Native Claude Code integration via MCP — one command to set up ✅ Auto-update hooks on every file edit and git commit The tagline says it best: it turns Claude from a "smart but forgetful tourist" into a "local expert who already knows the map." This is what AI-native dev tooling should look like. Worth a star ⭐ � https://lnkd.in/gTdB_cUs #AIEngineering #DeveloperTools #ClaudeCode #MCP #OpenSource #CodeReview

I recently tried Google OR Tools and it completely changed how I approach complex problems. Things like routing, scheduling, and resource allocation that usually feel messy can actually be solved cleanly with the right tools. You just define the problem and let the solver handle the heavy lifting. It is simple, powerful, and works across Python, Java, C++ and C#. If you are building anything that involves optimization, this is definitely worth exploring. it definitely helps manufacturing side for optimising manual their workflows instead of manual planning let the tool work on that github: https://lnkd.in/grTTDFej if you every used it, make sure to lmk in comments we can connect

Async in most languages is a mess. Rust has colored functions — if a function is async, every caller must be async too. It spreads through your codebase like a virus. JS gave us callback hell. Python's asyncio confuses even experienced devs. Zig's answer? Treat I/O like memory allocation. The new std.Io works exactly the same way: → Write your I/O code once → Caller passes in the I/O strategy (blocking, threaded, evented) → Zero changes to library code See the code above — doWork has zero async keywords. It's just a normal function. The caller decides how it runs. Swap std.Io.Threaded for std.Io.Evented at the call site. That's it. Tested this myself on 0.16.0-dev.2682 — it works today. Why this matters: ✅ No function coloring — async doesn't infect your codebase ✅ Library authors write I/O code once, works in any runtime ✅ Swap concurrency models at the call site, not the library ✅ Async is back in Zig — redesigned from the ground up Honest caveats: This is still experimental in 0.16.0-dev. The API isn't finalized. Io.Evented currently only works on Linux (io_uring). Andrew Kelley himself says it'll take a few iterations to get right — but the direction is clear.

"Why this matters:" Probably because LLM wrote this. LLMs loves "why this matters". async on function declaration is just a syntactic sugar turning the result in a future. If you have async IO in function without future that just means you're running a nested event loop. On some level this is actually worse than await, because with await you at least know that side-effects from being preempted might happen and can account for it.

Most debates about programming languages miss the real point. It’s not about which is better. It’s about trade-offs. Statically typed vs dynamically typed languages each come with their own advantages—and limitations. Statically typed (C, C++, Java): Pros: Catches errors early Better for large, complex systems Improves code readability and maintainability Cons: More verbose Slower to write and iterate Less flexible during rapid changes Dynamically typed (Python, JavaScript): Pros: Faster to write More flexible Great for rapid prototyping Cons: Errors show up at runtime Harder to maintain at scale Can lead to unexpected bugs Here’s what most beginners get wrong: They try to pick a “winner”. Experienced developers don’t do that. They choose based on context. Building a scalable backend system? You might prefer structure. Building a quick prototype or MVP? You might prefer speed. Different tools. Different strengths. The real skill is knowing when to use which. That’s what separates a learner from a professional. #programming #softwareengineering #developers #learning #java #python

Is there really a “better” programming language? A few weeks ago, I found myself at a crossroad; trying to choose the best backend language. It was the classic debate: Python vs PHP. I kept asking: Which one is better? Which one should I commit to? But here’s what I’ve come to realize… There is no universally “better” programming language. Every language is designed with a purpose: Some prioritize simplicity and readability Some focus on speed and performance Others are built for scalability or specific domains And the truth? Developers are building amazing, scalable, real-world solutions with all of them. So instead of asking “Which language is better?”, a more powerful question is: “Which language is more suitable for what I want to build?” Because at the end of the day: The problem you’re solving matters more than the language you choose Your understanding of fundamentals outweighs syntax and great developers aren’t defined by tools but by how they use them So yes… maybe not a better language, but definitely a preferable one; depending on your goals #365DaysChallenge.

software engineers use third-party libraries because they're either too lazy, or too overworked to write that single function they need. just import it! i think we will see more and more de-leveraging from third-party libraries to using languages with really strong standard libs and including those functions in our (secure) vibe coded apps. do your software engineers actually know WHY they are importing that code? how much of the imported libraries are actually being used? do you really need 20,000 lines of imported third-party code to serve a basic json api? leverage AI to create code that works without third-parties. make it a part of your coding agent prompts and context. avoid languages that allow you to do a single thing in a billion different ways. i like golang and rust. i hate python and javascript. 🐸

Here are 5 things you should know about Go vs Rust vs Zig for backend infrastructure in 2026: 1️⃣ **Rust's Rise**: OpenAI's recent acquisition of Astral, a Python tooling startup, underscores Rust's growing importance. Rust's zero-cost abstractions and safety features make it a top choice for mission-critical systems. 2️⃣ **Go's Stability**: Go remains a stalwart in backend development, with Oracle's Java 26 release showing how Java continues to evolve. Go's simplicity and ease of use in distributed systems make it a safe bet for reliability and maintainability. 3️⃣ **Zig's Innovation**: Ring, a lesser-known language gaining traction, inspired the design of Zig. Zig's focus on safety and performance makes it a compelling alternative, especially for embedded and systems programming needs. 4️⃣ **Performance Benchmarks**: Early benchmarks suggest that Zig can outperform both Go and Rust in certain scenarios, particularly in memory safety and performance. For example, a simple benchmark showing Zig's improved performance in a task scheduler can be found in the Zig documentation. 5️⃣ **Community and Ecosystem**: While Rust has a robust and growing ecosystem, Go's community is also expanding. Zig's community is smaller but rapidly growing, with a strong focus on contributions and improvements. In 2026, the choice of backend language depends on specific project requirements and team expertise. Which language do you think will dominate the backend space this year? #Golang #Rust #Programming #Backend

Where Did Your Favorite Programming Language Come From? 🌍💻 Every programming language we use today has a story — and often, a country where it was first created. These innovations have shaped the modern tech ecosystem we rely on daily. Here are some popular programming languages and their origins: 🇺🇸 C – United States 🇺🇸 C++ – United States 🇺🇸 Java – United States 🇳🇱 Python – Netherlands 🇺🇸 JavaScript – United States 🇺🇸 Go – United States 🇺🇸 Rust – United States 🇺🇸 TypeScript – United States 🇺🇸 Swift – United States 🇨🇦 PHP – Canada 🇯🇵 Ruby – Japan 🇺🇸 C# – United States 🇺🇸 COBOL – United States 🇺🇸 Fortran – United States What’s fascinating is how a few lines of code created decades ago can influence billions of devices today. For example: Python powers AI, data science, and machine learning. JavaScript runs the modern web. C and C++ still power operating systems and performance-critical software. This reminds us of something powerful: Technology is global, but innovation often starts with one idea, one person, and one line of code. For anyone entering tech today — whether in data analytics, software engineering, or AI — understanding these foundations is incredibly valuable. 💡 The tools may evolve, but the logic behind them remains timeless. #Programming #SoftwareEngineering #DataScience #Python #TechHistory #Innovation #Coding