GLM-Latest Surpasses Haskell Benchmark with 100% Code Integrity

Monads in Haskell Monads... So this time, I wanted to fix that. In this video and article, I’ll walk you through what Monads actually are — but not by dropping the term on you out of nowhere. We’ll start from the ground up: Functors, then Applicatives, and only then arrive at Monads. Step 1 — Functors: applying a function inside something A Functor is any type that can be “mapped over.” You already know this idea from other languages: The wrapper (Maybe) stays the same — you just apply a function inside it. Step 2 — Applicatives: applying a wrapped function to a wrapped value Applicatives go one step further. 2) <> Just 10 → gives Just 20. It’s basically saying: “I’ve got a boxed function and a boxed value. Apply one to the other.” And when something’s missing (Nothing), the whole thing fails gracefully. Step 3 — Monads: chaining things that return wrapped results Now comes the big one — Monads. For example: safeDivide :: Float -> Float -> Maybe Float Using the “bind” operator (>>=), we can write: Just https://lnkd.in/gUpBbeVY

🧠 𝑭𝒖𝒏𝒄𝒕𝒊𝒐𝒏𝒂𝒍 𝑷𝒓𝒐𝒈𝒓𝒂𝒎𝒎𝒊𝒏𝒈 - 𝑪𝒐𝒏𝒄𝒖𝒓𝒓𝒆𝒏𝒄𝒚 𝑾𝒊𝒕𝒉𝒐𝒖𝒕 𝑭𝒆𝒂𝒓 (𝑇ℎ𝑒 𝑀𝑎𝑛𝑦 𝐹𝑎𝑐𝑒𝑠 𝑜𝑓 𝐶𝑜𝑛𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑦 - 𝑃𝑎𝑟𝑡 2) 𝑀𝑜𝑠𝑡 𝑐𝑜𝑛𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑦 𝑏𝑢𝑔𝑠 𝑠𝑡𝑎𝑟𝑡 𝑤𝑖𝑡ℎ 𝑜𝑛𝑒 𝑐𝑢𝑙𝑝𝑟𝑖𝑡: 𝑴𝒖𝒕𝒂𝒃𝒍𝒆 𝒔𝒕𝒂𝒕𝒆. Two threads writing to the same variable = chaos. Functional programming solves this by removing mutability altogether. 𝑃𝑢𝑟𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑠, 𝑛𝑜 𝑠𝑖𝑑𝑒 𝑒𝑓𝑓𝑒𝑐𝑡𝑠, 𝑛𝑜 𝑠ℎ𝑎𝑟𝑒𝑑 𝑠𝑡𝑎𝑡𝑒. 𝑌𝑜𝑢 𝑑𝑜𝑛'𝑡 𝑚𝑎𝑛𝑎𝑔𝑒 𝑙𝑜𝑐𝑘𝑠 𝑦𝑜𝑢 𝑚𝑎𝑛𝑎𝑔𝑒 𝑑𝑎𝑡𝑎 𝑓𝑙𝑜𝑤. That's why languages like Scala, Elixir, and Haskell scale so effortlessly. Concurrency here isn't about coordination - it's about isolation. 💬 Do you think FP principles belong in every modern backend now? #FunctionalProgramming #Concurrency #Parallelism #CleanCode #SoftwareArchitecture

AI and Web3 are entering a new phase. With Hathor’s Nano Contracts, Python now runs natively on-chain, letting developers build smart contracts and AI agents using the tools they already know. No niche languages, just real software engineering for decentralized systems. Read the full breakdown by Cointelegraph: https://lnkd.in/dH6PxyQs

Excited to share my latest creation — an N-Puzzle Problem Solver built entirely in Java! 🎯 This project takes on the timeless AI challenge of the N-Puzzle, solved through the A* search algorithm and heuristic evaluation. Each unique board state is treated as a graph node, and every valid tile movement as an edge, making this a beautiful intersection of graph theory and artificial intelligence. Unlike most Python-based versions, I built this in Java to gain fine-grained control, explore immutability, and truly understand the internal workings of search algorithms. This experience deepened my grasp of state-space modeling, heuristic design, and optimal pathfinding. ✨ Highlights: Scalable for any N×N puzzle (3x3, 4x4, ...) Implements A* search with heuristic evaluation Extensible design for custom heuristics & visualizations 🔗 Explore the full project here: https://lnkd.in/gryHTAGN #ArtificialIntelligence #Java #GraphTheory #AStar #Algorithms #ProblemSolving #Coding

Leveling up my C journey at Emertxe: My fourth project takes me into compiler fundamentals with a Lexical Analyzer! 🔍 Constructed a sophisticated lexical analyzer in C that processes source code through advanced character-by-character analysis and tokenization. The system identifies and categorizes all C language elements including keywords, identifiers, numbers (supporting decimal, octal, hexadecimal formats), operators, and literals while managing complex states for nested comments, string literals, and preprocessor directives. It implements comprehensive syntax validation, detecting errors like missing semicolons, unmatched brackets, and invalid identifiers with precise line number reporting. 🛠️ Technologies Used: C, Finite State Machines, Pattern Matching, String Processing, Error Handling, File I/O 🔑 Key Challenges & Learnings: ⚡ Challenge: Handling nested comments and string literals within code 💡 Solution: Implemented stack-based state machine with context tracking 📚 Learning: Mastered finite automata design for language parsing ⚡ Challenge: Token ambiguity between operators and complex symbols 💡 Solution: Developed lookahead buffer with token precedence rules 📚 Learning: Learned compiler-level tokenization strategies ⚡ Challenge: Error recovery and meaningful error reporting 💡 Solution: Implemented error recovery heuristics with intelligent error localization 📚 Learning: Understood robust error handling in language processors ⚡ Challenge: Performance optimization for large source files 💡 Solution: Designed efficient buffering and streaming processing 📚 Learning: Mastered performance optimization in text processing 🌍 Real-World Applications: • Compiler development - Core component of programming language compilers • IDE development - Syntax highlighting and code analysis tools • Code quality tools - Static analysis and linting applications 🔗 GitHub Link: https://lnkd.in/gFW-Nejw #Emertxe #CompilerDesign #LexicalAnalysis #StateMachines #ProgrammingLanguages #CProgramming #SyntaxAnalysis #ComputerScience #SoftwareEngineering #Parsing #Tokenizer

Why Rust Feels “Crazy as F*ck” in System Programming Ever wonder why developers keep saying Rust is the future of performance? It’s not hype — it’s architecture. Rust is built differently, giving you C-level speed with modern-day safety and developer confidence. Here’s why 👇 1. Ownership System — No Garbage Collector, No Leaks Rust doesn’t rely on a garbage collector like Go or Node. Instead, it uses a unique ownership and borrowing model that frees memory automatically — at compile time. No leaks. No dangling pointers. No runtime cleanup. Memory is handled predictably and efficiently. 2. Zero-Cost Abstractions Rust gives you high-level features like traits, generics, and iterators, but compiles them down to pure machine-level instructions. You get readable, expressive code that runs as fast as hand-written C. No abstraction penalty — just clean, optimized performance. 3. Fearless Concurrency Rust’s concurrency model prevents data races before your code even compiles. The compiler enforces thread safety, so you can write multi-threaded systems without fear of race conditions or shared state bugs. That’s why Rust powers blockchain nodes, browsers, and servers handling millions of parallel tasks safely. 4.Low-Level Control When You Need It Rust gives you full access to system memory and even allows inline assembly when necessary — but with guardrails that keep you from shooting yourself in the foot. It’s powerful enough for operating systems, WebAssembly, and crypto, yet elegant enough for web APIs and game engines. 5. Compile-Time Safety = Runtime Confidence Rust’s compiler is famously strict. It catches: • Null pointer errors • Race conditions • Type mismatches • Uninitialized variables If your code compiles, it’s already passed through some of the most rigorous checks in the industry. That’s why devs say: “If it compiles, it runs.” TL;DR — Rust Is Not Just Fast. It’s Smart Fast. Ownership = no GC, no leaks Zero-cost abstraction = high-level feel, low-level speed Fearless concurrency = parallel safety Compile-time strictness = runtime confidence “Rust doesn’t just run fast — it forces you to write fast, safe code. Ownership, zero-cost abstraction, fearless concurrency — that’s the real madness

The Devil is in the Details (and So Are We, Programmers) TLDR: AI won't replace programmers because someone still needs to specify all the intricate details that make software work. Whether we write those details in code or prompts, we're still programming. Like compilers before it, AI is a powerful tool that changes how we work - not whether we're needed. I just watched a fascinating interview with Robert Martin (Uncle Bob) that perfectly distilled what I've been observing and feeling in our industry. Most executives now understand that AI won't replace programmers - it will boost their performance. I'll admit it: I have a toxic relationship with AI-assisted code generation. I love it and hate it in equal measure. Here's the uncomfortable truth: when AI fails miserably at generating code, the blame often lies with us - the programmers. Not because we should have written the code ourselves, but because we failed to specify enough details. Copilot and similar tools are fantastic, but here's the trap: the demon of blind trust. When we stop being the authors of our code and start being passive consumers, we're in trouble. Even when AI becomes excellent at producing code, the real challenge will be structuring the prompt - which is itself a form of programming. We programmers are detail specifiers. It doesn't matter whether those details are specified in Python, Java, or natural language prompts. From this perspective, AI is just another compiler or interpreter. The business doesn't want to (and shouldn't) get involved in these tiny details. That's our domain. AI alone cannot specify all these details; programmers must provide them. We're already seeing this with companies like HumanLayer using detailed specs to generate substantial amounts of Go code daily. I wouldn't be surprised if formal prompting languages emerge to help us guide AI more efficiently. Robert Martin made another brilliant parallel: When compilers arrived, programmers feared unemployment. That didn't happen. We're in a similar moment now. The fundamentals haven't changed -- programmers specify the details. The medium has evolved, but the essence of our craft remains. Go check his talk https://lnkd.in/dPxi26Qr

Understand the limits of vibe-coding. This article covers where AI-driven coding is useful for prototypes and where it falls short for production-grade systems that require accountability. By Dr. Elisha Rosensweig and Eitan Wagner

🧵 Threads vs Processes and what Concurrency & Parallelism really mean Once, I was asked this classic question in an interview: “What’s the difference between a thread and a process?” At first, I answered: “Threads are part of a process, and a process can contain multiple threads.” That’s true, but the real magic begins when you connect it with concurrency and parallelism Here’s how I think about it now 👇 💡 Threads - Run inside a process - Share the same memory and resources - Lightweight and fast to create - Great for concurrent tasks (doing multiple things at once, but not necessarily at the same exact time) ⚙️ Processes - Independent units of execution - Each has its own memory and resources - Safer, because one process crashing doesn’t affect others - Perfect for parallel tasks (doing things truly at the same time on multiple CPU cores) 🧠 So what’s the difference between concurrency and parallelism? - Concurrency is like juggling, switching between multiple tasks quickly. - Parallelism is like having multiple jugglers, truly doing tasks simultaneously. 💬 In Python: - Threads are concurrent (because of the GIL. only one thread runs Python code at a time). - Processes can be parallel (each process has its own interpreter and CPU core). 💬 In Go: - Goroutines (threads) can be both concurrent and parallel, since Go doesn’t have a GIL. In short: 🧩 Processes = isolation + true parallelism 🧵 Threads = shared memory + concurrency Understanding this difference completely changes how you approach performance and scalability, especially in backend systems. How do you handle concurrency in your projects threads, processes, or async? Would love to hear your experience 👇 #SoftwareEngineering #BackendDevelopment #Concurrency #Parallelism #Python #GoLang #Multithreading #DevelopersJourney

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