How to write safe and efficient Haskell code with pattern synonyms

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

Achieving Atomicity and Isolation in Concurrent Rust: Channels vs. Mutexes Here's the thing about concurrent programming in Rust: the moment you try to share data between threads, the compiler forces you to pick a side. You can't sit on the fence. Either you embrace the "one owner at a time" philosophy of channels, or you accept the "share but wait your turn" reality of mutexes. There's no magical third option where you get both compile-time guarantees and shared mutable access. This isn't Rust being difficult—it's Rust being honest about what concurrent programming actually entails. Picture this: you're building a banking system (because of course you are—it's the perfect example for concurrency). You need multiple threads handling transfers, deposits, and withdrawals. The moment you type thread::spawn, Rust's type system taps you on the shoulder and asks: "So, how exactly do you plan to handle shared state?" You have two answers, and only two: "I won't share state at all" - Each thread owns its data completely. Other threads can send messages asking for ch https://lnkd.in/gqDttk6U

Hey Elixir friends! :) We need help completing Elixir’s browser runtime by porting some Erlang functions to JavaScript. Hologram automatically transpiles Elixir, but the underlying Erlang functions - the building blocks of Elixir’s stdlib - must be ported manually. No Erlang knowledge required - just basic JS/Elixir skills and pattern-following. Tasks: 15 min to a few hours. AI tools encouraged! Each function you port unlocks multiple Elixir stdlib functions in the browser! -> Read the full blog post: https://lnkd.in/dgrkCQuR #Hologram #Elixir #ElixirLang #BEAM #WebDev

🚨 GLM-Latest(Base) Crushes the Haskell Code Benchmark! 🚀 I've just completed a rigorous, end-to-end evaluation of the GLM-Latest model on the challenging Haskell LLM Benchmark (112 problems), and the results are a massive win for functional programming adoption! These results were not expected but the model out performed as this benchmarking stress-tested the models capacity to handle complex exercises. Main part for this is that Docker got crashed everytime so then "nix" env fixed the problem of containerization. Benchmarking complex languages like Haskell requires specialized tools, and we're thrilled to confirm GLM-Latest's reliability and strong functional reasoning capabilities. 🏆 Key Takeaways from the Benchmark: 1. First-Try Success (Pass@1): 54.5%—Over half of the complex Haskell challenges solved immediately. 2. Overall Reliability (Pass@2): 63.4%—Excellent consistency confirmed with one retry. 3. Code Quality & Integrity: 100% Well-Formed Responses and Zero Syntax Errors! 4. Operational Excellence: Achieved this with a $0.00 Total Cost for the run—proving exceptional value and efficiency. This evaluation is crucial as we integrate advanced LLMs into our highly concurrent, performance-critical Haskell services. A huge shoutout to the team for executing this complex setup and validation! What's next? We're leveraging these insights to fine-tune our prompts and integrate GLM-Latest for improved code generation and review in our Haskell pipelines. #AI #LLM #Haskell #FunctionalProgramming #CodeGeneration #Benchmark #Engineering #Juspay #haskell_llm_benchmark

⚙️ "The first time Rust yelled at me, I thought it hated me." I remember writing what looked like perfect code — clean, elegant, and efficient. Then came the infamous borrow checker: red lines everywhere, cryptic errors about ownership and lifetimes. I almost gave up. But here’s what clicked one day — Rust wasn’t fighting me, it was teaching me to think differently about memory. Ownership isn’t a compiler rule. It’s a philosophy. It forces you to confront who owns data, when it’s borrowed, and when it should die. No garbage collector, no hidden runtime — just explicit, predictable control. In C or C++, memory bugs hide in shadows. In Rust, the compiler shines a flashlight on every dark corner before your code even runs. Once you get it, ownership feels less like restriction and more like discipline that leads to freedom. You start writing systems that are not only fast — but trustworthy. The funny part? That first angry compiler error was actually the sound of Rust saying, “Don’t worry. I’ll make sure you never segfault again.” What was your “aha” moment when the ownership model finally made sense? #RustLang #CloudNative #SystemProgramming #MemorySafety #SecurityEngineering

Bugs love hidden changes. Most languages let you mutate input parameters by default. That’s convenient—until a function changes something it shouldn’t. Treat inputs as readonly by default. Change should be intentional, not accidental. ✅ Predictable behavior ✅ Easier debugging ✅ Safer concurrency Great code doesn’t wait for compiler rules. Immutable by habit. Mutable by necessity.

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

Tired of AI coding assistants giving you circular advice? Concerned about your IP leaking through third-party AI tools? Chris Beck's talk from ElixirConf EU 2024 shows how to build your own code intelligence system - entirely in Elixir. 🎯 Key learnings: - How Retrieval Augmented Generation (RAG) works specifically for codebases (it's different from document RAG) - Building RAG systems without relying on Python/JS ecosystems - Maintaining control over your code and data with local tools - Creating a complete Elixir-only RAG system with LiveView https://lnkd.in/dxWQ2xra

Yeah, but how did they compile the first compiler? Just a simple thought I had that led to a really interesting exploration of fully self-hosted languages (like C). When your language's compiler is compiled in the language it supports, what happens when you go back in time to the first compiler? Well, you find "hand written" assembly code, of course. Keep going. How is that assembly then compiled into machine code? Somewhere down the line you get to just some person punching holes in a card. And that code lives on in everything we have today. Fascinating.

I was talking to a friend this week about how I can't believe how fast nix has taken off. It is actually everywhere. Random repositories you chance upon just happen to have flake.nix files in them. People who would run a mile from Haskell are all-in on nix. It's because Runs-on-my-machine is a well understood problem that everybody has and the benefits there are immediate.