标签：#RAM

submitted by /u/goto-con [link] [留言]

I run a music metadata API as a solo developer. Under it sits a catalog of 90M+ recordings aggregated from six platforms: Spotify, Apple Music, Tidal, Beatport, Discogs, and MusicBrainz. The core job is cross-referencing: take whatever you know about a track (an ISRC, a platform ID, or a messy "artist + title" string from a DJ export) and resolve it to one canonical recording with everything else attached. When I started, I assumed this was mostly a plumbing problem. Every platform has an API, recordings have a standard identifier, join on it, done. Almost none of that survived contact with real data. This post is the parts I had to learn the hard way: why one song legitimately carries many ISRCs, how fuzzy matching on artist and title actually has to work, why recording-to-composition mapping is many-to-many in both directions, and the failure modes I now check for routinely. The ISRC almost solves it The ISRC (International Standard Recording Code) is a 12-character identifier for a specific recording. Daft Punk's "One More Time" is GBDUW0000053 : country prefix GB , registrant code DUW , year 00 , then a designation number. Every commercially released recording is supposed to have one, and most platforms expose it. So the naive architecture writes itself: one isrc column on the track table, join all six platforms on it, ship. That was my first schema, and it was wrong in a way that took a while to surface. Labels mint a fresh ISRC for every commercial variant of a recording. The radio edit gets one. The extended mix gets one. The 2001 release and the anniversary remaster get different ones. A reissue through a new distributor often gets one even when the audio is bit-identical. Regional releases sometimes get their own. None of this is an error; it is how the system is designed to work, because each of those is a distinct commercial product even when it is the same performance. The consequence: one canonical recording legitimately carries many ISRCs, and differen

submitted by /u/donutloop [link] [留言]

submitted by /u/r_retrohacking_mod2 [link] [留言]

I was optimizing the Lua transpiler for my modding language grug and ran into a really weird LuaJIT performance bug. The same benchmark could randomly run 20× slower, and it turned out a LuaJIT NYI could silently blacklist an unrelated hot loop. I wrote up the investigation here: The LuaJIT NYI That Silently Poisoned an Unrelated Hot Loop It goes from the benchmark mystery through LuaJIT's trace recorder internals, and ends with a PR to get unpack off LuaJIT's NYI list. Feedback is very welcome! :) submitted by /u/MyNameIsTrez [link] [留言]

submitted by /u/mariuz [link] [留言]

2.5.1. Code Contracts Your code, whether explicitly or implicitly, contains a contract. A contract has two sides: A contract is a requirement, which is a restriction on how the code is used A contract is a promise, which is a guarantee about how the code behaves When designing APIs, there is a useful rule of thumb: avoid imposing unnecessary restrictions, and only make promises you can keep . Why? Adding restrictions or removing promises requires a major semantic version change and may break other code When you first design an API, loosening restrictions and later adding extra promises is usually backward-compatible 2.5.2. Restrictions and Promises Common forms of restrictions in Rust are: Trait bounds Argument types Common forms of promises are: Trait implementations Return types Some Examples Let's look at an API evolving through three versions: fn frobnicate ( s : String ) -> String The first version takes a String and returns a String Its contract is that the caller performs allocation (because both the parameter and return value are owned, allocation is inevitable), and its promise is that it returns an owned String The problem with this function is that, without changing the signature, it cannot later be turned into a “no-allocation” function, because both the argument and return value are owned fn frobnicate ( s : & str ) -> Cow < '_ , str > The second version relaxes the contract a bit Its contract is that it accepts only a string reference, and its promise is that it returns either a string reference or an owned String , namely the Cow type This version is still somewhat rigid. For example, the argument is &str ; if I pass in a String , I still have to convert it first. Also, because the return value is Cow , it cannot return string-owning types other than String and &str (for example, OsString ) fn frobnicate < T : AsRef < str >> ( s : T ) -> T The third version relaxes the contract further Now both the parameter and the return value only require a type th

Claimable Clouds are temporary Cloudinary environments for AI workflows that let AI Agents safely manage media with no signup required. Imagine you're a busy designer, with many satisfied clients who depend on you to take their images and make them look great across social media. All that manual cropping and scaling, it's enough to make a body cry. On top of that, you know that AI can give you a hand here, but managing the handoff between your AI, your own skilled hands and artistic taste and style, and your always-in-a-hurry client list is another big pain. Enter the concept of the Cloudinary Claimable Cloud, just released today. Take a look at the docs about these new temporary instances available now What we built and why Provision a disposable Cloudinary cloud with no signup, using npx @cloudinary/cloud Auto-detect a dropped image and upload it to that temporary cloud Auto-crop it into 6+ social formats (Instagram, LinkedIn, X, Facebook, Stories) using AI-based smart cropping Generate a side-by-side gallery of results automatically Hand off a Claim URL so a client can make the cloud permanent Now, you can hand off the main pain points to AI - the resizing and reshaping of your images for the various social media platforms, while giving your clients a clean handoff via a temporary Cloud environment that they can use to create a Cloudinary account and start using these assets. One side effect: this also nudges your whole client base toward the same toolset - Cloudinary. The bigger deal is working with an AI agent that makes your life easier but ALSO allows you to keep control of the output. Let's walk through how this works! It all boils down to a new command: npx @cloudinary/cloud Type that into your terminal to kick off the process. I built a small app around this concept to provide this AI agent with a simple harness, so let me show how that looks. The user experience is to drop any image you want resized into the /drop folder. Under the cover, there are a few

TypeScript Strict Null Checks in 2026: Real-World Patterns for Handling undefined Without the Noise This article was written with the assistance of AI, under human supervision and review. Most TypeScript null safety problems stem from teams treating strictNullChecks as a boolean toggle instead of a design constraint. The compiler flag eliminates an entire class of production bugs, but codebases that flip it on without adjusting their patterns end up drowning in type assertions and optional chaining operators. The result is worse than the original false confidence wrapped in noise. The fundamental issue is that JavaScript conflates absence and failure. A missing property, an API error, and an uninitialized variable all return undefined or null , but they represent completely different failure modes. When teams enable strictNullChecks without encoding these distinctions into their types, the compiler forces them to handle every potential undefined the same way. That leads to defensive checks that obscure intent and catch nothing of value. The correct approach treats null safety as a type design problem. Discriminated unions encode why a value is missing. Branded types prove non-nullability at the boundary. Type guards narrow only when the business logic demands it. The patterns are simple, but they require understanding what the compiler is actually checking and what guarantees your code actually needs. This post covers the essential patterns teams need to write null-safe TypeScript in 2026 without the noise. Apply these in production and the difference will be immediate. Key Takeaways strictNullChecks eliminates runtime null errors only if your types encode why values are missing, not just that they might be missing. Discriminated unions outperform null returns for API responses because they force exhaustive handling of failure cases at compile time. Non-null assertions ( ! ) are acceptable at proven boundaries where external systems guarantee non-null values, but ne

Why I'm Prioritizing Automation in My Stack and Business Strategy As a full-stack developer working with JavaScript, Python, and Supabase, and simultaneously building out Delight Softwares Inc. and Softchic, efficiency isn't a luxury – it's a core requirement. My journey involves balancing academic pursuits with real-world tech solutions, especially for the Nigerian market. This dual role has highlighted a critical need: intelligent automation . I'm currently in the strategic planning phase of deeply integrating automation across my development and business operations. This isn't just about saving time; it's about building scalable, resilient systems that allow me to focus on innovation rather than repetition. The Vision: Where Automation Fits In Development Workflow Optimization: CI/CD Pipelines: As I delve deeper into backend development and complex Next.js applications, automating testing, building, and deployment processes becomes non-negotiable. Imagine pushing code and having tests run, builds deployed to staging, and even production updates handled with minimal manual intervention. This frees up precious time for architecting robust database logic and crafting intricate APIs. Local Environment Setup: Scripting initial project setups, dependency installations, and database seeding can save hours per project. Tools like npm scripts or simple Python automation can standardize this. Code Quality & Linting: Automated pre-commit hooks or CI checks using tools like ESLint for JavaScript/TypeScript and Black/Flake8 for Python ensure consistent code quality without constant manual review. Business Operations & Growth: Market Intelligence Automation: For Softchic, understanding market trends for website templates is crucial. Automating data collection from various sources using Python scripts can provide invaluable insights for product development and pricing strategies. Content & Marketing Streamlining: While creativity is human-driven, the distribution of promotional

submitted by /u/iximiuz [link] [留言]

A Deep Dive into the Memory Model From Source Code to Machine Instructions A five-part journey through compilers, executables, virtual memory, and the CPU Introduction: What Really Happens When Code Runs Consider a simple C program: include <stdio.h> int value = 10; int add(int a, int b) { return a + b; } int main() { int x = 5; int result = add(x, value); printf("%d", result); return 0; } Most programmers look at this and see only the visible outcome: 5 + 10 = 15 But behind that single printed number lies a much deeper story. Where does the data actually live? Who moves it from one place to another? How does the CPU find the instructions it needs to run? And how does the result finally make its way to the screen? Answering these questions means understanding a concept that many programmers use daily but rarely examine closely: the memory model. What Is a Memory Model, Really? Ask most developers what a "memory model" means, and the answer usually comes back in two words: stack and heap. That answer isn't wrong - it's just incomplete. A memory model is really a description of five things at once: How data is stored How data is accessed How long data exists Who is responsible for managing that lifetime How different parts of a system communicate through memory A program never leaps directly from C source code into RAM. Several distinct layers sit between the two, each one translating the layer below it into something the layer above can reason about. This article walks through all of them, one at a time, and then reassembles the full picture. The Four Layers, at a Glance Layer What It Deals With Typical Concepts 1. Programming Language Human-readable code scope, lifetime, ownership 2. Compiler Translating code to instructions registers, optimization, assembly 3. Operating System Running the program as a process virtual address space, .text/.data/.bss 4. CPU Architecture Executing raw instructions registers, cache, pipeline, ALU The rest of this article follows a sing

submitted by /u/mr_gnusi [link] [留言]

This post is a lover letter to Emacs Lisp macros. I've been a long time user as a lisp hacker, and my recent obsessions with Douglas Hofstadter's strange loop concepts and M.C. Escher's mind bending artwork have enhanced my appreciation of this language's most beautiful and thought provoking feature. This post can teach you about macros and what makes them useful, but I also hope it can instill a fascination with their concept. https://www.chiply.dev/post-elisp-macros-are-beautiful submitted by /u/misterchiply [link] [留言]

submitted by /u/yogthos [link] [留言]

submitted by /u/mareek [link] [留言]

TL;DR MCP servers are powerful, but they can provide access to production systems if...

submitted by /u/TheSwedeheart [link] [留言]

submitted by /u/ZuleZI [link] [留言]

Mugshawtys and similar pages often frame mugshots as thirst traps. The women featured are humiliated and are sometimes harassed for months and years after their arrests.