标签：#ENSO

c_std: A Leak-Free, Cross-Platform Standard Library for Modern C Bringing the comfort of the C++ STL and Python's standard library to C17 — without leaving C A technical white paper. Executive summary C is still the substrate of the computing world — kernels, databases, language runtimes, embedded firmware, and the inner loops of nearly everything else. Yet the moment you step away from the kernel and try to write ordinary application code in C, you feel the gap: no growable vector, no hash map, no JSON parser, no string type that doesn't invite a buffer overflow. You either pull in a grab-bag of mismatched third-party libraries, each with its own conventions and failure modes, or you re-implement the same dynamic array for the hundredth time. c_std is an attempt to close that gap deliberately and coherently. It is a single, consistent library — written in pure C17 — that reimplements a large slice of the C++ Standard Library (containers, algorithms, smart pointers) alongside many Python-style conveniences ( json , regex , random , statistics , csv , config , even turtle graphics). It targets Windows and Linux from one source tree, compiles cleanly under -Wall -Wextra , and — this is the part I care about most — is verified leak-free under Valgrind , module by module, example by example. This paper explains the design philosophy, the architecture, and the engineering discipline that makes a library like this trustworthy enough to build on. 1. The problem: C's missing middle Every C programmer knows the two extremes. At the bottom, the language itself: pointers, malloc , memcpy , raw arrays. At the top, whatever the platform hands you — <windows.h> or POSIX, OpenSSL, a JSON library someone wrapped a decade ago. The middle — the layer the C++ STL and Python's batteries-included standard library occupy — is missing. That missing middle has a real cost. It shows up as: Re-invention. Teams write their own vector, their own string builder, their own linked list, each subt

GitHub热门项目 | 使用 NextJS + Notion API 实现的，支持多种部署方案的静态博客，无需服务器、零门槛搭建网站，为Notion和所有创作者设计。 (A static blog built with NextJS and Notion API, supporting multiple deployment options. No server required, zero threshold to set up a website. Designed for Notion and all creators.) | Stars: 11,496 | 44 stars this week | 语言: JavaScript

GitHub热门项目 | Skills Catalog for Codex | Stars: 20,894 | 975 stars this week | 语言: Python

GitHub热门项目 | A Kiro Client in Rust | Stars: 1,560 | 5 stars today | 语言: Rust

GitHub热门项目 | 💥 Blazing fast terminal file manager written in Rust, based on async I/O. | Stars: 38,839 | 74 stars today | 语言: Rust

GitHub热门项目 | 为键盘工作者设计的单词记忆与英语肌肉记忆锻炼软件 / Words learning and English muscle memory training software designed for keyboard workers | Stars: 22,191 | 62 stars today | 语言: TypeScript

GitHub热门项目 | 3D Gaussian Splat Editor | Stars: 8,839 | 57 stars today | 语言: TypeScript

GitHub热门项目 | | Stars: 7,489 | 66 stars today | 语言: TypeScript

GitHub热门项目 | The open source coding agent. | Stars: 167,404 | 385 stars today | 语言: TypeScript

GitHub热门项目 | Playwright is a framework for Web Testing and Automation. It allows testing Chromium, Firefox and WebKit with a single API. | Stars: 89,864 | 85 stars today | 语言: TypeScript

GitHub热门项目 | The React Framework | Stars: 139,608 | 19 stars today | 语言: JavaScript

GitHub热门项目 | CodeVibe is an interactive learning platform designed to help beginners understand programming through simple lessons and hands-on practice. It includes structured course sections, coding examples, and a built-in HTML compiler that lets users write and test code directly in the browser, making learning web development practical, engaging. | Stars: 28 | 2 stars today | 语言: JavaScript

GitHub热门项目 | Automated auditing, performance metrics, and best practices for the web. | Stars: 30,276 | 10 stars today | 语言: JavaScript

GitHub热门项目 | A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progress | Stars: 24,110 | 76 stars today | 语言: JavaScript

GitHub热门项目 | Open-source alternative to AI video platforms — Free AI image & video generation studio with 200+ models (Flux, Midjourney, Kling, Sora, Veo). No content filters. Self-hosted, MIT licensed. | Stars: 17,522 | 138 stars today | 语言: JavaScript

GitHub热门项目 | Apache Airflow - A platform to programmatically author, schedule, and monitor workflows | Stars: 45,602 | 13 stars today | 语言: Python

GitHub热门项目 | Eagle: Frontier Vision-Language Models with Data-Centric Strategies | Stars: 1,556 | 124 stars today | 语言: Python

GitHub热门项目 | VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning | Stars: 22,616 | 658 stars today | 语言: Python

Aider is the best terminal AI coding tool I've used. But by default it sends every diff through your OpenAI or Anthropic key, which gets expensive fast on real refactors — a single 100-file repo map can torch a few dollars before Aider even reads your prompt. This post shows how to run Aider against any LLM provider — Ollama for free local runs, OpenRouter for mixed-provider routing, AWS Bedrock for the enterprise plate — through a single OpenAI-compatible endpoint. I'll use Lynkr , the self-hosted gateway I maintain, but the pattern works with any OpenAI-compatible proxy. Full disclosure: I build Lynkr. I'll point out where it loses to LiteLLM and OpenRouter further down so you can make an honest call. The setup in three commands # 1. Start the gateway npx lynkr@latest # 2. Point Aider at it export OPENAI_API_BASE = http://localhost:8081/v1 export OPENAI_API_KEY = any-value # 3. Run Aider with any model name Lynkr knows about aider --model openai/gpt-4o That's it. Aider speaks the OpenAI Chat Completions protocol; Lynkr speaks it back and quietly translates the call to whichever upstream provider you've configured (Ollama, Bedrock, Anthropic, Azure, OpenRouter, Databricks, llama.cpp, LM Studio, ...). Aider has no idea it's talking to a router. Why bother? The cost math Aider's own leaderboard shows GPT-4o and Claude 3.5 Sonnet at the top — but you don't need a $3-per-million-tokens model to rename a variable. You need it for the architecture decisions. Lynkr's tier routing splits the work: Aider call type Routes to Cost Repo map summarization qwen2.5-coder:7b (Ollama, local) $0 File edits, single-function diffs gemini-flash-1.5 (OpenRouter) ~$0.075/M Architecture / multi-file refactors claude-3.5-sonnet (Anthropic) $3/M On a typical 4-hour Aider session, 80–90% of the calls are repo-map and small-diff calls. Routing those to local + cheap models while keeping Claude Sonnet for the hard reasoning has cut my own Aider spend by roughly 70%. Your mileage will vary base

Criei um gerenciador de downloads desktop em Python e quero feedback da comunidade! O PyFlowDownloader é um app desktop feito com Python + PySide6 que usa yt-dlp para baixar vídeos e áudios de forma assíncrona do youtube. Algumas coisas que ele já faz: Fila de downloads com progresso em tempo real Cancelamento de downloads ativos ou pendentes Suporte a MP4 e MP3, de 144p até 1080p Histórico com exportação para CSV Interface desktop com tema visual via QSS Build para Windows via PyInstaller + pipeline de release no GitHub Actions Está na versão v0.3.0 e ainda tem muito espaço pra crescer. Repositório: https://github.com/Vinny00101/PyFlowDownloader Se você puder **testar e deixar sua opinião nos comentários, ficaria muito grato! Quer saber: O que achou da experiência de uso? Algum bug que encontrou? O que você adicionaria ou melhoraria no projeto? Todo feedback é bem-vindo!