标签：#c

GitHub热门项目 | A network diagnostic tool | Stars: 7,361 | 56 stars today | 语言: Rust

GitHub热门项目 | Modern server management panel built with Rust and React. Sites, databases, Docker apps, Git deploy, mail, DNS, monitoring, backups, and security — all in one panel. | Stars: 467 | 13 stars today | 语言: Rust

GitHub热门项目 | Route, manage, and analyze your LLM requests across multiple providers with a unified API interface. | Stars: 1,456 | 6 stars today | 语言: TypeScript

GitHub热门项目 | Crawlee—A web scraping and browser automation library for Node.js to build reliable crawlers. In JavaScript and TypeScript. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Works with Puppeteer, Playwright, Cheerio, JSDOM, and raw HTTP. Both headful and headless mode. With proxy rotation. | Stars: 24,963 | 69 stars today | 语言: TypeScript

GitHub热门项目 | 💾 Self-hosted online file converter. Supports 1000+ formats ⚙️ | Stars: 17,911 | 240 stars today | 语言: TypeScript

GitHub热门项目 | High performance Node.js image processing, the fastest module to resize JPEG, PNG, WebP, AVIF and TIFF images. Uses the libvips library. | Stars: 32,496 | 9 stars today | 语言: JavaScript

GitHub热门项目 | 🌐 Make websites accessible for AI agents. Automate tasks online with ease. | Stars: 106,530 | 270 stars today | 语言: Python

GitHub热门项目 | FlashInfer: Kernel Library for LLM Serving | Stars: 6,017 | 6 stars today | 语言: Python

GitHub热门项目 | Pretty fancy and modern terminal file manager | Stars: 19,161 | 312 stars today | 语言: Go

There's a whole ecosystem of "agent skills" now — reusable instruction files you drop into Claude Code (or Cursor, or Copilot) to make the model write cleaner code, debug more carefully, use fewer tokens, and so on. Some of these repos have tens of thousands of GitHub stars. Almost none of them ship a single number telling you whether the skill actually does anything. That bothered me, because "adding a plausible-sounding instruction" and "adding an instruction that works" look identical until you measure them. So I built a benchmark with one rule, committed before I ran anything: No skill gets merged unless it beats both a no-instruction baseline AND a placebo prompt on its pre-registered target metric, measured on hidden hold-out tests, with accuracy not allowed to drop. Skills that fail are published anyway, with their numbers. The placebo arm is the part almost nobody runs, and it turned out to be the most important one. Why a placebo Most "battle-tested" skill collections that measure anything at all compare skill-on vs skill-off. The problem: that comparison can't separate "this skill works" from "adding any confident-sounding text changes the model's behavior." LLMs are suggestible. If you want to claim your skill did something, you have to show it beats a same-length instruction that contains no actual mechanism — just vibes. So every result here is a three-way comparison — off / placebo / on — run K=5–8 times per task per arm, in isolated git workspaces, graded by hold-out acceptance tests the agent never sees, with every raw run log committed to the repo and the README regenerated from those logs in CI. 516 runs total, all on claude-opus-4-8 . Finding 1: the placebo often made code bigger My anti-over-engineering skill ( underkill , ~20 lines) cut source LOC by -23.8% vs baseline at identical accuracy (60/60 hold-out passes). Good. But the interesting column is the placebo: a same-length "write clean, minimal, professional code" instruction didn't reduce c

Seller is a dedicated Marketplace app for people who list and sell items frequently.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. You've probably noticed this if you've tried to fetch a public webpage in the last two years. You write four lines of Python. You get a 403. You add a User-Agent header. You get a 403 with better manners. You spin up headless Chrome and get a challenge page that spins forever. Eventually you go looking at pricing pages for data APIs, where a cheerful landing page offers to sell you back the exact same public HTML for $100 a month. Meanwhile the big labs vacuumed up that same web at a scale none of us will ever match, and now the standard way to read a page programmatically is to pay somebody. The web went from "open by default" to "open, but only if you look like a person holding a mouse." So let's talk about eight open source projects quietly clawing that access back. I've used most of these in anger. Some are brilliant. Quick note on star counts: I mention a few, because they are a decent signal of "somebody else has hit the bugs before you." They are not a signal of maintenance. A repo can have 70k stars and a last commit from 2023. Always check the commit graph before you check the star count. 1. Firecrawl: the one that turned a URL into a prompt What it does: you hand it a URL, it hands you clean markdown. Not HTML with the nav bar and the cookie banner and four newsletter modals. Markdown. Headings, paragraphs, links, done. It also does more than single pages. The crawl endpoint walks a whole site, map gives you a URL inventory without fetching everything, and structured extraction pulls typed JSON out of a page if you describe the shape you want. from firecrawl import Firecrawl app = Firecrawl ( api_key = " fc-... " ) doc = app . scrape ( " https://example.com/docs/getting-started " , formats = [ " markdown " ]) print ( doc . markdown

I wanted to add a background-removal tool to my site's image cluster that stayed true to the 100% client-side processing principle I already use for PDFs and image conversions. The path there was anything but linear: a library dropped over a licensing problem, a carefully chosen model that turned out more limited than expected, and a bug that froze the entire page — not just the tool — during computation. Here's the full build, including the parts that didn't work the first time. The starting problem: what's actually feasible for free? The initial idea was broad: remove backgrounds, and maybe unwanted objects too. The two tasks have very different difficulty levels. Removing objects requires inpainting — plausibly reconstructing the erased area — which in practice still means heavy generative models, impractical to run client-side with good quality on an average device. Removing a background , on the other hand, is a segmentation problem: separating a subject from its surroundings. That has much lighter models available, runnable entirely via WebAssembly with no server involved at all. So: background removal only, object removal shelved for later. The AGPL trap The first library that looked like a perfect fit turned out to be distributed under AGPL , a strong copyleft license. Free to use — but with a real catch for anyone embedding it in a public, closed-source web service: AGPL can require releasing the full source of the project that embeds it, under the same license. "Free for the end user" and "safe to drop into a closed-source commercial product" are two different questions, and it's worth answering the second one before writing integration code, not after deploying it. Before wiring any "free" AI library into a commercial project, check the exact license, not just the price tag. AGPL, GPL, and other strong copyleft licenses are fine for personal or internal tools, risky for a public closed-source product. The fix: switch to Transformers.js — Hugging Face's li

A nova fase dos agentes de IA: menos chat, mais operação. A OpenAI apresentou o Presence, uma plataforma para empresas implantarem agentes de voz e chat em atendimento ao cliente e fluxos internos. Para quem desenvolve sistemas, o ponto não é apenas colocar mais um chatbot em produção. A mudança relevante é tratar a IA como uma camada operacional: ela precisa participar de processos reais, com objetivos claros e resultados verificáveis. Um agente confiável precisa de mais do que boas respostas. Precisa receber o contexto certo, operar dentro de permissões bem definidas, deixar rastros auditáveis e saber quando transferir o caso para uma pessoa. Isso altera a decisão técnica. Escolher um modelo continua importante, mas arquitetura, integrações, observabilidade, avaliação contínua e governança passam a fazer parte do mesmo problema. A pergunta mais útil deixa de ser “onde podemos colocar IA?” e passa a ser: qual processo já está pronto para ser redesenhado com um agente de confiança? Fonte oficial: https://openai.com/index/introducing-openai-presence