标签：#rce

GitHub热门项目 | A modern runtime for JavaScript and TypeScript. | Stars: 106,979 | 15 stars today | 语言: Rust

GitHub热门项目 | 🥧 Savoury implementation of the QUIC transport protocol and HTTP/3 | Stars: 11,540 | 7 stars today | 语言: Rust

GitHub热门项目 | A set of beautifully-designed, accessible components and a code distribution platform. Works with your favorite frameworks. Open Source. Open Code. | Stars: 115,842 | 73 stars today | 语言: TypeScript

GitHub热门项目 | Storybook is the industry standard workshop for building, documenting, and testing UI components in isolation | Stars: 90,212 | 11 stars today | 语言: TypeScript

GitHub热门项目 | Vite plugin that reimplements the Next.js API surface — deploy anywhere | Stars: 8,149 | 8 stars today | 语言: TypeScript

GitHub热门项目 | The swiss army knife of lossless video/audio editing | Stars: 40,995 | 67 stars today | 语言: TypeScript

GitHub热门项目 | the full-stack Vue framework | Stars: 60,358 | 14 stars today | 语言: TypeScript

GitHub热门项目 | Next generation frontend tooling. It's fast! | Stars: 81,082 | 71 stars today | 语言: TypeScript

GitHub热门项目 | Free, open-source and self-hosted CAPTCHA alternative to reCAPTCHA. Privacy-first and powered by proof-of-work and instrumentation challenges. | Stars: 6,767 | 59 stars today | 语言: JavaScript

GitHub热门项目 | A full-stack AI helpdesk platform that uses machine learning, NLP, and OCR to automatically analyze support requests, detect similar incidents, and help teams resolve technical issues faster. | Stars: 139 | 4 stars today | 语言: JavaScript

GitHub热门项目 | use ai analysis Performance issue with perfetto | Stars: 491 | 1 star today | 语言: JavaScript

GitHub热门项目 | A bundler for javascript and friends. Packs many modules into a few bundled assets. Code Splitting allows for loading parts of the application on demand. Through "loaders", modules can be CommonJs, AMD, ES6 modules, CSS, Images, JSON, Coffeescript, LESS, ... and your custom stuff. | Stars: 65,757 | 2 stars today | 语言: JavaScript

GitHub热门项目 | Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free. | Stars: 34,899 | 51 stars today | 语言: Python

GitHub热门项目 | Open-Source Frontier Voice AI | Stars: 48,317 | 219 stars today | 语言: Python

GitHub热门项目 | Robust Speech Recognition via Large-Scale Weak Supervision | Stars: 101,694 | 116 stars today | 语言: Python

GitHub热门项目 | The Go programming language | Stars: 134,434 | 24 stars today | 语言: Go

GitHub热门项目 | The official NGINX Open Source repository. | Stars: 30,602 | 39 stars today | 语言: C

GitHub热门项目 | Agentic AI Infrastructure for magnifying HUMAN capabilities. | Stars: 14,794 | 63 stars today | 语言: TypeScript

GitHub热门项目 | The sandbox agent framework. | Stars: 4,519 | 126 stars today | 语言: TypeScript

I Benchmarked Lynkr Against LiteLLM on the Same Backends. Lynkr Was Cheaper for Tool-Heavy Workloads Founder disclosure: I built Lynkr, so take this as a technical benchmark write-up, not a neutral industry report. The numbers below come from the same backend providers on both gateways. If you're routing AI coding traffic through a gateway, just switching providers is not enough. The real savings come from reducing the tokens that ever reach the model in the first place. I ran Lynkr and LiteLLM against the same backends — Ollama locally, Moonshot, and Azure OpenAI — across 9 scenarios. On the scenarios that actually look like agentic coding work, Lynkr was cheaper because it does three things before forwarding the request upstream: smart tool selection, TOON compression, and semantic caching. The short version Lynkr was measurably better on the cost-sensitive parts of the workload: Smart tool selection: 53% fewer input tokens, 52% lower cost TOON JSON compression: 87.6% fewer billed tokens on a large tool result, 50% lower cost Semantic cache: 171ms cache-hit response vs 3,282ms on the repeat query path Tier routing: escalated hard prompts to stronger models instead of blindly sending everything to the cheapest route Area Lynkr result Why it mattered Tool selection 53% fewer tokens Removes irrelevant tool schemas TOON compression 87.6% fewer tokens Shrinks large JSON tool outputs Semantic cache 171ms cache hit Avoids repeat model calls Tier routing Escalates hard prompts Doesn’t over-optimize for cheapest path This matters if you're running Claude Code, Codex, Cursor, or similar agent workflows where tools, file reads, grep output, and repeated context dominate your token bill. Setup Same benchmark inputs, same providers, same request shape. Machine: macOS on Apple Silicon Lynkr: v9.3.2 on Node 20 LiteLLM: v1.87.1 on Python 3.12 Backends used: Ollama local, Moonshot, Azure OpenAI Scenarios: 9 total across simple prompts, tools, history, cache, and routing Each scena