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GitHub热门项目 | The official Rust SDK for the Model Context Protocol | Stars: 3,694 | 13 stars today | 语言: Rust

GitHub热门项目 | Composio powers 1000+ toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action. | Stars: 29,435 | 23 stars today | 语言: TypeScript

GitHub热门项目 | Build production-ready applications in TypeScript | Stars: 15,064 | 26 stars today | 语言: TypeScript

GitHub热门项目 | An enterprise-class UI design language and React UI library | Stars: 98,868 | 21 stars today | 语言: TypeScript

GitHub热门项目 | | Stars: 414 | 39 stars today | 语言: JavaScript

GitHub热门项目 | JavaScript player library / DASH & HLS client / MSE-EME player | Stars: 8,174 | 3 stars today | 语言: JavaScript

GitHub热门项目 | 🎵 The Ultimate Open Source Suno Alternative - Professional UI for ACE-Step 1.5 AI Music Generation. Free, local, unlimited. Stop paying for Suno! | Stars: 4,596 | 82 stars today | 语言: JavaScript

GitHub热门项目 | TokenSpeed is a speed-of-light LLM inference engine. | Stars: 1,745 | 53 stars today | 语言: Python

GitHub热门项目 | SGLang is a high-performance serving framework for large language models and multimodal models. | Stars: 30,917 | 73 stars today | 语言: Python

GitHub热门项目 | Deepfakes Software For All | Stars: 56,008 | 135 stars today | 语言: Python

GitHub热门项目 | A free open source IT asset/license management system | Stars: 14,282 | 6 stars today | 语言: PHP

OpenWorker shipped in late July 2026. It is MIT-licensed, runs on your own machine, and takes your API key instead of selling you inference. The pitch is narrow and worth repeating exactly: it is an agent that hands you finished work , not a chat transcript. A drafted document on disk. A Slack reply with the real numbers in it. A calendar that has actually been rearranged. There are a lot of desktop agents right now. This post is about what makes this one structurally different, what state it is actually in, and how to get it running. What it is in one paragraph OpenWorker is a desktop app: a Tauri shell around a React UI, sitting on top of a local Python agent server. You give it an outcome ("prepare a customer brief from these three files and the Jira tickets"). It decomposes that into steps, reaches into your files, terminal, and connected SaaS apps, and produces an artifact. Before anything consequential happens - sending a message, running a shell command, writing to your calendar - it stops and asks. The engine is built on aisuite , Ng's provider-agnostic LLM library. That matters more than it sounds like: OpenWorker is explicitly positioned as a reference implementation of what you can build on aisuite, so the codebase doubles as a worked example if you are building your own harness. The four things that actually distinguish it 1. There is no OpenWorker inference service You paste a key, or you point it at Ollama and use none at all. The curated list covers OpenAI, Anthropic, Google, plus OpenAI-compatible vendors like DeepSeek, GLM, Kimi, Qwen, MiniMax, Mistral, and Grok, plus open-weight models through Together and Fireworks. Roughly thirty models are marked as verified for tool-calling work; you can point it at any other model string and accept the risk yourself. The practical consequence: your cost is your provider bill, and swapping models is a dropdown, not a migration. 2. The permission model is typed, not a confirmation dialog This is the part I would

The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog […]

Earlier this month, OpenAI gave several of its AI models a task: complete a test designed to measure their cybersecurity capabilities. It put the systems in a sandboxed environment without an internet connection and set them off to work. What happened next is almost laughably silly - but also, as Adam Gleave, cofounder and CEO […]

GitHub热门项目 | 面向 A 股日K、分钟K与ETF分钟数据的本地量化引擎，集成增量同步、本地缓存、复权、批量查询、回测与指标计算。 | Stars: 1,427 | 50 stars today | 语言: HTML

Agent Reach is easiest to understand as a setup layer: it gives a command-capable coding agent a local toolbox, then stops being the center of the workflow. What is Agent Reach CLI for? Agent Reach CLI is a local, open-source coordinator for AI coding agents that can run shell commands; it is not a hosted scraping API, managed crawler, or cloud browser service. The practical job is narrower and more useful: choose platform utilities, install them, verify they work, and route the agent toward the right upstream tool. The current setup story should be pinned to Agent Reach v1.5.0, with package metadata listing Python >=3.10 and an MIT license . The v1.5.0 release was published on June 11, 2026, and describes 162 total tests plus 32 end-to-end real-machine tests across 13 channels . That matters because the project is handling brittle platform tooling, not exposing one stable universal API. "Selects, installs, health-checks and routes" is the core model described by the Agent Reach project, which means the agent still calls tools such as OpenCLI, yt-dlp, GitHub CLI, Jina Reader, feedparser, and platform CLIs directly (source: Agent Reach GitHub repository ). Out of the box, the zero-config surface is deliberately limited: public web reading via Jina Reader, YouTube, GitHub, RSS, Exa Search, V2EX, and basic Bilibili are listed in the install guide . The seed video frames the tool as a way to give agents access to social and web platforms, but builders should read that through the repo’s stricter model: Agent Reach installs and checks local capabilities; it does not remove login, cookie, or platform constraints . Prerequisites before pipx Agent Reach prerequisites are mostly local-environment prerequisites: use Python >=3.10, a shell-capable workstation, and accounts where you can manage CLIs, browser sessions, environment variables, and cookies deliberately . Treat the setup as installing a local capability layer for an AI coding agent, not as signing up for a hosted sc

Hoi hoi! I'm @nyaomaru, a frontend engineer who was recently knocked off my feet by how good the...

A follow-up to How Old Is My Data? The failure mode that gets worse when a machine is reading the data In a classic dashboard, stale data is a human problem: someone looks at a number that's six hours old and makes a slightly worse decision. Annoying, rarely catastrophic. Now hand that same data to a retrieval-augmented-generation (RAG) pipeline, or to an autonomous agent. The stakes change. The system doesn't pause to sanity-check the timestamp — it acts. And when the data it acts on is stale, three things are true at once: The answer is confidently wrong. There is no error to fire on — the query succeeded, the model responded, latency was normal. Every other signal on your dashboard is green. That's the worst combination in observability: a real failure that is completely invisible to the signals we currently emit. Where staleness hides in AI systems RAG: index vs. corpus. Your vector index was built from a corpus at some point in time. The corpus keeps changing — documents get added, edited, retracted. If the re-embedding job stalls or falls behind, the index quietly drifts out of date. The retriever still returns plausible chunks; the model still writes a fluent answer. It's just answering from a version of reality that no longer exists. The quantity you care about is the age of the index relative to its source — not the age of either one alone. Feature stores: online–offline skew. The features your model trained on and the features it serves on are supposed to match. When the online store lags the offline pipeline, predictions degrade in a way that looks like model drift but is actually data staleness wearing a costume. Agents: stale shared state. Multi-agent systems coordinate through shared memory, scratchpads, and context. An agent reasoning over state that another agent updated ten steps ago — but which never propagated — makes locally reasonable, globally wrong decisions. This isn't a new or exotic problem: it's exactly the regime that Age of Information t

cal.com, Calendly, zcal... booking SaaS isn't short on options, and most of them are genuinely decent. Free tiers cover the basics for a lot of freelancers. The catch: you're the product (nothing's really free), and your customer data lives somewhere you don't fully control and can't fully audit. A dysfunction I ran into on another SaaS tool was the trigger. Trusting a third-party service by default, just because it's widely used and billed monthly, doesn't always hold up. That episode was enough to make me reconsider every external service this site was relying on for functionality that's actually simple to self-host — and the booking widget, running on Calendly, was one of them. Nothing wrong with Calendly specifically. It worked fine. But structural friction had been building regardless: a recurring subscription for something as simple as displaying open slots and recording a choice, a hard dependency on a third party for a component with nothing exceptional about it technically, and customization capped by whatever the vendor exposes in settings — no way to go further if a need falls outside that box. On top of that, an integration constraint that mattered more than any of the above: the site runs on Astro, generating lightweight static pages by design, specifically to avoid the weight of third-party scripts and dependencies — the exact opposite of what embedding a SaaS widget implies. So: could a self-hosted alternative match the experience, without the monthly bill and without handing a core commercial function (people booking a call with me) to an external vendor? This is the write-up of that search, the codebase audit that came out of it, and the production rollout. The landscape Four self-hosted candidates stood out as genuinely comparable — not just UI skins sitting on top of someone else's API, not just internal-scheduling tools with the public-facing UX as an afterthought. CloudMeet — Svelte + TypeScript, deployed on Cloudflare Pages/Workers/D1, free-tie

In a new disclosure, OpenAI says its agent used exposed logins to gain access to at least four “publicly available services” in its unhinged quest to solve a test.