标签：#c

GitHub热门项目 | LLM API 管理 & 分发系统，支持 OpenAI、Azure、Anthropic Claude、Google Gemini、DeepSeek、字节豆包、ChatGLM、文心一言、讯飞星火、通义千问、360 智脑、腾讯混元等主流模型，统一 API 适配，可用于 key 管理与二次分发。单可执行文件，提供 Docker 镜像，一键部署，开箱即用。LLM API management & key redistribution system, unifying multiple providers under a single API. Single binary, Docker-ready, with an English UI. | Stars: 35,709 | 30 stars today | 语言: JavaScript

GitHub热门项目 | Comet: agent skill harness for turning ideas into evaluated workflows | Stars: 2,264 | 26 stars today | 语言: JavaScript

GitHub热门项目 | Open-source meeting transcription API for Google Meet, Microsoft Teams & Zoom. Auto-join bots, real-time WebSocket transcripts, MCP server for AI agents. Self-host or use hosted SaaS. | Stars: 2,520 | 74 stars today | 语言: Python

GitHub热门项目 | Animation engine for explanatory math videos | Stars: 88,514 | 133 stars today | 语言: Python

GitHub热门项目 | A collection of projects showcasing RAG, agents, workflows, and other AI use cases | Stars: 13,132 | 18 stars today | 语言: Python

GitHub热门项目 | Our library for RL environments + evals | Stars: 4,344 | 15 stars today | 语言: Python

GitHub热门项目 | 26m function call model that runs on incredibly small devices | Stars: 3,071 | 113 stars today | 语言: Python

Luca Mezzalira shares proven learnings from guiding hundreds of teams through the migration from monolithic web applications to distributed frontend architectures. He explains the core architectural difference between components and micro-frontends, outlines a 6-step decision framework spanning client vs. server rendering, and discusses how to utilize edge compute for safe, iterative rollouts. By Luca Mezzalira

In today's mobile-first world, users expect authentication to be both secure and effortless. Typing passwords every time an app is opened not only impacts the user experience but also introduces security risks if passwords are weak or reused. Biometric authentication solves this problem by allowing users to verify their identity using Fingerprint , Face ID , Touch ID , Iris Scanner , or even their device's PIN/Password . If you're building a React Native application, @sbaiahmed1/react-native-biometrics is one of the most comprehensive biometric libraries available. Beyond simple authentication prompts, it offers hardware-backed cryptographic key management, biometric enrollment detection, device integrity checks, StrongBox support, and compatibility with both the React Native New Architecture and Expo. In this article, we'll explore everything this library offers and learn how to integrate biometric authentication into a React Native application. Why Biometric Authentication? Traditional authentication methods come with several drawbacks: Passwords are easy to forget. Weak passwords are vulnerable to attacks. OTP-based logins can be slow and frustrating. Users often abandon apps with poor login experiences. Biometric authentication addresses these challenges by providing: 🔒 Enhanced security ⚡ Faster authentication 😊 Better user experience 📱 Native platform support 🔑 Secure fallback using device credentials Whether you're building a banking app, healthcare platform, enterprise application, or e-commerce app, biometric authentication has become an expected feature. Installation Install the package using npm: npm install @ sbaiahmed1 /react-native-biometric s or with Yarn: yarn add @ sbaiahmed1 /react-native-biometric s For iOS: cd ios pod install Platform Configuration Before using biometric authentication, configure the required permissions for both Android and iOS. Android Open your android/app/src/main/AndroidManifest.xml file and add the following permissions: <

Many enterprise AI governance discussions start with frameworks. Frameworks are useful. They help organizations define principles, roles, controls and accountability. But when an enterprise starts using generative AI in real workflows, the practical governance problem often appears somewhere much more specific: the AI access path. That is the moment when an employee, application, copilot, agent or API workflow sends a request to an AI model. At that point, governance becomes operational. The practical governance questions Before an AI request reaches a model, an enterprise may need to answer several concrete questions: Who is sending the request? What business use case is involved? What data is being sent? Which AI model is being used? Is the model approved for this use case? Should sensitive data be masked or blocked? Was the access decision recorded? Can the activity be reviewed later? Can AI usage and token cost be explained by user, department, model and use case? These questions are not only policy questions. They are architecture questions. If the enterprise cannot answer them at the access path, AI governance may remain too far away from the real system behavior. Why the access path matters Many organizations already have AI policies. But policies are often written before or after the actual AI interaction. The access path is where policy meets execution. For example, a team may approve the use of generative AI for internal productivity. But the organization still needs to understand: whether customer data is being included in prompts; whether employees are using approved or unapproved models; whether sensitive content is being sent to external services; whether different departments are using AI in very different ways; whether audit evidence exists when an incident or review happens. This is why AI governance should not only be treated as a document, committee or training program. It also needs a technical control point. A simple access governance pattern A

Everyone can generate a website now. Type a prompt, get a decent page — that part is a commodity. The question nobody's answering is what happens on day 2 : the leads start arriving, a line of copy needs a tweak, someone asks for a section you forgot. That's when a website stops being a design project and becomes a thing you have to run — and where most tools hand you yet another dashboard to log into and dread. Sitelas makes a different bet. Because a Sitelas site lives inside Claude through an MCP connector, the same chat that built the site also runs it . You don't open an admin panel to see who filled out your form, write back, or change the page. You just ask. Here's what "running your site from a chat" actually looks like. First, the 30-second why Claude connects to outside tools through MCP connectors — you already use the ones for Gmail, Calendar, and Drive. Sitelas has one too. Add it once (in claude.ai: Customize → Connectors → Add custom connector , and paste https://sitelas.com/api/mcp ), and Claude can do things with your site, not just talk about it: publish it, read its submissions, restyle it, add a section. Your site becomes an automation endpoint sitting next to your other connectors — the thing a Webflow or Squarespace site can't be. New here? Start with How to Build a Website From a Claude Chat . "Did anyone fill out my form today?" That single question is the whole idea. You ask; Claude reads your site's submissions, surfaces the new lead — Maya, a bakery owner — and drafts a warm reply in your voice. One message, no tabs. It works because every form on a Sitelas site captures submissions to your inbox automatically — no integration required. You can open that inbox in the dashboard any time: …but running your site from a chat means you rarely need to. Claude reads those same submissions straight from your site, so "who wrote in today, and what do they want?" is answered in the thread you're already in — not in a panel you have to remember to ch

My CLAUDE.md has one line near the bottom that I wrote months ago and mostly forgot about until I started actually paying attention to what it does: ## Important Note after your work done codex will review what you done. Terse, no punctuation, clearly typed in a hurry. But it's a real instruction that fires on every session in this repo: I finish a change, and a second model reviews it before I consider the work done. I added it half as an experiment. A few months in, it's changed how I work more than almost anything else in the setup, and not in the way I expected. I thought it would catch bugs. Mostly it doesn't, not directly. What it actually does is force a triage decision on every single piece of feedback, and getting that triage wrong is where all the pain lives. The three buckets Early on I treated every review comment the same way: read it, do it. That lasted about a week before I was silently making changes I didn't agree with because a second AI suggested them, and separately burning a stupid amount of time re-litigating comments that were just wrong or out of scope. What actually works is sorting every comment into one of three buckets before touching code: Fix it, no discussion. The comment is unambiguous, low-risk, and doesn't touch anything architecturally significant. Just do it and move on. Ask first. The comment is ambiguous, or it touches something that would require a real judgment call, or the "fix" would be a bigger refactor than the comment implies. Stop and get a human decision before acting. Skip silently. The comment is a duplicate of something already handled, or genuinely doesn't apply. Don't reply just to say "not doing this," don't leave a comment thread as evidence of having read it. Silence is the correct response to a non-issue. The failure mode I kept falling into before I had these buckets explicitly was collapsing 2 into 1: treating "ambiguous" as "just pick an interpretation and go." That's the actual source of review fatigue, not