标签：#r

GitHub热门项目 | A massively parallel, high-level programming language | Stars: 19,619 | 28 stars today | 语言: Rust

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GitHub热门项目 | Deep Code 是专为 deepseek-v4 模型优化的终端 AI 编码助手，支持深度思考、推理强度控制以及 Agent Skills。 | Stars: 1,654 | 128 stars today | 语言: TypeScript

GitHub热门项目 | Open Source Qualtrics Alternative | Stars: 12,484 | 13 stars today | 语言: TypeScript

GitHub热门项目 | 🦸 AI 编程超能力 · 中文增强版 — superpowers（116k+ ⭐）完整汉化 + 6 个中国原创 skills，让 Claude Code / Copilot CLI / Hermes Agent / Cursor / Windsurf / Kiro / Gemini CLI 等 16 款 AI 编程工具真正会干活 | Stars: 6,504 | 66 stars today | 语言: JavaScript

GitHub热门项目 | All parts of Claude Code's system prompt, 27 builtin tool descriptions, sub agent prompts (Plan/Explore/Task), utility prompts (CLAUDE.md, compact, statusline, magic docs, WebFetch, Bash cmd, security review, agent creation). Updated for each Claude Code version. | Stars: 11,638 | 28 stars today | 语言: JavaScript

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GitHub热门项目 | Production-grade platform for building agentic IM bots - 生产级多平台智能机器人开发平台/ Agent、知识库编排、插件系统 / Bots for Discord / Slack / LINE / Telegram / WeChat(企业微信, 企微智能机器人, 公众号) / 飞书 / 钉钉 / QQ / Matrix e.g. Integrated with ChatGPT(GPT), DeepSeek, Dify, n8n, Langflow, Coze, Claude, Gemini, GLM, Ollama, SiliconFlow, Moonshot, openclaw / hermes agent, deerflow | Stars: 16,733 | 33 stars today | 语言: Python

GitHub热门项目 | An AI-boost cross-platform multi-protocol fluent-design concurrent downloader built with Python & Qt. | Stars: 5,688 | 59 stars today | 语言: Python

GitHub热门项目 | An OSINT tool to search for accounts by username and email in social networks. | Stars: 6,806 | 53 stars today | 语言: Python

GitHub热门项目 | AI-powered job application framework built on Claude Code. Fork it, fill in your profile, and let Claude evaluate jobs, tailor CVs, write cover letters, and prepare you for interviews. | Stars: 8,581 | 2,402 stars today | 语言: TypeScript

GitHub热门项目 | 💡 Download the complete source code of any website (including all assets). [ Javascripts, Stylesheets, Images ] using Node.js | Stars: 3,643 | 173 stars today | 语言: HTML

GitHub热门项目 | Show usage stats for OpenAI Codex and Claude Code, without having to login. | Stars: 16,922 | 377 stars today | 语言: Swift

GitHub热门项目 | Repository for skills to assist AI coding agents with .NET and C# | Stars: 4,218 | 200 stars today | 语言: C#

Solos announced a new version of its AirGo smart glasses, one that forgoes cameras for a sleeker design and an AI assistant that relies on voice interactions. Last year's AirGo A5 weighed 36 to 40 grams depending on the frame style, but the new AirGo A6 weigh around 19 grams. Part of the weight savings […]

Google is going to give content creators and website owners a better idea of how people find their social media profiles and YouTube content through Search. With a new feature in the Google Search Console called "platform properties," Google says that you'll be able to "easily track which search terms lead people to your Instagram, […]

You can clip a cover over the cameras, which could be a double-edged sword.

submitted by /u/itgforlife [link] [留言]

Most of the "AI for observability" work I see right now hands the language model the judgment. I think that's backwards. Feed it the alert, feed it some metrics, ask it what's wrong, what should be done, and let it make the judgement call. Based on my experience working with language models, I decided that inverting the process provides better results. The short version: in my alerting pipeline, the set of allowable classifications is fixed in deterministic Python, and the model has to pick from it. The LLM's only job is to turn a structured verdict into an easily digestible sentence. It never decides whether something is bad, how bad it is, or what category of problem it is. It narrates within a decision space the code has already locked down. TL;DR: Instead of letting an LLM decide what's wrong with an alert, I let deterministic Python make every operational decision and restrict the model to explaining the result in plain English. The code classifies, validates, and aggregates; the LLM only narrates. That keeps the data consistent, prevents hallucinated classifications, and ensures the monitoring pipeline continues working even if the model fails. The problem I run a small managed monitoring service. Alertmanager fires, a webhook lands, and historically that webhook produced a line like HighMemoryUsage on host web-vm, severity warning, which is accurate, but not terribly helpful. The person reading it still has to know what HighMemoryUsage implies, whether this host always runs hot, and whether to care. I wanted plain-English context attached to the alerts without altering the alert delivery process. The obvious move was to throw the whole alert at an LLM and ask it to explain. I tried that in the first iteration of this experiment, expecting it to be somewhat accurate, but not entirely reliable, and it did not disappoint, the model was confidently inconsistent. The same alert, fired three times, produced three different "root cause" categories. One run called a