谷歌反重力:改变计算世界的破坏者永远

您的计算机系统刚刚成为您的通用AI代理工具包! 如果你可以像和同事一样对你的计算机说话 - 它只是工作? What if you could talk to your computer the way you talk to a colleague — and it just ? did the work 做了工作 Not autocomplete. Not a chatbot that pastes code snippets. Not a suggestion engine that highlights the next three words. A system that takes your intent and orchestrates your entire machine — files, browser, terminal, internet — to deliver the result. That is exactly what Google Antigravity does. 这里是应该阻止你滚动的部分:它不仅适用于编码器。 And here's the part that should stop you mid-scroll: it's not just for coders. 是的,Antigravity可以从头开始构建一个完整的API,构建一个微服务架构,生成一个CI/CD管道,并在你煮咖啡所需的时间内写200个单元测试。 它的编码能力是无与伦比的 - 它没有 你在写代码时,它 ,生产验证文物(实施计划,屏幕截图,浏览器记录),以便您可以在批准之前审查所做的事情。 协助 orchestrates agents that write, test, debug, and deploy code autonomously 但是,这里有令人不安的部分:您的计算机现在是您的API。 But here is the mind-blowing part: your computer is now your API. Every file on your hard drive, every tab in your browser, every URL on the internet, every command in your terminal — all of it is now a tool that autonomous AI agents can access, read, analyze, and act upon. 当机器的界面是自然语言,执行者是AI代理人,具有浏览器手,文件系统访问和互联网眼睛时,“开发人员工具”和“专业工具”之间的区别完全蒸发了。 When the interface to your machine is natural language and the executor is an AI agent with browser hands, file system access, and internet eyes, the distinction between "developer tool" and "professional tool" evaporates completely. 一位研究人员说: “阅读这50个PDF,搜索谷歌学者,并制作一份系统的文献评论。 Done. 一个营销经理说: “访问这三个竞争对手的网站,分析他们的定价,并创建一个SWOT分析。 Done. 一个学生说: “阅读我的课程,在线找到免费资源,使用闪存卡生成6周的学习计划。 Done. 一个内容创作者说: “写一个关于WebSockets的技术准确的博客文章,通过实际运行测试每个代码片段,并对整个事情进行SEO优化。 Done — and the code was verified on your local machine before it was published. This isn't a coding tool that happens to do other things. This is a universal orchestration platform that happens to be good at coding as well. 在本文中,我们将向您展示谷歌反重力是什么,它提供的LLM,如何使用MCP服务器和开源后端进行涡轮充电,以及 Most importantly — 30个详细的,AI优化的提示,涵盖了六个职业,证明反重力不是编码的未来。 30 detailed, AI-optimized prompts spanning six professions that prove Antigravity isn't the future of coding. 这是计算机本身的未来。 It's the future of computing itself. 让我们潜入吧! 谷歌反重力:无代码的通用AI代理管弦乐器改变一切 谷歌反重力不仅仅是另一个代码编辑器 - 它是世界上第一个代理第一集成开发环境(IDE),由谷歌于2025年11月18日在公开预览中推出。 Google Antigravity is not just another code editor — it is the world's first agent-first integrated development environment (IDE), launched by Google in public preview on November 18, 2025. 基于与传统编码助手完全不同的哲学,Antigravity代表了从人工智能辅助编码到人工智能管弦开发的范式转变。 Built on a radically different philosophy from traditional coding assistants, Antigravity represents a paradigm shift from to AI-orchestrated development. AI-assisted coding AI辅助编码 与其提供自动完成的建议或基于聊天的代码片段,Antigravity部署了完全自主的AI代理,他们代表您计划,写作,测试,调试和部署整个软件项目。 阿尔法 Instead of offering autocomplete suggestions or chat-based code snippets, Antigravity deploys fully autonomous AI agents that plan, write, test, debug, and deploy entire software projects on your beh But to focus on coding alone ignores the real power of Antigravity. The real secret that very few know is that Google Antigravity is not just for coding. 这是在一台计算机上可以做的一切。 It is for everything that can be done on a computer. Everything! AI Agents can be used for anything you want! Your Computer is now an Automated AI Agent system with Google Antigravity. To highlight this, I have curated 30 prompts that will save you hours of work. Only 10 prompts out of 30 prompts are for developers! The Three Pillars of Power 1. Browser Integration 反重力具有专用的浏览器子代理,具有Chrome扩展,允许AI代理与网页互动,就像人类一样 - 点击按钮,滚动,键入表单,阅读DOM内容和捕捉屏幕截图。 这允许自动化用户界面测试,网页扫描,表单填写,并通过视觉感知层和可访问性树地图实时调试网页应用程序。 2. Internet Access 与孤立的编码助理不同,反重力代理可以搜索网络,获取文档,阅读URL,并从任何公开可用的来源提取实时数据。 这意味着您的AI代理可以搜索最新的API文档,找到Stack Overflow解决方案,或研究最佳实践 - 所有这些都是在构建您的项目时。 3. Local System Access 反重力代理直接在您的本地文件系统、代码编辑器和终端中工作。 他们可以运行壳命令,执行构建脚本,导航目录,创建和修改文件,并管理项目结构。 通过沙盒环境、终端命令自动执行策略、允许列表和拒绝列表保持安全性 - 敏感操作总是需要明确的用户批准。 为什么这不像任何其他助手 这不是第一次这样做是可能的。 Claude Desktop was released recently, and it is a very good product. But Google Antigravity blows it out of the water with token limits, generous refresh rates, and sheer breadth of capabilities. 关于编码: 传统的助手如GitHub Copilot或ChatGPT提供 你还在写代码。 建议 反重力是a . code orchestrator 你在高层次上描述了你想要的内容,多个AI代理人协作无同步地提供它。 该平台提供A 在那里,您可以同时生成,观察和协调跨不同工作区工作的代理。 Manager View (Mission Control) 代理人生产 实施计划、任务检查列表、屏幕截图、浏览器记录和代码分布 - 这样你总是可以准确地知道做了什么,并在批准之前查看它。 verification artifacts 这将开发人员从代码撰写者转变为 指挥自主代理人。 software architect 谷歌反重力已在 ,并且是 在公共预览期间,对于LLM使用的慷慨率限制。 Windows, macOS, and Linux free for individuals It completely redefines what it means to work on a computer system. But - Everything can be automated! Not just code! 支持谷歌反重力的LLMs Google Antigravity 提供访问 A 目前任何工具中最多样化的LLM系列: multi-model ecosystem Model Provider Strength (High/Low) Gemini 3 Pro Google Primary agent model, planning & execution Gemini 3 Flash Google Fast inference, checkpointing Gemini 3 Deep Think Google Deep reasoning, complex architecture Gemini 2.5 Pro UI Checkpoint Google Browser subagent, UI interactions Gemini 2.5 Flash Google Context summarization Gemini 2.5 Flash Lite Google Codebase semantic search Claude Opus 4.6 (Thinking) Anthropic Advanced planning, large context windows Claude Opus 4.5 (Thinking) Anthropic Debugging, complex code review Claude Sonnet 4.5 (Thinking) Anthropic Balanced speed & quality GPT-OSS-120B OpenAI (Open Source) Open-weight, community-driven tasks (高/低) Gemini 3 Pro 谷歌 主代理模型、规划与执行 Gemini 3 Flash 谷歌 快速推断,检查点 Gemini 3 Deep Think 谷歌 深度推理,复杂的建筑 Gemini 2.5 Pro UI Checkpoint 谷歌 浏览器子代理,UI互动 Gemini 2.5 Flash 谷歌 背景总结 Gemini 2.5 Flash Lite Google 语义搜索代码库 Claude Opus 4.6 (Thinking) Anthropic 先进的规划,大背景窗口 Claude Opus 4.5 (Thinking) 人类 Debugging, complex code review Claude Sonnet 4.5 (Thinking) 人类 平衡速度与质量 GPT-OSS-120B 开源(Open Source) 开放式、社区驱动的任务 Users can manually select models per task, giving unprecedented flexibility. No other product offers this breadth of frontier AI models in a single unified interface. As an Indian, I have free access to a 借助其与 Google AI Pro Jio. Paid Google Gemini was good. 付费谷歌香蕉 Pro 很棒。 But a paid subscription to Google Antigravity is irreplaceable. Turbocharge 反重力: MCP 服务器和开源后端 Connecting MCP Servers 该 is an open protocol by Anthropic that lets Antigravity connect to external tools, databases, and services. To add MCP servers: Model Context Protocol (MCP) Open the → click → → Agent pane ⋯ (more) MCP Servers Manage MCP Servers 点击查看原始配置以打开 mcp_config.json Add your server configuration in JSON format and save Popular MCP servers include ,和 servers. GitHub, Slack, Google Drive, Jupyter, File System SSH Connecting Claude Code with Cloud-Based Open-Source Models GLM-4/5 (Zhipu AI / BigModel API) Zhipu AI provides a high-concurrency API for the GLM series. Use the 或 identifiers for the best performance with coding tasks. glm-4 glm-4-plus And now 也被释放了! glm-5 # 1. Set your Zhipu AI API Key export ANTHROPIC_API_KEY=your_zhipu_api_key # 2. Point Claude Code to the Zhipu OpenAI-compatible endpoint export ANTHROPIC_BASE_URL=https://open.bigmodel.cn/api/paas/v4 # 3. Launch Claude Code (Specify the model if the tool allows) claude model="glm-5" Kimi K2.5 (Moonshot AI API) 使用 Moonshot AI的Kimi模型在长期背景处理和复杂推理方面表现出色,其API完全兼容OpenAI。 # 1. Set your Moonshot API Key export ANTHROPIC_API_KEY=your_moonshot_api_key # 2. Configure the Base URL for Moonshot export ANTHROPIC_BASE_URL=https://api.moonshot.cn/v1 # 3. Launch Claude Code claude model="kimi-latest" DeepSeek R1 (DeepSeek 官方 API) The official DeepSeek API is the most cost-effective way to access the full R1 reasoning model without needing local multi-GPU clusters. # 1. Set your DeepSeek API Key export ANTHROPIC_API_KEY=your_deepseek_api_key # 2. Point to DeepSeek's official API endpoint export ANTHROPIC_BASE_URL=https://api.deepseek.com/v1 # 3. Launch Claude Code with the Reasoning model claude model="deepseek-reasoner" Important Notes on Cloud Integration API 兼容性:大多数这些提供商使用 OpenAI 兼容性的 /v1 附加字符,而 Claude Code 通常在对基点 URL 进行上列时所期望的。 密钥掩盖:确保您的 ANTHROPIC_API_KEY 环境变量包含提供商的密钥;即使变量名称说“人为”,自定义 BASE_URL 将其路由到正确的提供商。 利率限制:云提供商有基于等级的利率限制。对于重量级的编码会话,请在相应的控制台上检查您的信用余额。 This will help you if you are unable to use a paid Google AI Pro subscription! This will help you if you are unable to use a paid Google AI Pro subscription! One more key point before we dive into the prompts. The best LLM in Google Antigravity is not Google Gemini Pro 3.0. The best LLM in Google Antigravity is not Google Gemini Pro 3.0. It is Claude Opus 4.6. It is Claude Opus 4.6. By far! For every prompt in this article, use Claude Opus 4.6 as of today (16th Feb 2026) for best results! For every prompt in this article, use Claude Opus 4.6 as of today (16th Feb 2026) for best results! Before You Begin: 10 Best Practices for Using Google Antigravity Safely 震撼开发者社区的警告故事 在2025年12月,在Antigravity的公开推出不到一个月后,一名开发人员在全球范围内发布了头条新闻。 , , ,和 for the worst possible reason: Tom's Hardware 技术 注册 How-To Geek Google Antigravity's AI agent wiped his entire D: drive — permanently. Google Antigravity's AI agent wiped his entire D: drive — permanently. The developer had issued a routine instruction to clear a project cache. But operating in Turbo mode with aggressive auto-execution settings, the AI agent misinterpreted the scope of the command. Instead of targeting the project folder, it executed a recursive deletion command against the root of the entire drive — and used the /q (quiet) flag, which bypassed all confirmation prompts and the Recycle Bin. 几个月的工作,个人文件,项目存储库 - 几秒钟就消失了。 Months of work, personal files, project repositories — gone in seconds. The AI agent itself then issued an apology, calling it a "critical failure." 这个事件不是传统意义上的错误,它是一个 一个开发者给一个自主代理太多的权力而没有保障。 trust calibration failure Antigravity is extraordinarily capable, but that power demands respect. 以下10个最佳实践,从这一事件和更广泛的社区经验中汲取,将确保您充分利用Antigravity的潜力,而不会冒险您的数据。 The following 10 best practices, drawn from this incident and the broader community experience, will ensure you harness Antigravity's full potential without risking your data. The 10 Best Practices For Using Google Antigravity Turbo mode auto-executes commands without confirmation. Reserve it for sandboxed test projects only — never on drives containing irreplaceable data. Never enable Turbo mode on sensitive directories. Before handing control to an AI agent, create a snapshot. Use Git commits, cloud backups, or disk images. If the agent makes a catastrophic mistake, you can roll back in minutes. Back up before every major session. Always set a specific project directory as your workspace. Never point Antigravity at a drive root ( , ) or a home directory — scope its access as narrowly as possible. Use a dedicated workspace folder. C:\ D:\ Antigravity supports Terminal Command Auto Execution policies. Explicitly deny destructive commands ( , , ) and whitelist only the tools your project actually needs. Configure Allow Lists and Deny Lists. rm -rf del /s /q format Antigravity generates implementation plans, task lists, and diffs before executing. The few minutes spent reviewing can prevent hours of disaster recovery. Always review artifacts before approving. Read them. Before running a complex prompt on your real codebase, test it on a throwaway copy. Validate the agent's behavior in a safe environment before trusting it with production code. Test prompts on dummy projects first. "Clean up this project" is dangerous. "Delete all files in the directory" is safe. The more precise your prompt, the less room the agent has to misinterpret your intent. Be specific, not vague. .pyc src/ For large operations, break your prompt into stages and review after each stage completes. Don't ask the agent to "refactor everything and deploy" in a single prompt. Use incremental approval for multi-step tasks. Always work inside a Git repository with frequent commits. If an agent makes unwanted changes, is your instant undo button. Keep version control active. git reset --hard Watch the terminal output in real time. If you see a command targeting unexpected paths or using unfamiliar flags, using the interrupt function. 📊 Monitor the agent's terminal commands. stop the agent immediately 警惕 Google Antigravity is the most powerful AI tool ever created — and with great power comes great responsibility. Google Antigravity is the most powerful AI tool ever created — and with great power comes great responsibility. These best practices aren't optional. They are the difference between a 10x productivity boost and a career-defining disaster. 信仰地跟随他们。 Follow them religiously. With that, here we go! (Alle, alle, alle) 所有领域的每个专业人士的30个改变游戏的机会 The Developers Section 1:全套API开发与自动测试 - Building a full-stack API means creating the back-end engine that powers websites and apps — the invisible machinery that handles user requests, stores data, and enforces security. - Traditionally, this requires days of manual setup: designing database structures, writing endpoints, configuring authentication, and creating tests. 这个提示要求Antigravity从头开始构建整个系统 - 模型,终端,身份验证,测试,文档和部署 - 一次。 - The result is a production-ready API that would normally take an experienced developer over a week to deliver. Google Antigravity Prompt: Build a production-ready REST API for [your-domain, e.g., e-commerce product catalog] using [framework: e.g., FastAPI/Express.js/Spring Boot]: Architecture: Design a clean MVC/service-repository pattern with dependency injection Models: Create database models for [entities, e.g., products, categories, reviews] with proper relationships, indexes, and validation constraints Endpoints: Implement full CRUD operations with: Pagination (cursor-based) Filtering by [fields, e.g., price range, category] Sorting (multi-field) Rate limiting (100 req/min) Auth: JWT authentication with refresh tokens and role-based access control Testing: Generate comprehensive pytest/jest test suite covering: Unit tests for all service methods Integration tests for each endpoint Edge cases (empty DB, invalid inputs, auth failures) Docs: Auto-generate OpenAPI/Swagger documentation Deploy: Create Dockerfile and docker-compose.yml with PostgreSQL Use [your project root: e.g., C:/Projects/my-api] as the workspace. Why This Is a Game-Changer: Eliminates 8–12 hours of boilerplate setup, architecture decisions, and test writing 确保行业最佳实践(标记页,率限制,RBAC)开发人员经常在时间压力下跳过 Auto-generated tests catch bugs before they reach production Produces deployment-ready code, not prototypes ⏱ Time Saved: ~10 hours manually → 15 minutes with Antigravity 2: Legacy Codebase Refactoring & Modernization 每个软件团队都继承了旧代码 - 代码由离开的人写,使用过时的模式,有隐藏的安全漏洞,没有文件。 重构意味着重组这个代码,使其更清洁,更安全,更可维护,而不会改变它所做的。 这个提示指示Antigravity对整个代码库进行审核,检测问题,现代化依赖,应用最佳实践,添加类型安全,写测试,并记录一切。 - The output is a clean, modern codebase with a detailed before-and-after report — weeks of risky manual work compressed into a single session. Google Antigravity Prompt: Analyze and refactor the legacy codebase at [path, e.g., C:/Projects/legacy-app]: Audit: Scan all files for: Code smells (duplicated logic, god classes, long methods) Security vulnerabilities (SQL injection, XSS, hardcoded secrets) Deprecated API usage and outdated dependencies Dependency Update: Upgrade all packages to latest stable versions, resolve breaking changes Architecture Refactor: Extract repeated logic into shared utility modules Apply SOLID principles to classes exceeding 200 lines Introduce proper error handling with custom exception classes Type Safety: Add TypeScript types / Python type hints to all functions Testing: Write unit tests for all refactored modules achieving >80% coverage Documentation: Generate JSDoc/docstring documentation for all public APIs Report: Create REFACTORING_REPORT.md with before/after metrics Preserve all existing functionality — run existing tests after each change. Why This Is a Game-Changer: 将几周的危险手动重构转化为安全、验证和自动化流程 Catches security vulnerabilities that manual code review frequently misses Preserves backward compatibility by running existing tests continuously Produces a comprehensive report that satisfies management and compliance audits ⏱ Time Saved: 40+ hours manually → 40 minutes with Antigravity 3、自动化CI/CD管道创建 CI/CD(连续集成/连续部署)是构建、测试和交付软件的自动组装线,每次开发人员做出更改。 - Without it, teams manually build, test, and deploy — a slow, error-prone process that leads to broken releases. 这个提示有反重力创建整个管道:构建自动化,测试,安全扫描,阶段部署,生产部署与回归,和基础设施代码。 - The result is a professional-grade software delivery system that catches bugs before users see them and deploys with zero downtime. Google Antigravity Prompt: Create a complete CI/CD pipeline for [repo URL or local path] targeting [platform: GitHub Actions/GitLab CI/Jenkins]: Build Stage: Configure multi-stage build with caching for [language: Node.js/Python/Java] Test Stage: Run unit tests with coverage reporting (minimum 80%) Run linting ([ESLint/Pylint/Checkstyle]) Run security scanning (Snyk/Trivy container scan) Quality Gates: Fail pipeline if coverage drops below threshold or critical vulnerabilities found Staging Deploy: Auto-deploy to [staging environment URL] on PR merge to develop Production Deploy: Deploy to [production environment] on tagged releases with: Blue-green deployment strategy Automatic rollback on health check failure Slack notification on success/failure to [channel] Infrastructure: Generate Terraform/Pulumi IaC for [AWS/GCP/Azure] resources Docs: Create DEPLOYMENT.md with architecture diagram and runbook Test the pipeline locally using act or equivalent tool before committing. Why This Is a Game-Changer: DevOps 管道通常需要几天的时间来正确配置,使用适当的安全扫描和回归。 Integrates security (shift-left) directly into the pipeline from day one Blue-green deployment and auto-rollback eliminate downtime risk Infrastructure-as-Code 确保团队之间可重现的环境 ⏱ Time Saved: 20+ hours manually → 30 minutes with Antigravity 4: Cross-Browser UI Testing Suite - When you visit a website, it should look and work the same whether you're using Chrome, Firefox, Safari, or a phone — but it often doesn't. 用户界面测试意味着在不同的浏览器和屏幕尺寸上自动检查每个页面,表单和按钮,以捕捉视觉错误。 - This prompt tells Antigravity to set up a complete testing framework that verifies user flows, checks visual appearance, measures performance, and ensures accessibility compliance. - The result is a safety net that catches broken layouts and usability issues before real users encounter them. Google Antigravity Prompt: Create an end-to-end browser testing suite for the web application at [URL or local path]: Framework Setup: Initialize [Playwright/Cypress] with TypeScript support Test Scenarios: Write automated tests for: User registration and login flow Navigation through all main pages ([list pages]) Form validation (required fields, email format, password strength) Shopping cart / core workflow ([describe primary user journey]) Responsive design at 320px, 768px, 1024px, 1440px breakpoints Visual Regression: Capture baseline screenshots for all pages, configure visual diff threshold at 0.1% Performance: Add Lighthouse CI checks for Core Web Vitals (LCP < 2.5s, CLS < 0.1) Accessibility: Run axe-core accessibility audit, fail on WCAG 2.1 AA violations Reporting: Generate HTML test report with screenshots of failures CI Integration: Add test run to [CI platform] pipeline config Use Antigravity's browser subagent to manually verify each critical flow after test creation. Why This Is a Game-Changer: Comprehensive browser testing is the most neglected quality practice — this automates it completely Visual regression catches CSS breakages that unit tests cannot detect Accessibility compliance (WCAG 2.1 AA) is legally required in many jurisdictions Antigravity's browser subagent can physically verify the tests work correctly ⏱ Time Saved: 30+ hours manually → 45 minutes with Antigravity 5:微服务架构设计 & Scaffolding - Microservices is a way of building large applications by splitting them into small, independent services that each handle one specific task — like separate departments in a company 这种方法使系统更容易扩展,更新和维护,但从头开始设计是一个复杂和耗时的系统。 - This prompt asks Antigravity to design the entire architecture, create each service, set up communication between them, configure an API gateway, and orchestrate everything with Docker 结果是一个完全分布式的分布式系统,通常需要数周的建筑规划和安装 Google Antigravity Prompt: Design and scaffold a microservices architecture for [application description, e.g., food delivery platform]: Domain Analysis: Identify bounded contexts and create a domain model diagram in Mermaid Services: Scaffold these microservices in [language]: [Service 1, e.g., User Service] — auth, profiles [Service 2, e.g., Order Service] — order lifecycle [Service 3, e.g., Payment Service] — Stripe integration [Service 4, e.g., Notification Service] — email/SMS via [provider] Communication: Implement async messaging via [RabbitMQ/Kafka] with event schemas API Gateway: Configure [Kong/Traefik] with routing, rate limiting, and JWT validation Database: Each service gets its own [PostgreSQL/MongoDB] instance — no shared DBs Observability: Add structured logging, Prometheus metrics, and distributed tracing (Jaeger) Docker: Create docker-compose.yml orchestrating all services, databases, message broker, and gateway Place all services under [workspace path] with a shared proto/schema directory. Why This Is a Game-Changer: Microservices architecture decisions (communication patterns, database per service) take weeks of design meetings Event-driven communication with proper schemas prevents integration nightmares Built-in observability stack means production debugging from day one Docker compose enables local development of the entire system on a single machine ⏱ Time Saved: 60 hours manually → 2 hours with Antigravity The Corporate Section - In the corporate world, success depends on how quickly you can turn raw information into clear decisions, professional reports, and organized action plans. - These prompts are designed to handle the "knowledge management" burden that slows down every office, from drafting quarterly reports to tracking meeting outcomes across large teams. 我们专注于自动化竞争情报,简化新员工入职,并通过实时监管研究进行复杂的政策合规性审计。 - Using these tools, a single employee can perform the work of an entire administrative department, ensuring that nothing is forgotten and every decision is backed by data. 6: Automated Business Report Generation - A quarterly business report transforms raw data — sales numbers, customer feedback, web analytics — into a polished document that executives use to make decisions - Creating one manually means hours of spreadsheet work, chart formatting, and insight writing - This prompt instructs Antigravity to read your data files, perform calculations, generate charts, write an executive summary with actionable recommendations, and produce a branded, navigable HTML report - The result is a presentation-ready report with data-driven insights that would typically consume two to three full workdays of analyst time Google Antigravity Prompt: Generate a comprehensive quarterly business report from raw data: Data Sources: Read and analyze: Sales data from [file path, e.g., C:/Reports/Q4_sales.csv] Customer feedback from [file path, e.g., C:/Reports/feedback.xlsx] Web analytics from [URL: e.g., Google Analytics export] Analysis: Calculate and present: Revenue by product line, region, and channel (tables + charts) Month-over-month growth rates with trend indicators Customer satisfaction scores (NPS) with sentiment breakdown Top 5 performing and bottom 5 underperforming products Insights: Generate executive summary (300 words) highlighting: Key wins and areas of concern Actionable recommendations (minimum 5) Competitive benchmarking insights from [competitor URLs] Formatting: Output as polished Markdown + export-ready HTML with: Company branding colors ([hex codes]) Professional charts using Chart.js Table of contents with clickable navigation Save to [output path] as Q4_Report.html and Q4_Report.md Why This Is a Game-Changer: 季度报告每周消耗2～3个完整工作日分析师时间 Automated competitive benchmarking via web access provides real-time market context 一致的格式化 eliminates back-and-forward with management on presentation quality Data-driven recommendations replace gut-feel analysis with quantified insights ⏱ Time Saved: 16+ hours manually → 20 minutes with Antigravity 提示 7:会议日程表和行动项目追踪器 - After every meeting, someone must write up what was discussed, what was decided, and who is responsible for what — and then follow up to make sure it actually gets done. 这种行政工作是无聊的,但却是至关重要的;没有它,决策就会被遗忘,任务就会陷入裂缝。 这个提示程序有反重力处理会议转录,提取结构化的分钟,创建具有所有者和截止日期的行动项目表,草案后续电子邮件,并生成日历邀请。 - The result is a complete accountability system that turns every meeting into tracked, actionable outcomes. Google Antigravity Prompt: Process meeting recordings/transcripts and create structured documentation: Input: Read the meeting transcript from [file path, e.g., C:/Meetings/standup_2026-02-16.txt] Structure: Extract and organize into: Attendees: List all participants mentioned Agenda Items: Numbered list of topics discussed Key Decisions: Bulleted list with rationale for each decision Action Items: Table with columns: Task | Owner | Deadline | Priority (High/Med/Low) Open Questions: Unresolved items requiring follow-up Follow-up Email: Draft a professional follow-up email summarizing decisions and action items, addressed to [team email/distribution list] Tracking: Create/update ACTION_TRACKER.md at [path] — append new items, mark previously completed items Calendar: Generate .ics calendar invites for action items with deadlines Format: Use Markdown with emoji indicators (✅ done, 🔴 overdue, 🟡 in progress) Cross-reference with previous meeting notes at [directory] to identify recurring unresolved items. Why This Is a Game-Changer: 会议追踪是生产率的第1个黑洞 - 行动项目在几个小时内丢失 Automatic cross-referencing with previous meetings surfaces chronic blockers Calendar invite generation ensures deadlines are visible in everyone's workflow 持久的行动追踪器可随着时间的推移创造组织责任 ⏱ Time Saved: 3+ hours per meeting manually → 5 minutes with Antigravity 8: Competitive Intelligence Dashboard - Competitive intelligence means systematically researching your competitors — their products, pricing, strategies, and market positioning — to inform your own business decisions. - Most companies either pay expensive consultants or spend weeks doing this manually across dozens of websites. 这个提示告诉Antigravity访问竞争对手的网站,提取产品和定价数据,分析技术堆栈,构建比较矩阵,进行SWOT分析,并提出战略建议。 结果是一份全面的来源情报报告,可在时间和成本的一小部分内实现数据驱动的战略规划。 Google Antigravity Prompt: Research competitors and build a competitive intelligence report: Competitors: Research these companies via their websites: [Competitor 1 URL] [Competitor 2 URL] [Competitor 3 URL] Data Collection: For each competitor, extract: Product/service offerings and pricing tiers Recent news, press releases, and blog announcements Technology stack (use [BuiltWith URL] or similar) Social media presence and engagement metrics from [social URLs] Analysis Framework: Create comparison matrices for: Feature parity (what they have that we don't, and vice versa) Pricing comparison (normalized per user/month) Market positioning (value vs. premium vs. budget) SWOT Analysis: Generate a SWOT table for our position relative to each competitor Recommendations: Provide 10 strategic recommendations prioritized by impact and effort Output: Save as Competitive_Intel_Feb2026.md at [output path] Include all source URLs as footnote references for verification. Why This Is a Game-Changer: Competitive analysis normally requires expensive subscriptions (Gartner, CB Insights) or weeks of manual research Antigravity's internet access enables real-time data collection from competitor websites 结构化的SWOT和功能矩阵提供可立即采取行动的战略输入 Footnoted sources ensure every data point is verifiable by leadership ⏱ Time Saved: 24+ hours manually → 30 minutes with Antigravity 9: Employee Onboarding Documentation System - When a new employee joins a company, they need clear guides on setting up their tools, understanding the team structure, learning internal terminology, and knowing who to ask for help. 虚弱的登机文档意味着每一次新招聘都出现了几周的混乱和生产力损失。 - This prompt asks Antigravity to analyze existing documentation, generate a day-by-day onboarding guide, create technical setup instructions with copy-pasteable commands, compile a FAQ from real questions, and build a glossary. - The result is a professional onboarding system that gets new employees productive in days instead of weeks. Google Antigravity Prompt: Create a comprehensive employee onboarding documentation system: Analyze: Read existing documentation at [docs directory path] and company wiki at [wiki URL] Onboarding Guide: Generate ONBOARDING.md covering: Day 1 checklist (accounts, tools, introductions) Week 1 learning path with links to internal resources 30-60-90 day milestones with measurable objectives Team structure diagram (Mermaid org chart) Key contacts table (Name | Role | Slack | Email) Technical Setup: Create DEV_SETUP.md with: Step-by-step environment setup for [OS: Windows/Mac/Linux] Required tools installation commands (copy-pasteable) Repository clone and configuration instructions Verification checklist (all green = ready to contribute) FAQ: Compile ONBOARDING_FAQ.md from common questions found in [Slack channel URL/export] Glossary: Create COMPANY_GLOSSARY.md of internal terminology Save all files to [output directory] Ensure all links are valid by checking each URL. Why This Is a Game-Changer: New hire onboarding typically takes 2–4 weeks to become productive; this cuts it to days 链接验证确保文档保持新鲜,不会指向死页 可复制粘贴的设置命令消除“它在我的机器上工作”问题 从真实的Slack问题中生成常见问题解决了实际的疼痛点,而不是假设的疼痛点 ⏱ Time Saved: 30+ hours manually → 45 minutes with Antigravity Prompt 10: Policy Compliance Audit & Gap Analysis - Compliance means ensuring your organization follows legally required standards like GDPR (data privacy), SOC2 (security), or HIPAA (healthcare data) — failure can result in massive fines. - Auditing your current policies against these standards is expensive, time-consuming, and requires specialized knowledge. - This prompt has Antigravity read your internal policies, research current regulatory requirements from official sources, identify gaps in a structured compliance matrix, assess risk levels, and generate a phased remediation roadmap. - The result is a comprehensive audit with a clear action plan that would normally cost tens of thousands in consultant fees. Google Antigravity Prompt: Perform a compliance audit of internal policies against regulatory standards: Input Documents: Read all policy files from [policies directory path] Regulatory Framework: Research current requirements for [standard: GDPR/SOC2/HIPAA/ISO 27001] from official sources: [Regulatory body URL, e.g., gdpr.eu] [Framework documentation URL] Gap Analysis: Create compliance matrix (table format): Requirement ID | Description | Current Status (✅ Compliant / ⚠️ Partial / ❌ Non-Compliant) | Evidence | Remediation Action Risk Assessment: For each gap: Severity (Critical/High/Medium/Low) Potential financial/legal impact Estimated remediation effort (hours) Priority ranking Remediation Plan: Generate COMPLIANCE_ROADMAP.md with: Phased timeline (30/60/90 days) Resource requirements per phase Template policy documents for critical gaps Executive Summary: One-page brief for C-suite with RAG status indicators Save all outputs to [compliance audit directory]. Why This Is a Game-Changer: Compliance audits cost $50K–$200K with external consultants — this provides a comprehensive first pass Real-time regulatory research ensures the audit reflects the latest requirements Phased remediation roadmap gives management a clear action plan with resource needs 模板策略文档加速关闭差距,而不是从零开始 ⏱ Time Saved: 80+ hours (consultant engagement) → 2 hours with Antigravity The Researcher Section - Scientific research is a race between great ideas and the overwhelming volume of existing data that must be searched, cleaned, and analyzed to prove those ideas correct. - These prompts transform the "literature review" and "data analysis" phases of research from manual months-long chores into fast, automated, and statistically rigorous processes. - We provide workflows for conducting systematic reviews, building reproducible analysis pipelines, drafting grant proposals, and ensuring your experiments meet global standards. - This section is for the scientist who wants to spend less time formatting citations and cleaning CSV files, and more time discovering new knowledge and interpreting results. 11: Systematic Literature Review Automation 系统的文献审查涉及搜索数千篇学术论文,以找到,分析和合成有关特定研究主题所写的一切。 - For a human researcher, this process typically takes three to six months of painstaking searching, reading, and organizing - This prompt directs Antigravity to search major academic databases, select papers based on strict criteria, extract key findings and methodologies, and generate a structured review with a gap analysis - The result is a comprehensive literature map and initial draft that accelerates the research process by months Google Antigravity Prompt: Conduct a systematic literature review on [research topic, e.g., "transformer architectures for time series forecasting"]: Search: Query these academic sources via web: [Google Scholar URL with search query] [arXiv URL with search filters] [Semantic Scholar URL] Selection Criteria: Include papers that: Published between [start year] and [end year] Minimum [N] citations (for older papers) or from top-tier venues Directly address [specific sub-topic or methodology] Extraction: For each paper, record: Title, Authors, Venue, Year, Citation Count Research question and hypothesis Methodology summary (3-4 sentences) Key findings and limitations Datasets and metrics used Synthesis: Create: Taxonomy of approaches (Mermaid diagram) Comparison table (Method | Dataset | Metric | Result) Research gap analysis identifying under-explored areas Recommended future research directions (minimum 5) Output: Save as Literature_Review_[topic].md at [path] Format all citations in [APA/IEEE/Chicago] style. Why This Is a Game-Changer: 系统的文献评论需要3～6个月的手动时间;这在几个小时内产生了一个全面的第一草案。 Web access enables real-time search across multiple academic databases simultaneously Structured extraction ensures no paper is reviewed superficially or inconsistently Gap analysis accelerates the transition from review to original research contribution ⏱ Time Saved: 200 hours manually → 2 hours with Antigravity 12:研究数据分析管道 - Research data analysis involves cleaning raw experimental data, performing statistical tests to see if results are significant, and creating clear charts for publication. 手动做这件事需要深厚的编码和统计专业知识,即使是小错误也会导致无效的科学结论。 - This prompt has Antigravity build a full analysis pipeline: it handles missing data, runs the correct statistical tests based on data distribution, and produces high-quality visualizations. - The result is a reproducible and statistically rigorous analysis that meets the highest standards for scientific publication. Google Antigravity Prompt: Build a reproducible data analysis pipeline for my research dataset: Data Loading: Read dataset from [file path, e.g., C:/Research/experiment_data.csv] Preprocessing: Create Python script that: Handles missing values (strategy: [mean imputation/KNN/drop]) Detects and treats outliers using IQR method Normalizes features using [StandardScaler/MinMax] Encodes categorical variables (one-hot / label encoding) Exploratory Analysis: Generate: Descriptive statistics summary table Correlation heatmap (Seaborn, publication quality) Distribution plots for all numeric variables Class imbalance analysis (if classification task) Statistical Tests: Run appropriate tests: Normality test (Shapiro-Wilk) [t-test/ANOVA/chi-square/Mann-Whitney] for hypothesis testing Effect size calculations (Cohen's d / Cramér's V) p-value corrections for multiple comparisons (Bonferroni) Output: Save all plots as 300 DPI PNGs, results as analysis_results.md Reproducibility: Create requirements.txt and README.md with run instructions All code must be PEP 8 compliant with comprehensive docstrings. Why This Is a Game-Changer: 确保许多研究人员忽略的统计严格性(效果大小,多个比较纠正) Publication-quality visualizations eliminate manual Matplotlib formatting struggles 完整的可复制性包(requirements.txt,README)符合开放科学标准 自动正常性测试选择正确的统计测试,防止方法错误 ⏱ Time Saved: 25 hours manually → 30 minutes with Antigravity 13: Grant Proposal Draft Generator - A research grant proposal is a detailed document explaining why a project deserves funding, how it will be conducted, how much it will cost, and what its impact will be. 写一份获胜的提议非常具有竞争力,通常需要研究人员工作时间的40%。 这个提示指示Antigravity研究具体的资助者的需求,将项目目标与这些优先事项一致,拟定所有所需的部分,创建时间表,并证明预算。 结果是战略一致的提案草案,最大限度地确保关键研究资金的机会。 Google Antigravity Prompt: Draft a research grant proposal for [funding body, e.g., NSF/ERC/NIH]: Research: Visit the funding body website at [URL] to extract: Current funding priorities and themes Eligibility criteria and deadlines Evaluation criteria and scoring rubric Maximum budget and duration Proposal Sections: Generate drafts for: Abstract (250 words): Concise problem-solution-impact summary Introduction: Background, gap in knowledge, research questions Literature Context: Key references supporting the proposal (cite 15–20 works) Methodology: Detailed research design, data collection, analysis plan Timeline: Gantt chart (Mermaid) with milestones across [duration] Budget Justification: Itemized budget table with line-by-line rationale Impact Statement: Societal relevance, knowledge contribution, dissemination plan Alignment: Map each section to the funder's evaluation criteria Formatting: Follow [funder's template/format guidelines] Save to [output path] as Grant_Proposal_[funder]_[topic].md Research topic: [your research topic and objectives]. Why This Is a Game-Changer: 授权写作消耗了研究人员的时间的20%至40%,成功率只有15%至25%。 实时访问资助者网站确保与最新优先事项和标准保持一致 Evaluation criteria mapping maximizes scoring potential on every section Budget justification templates prevent the #1 reason for administrative rejection ⏱ Time Saved: 60 hours manually → 2 hours with Antigravity 14: Experiment Reproducibility Package - Reproducibility is the cornerstone of science; it means another researcher should be able to run your experiment and get the exact same results. 许多研究项目由于缺乏依赖性、硬代码设置或未经文档的数据处理步骤而失败。 - This prompt tells Antigravity to audit your experiment code, create a standardized environment (Docker), automate the data processing, and provide a one-command reproduction script. - The result is a "science-ready" package that ensures your work is trusted, verified, and can be built upon by the global scientific community. Google Antigravity Prompt: Create a complete reproducibility package for my ML experiment: Code Audit: Review the experiment code at [repo/directory path] for: Hardcoded random seeds (ensure all are set and documented) Non-deterministic operations (flag and fix or document) Missing dependency version pins Environment: Generate: environment.yml (Conda) with exact version pins Dockerfile for containerized reproduction Makefile with targets: setup, train, evaluate, visualize Data Pipeline: Create: data/README.md with dataset source, license, and download instructions Checksum verification script for data integrity Preprocessing script that transforms raw → processed data deterministically Experiment Config: Create configs/ directory with: Hyperparameter YAML files for each experiment variant Ablation study configurations Results Verification: Script that re-runs experiment and compares outputs against stored baseline results (tolerance: [threshold]) Documentation: REPRODUCE.md with one-command reproduction instructions Verify the package by running the full pipeline end-to-end. Why This Is a Game-Changer: The reproducibility crisis affects 70%+ of ML research — this solves it systematically Docker集装箱化消除不同机器和操作系统版本的“依赖地狱” Checksum verification ensures data integrity — a step most researchers skip entirely 单命令播放(通过Makefile)允许评论员在几分钟内验证结果,而不是几天 ⏱ Time Saved: 20 hours manually → 1 hour with Antigravity 15:研究论文草案与格式化 - Writing a research paper requires following incredibly strict formatting rules that differ for every scientific journal and conference. 一个论文可以被拒绝,只是因为它有错误的字体大小,引用风格或页面布局,即使研究本身是突破性的。 - This prompt asks Antigravity to research specific venue guidelines, organize your notes and results into a structured draft, and apply the exact 100% required formatting. 结果是准备提交的专业论文草案,防止“桌面拒绝”,让您专注于科学而不是边缘。 Google Antigravity Prompt: Draft a research paper following [venue: e.g., IEEE/ACM/Nature] formatting guidelines: Template: Research the submission guidelines at [conference/journal URL] and create LaTeX/Markdown template matching requirements Content Input: Read: Experiment results from [results directory path] Literature review from [literature review file path] Methodology notes from [notes file path] Sections: Draft: Title: 3 alternative titles optimized for discoverability Abstract: Structured (Background-Methods-Results-Conclusion), 250 words Introduction: Problem motivation → gap → contribution → paper structure Related Work: Organized by theme, positioning our contribution clearly Methodology: Formal description with equations and algorithm pseudocode Results: Tables and figure captions for all experiment outcomes Discussion: Findings interpretation, limitations, and threats to validity Conclusion: Key contributions, implications, future work References: Format all citations in [required style] with DOI links Checklist: Create submission checklist from venue guidelines Save to [output path] as paper_draft_v1.md. Why This Is a Game-Changer: Paper writing takes 2–6 months; Antigravity generates a structured first draft in hours Venue-specific formatting compliance prevents desk rejection (which wastes months of review time) Structured abstract and clear contribution positioning improve acceptance odds significantly 提交检查清单确保没有额外的材料或格式化要求被错过 ⏱ Time Saved: 100 hours manually → 2 hours with Antigravity 学生部分 - Education is not just about what you know, but how effectively you can manage your time, organize your study materials, and prepare for high-stakes assessments. - These prompts act as a private AI tutor and project manager, helping you build personalized study plans based on your own weak areas and scientifically proven memorization techniques. 我们涵盖了整个学术旅程,从构建一个100页的论文和构建交互式教程到生成现实的实践考试和协调复杂的团队项目。 - Whether you are an undergraduate or a PhD candidate, these tools ensure that you spend your energy on deep learning rather than logistical frustration. 16: Personalized Study Plan Generator - A personalized study plan is a roadmap that breaks down a complex subject into manageable daily tasks, focusing on exactly what you need to learn most. - Most students study "linearly," which is inefficient; scientific evidence shows that "spaced repetition" (reviewing at specific intervals) is 3x more effective. 这个提示有反重力分析你的课程,识别你的弱点,并生成一个自定义的时间表与内置的审查会议和策划的免费学习资源。 结果是科学优化的学习系统,最大限度地提高您的记忆力,并最大限度地减少浪费的时间。 Google Antigravity Prompt: Create a comprehensive, personalized study plan for [subject/course, e.g., Data Structures and Algorithms]: Syllabus Analysis: Read the course syllabus from [file path or URL] Assessment: Based on my self-assessment ([list weak topics, e.g., graph algorithms, dynamic programming]), prioritize topics accordingly Schedule: Generate a [N-week] study plan with: Daily study sessions (2-hour blocks) Spaced repetition review days (Days 3, 7, 14, 30 after initial study) Practice problem sets (5 easy → 5 medium → 3 hard per topic) Weekly mock test dates Resources: Research and link to: Free online courses ([platforms: Coursera/MIT OCW/Khan Academy]) YouTube tutorials for each difficult topic Practice platforms ([LeetCode/HackerRank] problem links by topic) Textbook chapter references from [textbook name] Tracking: Create STUDY_TRACKER.md with checkboxes for each session Flashcards: Generate Anki-compatible flashcards (Q&A format) for key concepts Save all files to [study directory path]. Why This Is a Game-Changer: 基于弱点的个性化计划比通用语法更有效3倍 Spaced repetition scheduling is scientifically proven but rarely implemented manually 编制的免费资源(通过网络搜索)消除搜索高质量材料的时间 Anki flashcards enable mobile study and leverage the most effective memorization technique ⏱ Time Saved: 8 hours planning manually → 10 minutes with Antigravity 17:论文 / 论文结构和文学绘制 - A thesis or dissertation is the biggest project most students will ever lead, and starting can be paralyzing due to the sheer volume of research and writing required. - A "literature map" is a visual way to see how different research papers connect, helping you find your own unique "gap" to write about. 这个提示指示Antigravity来精简你的研究问题,绘制你的领域的种子论文,并创建一个详细的章节对章的概述与单词数。 结果是一个具体的结构框架,将一个可怕的100页项目变成一系列可实现的里程碑。 Google Antigravity Prompt: Help me structure my thesis on [topic, e.g., "Impact of Social Media on Political Polarization"]: Research Question Refinement: Based on my initial idea, suggest: 1 primary research question (specific, measurable) 3 secondary research questions Null and alternative hypotheses Literature Mapping: Search [Google Scholar/JSTOR/PubMed URL] for: 20 seminal papers in the field (sorted by citation count) 10 recent papers (last 2 years) showing current trends 5 contrarian/critical papers offering opposing viewpoints Create a literature concept map (Mermaid diagram) Thesis Structure: Generate chapter outline: Chapter titles with 3-5 sub-section headings each Estimated word count per chapter (totaling [target word count]) Key arguments to develop in each chapter Methodology Planning: Recommend: Research design (qualitative/quantitative/mixed) Data collection methods appropriate for my research questions Analysis framework with justification Timeline: Create a Gantt chart for [duration: e.g., 12 months] Save as THESIS_PLAN.md at [output path]. Why This Is a Game-Changer: 论文规划是最引发焦虑的阶段 - 这提供了具体的,可操作的结构 跨多个数据库的文献映射表面文件,关键字搜索错过 Including contrarian papers demonstrates critical thinking (a key examiner criterion) 单词计数分配防止过写方法和过写分析的常见陷阱 ⏱ Time Saved: 40 hours manually → 1 hour with Antigravity 18:交互式编码教程构建器 - Learning to code is best done through practice, but finding tutorials that are neither too easy nor too hard is difficult. 一个“互动”的教程包括解释,代码示例,以及自动检查正确性的练习。 这个提示是Antigravity为您构建一个定制的10课编码课程,完成了工作示例,指导练习和“自动分级器”,即时测试您的代码。 结果是一个私人,自助的编码课堂,捕捉你的错误,并提供即时反馈,就像人类导师一样。 Google Antigravity Prompt: Create an interactive coding tutorial for [programming topic, e.g., "Python Object-Oriented Programming"]: Structure: Design 10 progressive lessons: Each lesson: Concept explanation (200 words) → Code example → Exercise → Solution Difficulty progression: Beginner (1-3) → Intermediate (4-7) → Advanced (8-10) Code Examples: For each lesson, create: Working demonstration code in [language] Annotated comments explaining every non-obvious line Common mistake examples with explanations of why they fail Exercises: Generate per lesson: 1 guided exercise (with hints) 1 independent exercise (solution hidden) 1 challenge problem connecting to real-world applications Auto-Grading: Write unit test files for each exercise that students can run to verify their solutions Cheat Sheet: Create a one-page reference card for all concepts covered Prerequisites: List required knowledge with links to prerequisite tutorials Save complete tutorial to [tutorial directory path] with clear folder structure: lesson-01/, lesson-02/, etc., each containing README.md, code files, and test files. Why This Is a Game-Changer: 自动评级的自动步骤教程提供即时反馈 - 没有等待TAs 与现实世界背景的进步困难保持动机并展示相关性 Common mistake examples teach debugging skills, not just correct patterns Unit test verification teaches Test-Driven Development (TDD) as a bonus skill ⏱ Time Saved: 15+ hours creating manually → 20 minutes with Antigravity 19:考试准备和实践测试生成器 - Practice tests are the single most effective way to prepare for exams because they force you to recall information rather than just re-reading notes. 创建自己的实践问题是耗时的,往往缺乏真正的考试的难度校准。 这个提示告诉Antigravity阅读你的课程,研究你的大学的过去考试论文,并生成三个模糊的考试的难度增加与完整的模型答案。 结果是高压的“训练地面”,确保你在考试当天从不被问题所惊讶。 Google Antigravity Prompt: Generate comprehensive exam preparation materials for [subject, e.g., "Operating Systems"]: Syllabus: Read the exam syllabus/topics from [file path or URL] Study Notes: For each topic, create concise revision notes: Key definitions and concepts (bullet points) Important formulas/algorithms with worked examples Comparison tables (e.g., process vs thread, paging vs segmentation) Mnemonic devices for memorization-heavy topics Practice Tests: Generate 3 mock exams: Mock 1: 30 MCQs covering all topics (with answer key and explanations) Mock 2: 10 short-answer questions (model answers provided) Mock 3: 5 long-form essay/problem-solving questions mimicking real exam format Difficulty Calibration: Research past exam papers from [university/course URL] to match difficulty level Weak Area Drill: Extra 20 practice questions targeting my weak topics: [list topics] Quick Reference: Create a printable 2-page cheat sheet (if open-book) or key-term glossary Save to [exam prep directory] organized by topic. Why This Is a Game-Changer: 针对特定考试校准的自定义实践测试比通用问卷银行更有效5倍 通过网络研究过去的论文确保难度水平匹配实际考试 目标弱区钻探利用帕雷托原则最大限度地提高研究效率(80/20规则) 多种问题格式(MCQ、简短答案、论文)准备任何考试结构 ⏱ Time Saved: 12+ hours manually → 15 minutes with Antigravity 20: Group Project Coordination Hub 群体项目往往会失败,不是因为编码或写作不好,而是因为沟通不好,有人错过了截止日期,或者两个人做同一项任务两次。 协调中心是一个中央的文件和模板,确保每个团队成员都知道他们负责什么。 这个提示要求Antigravity将您的项目分解为任务,公平分配它们,创建共享时间表(甘特图),并设置标准化报告模板。 结果是“自动化项目经理”,让团队保持组织,负责,并专注于评级领域。 Google Antigravity Prompt: Set up a complete coordination system for our group project on [project topic]: Project Charter: Create PROJECT_CHARTER.md with: Project title, objective, and scope Team members: [names] with assigned roles Communication plan (meeting schedule, platform: [Slack/Discord/WhatsApp]) Grading criteria from [rubric file/URL] Task Breakdown: Create TASKS.md with: Work Breakdown Structure (WBS) in tree format Task assignments (balanced workload per member) Dependencies between tasks (Mermaid Gantt chart) Due dates aligned with submission deadline: [date] Templates: Generate standardized templates for: Individual progress report (weekly) Meeting minutes (with action items table) Peer evaluation form Repository Structure: Initialize project folder at [path] with: Organized directories: docs/, src/, data/, presentations/ .gitignore for [language/framework] README.md with project overview and setup instructions Integration Checklist: Create INTEGRATION.md for combining individual contributions Ensure workload is equitably distributed across [N] team members. Why This Is a Game-Changer: 群组项目往往由于协调而失败,而不是能力。 依赖性甘特图预防一个成员阻止另一个成员的瓶颈 Standardized templates ensure consistent quality across all members' contributions 从一开始就构建的同行评估表格鼓励全方位的问责 ⏱ Time Saved: 10+ hours manually → 15 minutes with Antigravity AI工程师部 对于那些构建下一代人工智能技术的人来说,挑战不再仅仅是“让它工作”,而是让它快速、安全、成本效益和准确。 这些提示器为当今最先进的AI工作流程提供工业级的支架:RAG评估,多代理管弦和本地模型精细调。 我们专注于人工智能的“工程”方面 - 构建监控堆栈,执行严格的模型基准,并部署可以扩展到数千名用户的系统。 这个部分是针对专业的人工智能工程师,他们需要超越“聊天机器人”并为企业环境构建可靠、可验证的自主系统。 提示21:ML模型基准和比较框架 Benchmarking 是指在不同的AI模型(如 Gemini、Claude 和 GPT)上运行相同的“测试”,看看哪一个模型实际上更智能、更快或更便宜。 随着每个月推出数十款新车型,公司通常会浪费数千美元使用“过度杀死”或仅仅不足以满足其需求的车型。 这个提示有抗重力构建一个完整的框架,测试模型在推理,编码和安全,同时跟踪每个响应的确切成本和速度。 结果是基于数据的建议,确保您为您的项目选择合适的AI“大脑”,而无需猜测。 Google Antigravity Prompt: Build a comprehensive ML model benchmarking framework: Models: Set up comparison for [model list, e.g., GPT-4, Claude 3.5, Gemini 2.0, Llama 3.1, Mistral Large] Benchmark Suite: Implement evaluations for: Reasoning: GSM8K, ARC-Challenge, HellaSwag Coding: HumanEval, MBPP, SWE-bench Knowledge: MMLU, TruthfulQA Safety: ToxiGen, BBQ bias benchmark Infrastructure: Create Python framework with: Unified API interface (adapter pattern) for all model providers Async batch inference for throughput measurement Token-level cost calculation per benchmark Latency tracking (TTFT, tokens/sec) Analysis: Generate: Radar charts comparing models across dimensions Cost-performance Pareto frontier visualization Statistical significance tests (paired t-test) between model pairs Confidence intervals for all metrics Report: Create BENCHMARK_REPORT.md with publication-quality tables and charts Reproducibility: Include all configs, seeds, and exact prompts used Run against [API keys config path] and save results to [output directory]. Why This Is a Game-Changer: 选择模型的决定会让公司花费10万美元 – 错误的API花费100万美元 – 基准测试阻止了这一点 Pareto成本绩效分析确定了质量和预算之间的甜点 Statistical significance testing prevents choosing models based on noise rather than real differences 统一的适配器模式使框架可以随着新型号的月度推出而扩展 ⏱ Time Saved: 50+ hours manually → 2 hours with Antigravity 22:RAG管道与评估链接 - RAG (Retrieval-Augmented Generation) is a technique that gives AI a "private library" (your documents) to read from so it can answer questions accurately without making things up. - A major problem with RAG is "hallucination"—when the AI sounds confident but is actually wrong—and evaluating how often this happens is notoriously difficult. - This prompt instructs Antigravity to build a production-grade RAG system that includes an "evaluation harness" to quantitatively measure its accuracy and faithfulness. 结果是一个值得信赖的AI助理,引用其来源并承认,当它不知道答案时,由硬性能指标支持。 Google Antigravity Prompt: Build a production-grade Retrieval-Augmented Generation (RAG) pipeline with evaluation: Data Ingestion: Process documents from [data directory path]: Support PDF, DOCX, Markdown, HTML formats Chunking strategy: recursive character splitting (chunk size: 512, overlap: 50) Extract metadata (source, page, section headings) Vector Store: Set up [ChromaDB/Pinecone/Weaviate] with: Embedding model: [text-embedding-3-large / all-MiniLM-L6-v2] Hybrid search (dense + sparse BM25) with reciprocal rank fusion Metadata filtering capabilities Retrieval: Implement: Query expansion (HyDE: Hypothetical Document Embeddings) Re-ranking with cross-encoder ([model name]) Context window optimization (stuff vs. map-reduce strategies) Generation: Configure [LLM] with custom system prompt enforcing: Source attribution (cite chunk IDs) Hallucination guardrails ("I don't know" when confidence < threshold) Evaluation: Implement RAGAS metrics: Faithfulness, Answer Relevancy, Context Precision, Context Recall Create 50 golden Q&A pairs for evaluation API: Wrap in FastAPI with streaming response support Save to [project directory]. Why This Is a Game-Changer: RAG管道是企业AI的第1用例,但大多数部署缺乏适当的评估 混合搜索(密度 + BM25)与重新排名优于简单的矢量搜索15-30% RAGAS evaluation framework provides quantitative quality metrics instead of "vibes-based" testing 具有源分配的幻觉屏幕对于企业信任和合规性至关重要 40小时以上手动 → 2小时与反重力 ⏱ Time Saved: 23:精细调节管道与实验跟踪 精细调节就像“将人工智能发送到专门的学校”,这样它就可以学会用特定的风格说话,掌握独特的数据集,或遵循复杂的格式规则。 这个过程以前需要大量的超级计算机,但现代技术现在允许我们在单一的高端消费机上做到这一点。 这个提示要求Antigravity设置一个完整的精密调节管道,清理您的数据,高效地训练模型,并记录每个实验,以便您找到“最佳”版本。 - The result is a custom-trained AI that deeply understands your specific domain, ready to be deployed locally at a fraction of the cost of large cloud models. Google Antigravity Prompt: Create an end-to-end LLM fine-tuning pipeline: Data Preparation: Process training data from [data file/directory]: Convert to instruction-following format (system/user/assistant) Train/validation/test split (80/10/10) with stratification Data quality checks (deduplication, length filtering, toxicity screening) Base Model: Set up [model: Llama 3.1 8B / Mistral 7B / Phi-3] with: QLoRA configuration (rank: 16, alpha: 32, target modules: all linear) 4-bit quantization (NF4) for memory efficiency Gradient checkpointing enabled Training: Configure: Learning rate: 2e-4 with cosine scheduler and 100-step warmup Batch size optimization (gradient accumulation for effective batch 32) Early stopping on validation loss (patience: 3 epochs) Experiment Tracking: Integrate [Weights & Biases / MLflow]: Log all hyperparameters, metrics, and system utilization Save checkpoints every [N] steps Evaluation: Benchmark against base model on [task-specific metrics] Export: Save merged model in GGUF format for local inference Documentation: Create TRAINING_REPORT.md with results and recommendations All code at [project path], GPU environment: [GPU type and count]. Why This Is a Game-Changer: 精密调节管道有50多个超参数 - 这为每个参数配置了最佳的默认值 QLoRA + 4 位量化可在单个消费者 GPU 上精确调整 7B 模型 集成实验跟踪防止“哪个运行是好的?”问题 GGUF 出口允许无 GPU 的本地部署,大幅降低推断成本 ⏱ Time Saved: 30+ hours manually → 1 hour with Antigravity 24:AI代理系统与多工具管弦乐队 人工智能代理是可以“使用工具”的程序 - 他们不仅可以聊天;他们可以搜索网络,发送电子邮件,查询数据库,并自主执行操作。 “多工具管制”是当今最先进的AI形式,其中“监督”AI管理几个专门的“工人”AI来解决复杂的多步问题。 这个提示告诉Antigravity建立一个复杂的代理系统,包括安全的警卫,专门的角色和人体在循环中的检查点,以做出敏感的决定。 结果是能够从终端处理整个业务流程(如客户支持或市场研究)的自主数字化劳动力。 Google Antigravity Prompt: Build a multi-agent AI system for [use case, e.g., automated customer support]: Architecture: Design using [LangGraph/CrewAI/AutoGen] with: Supervisor agent (task routing and quality control) Specialist agents: [list 3-5 agent types, e.g., FAQ Agent, Escalation Agent, Analytics Agent] Shared memory (conversation history + knowledge base) Tools: Implement tool integrations: Database query tool ([PostgreSQL/MongoDB] connection) API caller tool (REST endpoints for [external services]) Email sender tool (SMTP configuration) Web search tool (Tavily/Serper API) Orchestration Logic: State machine defining agent transitions (Mermaid diagram) Fallback/escalation paths when confidence < [threshold] Human-in-the-loop breakpoints for sensitive actions Safety: Implement: Input validation and prompt injection detection Output guardrails (PII redaction, toxicity filtering) Rate limiting per user Testing: Create 30 test scenarios covering happy paths and edge cases Deployment: Dockerize with health checks and Prometheus metrics Save entire project to [directory]. Why This Is a Game-Changer: 多代理系统是人工智能工程的边界 - 这提供了生产准备的起点 与专家代理人的监督模式自然地匹配企业组织结构 Human-in-the-loop breakpoints satisfy compliance requirements for sensitive operations 快速注射检测和PII编辑解决了企业AI安全的两大问题 ⏱ Time Saved: 60+ hours manually → 2 hours with Antigravity 25:模型部署和监控堆栈 部署是将人工智能模型带出“实验室”并将其放入“生产”环境中,在那里它可以为实际用户提供24小时的服务。 监控是监控在一个模型运行了很长时间后发生的放缓,错误或精度的“错误”的安全系统。 这个提示有Antigravity建立一个专业的托管基础设施与实时仪表板,提醒你,如果系统变得慢或昂贵。 结果是强大的,生产准备的AI服务,在现实世界使用的压力下保持健康,快速和成本效益。 Google Antigravity Prompt: Create a production model serving and monitoring infrastructure: Model Server: Deploy [model name] using [vLLM/TGI/Triton]: Quantization: [AWQ/GPTQ/FP8] Continuous batching with max batch size [N] Tensor parallelism across [N] GPUs OpenAI-compatible API endpoint Load Balancing: Configure [Nginx/Traefik] with: Health check endpoints Request queuing with priority levels Auto-scaling rules (CPU/GPU utilization thresholds) Monitoring Dashboard: Create Grafana dashboard tracking: Request latency (p50, p95, p99) Token throughput (tokens/sec) GPU memory and utilization Error rates and status codes Queue depth and wait times Alerting: Configure PagerDuty/Slack alerts for: Latency p99 > *[threshold]*ms Error rate > [threshold]% GPU memory > 90% A/B Testing: Implement traffic splitting between model versions Infrastructure: Full Kubernetes manifests or docker-compose.yml Save to [infrastructure directory path]. Why This Is a Game-Changer: 模型部署是大多数ML项目死亡的地方 - 这将缩小从笔记本到生产的差距 连续批量化 + 量化可降低服务成本60% - 80% vs. 天真部署 带有警报的格拉法纳监控可防止生产中昂贵的无声故障 A/B测试基础设施可实现安全、数据驱动的模型升级,而无需全面部署 Time Saved: 50+ hours manually → 2 hours with Antigravity 技术内容创作者 - Creating high-quality technical content—blogs, videos, newsletters, or documentation—is a massive task that requires juggling writing, coding, and production simultaneously. - These prompts are built to eliminate the risk of "broken code" in tutorials while automating the repetitive parts of scripting, repo creation, and SEO research. 我们为构建文档网站提供完整的套件,使用时间标记分支编写YouTube教程,并设计多部分电子邮件课程。 对于现代设计师来说,这意味着生产时间减少70%,同时为您的观众提供更专业、更精致的体验。 26:技术博客文章与工作代码示例 技术博客意味着通过写作和 - 最重要的是 - 代码实例向其他开发人员解释复杂的技术。 对于读者来说,一个主要的沮丧是“破坏代码”,其中一个博客插入了一段未经测试和未运行的片段,破坏了他们的信誉。 这个提示指示Antigravity研究最新最佳实践,撰写一个专业的教程,并在发布之前在本地测试每个代码行。 - The result is a high-authority blog post with a complete, verified project that readers can trust and use immediately. Google Antigravity Prompt: Write a production-quality technical blog post about [topic, e.g., "Building a Real-Time Chat App with WebSockets"]: Research: Search the web for: Latest best practices and official documentation for [technology] Common pitfalls developers face (Stack Overflow, Reddit, GitHub Issues) Competing approaches and when to use each Structure: Create the post with: Hook (2-3 sentences): Problem statement that resonates with developers Prerequisites: Tools, versions, and prior knowledge needed Step-by-step tutorial (5-8 sections): Each section with explanation → code block → expected output Troubleshooting FAQ: 5 common errors with solutions Conclusion: Key takeaways + links to further reading Code: Write all code examples in [language/framework]: Every snippet must be copy-pasteable and runnable Include inline comments for non-obvious logic Build a complete working project, not isolated fragments Test all code by running it locally before including SEO: Optimize with target keyword [keyword], meta description, and 5 internal link suggestions Save post to [output path] and working code to [code directory] Tone: conversational but technically precise. Target audience: [junior/mid/senior] developers. Why This Is a Game-Changer: Technical blog posts with broken code examples destroy creator credibility — Antigravity tests every snippet locally Real-time web research ensures the post reflects the latest API versions and best practices, not outdated patterns SEO优化嵌入到写作过程中,消除了单独的关键字研究步骤 完整的工作项目(不是片段)意味着读者可以立即克隆和运行,增加参与和分享 ⏱ Time Saved: 12 hours manually → 25 minutes with Antigravity 27: 使用 Timestamps & Repo 的 YouTube 教程脚本 通过视频教育人们需要的不仅仅是屏幕录制;它需要摄像头准备的脚本,清晰的结构和有组织的代码,观众可以遵循。 “伴侣复制片”是一个代码文件夹,匹配视频的不同阶段,允许观众在任何时候跳进而不会迷路。 - This prompt has Antigravity script your entire video with screen cues, generate SEO-optimized descriptions with timestamps, and build a branched repository that matches the video's timeline. 结果是专业的“创造者套件”,将生产时间缩短60%,同时为您的观众提供更顺利的学习体验。 Google Antigravity Prompt: Create a complete YouTube tutorial package for [topic, e.g., "Deploy a FastAPI App to AWS Lambda in 10 Minutes"]: Research: Check YouTube for existing tutorials on this topic via [YouTube search URL] — identify gaps and unique angles Script: Write a spoken-word script (target: [N minutes]) with: Hook (0:00–0:30): "Have you ever struggled with…" + what they'll learn Overview (0:30–1:30): Architecture diagram description (Mermaid) + tools needed Step-by-step coding sections: Each section labeled with timestamp markers: [TIMESTAMP: 01:30 — Project Setup] [SCREEN: show terminal] / [SCREEN: show browser] cues Exact words to say while typing each code block Demo (final 2 min): Show the working application live CTA (last 30 sec): Subscribe + link to repo + next video suggestion Companion Repo: Create complete project at [repo path] with: Branches matching video sections (step-1-setup, step-2-api, final) README.md linking to the video with timestamps Description & Tags: Generate YouTube description with timestamps, 30 SEO tags, and 3 thumbnail title options Save script to [output path] Why This Is a Game-Changer: 教程创作者花费了60%的制作时间编写脚本 - 这产生了摄像头准备的脚本与屏幕提示 分支的伴侣休息让观众跳到任何部分,减少沮丧和评论投诉 YouTube 描述与时刻印改进观看时间指标和算法推荐 竞争差距分析确保教程提供了一个独特的角度,而不是现有内容的重复 ⏱ Time Saved: 15 hours manually → 30 minutes with Antigravity 28:技术文档网站生成器 文档是软件的用户手册;如果没有良好的文档,即使是最强大的工具也无法使用,最终会死亡。 创建一个现代的“文档网站”涉及阅读数千行代码来解释每个函数的运作方式,创建指南,并确保所有链接都是有效的。 此提示指示Antigravity分析您的整个代码库,自动生成专业文档网站(如Docusaurus),甚至包括交互式“代码沙盒”,让用户可以进行实验。 结果是“黄金标准”的文档体验,使您的软件看起来专业,易于采用。 Google Antigravity Prompt: Generate a complete documentation site for [project/library name] from source code: Code Analysis: Read the entire codebase at [repo path] and extract: All public APIs, classes, functions with signatures and docstrings Configuration options and environment variables Data models and their relationships Documentation Structure: Create using [Docusaurus/MkDocs/VitePress]: Getting Started: Installation, quick start (3 commands to "Hello World"), system requirements Guides: 5 task-oriented tutorials for common use cases of [project] API Reference: Auto-generated from code with parameter types, return values, examples Configuration: Every option documented with default value, type, and usage example FAQ: 10 questions derived from [GitHub Issues URL] and README Migration Guide: Breaking changes between versions (if applicable) Enhancements: Add copy buttons to all code blocks Include runnable code sandboxes (CodeSandbox/StackBlitz links) for key examples Search functionality (Algolia DocSearch or built-in) Verify: Build the site locally and check for broken links Save to [docs output directory] Write documentation for [target audience: beginners/intermediate/advanced]. Why This Is a Game-Changer: 开源项目以文档质量生存或死亡 - 这将从代码中生成专业的文档网站 真正的GitHub问题研究表面上实际用户问的问题,而不是维护者假设他们问什么 可运行代码沙盒允许用户在没有本地设置的情况下进行实验,大幅减少贡献摩擦 自动破坏链接检查防止 #1 文档维护失败(复原器后死亡链接) ⏱ Time Saved: 40 hours manually → 2 hours with Antigravity 29:技术新闻稿 / 电子邮件课程建设者 - Email courses are highly effective because they teach people complex topics in "bite-sized" chunks over several days, preventing them from feeling overwhelmed. 设计一个好的课程需要平衡理论与实际作业,并写有吸引力的主题,以便人们实际上打开电子邮件。 这个提示要求Antigravity设计一个多天的课程,用现实世界的类似性写下每个电子邮件,并构建一个高转换的定位页面来收集登录。 其结果是自动化的“教育引擎”,每天建立您的个人品牌,并为您的订阅者提供真正的价值。 Google Antigravity Prompt: Create a [N-part] email course on [topic, e.g., "Mastering Docker in 7 Days"]: Curriculum Design: Structure [N] emails with progressive difficulty: Each email: 500 words max, 1 key concept, 1 hands-on exercise Subject line A/B variants (2 options per email optimized for open rates) Preview text (90 characters) for inbox display Content Per Email: Day [N] — [Topic]: One clear learning objective Concept (200 words): Explain with a real-world analogy Code Exercise: Minimal, self-contained, 15-minute task Pro Tip: One advanced insight most tutorials miss Homework: Optional stretch challenge linking to next day's topic Resources: Research and include per email: 1 recommended free resource (blog/video) via web search 1 common mistake with debugging steps Landing Page: Create landing_page.html with: Course overview, curriculum outline, signup form placeholder Social proof section (testimonial placeholders) FAQ (5 questions) Save all emails as individual Markdown files to [output directory] Tone: friendly mentor, not formal textbook. Include emoji sparingly. Why This Is a Game-Changer: 电子邮件课程的完成率比视频课程高5倍 - 这种格式最大限度地提高了学习成果 A/B主题行和预览文本对于开放率至关重要,但内容创作者很少优化 每天微观锻炼的渐进困难防止压倒(学习者放弃的第1个原因) 具有社交证据结构的定位页面以显著更高的速度将访问者转换为订阅者 ⏱ Time Saved: 20 hours manually → 30 minutes with Antigravity 30:会议演讲 / 研讨会准备套件 准备会议演讲或研讨会需要数十个小时,从研究最新趋势到写演讲员笔记,并构建实时演示反馈计划。 一个“实时演示反弹”是一个安全网(如屏幕截图或录音),如果互联网失败或你的代码破裂,而你在舞台上,在数百人面前。 这个提示有反重力研究你的观众,概述你的幻灯片,写你的扬声器笔记,并为研讨会参与者准备一个“初始”和“完整”存储库。 结果是防弹的“扬声器包”,确保你从你踏上舞台的那一刻起保持冷静,准备和专业。 Google Antigravity Prompt: Prepare a complete conference talk/workshop package for [topic, e.g., "Building Production AI Agents with LangGraph"]: Research: Search for: Recent talks on this topic at [conference names/URLs] — identify unique angles Latest developments (last 3 months) via [tech news URLs] Audience profile of [conference name] from past attendee surveys or descriptions Slide Outline: Create [N-slide] presentation outline: Opening (3 slides): Hook question + "By the end, you'll be able to…" + agenda Core Content (15-20 slides): 3-4 key sections with: Concept explanation → live demo script → key takeaway per section Speaker notes (exact words to say per slide, 1-2 min each) Live Demo Plan: Step-by-step script with fallback screenshots if demo fails Closing (3 slides): Summary, resources QR code content, Q&A prompts Workshop Repo (if workshop): Create at [repo path] with: starter/ branch (skeleton code for attendees) complete/ branch (finished working solution) SETUP.md with pre-workshop preparation checklist Handout: Create CHEAT_SHEET.md — single-page reference attendees can take home Bio & Abstract: 3 title options + 200-word abstract + 50-word speaker bio Save all materials to [output directory]. Why This Is a Game-Changer: 会议谈话准备平均40小时以上 - 这在不到一个小时内产生了80%的材料 直播演示文稿反弹截图防止舞台上失败演示文稿的噩梦场景 初始/完整分支模式是研讨会的黄金标准,但由于时间限制,很少实施 具有精确时刻的扬声器笔记确保演讲适合分配的插槽,而不超过或低于 ⏱ Time Saved: 40 hours manually → 1 hour with Antigravity 结论:计算机历史上的新黎明 Look at those 30 prompts above. Look at those 30 prompts above. 现在计算一下其中有多少涉及写一行代码。 Now count how many of them involve writing a single line of code. 答案是:大多数人不会! The answer is: most of them don't! 一个遵守监管官,审计GDPR政策。 一个准备操作系统考试的学生。 一位研究人员在三个学术数据库中绘制了200篇论文。 一个技术内容创作者编写一个YouTube教程,具有时刻标记的伴侣 repos。 公司经理将原始销售 CSV 转换为执行人员准备的季度报告,包括图表。 这些人中没有一个是开发者。 None of these people are developers. And yet, every single one of them just had their workflow revolutionized by Google Antigravity. 这是没有人谈论的突破 - 坦率地说,令人惊讶的是,它以前没有被突出。 我们如此忙于将反重力框架作为一个“编码工具”,以至于我们错过了隐藏在清晰的视野中的纹理变化。 We have been so busy framing Antigravity as a "coding tool" that we missed the tectonic shift hiding in plain sight. When you give an AI agent access to your 你的 ,和该 然后让它 你不只是建立一个更好的代码编辑器。 local file system web browser open internet plan, execute, verify, and iterate autonomously 你已经建立了一个通用工作管弦乐队。 You have built a universal work orchestrator. 计算机本身成为了API。 每个文件,每个文件夹,每个表格,每个浏览器标签,每个终端命令 - 所有这些都变得可编排。 想想这意味着什么。 Think about what that means. 在个人计算的历史上,第一次“我想要做什么”和“我想要做什么”之间的障碍已经崩溃到一个单一的自然语言提示。 Not just for code. For . everything 律师可以指向Antigravity的合同文件夹,监管网站和合规模板 - 然后离开。 教师可以将其指向课程,教科书和问题银行 - 并获得完整的,分级考试包。 营销经理可以将其指向竞争对手的网站,内部销售数据和品牌指南 - 并获得源市场数据的投标板概述。 这不是一个渐进的改善。 这是一个新的软件类别,操作系统本身成为面具,人工智能代理成为刷子。 This is a new category of software — one where the operating system itself becomes the canvas, and AI agents become the brush. We went from command-line interfaces to graphical interfaces to voice interfaces. 现在,我们进入了意图界面时代:你声明了你的意图,自主代理人组织了你的整个数字环境来实现它。 Now we are entering the intent interface era: you state your intent, and autonomous agents orchestrate your entire digital environment to fulfill it. The sunrise you see on the horizon isn't just for developers. It's for every knowledge worker, every student, every researcher, every creator, every professional who sits in front of a computer screen and thinks, "I wish this could just... get done." “我希望这只能......完成。 Now it can. 世界还没有意识到这一点! And the world hasn't realized it as yet! 参考 谷歌反重力官方网站 — antigravity.google 谷歌反重力文档 — antigravityide.help 谷歌博客 - 引进 反重力 - blog.google/technology/developers 维基百科网页 — en.wikipedia.org/wiki/Google_Antigravity MCP 协议规格 — modelcontextprotocol.io 人类克劳德文档 — docs.anthropic.com GLM-5 GitHub 存储库 (Zhipu AI) — github.com/THUDM/GLM DeepSeek 官方网站 — deepseek.com Kimi K2 官方文件 — kimi-k2.ai Ollama (本地LLM部署) — ollama.com vLLM 引用服务器 — docs.vllm.ai SGLang 引用框架 — github.com/sgl-project/sglang 谷歌代理开发套件(ADK) — google.github.io/adk-docs Codecademy — Google 反重力指南 — codecademy.com/article/what-is-google-antigravity RAGAS 评估框架 — docs.ragas.io 文档文件 — langchain-ai.github.io/langgraph 重量和偏见 — wandb.ai HuggingFace Hub — huggingface.co 首页 组件 MCP 集成 — composio.dev 分析 Vidhya — MCP 服务器指南 — analyticsvidhya.com/blog/mcp-servers Docusaurus 文档框架 — docusaurus.io MkDocs 文档生成器 — mkdocs.org VitePress 静态网站生成器 — vitepress.dev ConvertKit电子邮件营销 — convertkit.com 阿尔戈利亚 DocSearch — docsearch.algolia.com YouTube 创作者学院 — creatoracademy.youtube.com Sessionize(会议CFP平台) — sessionize.com 上面的所有图像都是由Google Nano Banana生成的,快速版本。 All images above were generated by Google Nano Banana, fast version. Claude Opus 4.6 被用来研究和彻底验证文章。 Claude Opus 4.6 was used to research and verify the article thoroughly. 谷歌反重力与 Gemini AI Pro 计划用于第一个草案。 Google Antigravity with the Gemini AI Pro Plan was used for the first draft.