| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Enables users to browse and filter a curated JSON-based prompt library across 13 languages (English, Chinese, Spanish, Arabic, Portuguese, etc.) using Docusaurus's built-in i18n system with client-side tag-based filtering. The system stores prompts as structured JSON objects with language-specific content, metadata, and category tags, allowing real-time filtering without backend queries. Filtering operates on prompt attributes like category, use-case, and difficulty level through React Context state management.
Unique: Uses Docusaurus's native i18n system with JSON-based prompt storage and client-side filtering, enabling zero-latency discovery across 13 languages without backend infrastructure. Custom JSON-splitting mechanism allows language-specific content to be served statically, reducing deployment complexity compared to database-backed alternatives.
vs alternatives: Faster discovery than PromptBase or OpenAI's prompt library because filtering happens client-side with no server round-trips, and multilingual support is built-in rather than bolted-on.
Allows users to create, edit, save, and organize custom prompts in a personal library using React Context API for state management and browser LocalStorage for persistence. Users can fork existing prompts from the catalog, modify them, and save them locally without backend infrastructure. The system maintains a User context that tracks favorites, custom prompts, and user preferences, with data persisted across browser sessions via LocalStorage.
Unique: Implements a React Context-based user state system that persists to browser LocalStorage, enabling offline-first prompt management without requiring backend authentication or database. The architecture allows users to fork and modify catalog prompts locally, creating a personal variant library without server-side storage.
vs alternatives: Simpler than cloud-based prompt managers like Prompt.com because it requires no account creation or API keys, and faster for local access since data is stored client-side rather than fetched from a server.
Renders ChatGPT-Shortcut as a responsive web application using Ant Design 5.x components and custom React components, ensuring usability across desktop, tablet, and mobile devices. The Docusaurus framework handles responsive layout through CSS media queries and flexible grid systems, while Ant Design provides pre-built responsive components. The UI adapts to different screen sizes without requiring separate mobile or tablet versions.
Unique: Leverages Ant Design 5.x's built-in responsive components combined with Docusaurus's CSS framework to achieve responsive design without custom media queries. This approach reduces custom CSS and ensures consistency with Ant Design's design system across all screen sizes.
vs alternatives: More maintainable than custom responsive CSS because Ant Design components handle responsive behavior automatically, reducing the need for custom breakpoints and media queries.
Implements instant page loading through a custom Docusaurus plugin (plugins/instantpage.js) that preloads pages on hover or link focus, reducing perceived latency when navigating between prompts. The plugin likely uses the Instant.page library or similar approach to prefetch linked pages before the user clicks, creating a snappy navigation experience. Combined with Docusaurus's static site generation, this enables near-instant page transitions.
Unique: Uses a custom Docusaurus plugin to integrate instant page loading, enabling prefetching without modifying individual page components. This approach is more maintainable than adding prefetch logic to each page because it's centralized in the plugin system.
vs alternatives: More efficient than service workers for prefetching because it uses simple link prefetching without the complexity of service worker registration and cache management, reducing bundle size and implementation complexity.
Enables users to share custom prompts with the community and contribute new prompts to the public catalog through a GitHub-based contribution workflow. The system uses a community-prompts page where users can view shared prompts, and contributions are managed via pull requests to the prompt.json file in the repository. The architecture leverages GitHub as the backend for version control, review, and merging of new prompts, with Docusaurus rendering the community content statically.
Unique: Uses GitHub as the primary backend for community contributions, leveraging pull requests as the contribution mechanism and the repository as the source of truth. This eliminates the need for a custom backend while maintaining version control, review workflows, and contributor attribution natively through GitHub.
vs alternatives: More transparent and decentralized than centralized prompt marketplaces because all contributions are public, auditable, and version-controlled in GitHub, enabling community-driven curation rather than platform gatekeeping.
Provides browser extension and Tampermonkey userscript implementations that inject ChatGPT-Shortcut prompts directly into ChatGPT, Claude, and other LLM interfaces. The extensions use browser extension APIs to communicate with the main Docusaurus site, fetch prompts from the catalog, and inject them into the LLM chat interface via DOM manipulation. The userscript approach enables cross-browser compatibility without requiring formal extension store approval.
Unique: Implements dual distribution model via both formal browser extensions and Tampermonkey userscripts, enabling reach across browsers and users who prefer lightweight script-based solutions. Uses DOM manipulation to inject prompts directly into LLM interfaces, eliminating the need for API integrations with ChatGPT or Claude.
vs alternatives: More accessible than ChatGPT plugins because it works without requiring ChatGPT Plus or plugin approval, and more flexible than native integrations because it can target multiple LLM platforms simultaneously.
Defines and enforces a structured schema for prompts using TypeScript interfaces (LanguageData, prompt objects) that specify required fields like title, description, category, tags, and language-specific content. The system validates prompts against this schema during contribution and rendering, ensuring consistency across the catalog. Metadata includes multilingual content, difficulty levels, use-case categories, and contributor attribution, all stored in the prompt.json file with strict JSON structure.
Unique: Uses TypeScript interfaces to define prompt schema, enabling compile-time type checking and IDE autocomplete for contributors. The schema is embedded in the codebase rather than exposed as a separate JSON schema file, making it tightly coupled to the application logic but reducing external dependencies.
vs alternatives: More developer-friendly than JSON schema because TypeScript interfaces provide IDE support and compile-time checking, but less portable because the schema is not exposed as a standalone artifact that external tools can consume.
Supports 13+ languages through Docusaurus's built-in i18n system combined with a custom JSON-splitting mechanism that separates language-specific prompt content. Each prompt stores language variants in a LanguageData structure, and Docusaurus automatically routes users to the appropriate language version based on browser locale or user selection. The system uses i18n configuration in docusaurus.config.js to define supported locales and default language, with translation resources organized in i18n/ directory structure.
Unique: Combines Docusaurus's native i18n routing with a custom JSON-splitting mechanism for prompt content, enabling language variants to be stored in a single prompt.json file while being served through language-specific routes. This approach avoids duplicating the entire prompt catalog per language while maintaining Docusaurus's static site generation benefits.
vs alternatives: More efficient than duplicating the entire site per language because it uses Docusaurus's i18n system to route users to language-specific content without duplicating the underlying data structure, reducing maintenance burden.
+4 more capabilities
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs ChatGPT-Shortcut at 38/100.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities