| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Enables users to preview individual messages within AI chat conversations without full page navigation by injecting DOM manipulation logic into ChatGPT, Gemini, and other AI chat platforms. Uses Chrome extension content script injection to intercept and augment the native chat UI, adding preview overlays and jump-to-message functionality that preserves scroll position and conversation context.
Unique: Implements platform-agnostic message preview through content script injection with multi-platform support (ChatGPT, Gemini, Claude) rather than building a separate chat interface; uses lightweight DOM traversal to locate and preview messages without requiring API access or conversation re-fetching
vs alternatives: Lighter weight than conversation export tools and faster than manual scrolling; works directly within native chat UIs without requiring separate windows or tabs
Allows users to mark specific messages as favorites and organize them with custom tags, storing metadata in Chrome's local storage API. The extension maintains a JSON-based index of favorited messages (including message text, timestamp, conversation ID, and user-defined tags) that persists across browser sessions and enables quick filtering and retrieval without re-accessing the original conversation.
Unique: Uses Chrome's native localStorage for lightweight persistence without requiring backend infrastructure or user authentication; implements tag-based filtering on client-side with in-memory indexing for fast retrieval, avoiding the need for full-text search infrastructure
vs alternatives: Simpler and faster than cloud-based bookmark services because it operates entirely locally; no sync latency or privacy concerns about sending conversation data to external servers
Provides client-side filtering of messages within a conversation by message content, timestamp, or custom tags through DOM query logic and localStorage index lookups. The extension builds an in-memory index of all messages in the current conversation and applies filter predicates to surface matching messages, enabling fast substring search and tag-based filtering without requiring API calls or conversation re-fetching.
Unique: Implements lightweight client-side search using DOM traversal and localStorage index queries rather than requiring backend search infrastructure; combines tag-based filtering (from favorites system) with substring search for dual-mode retrieval without external dependencies
vs alternatives: Faster than exporting conversations and searching externally because it operates in-browser; no latency from API round-trips or data serialization
Extends the native UI of multiple AI chat platforms (ChatGPT, Gemini, Claude) through a unified content script architecture that detects the current platform and applies platform-specific DOM selectors and event handlers. Uses feature detection and CSS class/ID matching to identify message containers, input fields, and UI elements across different platform implementations, then injects custom UI controls (preview buttons, favorite icons, filter inputs) into the native interface.
Unique: Uses platform-detection logic to apply different DOM selectors and event handlers per platform, enabling a single extension to work across ChatGPT, Gemini, and Claude without requiring separate extensions; stores unified favorite index that can reference messages from any platform
vs alternatives: More maintainable than separate per-platform extensions because shared logic (favorites, filtering) is centralized; more flexible than platform-specific tools because it adapts to multiple services
Provides keyboard shortcuts for jumping to next/previous messages, toggling favorite status, and opening the filter panel without using the mouse. Implements a global keyboard event listener in the content script that intercepts key combinations (e.g., Ctrl+J for jump, Ctrl+F for favorite) and triggers corresponding navigation or UI state changes, with support for customizable keybindings stored in extension options.
Unique: Implements global keyboard event interception at the content script level with support for customizable keybindings stored in extension options, allowing users to define their own shortcuts rather than forcing a fixed set; integrates with the message navigation and favorite systems to provide end-to-end keyboard-driven workflows
vs alternatives: More accessible than mouse-only navigation and faster for power users; customizable keybindings provide flexibility that fixed shortcuts cannot match
Enables users to export selected or all favorited messages from a conversation in multiple formats (JSON, CSV, Markdown) with metadata (timestamp, tags, conversation ID). Implements a batch processing pipeline that iterates over the favorite index or selected messages, formats them according to the chosen export template, and generates a downloadable file through the browser's download API.
Unique: Implements multi-format export (JSON, CSV, Markdown) with metadata preservation, allowing users to choose the format that best fits their downstream workflow; uses browser download API for client-side file generation without requiring backend infrastructure
vs alternatives: More flexible than copy-paste because it handles bulk operations and multiple formats; more privacy-preserving than cloud-based export services because data never leaves the browser
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 AiChat-QuickJump at 25/100.
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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.
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