| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Automatically downloads videos from YouTube and Bilibili platforms using dedicated API modules (backend.api.v1.youtube and backend.api.v1.bilibili) that handle platform-specific authentication, URL parsing, and video format selection. The system abstracts platform differences behind a unified video ingestion interface, storing downloaded content in a standardized format for downstream processing. Supports both direct URL input and account-based authentication for platform-specific features.
Unique: Dual-platform abstraction layer (backend.api.v1.youtube and backend.api.v1.bilibili) that normalizes platform-specific download APIs into a unified interface, handling authentication, format negotiation, and metadata extraction without requiring users to manage platform-specific logic
vs alternatives: Supports both Western (YouTube) and Chinese (Bilibili) platforms natively in a single system, whereas most video processing tools focus on YouTube-only or require separate tools per platform
Extracts structured outlines from video content by feeding transcripts or visual keyframes to DashScope API (Alibaba's LLM service), generating hierarchical topic breakdowns with timestamps. The pipeline step (backend.pipeline.step1_outline) uses prompt engineering to convert unstructured video content into machine-readable outlines that segment the video into logical sections. This structured outline becomes the foundation for all downstream analysis, enabling timeline analysis and highlight detection.
Unique: Integrates DashScope API (Alibaba's LLM) specifically for Chinese-language video content understanding, with prompt engineering optimized for both English and Chinese transcripts, producing structured JSON outlines with timestamp precision rather than free-form summaries
vs alternatives: Purpose-built for bilingual video analysis (English + Chinese) with DashScope integration, whereas generic video summarization tools typically use OpenAI/Anthropic APIs and lack Chinese language optimization
Exposes all system functionality through a RESTful API built with FastAPI (backend/main.py and backend/api/v1/) with automatic OpenAPI documentation. Provides endpoints for project CRUD operations, video download/processing, clip retrieval, and status monitoring. Uses FastAPI's dependency injection for authentication, validation, and error handling. Implements proper HTTP status codes, error responses, and request/response schemas with Pydantic validation.
Unique: FastAPI-based REST API with automatic OpenAPI documentation and Pydantic validation, providing type-safe endpoints for all video processing operations with clear error handling and status codes
vs alternatives: FastAPI provides automatic API documentation and async support out-of-the-box, whereas Flask/Django require manual documentation and have less elegant async handling
Implements internationalization (i18n) infrastructure supporting English and Chinese languages across frontend and backend. Frontend uses i18n library for dynamic language switching with locale-specific formatting. Backend provides language-specific API responses and LLM prompts. Documentation is maintained in both languages with synchronization mechanisms. Enables global user base without requiring separate deployments.
Unique: Dual-language support (English + Chinese) built into core architecture with language-specific LLM prompts and documentation synchronization, rather than bolted-on translations
vs alternatives: Native bilingual support with optimized prompts for each language beats generic translation layers that may lose semantic meaning or cultural context
Provides Docker configuration for containerized deployment of the entire system (frontend, backend, Celery workers, Redis). Includes Dockerfile for building application images, docker-compose for local development with all services, and deployment guidance for production environments. Enables consistent deployment across development, staging, and production with minimal configuration drift.
Unique: Complete Docker setup including frontend, backend, Celery workers, and Redis in single docker-compose file, enabling full-stack local development and production deployment with minimal configuration
vs alternatives: Docker-based deployment provides reproducible environments and easy scaling, whereas manual installation requires platform-specific setup and is error-prone
Analyzes structured outlines from step 1 to create fine-grained timeline segments with topic labels and temporal boundaries (backend.pipeline.step2_timeline). Uses LLM-powered analysis to detect topic transitions, segment boundaries, and content coherence across the video duration. Produces a timeline data structure that maps each second of video to its corresponding topic, enabling precise highlight detection and clip generation downstream.
Unique: Creates a dense timestamp-to-topic mapping across entire video duration using LLM analysis of outline structure, enabling sub-second precision for highlight detection, rather than coarse segment boundaries typical of rule-based segmentation
vs alternatives: Produces granular timeline data structures (second-level topic mapping) that enable precise clip boundaries, whereas traditional video editing tools rely on manual chapter markers or scene detection algorithms that lack semantic understanding
Scores video segments for highlight potential using LLM analysis (backend.pipeline.step3_scoring) that evaluates engagement, information density, emotional impact, and viewer interest signals. Assigns numerical scores to each timeline segment indicating likelihood of being a good highlight clip. Uses multi-dimensional scoring criteria (entertainment value, educational value, emotional peaks, etc.) to rank segments, enabling intelligent selection of top-N highlights without manual review.
Unique: Multi-dimensional LLM-based scoring that evaluates segments across entertainment, educational, emotional, and information density dimensions simultaneously, producing explainable scores rather than black-box neural network rankings
vs alternatives: Combines semantic understanding (via LLM) with explicit scoring dimensions, enabling interpretable highlight selection and customizable scoring criteria, whereas ML-based approaches (scene detection, audio analysis) lack semantic reasoning about content value
Generates actual video clip files from scored segments using FFmpeg operations orchestrated through backend.services.video_service. Handles video codec selection, bitrate optimization, format conversion (MP4, WebM, etc.), and audio track management. Implements efficient frame-accurate clipping by calculating exact seek positions and duration parameters, avoiding re-encoding when possible to minimize processing time. Supports batch clip generation with parallel FFmpeg processes.
Unique: Wraps FFmpeg operations in a service layer (backend.services.video_service) that abstracts codec selection, bitrate optimization, and parallel processing, with intelligent keyframe detection to minimize re-encoding overhead and support frame-accurate clipping without full video re-encoding
vs alternatives: Provides intelligent codec selection and parallel batch processing with keyframe-aware clipping, whereas naive FFmpeg usage re-encodes entire videos; more efficient than Python-only libraries (moviepy) which lack hardware acceleration
+5 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 autoclip at 44/100.
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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