| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Recursively discovers files in a codebase while respecting .gitignore rules through native git integration, building an in-memory file tree that filters out ignored paths before processing. Uses the ignore crate to parse .gitignore patterns and applies them during traversal, avoiding unnecessary I/O on excluded directories. This enables developers to automatically exclude vendor directories, build artifacts, and other non-essential files without manual configuration.
Unique: Integrates the Rust `ignore` crate for native .gitignore parsing during traversal rather than post-filtering, eliminating I/O on ignored paths and providing performance benefits on large repositories with deep ignore rules
vs alternatives: Faster than tools that traverse all files then filter (e.g., simple glob-based tools) because it skips I/O on ignored directories entirely, and more reliable than regex-based .gitignore emulation because it uses the standard ignore crate
Applies glob patterns to filter files discovered during directory traversal, supporting both inclusion and exclusion patterns with explicit user overrides that take precedence over defaults. The filtering engine evaluates patterns in sequence (include patterns first, then exclusions) and allows users to force-include files that would normally be filtered out via CLI flags or configuration. This enables fine-grained control over which files appear in the final prompt without re-running the entire traversal.
Unique: Implements a two-pass filtering system where user-specified overrides (via --include and --exclude flags) take precedence over default patterns, allowing developers to surgically override filtering rules without modifying configuration files
vs alternatives: More flexible than static .gitignore-only filtering because it supports dynamic inclusion/exclusion patterns, and more intuitive than regex-based filtering because it uses familiar glob syntax
Implements a Code2PromptSession struct that maintains state across multiple configuration and generation steps, enabling developers to build multi-step workflows (configure filters, select files, generate prompt) without re-traversing the filesystem. Sessions encapsulate the file tree, token map, configuration, and template state, allowing incremental modifications and multiple prompt generations from the same session. This is particularly useful for interactive workflows where users make multiple selections before final output.
Unique: Implements a stateful session object that encapsulates the entire processing pipeline (file tree, token map, configuration, template) and allows incremental modifications without re-traversal, enabling efficient multi-step workflows and interactive tools
vs alternatives: More efficient than stateless tools because it avoids repeated filesystem traversals, and more flexible than single-shot tools because it supports incremental modifications and multiple generations
Detects binary files using magic byte analysis (checking file headers for known binary signatures) and handles them safely by either skipping them or base64-encoding them for inclusion in prompts. This prevents binary data from corrupting text-based prompts while preserving the option to include binary metadata if needed. The detection uses heuristics (null bytes, non-UTF8 sequences) to identify binary files with high accuracy.
Unique: Uses magic byte analysis (checking file headers for known binary signatures) combined with heuristic detection (null bytes, non-UTF8 sequences) to identify binary files with high accuracy, preventing corruption of text-based prompts
vs alternatives: More robust than extension-based detection because it identifies binaries by content rather than filename, and more efficient than reading entire files because it only examines headers
Organizes files in the generated prompt using customizable sorting strategies (alphabetical, by size, by modification time, by directory depth) to improve readability and enable LLMs to process related files together. Files can be grouped by directory, sorted within groups, and presented in a hierarchical structure that mirrors the filesystem. This enables developers to control how files appear in the prompt without modifying the underlying file tree.
Unique: Implements multiple sorting strategies (alphabetical, by size, by modification time, by directory depth) that can be applied independently or combined, allowing developers to optimize file presentation for different use cases
vs alternatives: More flexible than fixed ordering because it supports multiple strategies, and more efficient than manual file organization because it's automated and reproducible
Processes specialized file types (CSV, JSONL, Jupyter notebooks, binary files) into structured text representations suitable for LLM consumption, with format-specific handlers that preserve semantic information. CSV files are converted to markdown tables, JSONL is pretty-printed with indentation, Jupyter notebooks extract code cells and markdown, and binary files are detected and either skipped or base64-encoded. Each processor is modular and can be extended to support additional formats without modifying the core pipeline.
Unique: Implements a pluggable processor architecture where each file format has a dedicated handler (CSVProcessor, JSONLProcessor, NotebookProcessor) that can be extended independently, allowing developers to add custom processors without touching the core pipeline
vs alternatives: More comprehensive than simple text extraction because it preserves semantic structure (tables for CSV, code cells for notebooks), and more robust than naive file reading because it detects binary files and prevents corruption
Counts tokens using tiktoken-rs (OpenAI's tokenizer) to track context usage and prevent exceeding LLM context window limits, providing per-file token counts and cumulative totals. The system tracks tokens for file content, templates, and metadata separately, allowing developers to see exactly which files consume the most tokens and make informed decisions about inclusion. A token map is maintained during processing to enable interactive token-aware file selection in the TUI.
Unique: Maintains a detailed token map during processing that tracks tokens per file and enables interactive token-aware file selection in the TUI, allowing users to see real-time token impact of including/excluding files
vs alternatives: More granular than simple total token counts because it breaks down tokens by file, enabling informed decisions about which files to include; more accurate than manual estimation because it uses tiktoken-rs
Integrates with git to include version control information in prompts, supporting git diffs (staged/unstaged changes), commit logs, and branch comparisons. Developers can include recent commits, changes between branches, or the current diff to provide LLMs with context about recent modifications. This is implemented via git2-rs bindings that query the repository's git objects directly, avoiding shell invocations and enabling cross-platform compatibility.
Unique: Uses git2-rs for direct git object access rather than shelling out to git commands, enabling cross-platform compatibility and avoiding subprocess overhead while maintaining full access to git history and diff generation
vs alternatives: More efficient than shell-based git integration because it avoids subprocess overhead, and more reliable than parsing git CLI output because it uses the native libgit2 library
+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 code2prompt at 38/100. code2prompt leads on quality, while TrendRadar is stronger on adoption and ecosystem.
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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