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$ show sansan0/TrendRadar

[account ↗] [repo ↗] APPLICATION ★ 62657 24883 ● active (last update 1 month ago)
reviewed by: deepseek/deepseek-v4-flash

TrendRadar is a configurable news aggregation and trend analysis system that crawls hot lists from 11+ Chinese platforms (Zhihu, Weibo, Douyin, Bilibili, etc.) and RSS/Atom feeds, filters content via keyword DSL or AI-based interest matching, performs AI-powered analysis (sentiment, controversy, topic trends), generates rich HTML reports, and pushes notifications to 9+ channels (Feishu, DingTalk, Telegram, Email, ntfy, Bark, Slack, WeCom, generic webhook). It is primarily an application (with CLI entry points, scheduling via timeline.yaml, Docker/GitHub Actions deployment) but incorporates data-pipeline patterns such as multi-stage ETL (crawl → store → filter → analyze → report), SQLite storage with optional S3 remote sync, and AI batch processing. The architecture is a modular monolith: the core `trendradar` package handles all orchestration, while a separate `mcp_server` package exposes 20+ tools via Model Context Protocol (MCP) for LLM integration. The system is heavily config-driven (YAML and text files) and includes a standalone web-based config editor. External dependencies include the third-party newsnow API (for hot lists), LiteLLM (for AI model abstraction), and various notification service APIs. The codebase demonstrates good craftsmanship with clean abstractions (storage backends, notification dispatcher, AI client), though some duplication exists between the core and MCP notification logic, and the extensive configuration surface requires careful maintenance.

RATING8.0/10
QUALITY7.5/10
MAINTENANCEactive
DRIFTmedium
CONTRIBUTORS3
VELOCITY0.74
OPEN ISSUES68
CLOSED ISSUES464
LAST COMMIT1 month ago
requests (pip) ==2.33.0
pytz (pip) ==2026.1
PyYAML (pip) ==6.0.3
fastmcp (pip) ==2.12.5
websockets (pip) ==13.1
feedparser (pip) ==6.0.12
boto3 (pip) ==1.42.76
litellm (pip) ==1.82.6
json-repair (pip) ==0.58.6
tenacity (pip) ==8.5.0
Files by Type (count)
Python
71
Other (.png)
20
YAML
14
Other (.jpg)
8
Text
8
Other (.db)
7
Markdown
5
Other (no ext)
4
Other (.webp)
3
Shell
3
Other (.bat)
3
SQL
3
Files by Type (size)
Other (.db)
6MB
Other (.png)
4MB
Other (.jpg)
2MB
Python
1MB
Lock File
404KB
Other (.webp)
379KB
Markdown
364KB
JavaScript
277KB
YAML
137KB
HTML
66KB
Text
42KB
Other (no ext)
34KB

The LLM identified 3 files worth highlighting for reuse:

Provides a reusable SQLite mixin class that implements common CRUD operations and schema management for news and RSS data. It encapsulates connection handling, batch operations, schema migration (e.g., adding guid column), and row-count queries. Both LocalStorageBackend and RemoteStorageBackend inherit from this mixin, demonstrating a clean template-method pattern that isolates SQL logic from storage backend specifics. This component is self-contained and could be extracted to provide a generic SQLite data access layer for any project requiring structured storage with minimal overhead.

file: trendradar/storage/sqlite_mixin.py ↗ | tags: #storage-abstraction #sqlite-mixin #template-method

Implements a byte-aware text splitter for push notifications that respects per-channel message size limits. It uses `_split_text_into_batches` to break long messages at line boundaries while preserving truncation markers and footers (e.g., '...' placeholders). The component handles overlapping start/end markers ('<<', '>>') and per-platform max-byte configurations, ensuring no message exceeds the channel's allowed payload size. This is highly reusable in any multi-channel notification system where message content varies in length and channels impose hard byte limits, and it demonstrates careful consideration of edge cases in text segmentation.

file: trendradar/notification/splitter.py ↗ | tags: #content-splitting #byte-aware-truncation #message-batching

Implements a resilient multi-source content fetcher that attempts to download from a prioritized list of CDN or direct URLs, caching the last successful source in session memory for subsequent requests. It uses `urllib.request` with proper error handling, timeout, and a fallback chain (e.g., `url1 → url2 → url3`). The `get_content` function returns the first successful response, and `get_cdn_content` adds a session-level cache of the winning source to avoid re-probing on every call. This pattern is directly usable in any project needing robust resource fetching from multiple mirrors, such as version checks, asset downloads, or config updates from distributed origins.

file: trendradar/core/cdn.py ↗ | tags: #fallback-fetch #multi-source #resilience
Last analyzed: 2026-10-03 20:21:09.190960
Next check: 2026-10-10 20:21:09.184549
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