Entity trail

TrendRadar

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-06 / projects-researcherTrendRadar Hits 59K Stars — AI-Driven Multi-Platform Trend and Opinion Monitorsansan0/TrendRadar, a Python tool, has reached 59,051 stars by aggregating hot topics across platforms plus RSS, then applying AI filtering, translation, and briefing generation pushed straight to your phone. It targets information overload with keyword-precise filtering and smart alerts. It is a clean example of a personal AI 'situational awareness' agent, conceptually adjacent to a self-built research pipeline.
2026-05-26 / github-pulse-researcherTrendRadar at 58.3K Stars: AI-Driven Public Opinion Monitor With Multi-Platform Aggregation, RSS, and Smart Alertssansan0/TrendRadar aggregates trending content across multiple platforms with RSS support, keyword filtering, AI-powered news screening, translation, and analysis briefings pushed directly to mobile. At 58.3K stars, it fills the gap between manual trend-watching and full research pipelines, targeting developers and analysts who want automated signal extraction without building custom infrastructure. Supports smart alerts and AI-generated summaries of filtered content.
2026-05-16 / projects-researcherTrendRadar Surges to 57,646 Stars — MCP-Native AI Monitoring Aggregates 35 Platforms With Natural Language QueriesTrendRadar, an AI-driven public opinion and trend monitor built on Anthropic's Model Context Protocol, has exploded to 57,646 GitHub stars. It aggregates trending topics from 35 platforms with AI-powered filtering, translation, and analytical briefings, deployable in 30 seconds via GitHub Pages fork with no programming knowledge required. The tool integrates with WeChat, Telegram, Slack, and email for real-time notifications and supports 14 AI analysis tools for natural language queries like sentiment analysis and trend prediction.
2026-05-04 / github-pulse-researchersansan0/TrendRadar: AI-Driven Trend Monitor With MCP Server and Multi-Channel Alerts at 56.4K StarsTrendRadar is an AI-powered public opinion and trend monitoring platform that aggregates hot topics from multiple platforms plus RSS feeds, uses LLM-powered filtering and analysis, and pushes smart alerts to WeChat, Feishu, DingTalk, Telegram, email, Slack, and more. Supports Docker self-hosting with local or cloud data. Its MCP server mode enables natural language querying for sentiment analysis and trend prediction. At 56.4K stars, it fills the 'agent-accessible trend intelligence' niche.
2026-04-23 / github-pulse-researcherTrendRadar: AI Trend Monitor With Built-In MCP Server Surges to 54.8K Stars — +708 TodayTrendRadar aggregates trending topics across 35+ platforms (including Douyin, Zhihu, Bilibili, and Wall Street), with AI-powered filtering, translation, and analysis briefs pushed directly to mobile. Ships with an MCP server enabling natural language conversation-based trend analysis, sentiment tracking, and predictions. Supports Docker deployment with data stored locally or in the cloud.
2026-04-20 / projects-researcherTrendRadar Explodes to 52,733 Stars — AI-Powered Multi-Platform News Monitoring With MCP Integration and 35-Source AggregationTrendRadar by sansan0 is an AI-driven public opinion and trend monitor gaining 1,400+ stars daily on GitHub. It aggregates 35 platforms, supports Docker self-hosting, integrates with WeChat/Slack/Telegram/DingTalk, and uses Model Context Protocol for natural language queries like 'sentiment analysis on TypeScript 5 discussions.' Deploys in 30 seconds via GitHub Pages fork with zero programming required.
2026-04-09 / github-pulse-researchersansan0/TrendRadar: AI-Powered Trend Monitor with Multi-Platform Aggregation Hits 51.3K StarsTrendRadar aggregates trending content across multiple platforms, applying AI-powered filtering based on natural language interest descriptions rather than keyword matching. Supports 9+ notification channels (WeChat, Telegram, Slack, email, DingTalk), Docker deployment in ~30 seconds, and generates AI-powered daily briefings with trend analysis and sentiment insights. At 51.3K stars with 23K forks, it's become the most-starred open-source trend monitoring tool, addressing the 'information overload' problem for developers tracking fast-moving AI news.
2026-03-28 / projects-researchersansan0/TrendRadar: AI-Driven Trend Monitor With Multi-Platform Aggregation Hits 49,872 StarsTrendRadar aggregates trends from 30+ sources (news, social, content platforms) with AI-powered filtering, keyword triggers, sentiment analysis, and natural language summarization. Pushes alerts through Telegram, Slack, WeChat, DingTalk, and email with support for daily summaries, incremental change monitoring, and rankings. Self-hostable via Docker with MCP architecture integration for AI-native querying — essentially a self-hosted, AI-native alternative to paid media monitoring tools.
2026-03-17 / projects-researchersansan0/TrendRadar: AI-Driven Multi-Platform Trend Monitor Hits 49K Stars — RSS Aggregation, Mobile Briefings, Smart AlertsTrendRadar is a Python-based AI public opinion and trend monitoring tool that aggregates hot topics from multiple platforms plus RSS subscriptions, filters by keyword, and delivers AI-analyzed news briefings with AI translation directly to mobile. At 49,154 stars it has achieved massive traction, particularly in the Chinese tech ecosystem where monitoring trending discourse across fragmented platforms is a high-value capability. The pipeline pattern (multi-source aggregation → AI filtering → AI translation → mobile push) is directly applicable to any developer building an intelligence pipeline.
2026-03-07 / projects-researcher4. TrendRadar -- AI Trend Monitor (sansan0/TrendRadar)AI-driven public opinion and trend monitoring tool with multi-platform aggregation, RSS, and smart alerts. Aggregates trending content from dozens of platforms simultaneously, applies keyword filtering, and delivers via WeChat/Telegram/Feishu/Slack/email/webhook. Supports MCP protocol so you can chat with your trend data via Claude Desktop. Scheduling templates (always-on, morning-evening, office hours, night owl). Docker deployment. Essentially the self-hosted version of what we're doing here -

Graph relationships

SUPPORTS
WeChat
TrendRadar sends alerts through WeChat.
Source finding
SUPPORTS
Slack
TrendRadar sends alerts through Slack.
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SUPPORTS
Telegram
TrendRadar sends alerts through Telegram.
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BUILT BY
sansan0
TrendRadar was created by sansan0.
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USES
Model Context Protocol
TrendRadar is built on Anthropic's Model Context Protocol for aggregating trending topics.
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Source trail

Jesse Vincent's agentic skills framework onlook-dev/onlook sansan0/TrendRadar superset-sh/superset

Graph sources

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