The AI Coding Agent space moves fast. Claude Code, Codex, Gemini CLI, Qwen Code, and others generate a constant stream of Issues, PRs, and discussions daily. No one can keep up with all of it.

I recently discovered Big Model Radar, a project that uses GitHub Actions to automatically scrape development activity from these tools and generates bilingual (Chinese/English) daily reports using LLMs. It has been producing reports steadily every day since July 7, 2026.

Note: The Web UI is a snapshot of the old version (content up to 2026-05). For the latest reports, check Issues or Telegram.

What It Tracks

7 major CLI tools: Claude Code / OpenAI Codex / Gemini CLI / GitHub Copilot CLI / Kimi Code CLI / OpenCode / Qwen Code.

It generates 5 types of daily reports:

  • digest: Cross-tool community activity comparison
  • hn: Hacker News AI hot topics
  • trending: GitHub AI trending repositories
  • web: Anthropic/OpenAI official site updates
  • openclaw: OpenClaw ecosystem report

More Than Just Summaries

Each tool’s daily report contains 6 sections:

  1. Today’s Overview: A paragraph summarizing the day’s key signals
  2. Releases: New version listings
  3. Top 10 Hot Issues: Each with priority + comment count + significance analysis
  4. Top 7-10 Important PRs: Each with feature description + impact assessment
  5. Feature Request Trends: Directional insights extracted from Issues/PRs
  6. Developer Focus Areas: Pain point clustering + high-frequency demand aggregation

This isn’t simple Issue forwarding. It’s a hybrid of AI-generated summaries + trend analysis.

Three-Stage Pipeline: Production → Bridge → Consumption

The project’s architecture is interesting:

  1. Production: The maintainer pays for LLM tokens. GitHub Actions automatically generates daily reports at 08:00 CST, writing to GitHub Issues + Markdown files
  2. Bridge: The project provides MCP Server source code. Users deploy their own instance to Cloudflare Workers, exposing query APIs (list_reports / get_latest / get_report / search)
  3. Consumption: Users connect their own Claude Desktop to the MCP Server for queries, consuming their own LLM tokens

The essence: producers pay to generate content, consumers pay to query it. The MCP Server is a data bridge that lets AI agents consume Issue data directly, instead of humans browsing pages.

Access Methods

An Interesting Reality

As of 2026-07-12, there are zero human comments across 41 issues.

Issues in this project serve as persistent storage for AI-generated content, not a discussion forum. All channels are unidirectional — AI generates → humans/AI consume. There is no “human feedback to AI” loop.

An accurate description: AI generates, AI consumes, humans observe.

Value

Reduces information acquisition costs — from “manually checking 7 repositories daily” to “glancing at a daily report”; enables cross-tool comparison; provides trend awareness; AI analysis offers directional insights.