<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Chinese LLM on Hummingbird Labs</title><link>https://hummingbirdlabs.github.io/tags/chinese-llm/</link><description>Recent content in Chinese LLM on Hummingbird Labs</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 22 May 2026 21:16:00 +0800</lastBuildDate><atom:link href="https://hummingbirdlabs.github.io/tags/chinese-llm/index.xml" rel="self" type="application/rss+xml"/><item><title>More on TRAE China Version: Free Models Are Great But Slow</title><link>https://hummingbirdlabs.github.io/posts/post-005/</link><pubDate>Fri, 22 May 2026 21:16:00 +0800</pubDate><guid>https://hummingbirdlabs.github.io/posts/post-005/</guid><description>A detailed performance comparison of TRAE China&amp;#39;s free LLMs vs paid models: DeepSeek V4 Pro with think mode takes 50 minutes for large tasks, while free Qwen3.6 Plus is significantly faster with shorter queue times. Real data from actual coding projects, plus thoughts on DeepSeek&amp;#39;s mission to democratize AI.</description></item><item><title>Why I Use TRAE: Free LLMs, Stability, and 1M Token Context</title><link>https://hummingbirdlabs.github.io/posts/post-004/</link><pubDate>Fri, 22 May 2026 14:37:00 +0800</pubDate><guid>https://hummingbirdlabs.github.io/posts/post-004/</guid><description>A hands-on review of TRAE IDE&amp;#39;s China version: 14 free LLMs including DeepSeek v4 Pro and Qwen 3.6 Plus, 3-minute average wait times for free tier, fewer task freezes vs GitHub Copilot, and how DeepSeek&amp;#39;s 1M-token context window beats Claude&amp;#39;s 168K limit for large codebases.</description></item></channel></rss>