<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepSeek on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/tags/deepseek/</link><description>Recent content in DeepSeek on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 11 Sep 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/tags/deepseek/index.xml" rel="self" type="application/rss+xml"/><item><title>The Complete Guide to LLMs for Novel Writing, September 2026: A Data-Driven Comparison Across Five Dimensions (with Real Benchmarks for DeepSeek V4.1 / GLM-5.3 / Kimi K3 / Qwen3.8)</title><link>https://blog.lynxflow.co/en/posts/best-llm-for-novel-writing-2026-09/</link><pubDate>Fri, 11 Sep 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/best-llm-for-novel-writing-2026-09/</guid><description>Picking an AI model for novel writing, the internet is full of claims — &amp;ldquo;Claude has the best prose,&amp;rdquo; &amp;ldquo;DeepSeek has the densest foreshadowing,&amp;rdquo; &amp;ldquo;Kimi is in a league of its own for ultra-long-context continuation.&amp;rdquo; Which of these are backed by actual testing, and which are marketing? This article pulls together all publicly available raw benchmark data as of September 2026, evaluates models across the five dimensions that actually matter for novel writing, and g</description></item><item><title>5 Days, 150K Stars: DeepSeek Harness Sells Agents à La Carte</title><link>https://blog.lynxflow.co/en/posts/deepseek-harness-github-record-2026/</link><pubDate>Wed, 19 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/deepseek-harness-github-record-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/deepseek-harness-github-record-2026.png?v=083021" alt="Featured image of post 5 Days, 150K Stars: DeepSeek Harness Sells Agents à La Carte" /&gt;On the evening of August 13, DeepSeek dropped this year&amp;rsquo;s most significant open-source move—DeepSeek Harness (DSH). This isn&amp;rsquo;t just another large model update; it&amp;rsquo;s DeepSeek&amp;rsquo;s first time open-sourcing its own AI Agent runtime framework, released under the MIT license and free for commercial use.
Five days later, GitHub stars surged past 150,000 with 15,000 forks, shooting it into the ranks of GitHub&amp;rsquo;s fastest-rising projects of all time. For comparison: DeepSeek&amp;rsq</description></item><item><title>ChatGPT pro 20x Subscription vs China's Top LLMs: Per-Million-Token Cost</title><link>https://blog.lynxflow.co/en/posts/pro20x-vs-china-llm/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/pro20x-vs-china-llm/</guid><description>&lt;img src="https://blog.lynxflow.co/images/pro20x-vs-china-llm.png?v=090818" alt="Featured image of post ChatGPT pro 20x Subscription vs China's Top LLMs: Per-Million-Token Cost" /&gt;A counterintuitive ledger Suppose you pay 1000 RMB to activate a ChatGPT &amp;ldquo;pro 20x&amp;rdquo; subscription, equivalent to roughly $1700 of API value per week, and stack OpenAI&amp;rsquo;s ~10 monthly &amp;ldquo;Goodwill Resets&amp;rdquo; (announced by tibo — each one refreshes a brand-new fully-loaded 7-day cycle). Under that assumption, your effective cost per million tokens lands at 0.036 ~ 0.91 RMB — while China&amp;rsquo;s cheapest DeepSeek-V4-Flash costs 1.06 RMB, ~1.2x higher; against the priciest Kimi K</description></item></channel></rss>