LLM on Lynx Tech Blog

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)

Fri, 11 Sep 2026 00:00:00 +0800

Picking an AI model for novel writing, the internet is full of claims — “Claude has the best prose,” “DeepSeek has the densest foreshadowing,” “Kimi is in a league of its own for ultra-long-context continuation.” 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

Free AI API Quotas in 2026: A Survey of LLM Free Tiers and New-User Credits

Fri, 04 Sep 2026 00:00:00 +0800

Are free LLM APIs still a thing in 2026? Yes—and the landscape is more competitive than two years ago. Chinese platforms compete on new-user credits; overseas platforms compete on permanent free tiers. But free quotas are not a promise: some are permanently free small models, some are one-time signup gifts, and some only exist inside a short activity window. This post surveys the free channels that were publicly verifiable as of 2026-09-04, organized into three layers with the fine print for eac

Stop Pasting API Keys and Bank Cards into LLM Relays: A Guide to Self-Hosted Privacy Gateways

Mon, 31 Aug 2026 00:00:00 +0800

Using a third-party LLM relay to access Claude, GPT, or Gemini is cheap and convenient—but have you considered this: the relay operator can see every single word you send to the model. That includes API keys you casually paste in, bank card numbers, login passwords, ID numbers, medical records… all sitting in someone’s server logs, in plaintext. This isn’t paranoia—it’s architecture. A relay is fundamentally a reverse proxy: your request hits their server, gets unwrapped

Full Health Check of CPA Model Pool: Which of 80 Models Are Alive, Smart, or Running Naked

Sun, 23 Aug 2026 00:00:00 +0800

Last month I wrote an article testing six Claude Code models. This time I’m broadening the scope: my local CPA (local model gateway) has 80 models connected, and I did two things — first, sent real requests to each one to check liveness, then gave the survivors a puzzle-resistant intelligence test, and finally cross-validated against public leaderboards. Bottom line up front: listing a model ≠ it works. Of the 80 models, 59 are text models, and only about 30 could actually hold a conversat

Solo Multi-Channel Content: How I Built My AI Automation Pipeline LynxPipe

Thu, 20 Aug 2026 00:00:00 +0800

Running a blog, a Telegram channel, and a WeChat public account all by myself—the real bottleneck isn’t running out of ideas, it’s keeping up. Every day there’s an overwhelming flood of valuable AI news, and manually translating, rewriting, sourcing images, laying out, and distributing it all can eat up half a day before you even start. So I architected it as a pipeline I call LynxPipe: raw inputs go in, polished articles come out, and distribution to the blog, TG, and WeChat h

Hands-on Test of Six Claude Code Models: Who’s Fast, Who’s Smart, and Who’s a Lottery

Sat, 15 Aug 2026 00:00:00 +0800

Claude Code’s model picker currently has six options sitting in it: qwen3.8-max, glm-5.2-fast-preview, glm-5.2, deepseek-v4-pro-0813, qwen3-coder-next, and grok-4.6. All of them are routed through my local CPA, a local model gateway, to their respective upstream providers. In day-to-day use, my impressions were vague: “this one feels faster,” “that one feels smarter.” Gut feel is unreliable, so I spent an evening putting them on the same starting line and benchmarked speed, thinking time, long-i

Where Do LLM Companies Get Their Data? And How to Sell Data to Them

Sat, 08 Aug 2026 00:00:00 +0800

Research date: 2026-08-05 · Research method: two parallel ultracode workflows (Grok deep research with 106 agents + local sources across 6 angles with 10 agents), totaling 116 sub-agents and 4,500+ tool calls; key conclusions were validated through 3-vote adversarial review (25 claims submitted, only 3 survived with unanimous approval), while the rest are labeled by strength of evidence. One-sentence usage guide: Use “High confidence” for decision-making; treat “Medium/Low confidence” as leads;

AI Token-Intensive Annotation Industry Whitepaper (2026)

Wed, 05 Aug 2026 00:00:00 +0800

Generated: 2026-07-31 · ultracode multi-agent research pipeline (41 agents) · Nine major sections + in-depth analysis of 12 sub-sectors Data sources are provided in the “References” section of each chapter; all judgments without sources are marked [Inferred]