For AI builders
Track coding agents, model APIs, local LLM stacks, RAG workflows, and benchmark signals before choosing what to build with.
Global AI field guide for people who love AI
zglg.work is an English-first AI intelligence hub for global readers. It connects daily AI news, model rankings, hands-on field notes, tool guides, and local LLM practice in one practical reading path.

Hi, I am Guozhen. A lot of readers have told me the same thing: running MyClaw-style agent work burns tokens very quickly. Say one sentence and 100,000 tokens m...
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A fast reading surface for model launches, AI agents, coding tools, open-source projects, and infrastructure shifts.
Open sectionComposite rankings and practical interpretation across Arena, Artificial Analysis, Vals-style tasks, HELM, and real usage.
Open sectionOriginal experiments with models and tools, with screenshots, workflows, and honest notes about what actually worked.
Open sectionTool guides, local deployment notes, calculators, model selectors, and practical setup paths for builders.
Open sectionOriginal Field Notes

Hi, I am Guozhen. A lot of readers have told me the same thing: running MyClaw-style agent work burns tokens very quickly. Say one sentence and 100,000 tokens m...
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2026-05-31 · 29 visuals
Hi, I am Guozhen. Recently, two new models were released: Qwen3.7-Max and Claude Opus 4.8. On the benchmark list I was watching, Claude Opus 4.8 ranked first. Q...

2026-05-28 · 27 visuals
Hi, I am Guozhen. In the past, if I needed to read several PDF reports, summarize them into a Word document, create a few charts, and then turn everything into...

2026-05-27 · 26 visuals
Hi, I am Guozhen. Some readers told me they are interested in building a "one-person company," but they do not know where to start. In this article, I want to t...

2026-05-26 · 19 visuals
Hi, I am Guozhen. Many people have a large amount of material on their computers: PDFs, Word documents, Excel files, meeting notes, project documents, papers, c...
Track coding agents, model APIs, local LLM stacks, RAG workflows, and benchmark signals before choosing what to build with.
Use the daily brief and field notes to follow what changed in AI without getting buried under generic tech noise.
Compare public benchmark signals with practical constraints: speed, cost, context window, reliability, and deployment fit.
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