🛰️ 射电天文 × 人工智能科普站

大模型 · Harness · Agent LLM · Harness · Agent

三个词,说清这两年 AI 为什么突然"能干活了"——以及如何用这些技术,加速你的射电天文数据处理与研究。 Three words that explain why AI suddenly "gets things done" these past two years — and how to use them to speed up your radio-astronomy data processing and research.

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一句话读懂三概念 Three Concepts in One Line Each

🧭 一个类比 An analogy 大模型像一位学识渊博但没有手脚的专家;Harness 是给他配的实验室、仪器和安全规程;Agent 则是"接到你的课题后,自己查文献、做实验、写报告直到交卷"的那位研究员An LLM is like a brilliant expert without limbs; a harness is the lab, instruments, and safety rules you give them; an agent is the researcher who — given your project — reads literature, runs experiments, and writes the report until it's done.

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1 · 大模型 What is an LLM

定义、工作原理(Token 预测演示)、能力与局限、代表模型。

Definition, how it works (interactive token demo), capabilities & limits, representative models.

2 · Harness What is a Harness

组件全景图、裸模型对比、MCP 协议、DeepSeek Harness 实例。

Component map, bare-model comparison, MCP, a DeepSeek Harness case study.

3 · Agent What is an Agent

ReAct 循环动画、Agent 类型、代表产品、风险与边界。

Animated ReAct loop, agent types, representative products, risks & boundaries.

4 · 主流 AI 编程工具 Popular AI Coding Tools

国内推荐(Qoder / Trae / Kimi Code / WorkBuddy)与国外简述(Claude Code / OpenCode 等);从编程 Agent 到通用 Agent。

Domestic picks (Qoder / Trae / Kimi Code / WorkBuddy) & foreign briefs (Claude Code / OpenCode…); from coding to general agents.

4+ · 技能生态 Skills & Ecosystem

Skill / 插件 / MCP / 工作流与 SOP——未必要写代码,把流程写清楚同样有价值。

Skills, plugins, MCP, workflows & SOPs — documenting a clear process counts as programming.

5 · 射电天文实战 ⭐ Radio Astronomy in Practice

端到端案例:HI 谱线立方体 → 矩图 → CASA 交叉验证 → 科学图,每步配提示词模板。

End-to-end case: HI cube → moment maps → CASA cross-check → figure, with prompt templates.

6 · 学习资源 Resources

官方文档、公开数据(THOR / HI4PI / ALMA)、论文与学习路径。

Official docs, public data (THOR / HI4PI / ALMA), papers, learning path.

考一考:你学会了吗? Quick Quiz: Did You Get It?

5 道题,选完即看答案。答对 4 题以上就大胆开始用 AI 编程工具吧!

Five questions, instant feedback. Score 4+ and you're ready to start using AI coding tools.

1. 大语言模型(LLM)的核心任务是什么?What is the core task of an LLM?
✅ 正确!模型本质是"预测下一个 Token",其余能力都由此涌现。Correct! Everything else emerges from next-token prediction.
2. Harness 的主要作用是什么?What does a harness mainly do?
✅ 正确!Harness 是模型的"身体 + 工具箱 + 安全规程"。Correct! The harness is the body, toolkit and safety rules.
3. 一个"裸模型"(没有接 Harness)能做到以下哪件事?Which can a bare LLM (no harness) do?
✅ 正确!裸模型只能"文字进、文字出"。Correct! A bare model only takes text in and emits text out.
4. 以下哪个属于"终端 CLI 编程 Agent"?Which of these is a terminal CLI coding agent?
✅ 正确!Cursor/Trae 是 AI 原生 IDE,Copilot 是 IDE 助手,Claude Code 是终端 CLI。Correct! Cursor/Trae are AI-native IDEs, Copilot is an IDE assistant, Claude Code is a terminal CLI.
5. 处理谱线数据立方体、计算矩图之前,关键的一步是什么?Before computing moment maps from a spectral cube, the key step is…
✅ 正确!先掩膜再算矩,否则 NaN 与噪声会污染结果。Correct! Mask first, or NaN and noise will contaminate the moments.