修改文档
This commit is contained in:
Shen Wei
@@ -1,102 +1,102 @@
|
||||
---
|
||||
title: GitHub 上 5000 人收藏的 Vibe Coding 神级指南。
|
||||
source: https://mp.weixin.qq.com/s/QMPMSGW6XXk8L-yx4ujQcw
|
||||
author: shenwei
|
||||
published:
|
||||
created: 2025-12-30
|
||||
description:
|
||||
tags: [ai, github, vibe-coding]
|
||||
---
|
||||
|
||||
|
||||
#vibe-coding #ai #github
|
||||
|
||||
|
||||
|
||||
原创 逛逛 [逛逛GitHub](https://mp.weixin.qq.com/s/) *2025年12月27日 15:03*
|
||||
|
||||
Vibe Coding 说白了就是开发个应用不再像程序员一样，苦哈哈地写每一行代码，而是化身为导演。
|
||||
|
||||
只需要 保持一种感觉 ，这种感觉可能是对产品逻辑、用户流程、审美和交互的把握，剩下的体力活全交给 Cursor、Windsurf、Trae 等 AI 编程工具。
|
||||
|
||||
用 Karpathy 的话说： 我几乎不写代码了，我只负责调整氛围（Vibe），代码会自动长出来。
|
||||
|
||||
|
||||
|
||||
前段时间发了一篇文章，盘点了 GitHub 上比较有用的 Vibe Coding 相关开源项目。
|
||||
|
||||
然后在一个 AI 编程的群里，有一个读者分享了另外一个开源项目： vibe-coding-cn
|
||||
|
||||
仔细研究了一下，还挺不错的，分享给大家。
|
||||
|
||||
|
||||
|
||||
01
|
||||
|
||||
**项目简介**
|
||||
|
||||
这个叫 vibe-coding-cn 的开源项目 让国内开发者能光速跟上这波浪潮。
|
||||
|
||||
是 Vibe Coding 氛围感编程的 中文指南 ，汇集了目前全球最顶尖的 AI 编程资源。
|
||||
|
||||
下面是这个开源项目的核心目录：
|
||||
|
||||
|
||||
|
||||
这个开源项目对 Vibe Coding 进行了定义，还挺有意思的。
|
||||
|
||||
Vibe Coding \= **规划驱动 + 上下文固定 + AI 结对执行** ，让「从想法到可维护代码」变成一条可审计的流水线，而不是一团无法迭代的巨石文件。
|
||||
|
||||
|
||||
|
||||
这个中文的 Vibe Coding 中文指南，包括如下几个新的点：
|
||||
|
||||
方法论： 这一部分感觉还是比较玄乎的，其实就是几种准则，看一看就好。
|
||||
|
||||
|
||||
|
||||
AI 编程资源
|
||||
|
||||
还推荐了 AI 模型、IDE 等环境。如果你懒得筛选，直接 Cursor + claude-opus-4.5-xhigh，准没错。
|
||||
|
||||
|
||||
|
||||
除此之外，还有很多学习资源和文档， 大量提示词 Prompt 优化技巧。
|
||||
|
||||
包含数百个精选提示词，涵盖了需求澄清、系统架构设计、分步执行、自测等全链路脚本。支持 Excel 与 Markdown 互转。
|
||||
|
||||
教你如何用自然语言清晰地定义需求，如何让 AI 保持上下文一致，如何一分钟写出一个完整的 Web 应用， 也可以一同学习一下。
|
||||
|
||||
|
||||
|
||||
紧接着这个开源项目，提供一个一个完整流程。帮助你完成基础的设置、开发基础游戏、丰富细节，修复 Bug。
|
||||
|
||||
|
||||
|
||||
给我的感觉，这个开源项目践行 规划就是一切 的理念。
|
||||
|
||||
让 AI 写代码前，必须有清晰的技术选型、实施规划和模块化设计，防止 AI 因为理解偏差导致项目逻辑混乱。
|
||||
|
||||
总而言之，这个开源项目就是 专门为中文开发者设计的 **Vibe Coding 资源库与工作站。**
|
||||
|
||||
**它不仅包含了相关的哲学理论，还提供了一套成体系的工具链、提示词库和开发经验总结，旨在帮助开发者更高效地利用 AI 进行软件开发。**
|
||||
|
||||
```javascript
|
||||
开源地址：https://github.com/tukuaiai/vibe-coding-cn
|
||||
```
|
||||
|
||||
02
|
||||
|
||||
**点击下方卡片，关注逛逛 GitHub**
|
||||
|
||||
这个公众号历史发布过很多有趣的开源项目，如果你懒得翻文章一个个找，你直接关注微信公众号：逛逛 GitHub ，后台对话聊天就行了：
|
||||
|
||||
|
||||
|
||||
继续滑动看下一个
|
||||
|
||||
逛逛GitHub
|
||||
|
||||
向上滑动看下一个
|
||||
|
||||
---
|
||||
title: GitHub 上 5000 人收藏的 Vibe Coding 神级指南。
|
||||
source: https://mp.weixin.qq.com/s/QMPMSGW6XXk8L-yx4ujQcw
|
||||
author: shenwei
|
||||
published:
|
||||
created: 2025-12-30
|
||||
description:
|
||||
tags: [ai, github, vibe-coding]
|
||||
---
|
||||
|
||||
|
||||
#vibe-coding #ai #github
|
||||
|
||||
|
||||
|
||||
原创 逛逛 [逛逛GitHub](https://mp.weixin.qq.com/s/) *2025年12月27日 15:03*
|
||||
|
||||
Vibe Coding 说白了就是开发个应用不再像程序员一样，苦哈哈地写每一行代码，而是化身为导演。
|
||||
|
||||
只需要 保持一种感觉 ，这种感觉可能是对产品逻辑、用户流程、审美和交互的把握，剩下的体力活全交给 Cursor、Windsurf、Trae 等 AI 编程工具。
|
||||
|
||||
用 Karpathy 的话说： 我几乎不写代码了，我只负责调整氛围（Vibe），代码会自动长出来。
|
||||
|
||||
|
||||
|
||||
前段时间发了一篇文章，盘点了 GitHub 上比较有用的 Vibe Coding 相关开源项目。
|
||||
|
||||
然后在一个 AI 编程的群里，有一个读者分享了另外一个开源项目： vibe-coding-cn
|
||||
|
||||
仔细研究了一下，还挺不错的，分享给大家。
|
||||
|
||||
|
||||
|
||||
01
|
||||
|
||||
**项目简介**
|
||||
|
||||
这个叫 vibe-coding-cn 的开源项目 让国内开发者能光速跟上这波浪潮。
|
||||
|
||||
是 Vibe Coding 氛围感编程的 中文指南 ，汇集了目前全球最顶尖的 AI 编程资源。
|
||||
|
||||
下面是这个开源项目的核心目录：
|
||||
|
||||
|
||||
|
||||
这个开源项目对 Vibe Coding 进行了定义，还挺有意思的。
|
||||
|
||||
Vibe Coding \= **规划驱动 + 上下文固定 + AI 结对执行** ，让「从想法到可维护代码」变成一条可审计的流水线，而不是一团无法迭代的巨石文件。
|
||||
|
||||
|
||||
|
||||
这个中文的 Vibe Coding 中文指南，包括如下几个新的点：
|
||||
|
||||
方法论： 这一部分感觉还是比较玄乎的，其实就是几种准则，看一看就好。
|
||||
|
||||
|
||||
|
||||
AI 编程资源
|
||||
|
||||
还推荐了 AI 模型、IDE 等环境。如果你懒得筛选，直接 Cursor + claude-opus-4.5-xhigh，准没错。
|
||||
|
||||
|
||||
|
||||
除此之外，还有很多学习资源和文档， 大量提示词 Prompt 优化技巧。
|
||||
|
||||
包含数百个精选提示词，涵盖了需求澄清、系统架构设计、分步执行、自测等全链路脚本。支持 Excel 与 Markdown 互转。
|
||||
|
||||
教你如何用自然语言清晰地定义需求，如何让 AI 保持上下文一致，如何一分钟写出一个完整的 Web 应用， 也可以一同学习一下。
|
||||
|
||||
|
||||
|
||||
紧接着这个开源项目，提供一个一个完整流程。帮助你完成基础的设置、开发基础游戏、丰富细节，修复 Bug。
|
||||
|
||||
|
||||
|
||||
给我的感觉，这个开源项目践行 规划就是一切 的理念。
|
||||
|
||||
让 AI 写代码前，必须有清晰的技术选型、实施规划和模块化设计，防止 AI 因为理解偏差导致项目逻辑混乱。
|
||||
|
||||
总而言之，这个开源项目就是 专门为中文开发者设计的 **Vibe Coding 资源库与工作站。**
|
||||
|
||||
**它不仅包含了相关的哲学理论，还提供了一套成体系的工具链、提示词库和开发经验总结，旨在帮助开发者更高效地利用 AI 进行软件开发。**
|
||||
|
||||
```javascript
|
||||
开源地址：https://github.com/tukuaiai/vibe-coding-cn
|
||||
```
|
||||
|
||||
02
|
||||
|
||||
**点击下方卡片，关注逛逛 GitHub**
|
||||
|
||||
这个公众号历史发布过很多有趣的开源项目，如果你懒得翻文章一个个找，你直接关注微信公众号：逛逛 GitHub ，后台对话聊天就行了：
|
||||
|
||||
|
||||
|
||||
继续滑动看下一个
|
||||
|
||||
逛逛GitHub
|
||||
|
||||
向上滑动看下一个
|
||||
|
||||
逛逛GitHub
|
||||
@@ -1,58 +1,58 @@
|
||||
---
|
||||
title: YouTube Content Pipeline
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# YouTube Content Pipeline
|
||||
|
||||
As a daily YouTube creator, finding fresh, timely video ideas across the web and X/Twitter is time-consuming. Tracking what you've already covered prevents duplicates and helps you stay ahead of trends.
|
||||
|
||||
This workflow automates the entire content scouting and research pipeline:
|
||||
|
||||
• Hourly cron job scans breaking AI news (web + X/Twitter) and pitches video ideas to Telegram
|
||||
• Maintains a 90-day video catalog with view counts and topic analysis to avoid re-covering topics
|
||||
• Stores all pitches in a SQLite database with vector embeddings for semantic dedup (so you never get pitched the same idea twice)
|
||||
• When you share a link in Slack, OpenClaw researches the topic, searches X for related posts, queries your knowledge base, and creates an Asana card with a full outline
|
||||
|
||||
## Skills you Need
|
||||
|
||||
- `web_search` (built-in)
|
||||
- [x-research-v2](https://clawhub.ai) or custom X/Twitter search skill
|
||||
- [knowledge-base](https://clawhub.ai) skill for RAG
|
||||
- Asana integration (or Todoist)
|
||||
- `gog` CLI for YouTube Analytics
|
||||
- Telegram topic for receiving pitches
|
||||
|
||||
## How to Set it Up
|
||||
|
||||
1. Set up a Telegram topic for video ideas and configure it in OpenClaw.
|
||||
2. Install the knowledge-base skill and x-research skill.
|
||||
3. Create a SQLite database for pitch tracking:
|
||||
```sql
|
||||
CREATE TABLE pitches (
|
||||
id INTEGER PRIMARY KEY,
|
||||
timestamp TEXT,
|
||||
topic TEXT,
|
||||
embedding BLOB,
|
||||
sources TEXT
|
||||
);
|
||||
```
|
||||
4. Prompt OpenClaw:
|
||||
```text
|
||||
Run an hourly cron job to:
|
||||
1. Search web and X/Twitter for breaking AI news
|
||||
2. Check against my 90-day YouTube catalog (fetch from YouTube Analytics via gog)
|
||||
3. Check semantic similarity against all past pitches in the database
|
||||
4. If novel, pitch the idea to my Telegram "video ideas" topic with sources
|
||||
|
||||
Also: when I share a link in Slack #ai_trends, automatically:
|
||||
1. Research the topic
|
||||
2. Search X for related posts
|
||||
3. Query my knowledge base
|
||||
4. Create an Asana card in Video Pipeline with a full outline
|
||||
```
|
||||
---
|
||||
title: YouTube Content Pipeline
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# YouTube Content Pipeline
|
||||
|
||||
As a daily YouTube creator, finding fresh, timely video ideas across the web and X/Twitter is time-consuming. Tracking what you've already covered prevents duplicates and helps you stay ahead of trends.
|
||||
|
||||
This workflow automates the entire content scouting and research pipeline:
|
||||
|
||||
• Hourly cron job scans breaking AI news (web + X/Twitter) and pitches video ideas to Telegram
|
||||
• Maintains a 90-day video catalog with view counts and topic analysis to avoid re-covering topics
|
||||
• Stores all pitches in a SQLite database with vector embeddings for semantic dedup (so you never get pitched the same idea twice)
|
||||
• When you share a link in Slack, OpenClaw researches the topic, searches X for related posts, queries your knowledge base, and creates an Asana card with a full outline
|
||||
|
||||
## Skills you Need
|
||||
|
||||
- `web_search` (built-in)
|
||||
- [x-research-v2](https://clawhub.ai) or custom X/Twitter search skill
|
||||
- [knowledge-base](https://clawhub.ai) skill for RAG
|
||||
- Asana integration (or Todoist)
|
||||
- `gog` CLI for YouTube Analytics
|
||||
- Telegram topic for receiving pitches
|
||||
|
||||
## How to Set it Up
|
||||
|
||||
1. Set up a Telegram topic for video ideas and configure it in OpenClaw.
|
||||
2. Install the knowledge-base skill and x-research skill.
|
||||
3. Create a SQLite database for pitch tracking:
|
||||
```sql
|
||||
CREATE TABLE pitches (
|
||||
id INTEGER PRIMARY KEY,
|
||||
timestamp TEXT,
|
||||
topic TEXT,
|
||||
embedding BLOB,
|
||||
sources TEXT
|
||||
);
|
||||
```
|
||||
4. Prompt OpenClaw:
|
||||
```text
|
||||
Run an hourly cron job to:
|
||||
1. Search web and X/Twitter for breaking AI news
|
||||
2. Check against my 90-day YouTube catalog (fetch from YouTube Analytics via gog)
|
||||
3. Check semantic similarity against all past pitches in the database
|
||||
4. If novel, pitch the idea to my Telegram "video ideas" topic with sources
|
||||
|
||||
Also: when I share a link in Slack #ai_trends, automatically:
|
||||
1. Research the topic
|
||||
2. Search X for related posts
|
||||
3. Query my knowledge base
|
||||
4. Create an Asana card in Video Pipeline with a full outline
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user