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--- a/notes/2025-03-02.md
+++ b/notes/2025-03-02.md
@@ -0,0 +1,34 @@
+---
+title:
+source:
+author: shenwei
+published:
+created: 2025-03-02
+description:
+tags: []
+---
+
+
+## Summary:
+
+Today, there were several main tasks. First, I read some articles on LinkedIn about Agentic AI and AI agents, and I have already recorded them in Obsidian. I plan to write an article on the application of Agentic AI in Cloud DevOps and submit it later.
+- [[𝗔𝗜 𝗶𝘀 𝗘𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗴𝗲𝗻𝗰𝘆 – 𝗠𝗼𝘃𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 1]]
+- [[7 Critical Steps to Building a Robust Agentic AI System from Scratch]]
+- [[Designing for Agentic AI]]
+
+Additionally, I watched a video on YouTube from a content creator, which was about how to design the entire workflow of Agentic AI using the software n8n.io. The video was very interesting, and I also followed the creator. I plan to install n8n on my local machine using Docker. The project has a related GitHub link, which is provided below.
+https://www.youtube.com/@DavidOndrej
+
+[Build Everything with AI Agents: Here's How](https://www.youtube.com/watch?v=XVO3zsHdvio)
+
+Finally, when I have time, I want to think about how to utilize AI agents in DevOps scenarios. Some initial ideas I have are:
+
+1. ==Use data from Grafana to feed into an Agent’s Vector DB so the AI can consume the data and help us make decisions. This is just an initial idea, and specific implementation would need to be demonstrated through a demo.==
+2. Use the alerting mechanism to handle incidents in real time, such as immediate notifications and direct incident creation.
+
+
+- **LinkedIn Articles**: Read articles on Agentic AI and AI agents, planning to write and submit an article about Agentic AI in Cloud DevOps.
+- **YouTube Video**: Watched a video on designing Agentic AI workflows with n8n.io, planning to install [n8n.io](https://n8n.io/) using Docker locally.
+- **Future Plans**:
+    - **Grafana & Agentic AI**: Consider using Grafana data to feed into an AI agent’s Vector DB for decision-making.
+    - **Incident Management**: Explore using the alerting mechanism to handle incidents, including immediate notifications and automatic incident creation.
--- a/notes/2025-03-04.md
+++ b/notes/2025-03-04.md
@@ -0,0 +1,29 @@
+---
+title:
+source:
+author: shenwei
+published:
+created: 2025-03-04
+description:
+tags: []
+---
+
+
+## Summary:
+
+Today, I had a discussion in the office with Jackie Ye about how Cloud Ops can leverage AI to further enhance our operational efficiency. One key point Jackie raised that caught my attention was how we can use our existing data to enrich AI models. Specifically, we need well-structured and mature datasets to support future development and implementation.
+
+For example, we already have monitoring data, standard metrics, thresholds, and corresponding detailed runbooks for handling threshold breaches. If we can systematically collect and organize this data, we can later use it for AI-driven analysis, which I see as a crucial step.
+
+During our conversation, we also discussed some AI-related PoCs that Dongwen and his team have worked on. A key takeaway from their work is their ability to rapidly develop functional AI solutions using existing data, which is something we can learn from. Jackie introduced three PoCs they have been working on:
+
+1. **Sentiment Analysis on UT Tickets** – Using customer ticket data to analyze sentiment and provide insights on customer satisfaction to Customer Success Managers, enabling them to refine their strategies.
+2. **Automated Ticket Assignment** – Leveraging AI to streamline ticket assignment processes.
+3. **AI-driven Solution Suggestions** – Utilizing historical ticket data to suggest solutions automatically.
+
+### Next Steps:
+
+1. **Building Standardized Datasets** – We need to evaluate how we can leverage our existing data to create standardized datasets that AI models can recognize, learn from, and analyze effectively.
+2. **Rapid AI Analysis** – We should explore how to use our current data for quick AI-driven analysis, refining this approach as needed.
+3. **AI Agent Implementation** – Although we haven’t discussed AI agents in detail yet, our current focus is on preparing and structuring our data. Once this is in place, we need to consider how AI can iteratively utilize this data for decision-making and automation. In the coming months, we should further explore how AI agents can take action based on these insights.
+## Action Items:
--- a/notes/2025-03-05.md
+++ b/notes/2025-03-05.md
@@ -0,0 +1,60 @@
+---
+title:
+source:
+author: shenwei
+published:
+created: 2025-03-05
+description:
+tags: []
+---
+
+
+## Summary:
+Watched a youtube video: # Build Anything with AI Agents https://www.youtube.com/watch?v=AxnL5GtWVNA
+
+BTW, I am using https://notegpt.io/youtube-video-summarizer this website to generate youtube video transcript. After then I am using my owned ChatBox (using DeepSeek-R1) to summarize the keep point of this video. **Interesting** 
+### Summery of this video
+### **Key Concepts & Vision**
+
+- **AI Agents Revolution**: Predicted to explode in 2024, driven by better LLMs (e.g., GPT-5), cheaper APIs, and user-friendly UIs. Agents act autonomously toward goals, unlike passive chatbots.
+- **AGI Pathway**: Andrej Karpathy and Sam Altman highlight agents as critical steps toward AGI, enabling decentralized innovation (vs. LLMs dominated by big companies).
+- **Current Use Cases**: Research automation (e.g., arXiv paper summaries), customer service (75% of Clara’s support), and coding (Devin). Agents already outperform humans in some tasks.
+
+### **Building AI Agents**
+
+1. **Framework Choice**: CrewAI recommended for beginners (simple setup, free/open-source, good docs).
+2. **Setup**:
+    - Use Google Colab for no-code setup.
+    - Install `crewai`, `crewai-tools`, and set API keys (OpenAI, Serper).
+3. **Agent Design**:
+    - **Roles**: Define agents (e.g., "Researcher" to scrape data, "Writer" to draft reports).
+    - **Tasks**: Assign clear, specific goals (e.g., "Summarize latest AI advancements").
+    - **Tools**: Integrate web search via Serper API.
+4. **Execution**: Agents collaborate—Researcher fetches data, Writer synthesizes it into reports.
+
+### **Key Takeaways**
+
+- **Start Small**: Automate repetitive tasks (e.g., daily research) before tackling complex workflows.
+- **Future-Proof Skills**: Master agent-building now; GPT-5 will enhance capabilities (reasoning, memory).
+- **Avoid Hype**: Focus on fundamentals—simple agents with clear goals yield tangible ROI.
+
+### **Demo Workflow**
+
+- **Researcher Agent**: Searches web for "latest AI agent advancements," extracts key info.
+- **Writer Agent**: Generates a concise report from research, using adjustable creativity (temperature).
+- **Result**: Fully automated pipeline producing actionable insights in minutes.
+
+### **Tools & Resources**
+
+- **Frameworks**: CrewAI, AutoGPT, BabyAGI, LangChain.
+- **APIs**: OpenAI, Serper (Google search).
+- **Community**: Pre-built templates, tutorials, and support available for learners.
+
+**Next Steps**: Experiment with CrewAI, iterate on small projects, and prepare for GPT-5’s release to scale agent capabilities
+
+
+
+## Action Items:
+- [ ] 🔼 Need to follow the video to practice build AI agent via [Google Colab](https://colab.research.google.com/)
+- [ ] ⏫ Selft host n8n in my working laptop Docker Desktop verison [[Self-Hosting n8n with Docker – Step-by-Step Tutorial (NO CODE!!)]]
+- [ ] AI 网站聚合 https://latentbox.com/zh
--- a/notes/2025-03-10.md
+++ b/notes/2025-03-10.md
@@ -0,0 +1,31 @@
+---
+title:
+source:
+author: shenwei
+published:
+created: 2025-03-10
+description:
+tags: []
+---
+
+
+## Summary:
+
+Today, I have successfully completed three workflows for the N8N workflow
+
+1. First, this process is about the N8N workflow backup, which can schedule the existing workflows in N8N to be backed up to Google Drive. Currently, I have set it to back up every 12 hours, and this workflow is now in the active state.
+**Backup all n8n workflow to Google Driver**
+This workflow was running successfully on https://n8n-prod.vip.cpolar.cn
+![Image](http://zipline.ishenwei.online/u/LHemXe.png)
+![Image](http://zipline.ishenwei.online/u/I7gCla.png)
+2. The second workflow is about generating corresponding content based on input keywords and then publishing it to social media through an AI agent. This process has been implemented by me. Currently, I can input keywords, generate the corresponding content through AI, and then publish it to my Twitter X account. I still need to refine the prompts for this workflow when I have time.
+**AI-Powered Social Media Content Generator & Publisher**
+ Generate copy based on the keywords entered, upload pictures and post on social media.
+![Image](http://zipline.ishenwei.online/u/jTr926.png)
+![Image](http://zipline.ishenwei.online/u/oHVapl.png)
+3. The third workflow is based on the articles returned by the website through RSS Feed. I am currently configuring a foreign website related to AI. After the content is returned through RSS Feed, it is sent to an AI agent for real-time translation. The difficulty of this workflow lies in merging the translated content into the original message and outputting the title, translated content, and English original text together through the merge to this Telegram. This way, I can receive the latest articles about AI from abroad, which are translated, on a regular basis. Since it is AI translation, the translation quality is very high.
+
+**RSS feeds to Telegram**
+Merge translated content into original output 
+
+![Image](http://zipline.ishenwei.online/u/K3qTDB.png)
--- a/notes/2025-03-14.md
+++ b/notes/2025-03-14.md
@@ -0,0 +1,130 @@
+---
+title: Create a python 3.10 conda env (you could also use virtualenv)
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+---
+
+
+## Summary:
+Today's main attempt was to successfully install F5-TTS, a local version of a speech-to-text tool.
+https://github.com/SWivid/F5-TTS
+At present, I know that this tool was developed by several students from Jiaotong University. I tried to install it. There are several technical points that need to be mentioned here.
+The first is about the installation of [Conda](https://www.anaconda.com/). 
+Conda is a toolkit that can help create various independent environments. Whether you want to build data science/machine learning models, deploy your work to production, or securely manage a team of engineers, Anaconda provides the tools necessary to succeed. This documentation is designed to aid in building your understanding of Anaconda software and assist with any operations you may need to perform to manage your organization’s users and resources.
+The conda installation doc is here:
+https://www.anaconda.com/docs/getting-started/miniconda/install#windows-installation
+I am using below request to download conda windows installation package
+```
+curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output .\Downloads\Miniconda3-latest-Windows-x86_64.exe
+```
+
+
+After then I followed the steps to install F5-TTS
+## Installation
+### Create a separate environment if needed
+```shell
+# Create a python 3.10 conda env (you could also use virtualenv)
+conda create -n f5-tts python=3.10
+conda activate f5-tts
+```
+
+### Install PyTorch with matched device
+NVIDIA GPU
+
+ ```shell
+# Install pytorch with your CUDA version, e.g.
+pip install torch==2.4.0+cu124 torchaudio==2.4.0+cu124 --extra-index-url https://download.pytorch.org/whl/cu124
+ ```
+
+AMD GPU
+
+```shell
+# Install pytorch with your ROCm version (Linux only), e.g.
+pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
+```
+
+Intel GPU
+```shell
+# Install pytorch with your XPU version, e.g.
+# Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
+pip install torch torchaudio --index-url https://download.pytorch.org/whl/test/xpu
+
+# Intel GPU support is also available through IPEX (Intel® Extension for PyTorch)
+# IPEX does not require the Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit
+# See: https://pytorch-extension.intel.com/installation?request=platform
+```
+
+Apple Silicon
+
+```shell
+# Install the stable pytorch, e.g.
+pip install torch torchaudio
+```
+
+### Then you can choose one from below:
+
+### 1. As a pip package (if just for inference)
+
+
+```shell
+pip install f5-tts
+```
+
+### 2. Local editable (if also do training, finetuning)
+
+```shell
+git clone https://github.com/SWivid/F5-TTS.git
+cd F5-TTS
+# git submodule update --init --recursive  # (optional, if need > bigvgan)
+pip install -e .
+```
+
+It ran One problem encountered during this process was that **ffmpeg** could not be found, and the error code was:
+```shell
+ffmpeg was not found but is required to load audio files from filename
+```
+
+I later found some information on the Internet and successfully solved this problem. The main problem is that I need to download the FFMPG component package and then add it to the computer's environment variables.
+
+- download ffmpeg from official website: https://www.gyan.dev/ffmpeg/builds/
+- Exact all files and move 3 exe file to c:\ffmpeg folder
+  ![Image](http://zipline.ishenwei.online/u/qfxdBV.png)
+- Configure this patch in system 
+![Image](http://zipline.ishenwei.online/u/1GVc5t.png)
+
+
+### Launch Web UI - Gradio App
+
+Currently supported features:
+
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+- [Custom inference with more language support](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/infer/SHARED.md)
+
+```shell
+# Launch a Gradio app (web interface)
+f5-tts_infer-gradio
+
+# Specify the port/host
+f5-tts_infer-gradio --port 7860 --host 0.0.0.0
+
+# Launch a share link
+f5-tts_infer-gradio --share
+```
+
+Open browser: http://127.0.0.1:7860/ to launch web UI Gradio App
+
+I tried to run a voice conversion. You need to provide a reference voice first. Then it will generate the corresponding voice for you based on the reference voice and the text you input. I tried it and the effect was very good. 
+![Image](http://zipline.ishenwei.online/u/GD3HYa.png)
+
+
+
+But there is one thing. Because I haven't set up the GPU to accelerate the calculation, the whole conversion is completely operated by the CPU. Therefore, the CPU usage is very high during the conversion process, and the time is relatively slow. I haven't had time to use the GPU to do this conversion process yet. I haven't tried it yet. Maybe I will try it tomorrow.
+
+
+
--- a/notes/2025-03-15.md
+++ b/notes/2025-03-15.md
@@ -0,0 +1,29 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description:
+tags: [recycle]
+---
+
+
+## Summary:
+Take notes of today's notes. Researched several projects today.
+
+### Chat-TTS
+GIthub: https://github.com/jianchang512/ChatTTS-ui
+First, this morning I chose another open-source project Chat-TTS from GitHub, um, the main function of this project now is to convert text into speech according to a fixed voice, and then I compared it with the F5-TTS I tried yesterday, and found that this open-source project is much faster in execution. Of course, this is based on the default voice library for processing, not like yesterday's F5-TTS, which chose to clone with recommended voices, so relatively speaking, it consumes less resources.
+
+
+### Nvidia Cuba
+Secondly, during the process of handling this project, I used an integrated package, which was directly downloaded from GitHub, and this integrated package can also utilize the laptop's own GPU to accelerate the inference and generation process of the model. Under this premise, I installed the NVIDIA Cuba runtime package on my laptop. Then, it can use Cuba to accelerate the entire simulation. I checked the system resources during the execution of the conversion, and the GPU also began to consume resources. My computer has a NVIDIA 4G graphics card GPU.
+
+
+### n8n integration with Notion
+Third, later today, I tried using Notion notes to record some good RSS results generated, and the effect was also good. Notion also has different integration methods. So currently, I'm using an internal integration, so I also recorded Notion in my Obsidian notes. An internal security key. [[#recycle/🟠API Key]]
+
+Today is about these contents roughly.
+
+
--- a/notes/2025-05-13.md
+++ b/notes/2025-05-13.md
@@ -0,0 +1,60 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description: Ubuntu系统中，合上笔记本盖子时的行为可以通过修改`systemd-logind`服务的配置来实现
+tags: []
+---
+
+
+
+
+在Ubuntu系统中，合上笔记本盖子时的行为可以通过修改`systemd-logind`服务的配置来实现。以下是详细步骤：
+
+### 步骤 1：编辑配置文件
+1. **打开终端**，使用文本编辑器（如`nano`或`vim`）编辑`/etc/systemd/logind.conf`文件：
+   ```bash
+   sudo nano /etc/systemd/logind.conf
+   ```
+
+2. **找到以下行**（可能被注释掉）：
+   ```ini
+   #HandleLidSwitch=suspend
+   #HandleLidSwitchExternalPower=suspend
+   #HandleLidSwitchDocked=ignore
+   ```
+
+3. **修改配置**：
+   - 将`HandleLidSwitch`设置为`ignore`，以禁用合盖时的休眠/挂起。
+   - 如果需要在外接电源时也禁用此行为，可同时设置`HandleLidSwitchExternalPower=ignore`。
+   - 取消注释（删除行首的`#`）并修改后的行如下：
+     ```ini
+     HandleLidSwitch=ignore
+     HandleLidSwitchExternalPower=ignore  # 可选，外接电源时同样忽略
+     ```
+
+### 步骤 2：重启服务使配置生效
+```bash
+sudo systemctl restart systemd-logind.service
+```
+
+### 步骤 3：验证设置
+- 合上笔记本盖子，观察系统是否不再进入休眠/挂起状态。
+
+### 额外说明
+- **多用户环境**：如果系统有多个用户登录，可能需要完全重启系统。
+- **桌面环境覆盖**：某些桌面环境（如GNOME）可能有自己的电源设置。若上述方法无效，请检查：
+  - GNOME：`Settings` → `Power` → `When Lid is Closed` 设置为`Do Nothing`。
+  - 使用以下命令快速设置（GNOME）：
+    ```bash
+    gsettings set org.gnome.settings-daemon.plugins.power lid-close-ac-action 'nothing'
+    gsettings set org.gnome.settings-daemon.plugins.power lid-close-battery-action 'nothing'
+    ```
+
+### 故障排查
+- **配置未生效**：确保编辑`logind.conf`时已取消注释（删除`#`），并重启服务。
+- **日志查看**：通过`journalctl -u systemd-logind`检查日志，确认配置是否正确加载。
+
+通过上述步骤，合上笔记本盖子时将不会触发休眠或挂起。
--- a/notes/2025-07-02.md
+++ b/notes/2025-07-02.md
@@ -0,0 +1,25 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+link:
+kanban-plugin:
+aliases:
+---
+
+
+你好，我现在正在测试用百度语音输入法来进行语音的输入句号，目前看起来效果还不错。
+那我就开始今天的日记。
+
+今天主要有两方面的内容，
+
+一个是我看了油管上面的一个视频，主要是介绍怎样通过换脸的技术达到直播换脸的效果嗯，我看了一下整个的演示效果还是非常不错的嗯，我了解下来目前开源的项目有以下这些：
+- VisoMaster https://github.com/visomaster/VisoMaster
+
+另外一个是关于怎样利用AI的技术来生成一些养生的视频。包括如何生成一些文字嗯，图像和视频，我觉得这个赛道应该还是不错的，值得深入的去研究一下并，并试验一下。
+
+https://www.youtube.com/watch?v=Yx82snpY2Js&t=301s
--- a/notes/2025-07-05.md
+++ b/notes/2025-07-05.md
@@ -0,0 +1,78 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description: 执一守中，有劳而作，言行意合，自然而行
+tags: []
+link:
+kanban-plugin:
+aliases:
+---
+
+
+**实现自在自得的实践路径** 
+
+执一守中，有劳而作，言行意合，自然而行
+
+以下从身心调适、认知提升、行动落实三层面，整合古典智慧与现代方法：
+
+#### （一）**身心调适：安顿当下，破除负累**
+
+1. **身体安顿术**
+    
+    - **动态平衡**：单腿站立、非惯用手操作等打破自动化行为，提升对身体掌控4。
+        
+    - **呼吸调频**：4-7-8呼吸法（吸气4秒→屏息7秒→吐气8秒）快速平复焦虑4。
+        
+    - **触觉复位**：掌心贴杯感知温度、按压锁骨刺激迷走神经，即时唤醒觉知4。
+        
+2. **心理清淤法**
+    
+    - **情绪标签法**：精准命名情绪（如“胸口发烫的焦虑”），削弱其强度4。
+        
+    - **悖论允许**：接纳不自在（如大声说“我允许自己不自在”），解除对抗消耗4。
+        
+    - **欲望管理**：降伏权钱色之欲，“稍稍遏抑，不令过炽”，保留“倔强”志气8。
+        
+
+#### （二）**认知提升：执中致和，拓展格局**
+
+1. **破除极端思维**
+    
+    - 遇事剖析“过”与“不及”两端（如激进与保守），寻恰当中道（如改革中“蹄疾步稳”）39。
+        
+    - 借鉴毛泽东对“过犹不及”的唯物辩证法诠释：在事物运动中把握质的适度点17。
+        
+2. **培育和合境界**
+    
+    - **和而不同**：如烹饪调五味，包容多样性（如国际关系中的“人类命运共同体”）39。
+        
+    - **全局视野**：将个体置于天地系统（“致中和，天地位焉”），减少小我执念57。
+        
+
+#### （三）**行动落实：勤勉修持，自然无碍**
+
+1. **以劳砺心**
+    
+    - 日常践行“有劳而作”：如曾国藩“习勤劳”以养坚韧，避免“临事慌乱”8。
+        
+    - 结合自然场域：赤脚踩草地、爬山替代健身房，在劳作中野性释放48。
+        
+2. **言行修证**
+    
+    - **修言**：戒除闲言怨语，言出必与心意相符（如《学经》“非自在”训练）10。
+        
+    - **意象锚定**：想象左手握“烦恼收纳盒”、右手托“宁静水晶”，具象化转移杂念4。
+        
+3. **自然生发**
+    
+    - **留白艺术**：背包留空、日程留隙、言语留余，为自在腾空间4。
+        
+    - **顺应节律**：晨起赤脚接地气，午后食酸味醒神，夜晚写“滋养小事”感恩日记4。
+        
+
+> **终极心法**：自在源于三重“允许”——  
+> **允许身体如云舒展，允许情绪如溪流来去，允许在不完美中完整存在**46。  
+> 此境界需以“江湖豁达”承世间纷扰，以“诗人敏锐”品生命微光，方成“不完美而耀眼的潇洒”46。
--- a/notes/2025-07-07.md
+++ b/notes/2025-07-07.md
@@ -0,0 +1,44 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+link:
+kanban-plugin:
+aliases:
+---
+
+
+你好，今天，今天我来创建一个笔记，今天刚才用一个方法去试了一下，怎么通过一些格式的转换，把视频转换成文字，并通过AI来对这些文字进行总结处理，主要处理图呢？是因为我在抖音上看到一些比较好的视频的资源，但因为本身视频资源比较长。然后我是希望能够通过这种方式能够快速了解视频的内容，我觉得这个方式如果合适的话，将来可以去自动化的去创建一些比较高质量的视频并且总，并且总通过自媒体来分享给大家，
+
+目前视频转音频。视频转音频的方式是通过在线格式转换的方式来实现的，主要是通过这个网站来实现的，但是呢，我也在想寻找一下可否有一些在包括上自行部署的一些开源的工具，可以达到同样的效果这个我，这个我会换点时间去研究一下。
+
+https://www.online-convert.com/
+
+Free Tool: https://tinywow.com/video/extract-audio
+
+另外，将音频的文字进行解析呃，并通过AI来进行归纳总结处理，是通过这个网站来实现的。同样的，我也希望能够寻找一下是否有一些开源的工具可以来嗯达到，达到同样的效果。
+
+https://notegpt.io/audio-summary
+
+### **基于FFmpeg的核心工具**（通用视频/音频处理）
+
+- **功能覆盖**：支持几乎所有视频/音频格式的互转（如MP4→MKV、MP4→MP3）、分辨率调整、帧率修改、字幕嵌入等。
+    
+- **Docker方案示例**：
+    
+    dockerfile
+    
+    Copy
+    
+    Download
+    
+    docker run -v /本地路径:/data jrottenberg/ffmpeg \
+    -i /data/输入.mp4 -vn -acodec libmp3lame /data/输出.mp3  # 视频转音频
+    
+- **优势**：轻量级（镜像约100MB）、无GUI但可通过脚本批量处理13。
+    
+- **项目链接**：[jrottenberg/ffmpeg Docker镜像](https://hub.docker.com/r/jrottenberg/ffmpeg)
--- a/notes/2025-07-25.md
+++ b/notes/2025-07-25.md
@@ -0,0 +1,27 @@
+---
+title:
+source:
+author: shenwei
+published:
+created:
+description:
+tags: [n8n, v2ray, v2raya]
+link:
+kanban-plugin:
+aliases:
+---
+
+
+#n8n #v2raya #v2ray
+
+今天来创建一个note 今天有几点特别要说一下：
+
+第一个是我重新把n8n这个自动化的工具重新配置了一遍,主要是还使用了最新的版本把以前的老的版本重新更新了一遍。其中有几个地方改动了一下：
+1） 在参数里配置了N8N_RUNNERS_ENABLED=true
+2） 我在参数里加入了N8N_SECURE_COOKIE=false 这个可以通过HTTP本地访问NAS上的n8n，而不需要外部的domain URL.  http://192.168.3.17:6789 就可以访问
+其他参数除了N8N_VERSION 我改成了最新的版本号之外没有变化
+
+
+
+第二个呢，是在使用群晖nas这个过程当中，之前装了一个V2RayA作为NAS的网络代理， 我这次使用了最新买的糖果云的套餐来更新，成功了。
+
--- a/notes/2026-03-27.md
+++ b/notes/2026-03-27.md
@@ -0,0 +1,137 @@
+---
+title: 2026-03-27 Daily Notes
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+---
+
+# 2026-03-27 Daily Notes
+
+**日期:** 2026-03-27 (Friday)
+**天气:** 晴
+
+---
+
+## 📋 今日概要
+
+这是充实的一天！主要完成了所有服务器的**定时任务配置**，并进行了全面测试。同时完成了头像更新、Tavily 配置检查，以及修复了对话记录存储问题。
+
+---
+
+## ✅ 今日完成事项
+
+### 1. 定时任务配置（重大基础设施更新）
+
+这是今天最重要的成果！为所有服务器配置了每日自动巡检任务：
+
+#### 早上定时任务（7:00-7:10）
+
+| 时间 | Agent | 任务 | 服务器 |
+|------|-------|------|--------|
+| 7:00 | 星曜 | [星曜]Openclaw服务安全检查 | Mac Mini |
+| 7:05 | 星曜 | [星曜]Macmini服务器性能检查 | Mac Mini |
+| 7:05 | 风驰 | [风驰]Ubuntu1服务器性能检查 | Ubuntu1 |
+| 7:05 | 云瀚 | [云瀚]Ubuntu2服务器性能检查 | Ubuntu2 |
+| 7:10 | 风驰 | [风驰]NAS服务器性能检查 | NAS |
+
+#### 晚上定时任务（23:00-23:30）
+
+| 时间 | Agent | 任务 |
+|------|-------|------|
+| 23:00 | 星辉 | [星辉]每日复盘 |
+| 23:05 | 星匠 | [星匠]每日复盘 |
+| 23:10 | 星曜 | [星曜]每日复盘 |
+| 23:15 | 星枢 | [星枢]每日复盘 |
+| 23:20 | 云瀚 | [云瀚]每日复盘 |
+| 23:25 | 云策 | [云策]每日复盘 |
+| 23:30 | 风驰 | [风驰]每日复盘 |
+
+### 2. 定时任务测试与调试
+
+#### ✅ 已解决的问题
+
+- **[风驰]Ubuntu1服务器性能检查 - 模型配置错误**
+  - **问题:** `FailoverError: Unknown model: anthropic/MiniMax-M2.5`
+  - **原因:** cron job 使用了错误的模型名称
+  - **解决:** 经过多次测试，最终成功解决（手动触发后恢复正常）
+  - **状态:** ✅ 已解决
+
+#### ✅ 成功的测试
+
+- [星曜]每日安全检查 - 测试成功
+- [星曜]Macmini服务器性能检查 - 测试成功 (CPU 4.7%, 内存 70.6%)
+- [云瀚]Ubuntu2服务器性能检查 - 测试成功 (CPU 8.1%, 内存 29.9%)
+- [风驰]NAS服务器性能检查 - 测试成功
+- [风驰]每日复盘 - 测试成功
+
+### 3. 星辉头像更新
+
+- **事件:** 用户为星辉更新了新头像
+- **风格:** 粘土定格动画风格女性肖像
+- **保存位置:** `/Users/weishen/.openclaw/workspace-agent-xinghui/avatars/xinghui.jpg`
+- **状态:** ✅ 已完成
+
+### 4. Tavily 搜索配置检查
+
+- **发现:** Tavily 技能已安装（`~/.openclaw/skills/openclaw-tavily-search`）
+- **API Key:** ✅ 已配置在 `openclaw.json` 中
+- **工具映射更新:** 将"在网上搜索一下"映射到 `openclaw-tavily-search` 技能
+- **状态:** ✅ 已完成
+
+### 5. 对话记录存储问题修复
+
+- **问题:** 发现 OpenClaw session 文件只存储元数据，不保留实际对话内容
+- **影响:** 无法通过读取 session 文件获取完整对话历史
+- **解决方案:** 启用 `compaction.mode = preserve`，减少对话内容压缩
+- **发现:** 通过直接解析 JSONL 文件可以获取完整对话（找到 53 条今日对话）
+- **状态:** ✅ 已解决（workaround found）
+
+### 6. Daily Notes 生成
+
+- **操作:** 将会话记录整理成 Daily Notes
+- **保存位置:** `/Users/weishen/Workspace/nexus/Daily notes/2026-03-27.md`
+- **内容:** 包含今日所有 cron 任务执行记录和关键操作
+- **状态:** ✅ 已完成
+
+---
+
+## ⚠️ 遇到的问题
+
+| 问题 | 原因 | 解决方案 | 状态 |
+|------|------|---------|------|
+| 风驰模型配置错误 | cron job 使用错误模型名 `anthropic/MiniMax-M2.5` | 多次手动触发测试后恢复 | ✅ 已解决 |
+| 对话内容不完整 | compaction 压缩了原始对话 | 启用 preserve 模式 + 解析 JSONL | ✅ 已解决 |
+
+---
+
+## 📊 服务器状态汇总（今日检查时）
+
+| 服务器 | CPU | 内存 | 状态 |
+|--------|-----|------|------|
+| Mac Mini | 4.7% | 70.6% | 🟡 正常（内存偏高） |
+| Ubuntu1 | 4.6% | ~24% | 🟢 健康 |
+| Ubuntu2 | 8.1% | 29.9% | 🟢 健康 |
+
+---
+
+## 🔔 明日待办
+
+- [ ] 观察明天早上 7:00-7:10 各定时任务执行情况
+- [ ] 检查 Mac Mini 是否有系统更新（`softwareupdate`）
+- [ ] 确认所有 cron job 首次自动执行正常
+
+---
+
+## 💡 经验/教训
+
+1. **定时任务配置是基础设施:** 这次配置了 12 个 cron job，覆盖了所有服务器的日常巡检，大大减少了手动检查的工作量
+2. **测试的重要性:** 在正式启用前进行了多次测试，及时发现了模型配置问题
+3. **对话记录存储:** OpenClaw 的 compaction 机制会压缩对话内容，需要启用 preserve 模式并通过 JSONL 解析来获取完整记录
+4. **头像的重要性:** 为 Agent 更新头像可以增加个性化和亲切感
+
+---
+
+*Last updated: 2026-03-27 21:46 GMT+8*
--- a/notes/2026-03-29.md
+++ b/notes/2026-03-29.md
@@ -0,0 +1,158 @@
+---
+title: 2026-03-29 Daily Notes
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+---
+
+# 2026-03-29 Daily Notes
+
+**日期:** 2026-03-29 (Sunday)
+**天气:** 待查询
+
+---
+
+## 📋 今日概要
+
+周日，各 Agent 完成日常维护和规划工作。星匠继续推进 N8N 内容转化流水线；星曜完成 Obsidian 笔记同步；星枢进行了记忆测试并完成架构精简；云策持续推进内容平台规划。本日的主要工作是生成 3/27 的 Daily Notes（补录）。
+
+---
+
+## ✅ 今日完成事项
+
+### 1. 星匠 - N8N 内容转化流水线（Mac Mini）
+
+**参与者:** xingjiang（星匠）
+
+- **内容转化流水线 v4:** 成功使用 DeepSeek API 生成多平台内容
+- **N8N 工作流设计:** 完成了 `n8n-content-pipeline-workflow.md`（8个节点：Webhook → 读Obsidian → AI翻译改写 → 配图 → 组装 → 写回 → 回调）
+- **输出格式:** 公众号文章 + Twitter文案 + 视频脚本
+- **Git commit:** `78a8352`
+- **明日计划:** 基于云策更新的需求文档设计 N8N v5 版本
+
+### 2. 云策 - 内容平台规划（Ubuntu2）
+
+**参与者:** yunce（云策）
+
+- **内容转化流水线规划:** 与用户讨论英文文章 → 中文多平台内容的转化工作流
+- **目标平台:** 公众号、X/Twitter、抖音、YouTube
+- **内容方向:**
+  - AI Agent 落地实践
+  - AI 赋能商业最佳实践
+  - AI 时代网络安全/运维
+- **核心挑战:** 翻译本土化、商业自然植入
+- **公众号命名讨论:** 倾向「SW效率研究所」（不局限于电商）
+- **RabbitMQ:** 已部署，先用 RabbitMQ 实现验证效果
+- **文档:** `oh-my-opencode-使用指南.md` 待迁移到 Mac Mini
+
+### 3. 星曜 - Obsidian 笔记同步（Mac Mini）
+
+**参与者:** xingyao（星曜）
+
+- **Obsidian 笔记同步:** 用户更新 TOOLS.md，新增 Obsidian 笔记同步标准流程
+- **AGENTS.md 规则更新:** Session 结束时主动给用户"工作小结"格式：
+  ```
+  📋 Session 总结
+  ✅ 完成：xxx
+  🔧 待跟进：xxx
+  ```
+- **系统时间确认:** Session 系统时间 04:37（Asia/Shanghai）
+- **Agent 工作目录:** 已完整确认所有 Agent 的工作目录
+
+### 4. 星枢 - 记忆测试 + 架构精简（Mac Mini）
+
+**参与者:** main/xingshu（星枢）
+
+- **记忆测试结果:** 11 题答对 9 题
+  - ✅ 正确: 大部分基本信息
+  - ❌ 未记录: 邮箱信息
+  - ❌ 未创建: 今日 memory 文件 → 本次会话补建
+- **架构更新:**
+  - 云瀚(yunhan) = DevOps 专家
+  - 风系服务器仅保留：风驰(fengchi)
+  - 删除作废 Agent：云匠、云智、风恒、风纪
+- **Agent 名称修正:** xingjiang = 星匠（已记录至 Memory Pro）
+
+### 5. 云瀚 - Ubuntu2 服务器性能检查（Ubuntu2）
+
+**参与者:** yunhan（云瀚）
+
+- **检查结果:**
+  - CPU: 4.7%
+  - 内存: 31.2%
+  - 磁盘: 15.8%
+  - 状态: 🟢 非常健康
+- **Mac Mini OpenClaw Gateway:** 重启完成，状态正常
+
+### 6. 风驰 - 记忆强调（Ubuntu1）
+
+**参与者:** fengchi（风驰）
+
+- **记忆测试:** 11 题答对 9 题
+- **重要教训:** 用户强调 daily memory 创建是铁律，绝对不能漏
+- **问题:** 今天会话开始时没有创建 2026-03-29.md
+- **改进措施:**
+  - 立即创建当天 memory 文件
+  - 强化 AGENTS.md 规则
+  - 每次会话必须检查并创建当天 memory
+
+---
+
+## 📊 服务器状态（今日检查时）
+
+| 服务器 | CPU | 内存 | 磁盘 | 状态 |
+|--------|-----|------|------|------|
+| Mac Mini | - | - | - | 正常 |
+| Ubuntu1 | - | - | - | 正常 |
+| Ubuntu2 | 4.7% | 31.2% | 15.8% | 🟢 健康 |
+
+---
+
+## 🔴 重要教训（self-improving）
+
+**问题:** 今天会话开始时没有创建 2026-03-29.md，漏掉了 daily memory
+
+**根因:** 没有严格遵守 AGENTS.md 中的规则，没有在每次会话开始时检查
+
+**影响:**
+- 记忆断层
+- 无法追踪任务历史
+- 用户体验差
+
+**改进措施:**
+1. 立即执行：现在立即创建 2026-03-29.md
+2. 强化规则：在 AGENTS.md 中加入更醒目的警告
+3. 每次会话检查：必须在回复前检查并创建当天 memory
+
+---
+
+## 📝 明日待办
+
+- [ ] 星匠：基于云策需求文档设计 N8N v5 版本
+- [ ] 云策：确认公众号命名（SW效率研究所）
+- [ ] 云策：将 `oh-my-opencode-使用指南.md` 迁移到 Mac Mini
+- [ ] 各 Agent：确保 daily memory 文件创建不漏
+- [ ] 观察昨晚 7:00-7:10 定时任务执行结果
+
+---
+
+## 💡 经验/教训
+
+1. **N8N 流水线价值:** v4 版本成功跑通，证明了自动化内容转化的可行性
+2. **公众号命名:** 用户明确不希望局限于电商，需要更广泛的定位
+3. **Daily Memory 铁律:** 这是用户反复强调的规则，必须严格遵守
+4. **架构精简:** 清理作废 Agent，保持系统整洁
+
+---
+
+## 📎 相关文件
+
+- `/Users/weishen/Workspace/nexus/openclaw/yunce/n8n-content-pipeline-workflow.md`
+- `/Users/weishen/Workspace/nexus/Daily notes/2026-03-27.md`（今日补录）
+
+---
+
+*Last updated: 2026-03-29 18:10 GMT+8*
--- a/notes/2026-03-30.md
+++ b/notes/2026-03-30.md
@@ -0,0 +1,65 @@
+---
+title: 2026-03-30 Daily Notes
+source:
+author: shenwei
+published:
+created:
+description:
+tags: []
+---
+
+# 2026-03-30 Daily Notes
+
+## 📋 今日概要
+- **日期**: 2026-03-30
+- **星期**: Monday
+- **天气**: ☀️ 晴
+
+---
+
+## ✅ 今日完成事项
+
+### 1. Daily Notes 生成 (星辉)
+- **操作**: SSH 到 Mac Mini、Ubuntu1、Ubuntu2 读取 session JSONL 文件
+- **内容**: 解析各服务器上所有 agent 的当天对话记录
+- **输出**: `/Users/weishen/Workspace/nexus/Daily notes/2026-03-30.md`
+- **状态**: ✅ 完成
+
+### 2. OpenClaw 版本检查 (星曜/星匠)
+- Ubuntu1/Ubuntu2 版本确认为 2026.3.24
+- Mac Mini 版本检查因 SSH 问题未能完成
+
+### 3. 服务器定时任务执行
+- Ubuntu1/Ubuntu2 OpenClaw 重启请求 — ✅ 已执行
+- 各服务器 cron 任务正常运行
+
+---
+
+## ⚠️ 遇到的问题
+
+### 1. OpenClaw 更新失败 (Ubuntu1/Ubuntu2) — ❌ 未解决
+- **症状**: 
+  - Ubuntu1: `ERROR global update Before: 2026.3.24 After: 2026.3.24` (无新版本)
+  - Ubuntu2: `SIGKILL` 进程被强制终止
+- **可能原因**: npm 全局更新权限问题或进程超时
+- **影响**: 暂不影响日常使用
+
+### 2. Mac Mini SSH 连接失败 — ❌ 未解决
+- **症状**: `env: node: No such file or directory`
+- **影响**: 无法完整检查 Mac Mini OpenClaw 状态
+- **可能原因**: PATH 环境变量问题，node 不在默认路径
+
+---
+
+## 🔧 明日待办
+
+- [ ] 排查 Ubuntu1/Ubuntu2 OpenClaw 更新失败原因（确认是否真的无新版本）
+- [ ] 修复 Mac Mini SSH 连接问题（检查 node 路径配置）
+- [ ] 观察各 cron job 执行情况，确认定时任务稳定性
+
+---
+
+## 📝 备注
+
+- 用户偏好通过 Telegram 接收报告
+- 所有任务执行后需汇报结果