TrendPublish: track and summarize AI news in real time, automatically publish to WeChat public number

General Introduction

ai-trend-publish is an open source project hosted on GitHub and developed by the OpenAISpace team that focuses on tracking and publishing the latest trends in the field of artificial intelligence in real time. The tool is designed to help developers, tech enthusiasts, and researchers quickly access dynamic information in the AI field, such as cutting-edge technologies, hot projects, and industry news. By automating the collection and organization of data, users can easily grasp the latest trends in the AI ecosystem. The project relies on the GitHub platform and encourages the community to participate in contributing code or making suggestions for improvement, which is suitable for users interested in AI development. The project is currently in the development stage, and its functions are still being improved, but it has already shown its potential in analyzing technology trends.

Function List

• Tracking AI Trends in Real Time: Gather the latest news in AI from the web and social platforms.
• Data collation and dissemination: Organize the information gathered into easy-to-read content and publish it.
• Open Source Community Collaboration: Support users to participate in project development by submitting code or suggestions via GitHub.
• Customizable configurations: Allow users to adjust the scope of tracking and release format according to their needs.
• multi-source information aggregation: Integrate data from multiple platforms such as web, Twitter, and more.

Using Help

ai-trend-publish is an open source project based on GitHub, which requires some basic preparation and operation before use. The following is a detailed guide to help users quickly get started and take full advantage of its features.

Installation process

Since this is an open source project on GitHub, there is no direct online service and it needs to be deployed locally to work. Here are the installation steps:

1. Preparing the environment
   • Make sure you have Git (a version control tool) and Python (recommended version 3.8 or higher) installed on your computer.
   • Optional: Install Node.js or other dependencies (depending on project-specific requirements, we recommend checking the README file for confirmation).
2. Project Clone to Local
   • Open a terminal or command line tool and enter the following command to clone the repository:

     git clone https://github.com/OpenAISpace/ai-trend-publish.git
     

   • Once the cloning is complete, go to the project directory:

     cd ai-trend-publish
     

3. Installation of dependencies
   • Check that the project root directory contains the requirements.txt file (commonly used in Python projects).
   • If available, run the following command to install the Python dependency:

     pip install -r requirements.txt
     

   • If the project uses another language or toolchain (e.g. Node.js), please refer to the README file on the GitHub page for specific dependency installation instructions. This usually lists something like npm install of the order.
4. Configuring Environment Variables
   • Projects may require API keys (such as Twitter API or other data source keys) to grab information.
   • In the project directory, create a .env file (if required by the README), fill in the key in the sample format:

     TWITTER_API_KEY=your_key
     TWITTER_API_SECRET=your_secret
     

   • Please refer to the project documentation for details on how to configure this, usually in the README or the config Instructions will be provided in the folder.
5. Running Projects
   • Run the main program in a terminal, for example:

     python main.py
     

   • In the case of other types of scripts or services (e.g. Node.js), the run command may be different, for example node index.js. Please check the item description to confirm the startup method.
   • After a successful run, the terminal displays a log or output indicating that the tool is starting to work.

Main function operation flow

1. Tracking AI trends in real time

• procedure::
  1. After launching the tool, it will start crawling AI-related information based on preset data sources (e.g., Twitter, web pages).
  2. Data sources may include GitHub popular repositories, Twitter trending topics, or other technical sites, depending on the code implementation.
  3. Check the configuration file (e.g. config.yaml or similar document), identifying the keywords tracked (e.g., "AI", "machine learning") and the frequency (e.g., hourly updates).
• Customized settings::
  • Edit the profile to add the keywords you are interested in. Example:

    keywords.
    - "artificial intelligence"
    - "deep learning"
    update_interval: 3600 # in seconds, 3600 seconds = 1 hour
    

  • Save and restart the tool for the new settings to take effect.

2. Data collation and dissemination

• procedure::
  1. The tool will organize the crawled data into a structured format (e.g. JSON or Markdown).
  2. By default, organized content may be saved in a local folder (such as the output/), the filename may be ai_trends_date.mdThe
  3. If you need to automatically publish to a specific platform (such as a blog or GitHub Pages), you need to additionally configure the publish script.
• Posting Example::
  • compiler publish.py(if it exists), set the release target:

    destination = "https://your-blog.com/api/post"
    upload_data(file_path, destination)
    

  • Run the release command:

    python publish.py
    

3. Open source community collaboration

• Participation in contributions::
  1. fork the project to your own account on GitHub.
  2. Modify the code locally, for example to add a new data source or optimize the output format.
  3. Submit the Pull Request:
     • Push changes to your fork repository:

       git add .
       git commit -m "Add new feature: support for Reddit data sources"
       git push origin main
       

     • Create a Pull Request on GitHub and wait for maintainers to review it.

Featured Functions

multi-source information aggregation

• How to use::
  • The tool grabs information from multiple data sources at once, such as Twitter's live tweets and GitHub's trending repositories.
  • Check the log file (if any, such as logs/trend.log) to see the status of the crawl:

    2025-02-28 03:24:10 [INFO] Crawling 50 AI Trends from Twitter
    2025-02-28 03:24:15 [INFO] Crawling 20 Popular AI Projects from GitHub
    

  • The output integrates this data to produce a comprehensive report.
• Adjustment of data sources::
  • Add new sources in code or configuration files. For example, add Reddit support:

    sources.append({"type": "reddit", "url": "https://www.reddit.com/r/MachineLearning"})
    

caveat

• Debugging Issues: If something goes wrong at runtime, check the terminal logs, common problems can be missing dependencies or invalid API keys.
• documentation reference: Since the project is still under development, the README is probably the most authoritative guide, so be sure to read it carefully.
• Community Support: If you have questions, ask them on the GitHub Issues page to get developer or community help.

With these steps, you can fully deploy and use ai-trend-publish to stay on top of AI trends in real-time and participate in project improvements!