Recommendations: PLEASE ADD YOuR IDEAS

  • Create recommendations for community: 
    • Documentation

      1. Automated Content Generation: Creating standard documents, reports, or templates automatically.
      2. Document Classification and Organization: Sorting and categorizing documents based on content, relevance, and context.
      3. Real-Time Collaboration Support: Providing intelligent suggestions and corrections during collaborative writing efforts.
      4. Content Translation: Instantly translating documents between different languages to enhance global collaboration.
    • Customer Support

      1. Chatbots and Virtual Assistants: Providing 24/7 support to answer customer queries.
      2. Sentiment Analysis: Monitoring and analyzing customer feedback to improve service.
      3. Personalized Marketing: Offering personalized recommendations and content based on user behavior.
    • AI assisting in Best Practice Awarding



               Who we are

               Why we are here

Slide 1 AI for Enterprises
What Is TYPES of AI tools

AI why (enterprise advantage)

Slide 2 AI for Enterprise Documentation

               What is it

Slide 4 This is what we are doing at Hyperledger / What we want to get out of the tools Techniques

Slide 5 Deliverables

Slide Action Slide
subcommittees and how they will incorporate

Slide 3 Tools

** need time to evaluate vast tools available

Open Source Model for adopting AI tools

My Favorite tool DEMO ( all use same prompt with different tools)

Next Steps
               Educate Us (MIT course)
               Tool Evaluation

Action Items
A&A work on presentation
DEMO – let us know your tool, let us know the prompt


everyone's presentation

Gianluca Capuzzi 9:21 AM

Akanksha Rani left

Akanksha Rani joined as a guest

Tripur 9:40 AM

Tripur 9:41 AM

Potential Project Plan for presenting possible AI engines


  1. How do you see AI fitting into the overall enterprise solution, general observation from 10,000 feet

    BobbiBig Picture: white label ChatGBT for Linux with a learning engine specific to the community, each "marketing effort" should incorporate common Template, presentation and videos
    AkankshaEnterprises can leverage NLP to analyze unstructured data, extract valuable information from text, and enhance communication with customers and employees.
    ArunimaAs a tool to improve our productivity. But at the same time, we should use our creativity, thinking power and research. 
    GianlucaSoon AI will pervade production process starting from routine activities
    TripurReducing the workload and shifting the focus to more important areas. Could also add AI to our existing tools and get ahead in the field.
  2. How do you see AI fitting into the Documentation task force?

  3. BobbiOverall it will enable us to meet the community needs by creating professional guides.
    AkankshaBy using specific AI tools to reduce time and accuracy of our work templates , documentation ,user guides
    ArunimaAutomate some monotonous tasks which take up a lot of time. This way we will have more time to focus on complex tasks that require more thinking.
    GianlucaOne idea is to integrate a ChatBot or Large Language Model that replies to user questions
    TripurCreating the first draft of user guides, and presentations, and also keeping the log updated, intelligent search recommendations.
  4. How do you see AI fitting into each sub-committee

  5. Bobbi, this is where we get into specific workflows and actually applying 
    AkankshaUsing or creating specific AI tool for specific work, for example talking about onboarding , we need to know the particular interests of different personas. We can use different ML models to exract the particular needs
    ArunimaFigure out what are some of the tasks that can be automated using AI. This way we can focus our energy on more brainstorming and discussing ideas
    GianlucaTwo example: could analize comments from users, could propose intellisense in github
    TripurDivision of workflow, setting a pseudo deadline to get the idea.
  6. How do you envision the best way to accomplish this goal?

  7. BobbiPresent a message, maybe even a catch phrase for our endeavor.  Present an overall goal and the focus on a Specific use case (solana) and use it for all examples
    AkankshaAligning and planning our requirments with currently present tools and figure out the way to accomplish our goals
    ArunimaHaving proper knowledge of AI tools and how to properly use them to boost our productivity
    GianlucaIntegrating AI engine and AI API
    TripurTesting different AI- tools.
  8. What supporting products are needed for each implementation?

  9. BobbiSee list below
    AkankshaData preprocessing tools -Google Colab , Jupyter notebook, Pytorch  , Libraries , AI tools 
    ArunimaChatGPT, Gamma app
    GianlucaMachine Learning and AI libraries like TensorFlow
    TripurPhind, Copyai
  10. What is the best way to present this info on Thursday Format and use cases

  11. BobbiAI generated presentation with overall strategy for AI and thebn demo with use case for user guides in Solana 
    AkankshaIntroducing ALL the available solutions and aligning them with Hyperledger needs and make the presentation with examples
    ArunimaAI-Generated presentation + some demo videos of using AI tools
    GianlucaLet me think..I have a presentation and a simple software in python but is not appliable to blockchain at the moment
    TripurLet everyone present their favorite AI tools and live demo on them

What would we need to make it happen?

MIT online course 8/15

Overall Community Analysis


  1. Enhanced Efficiency: AI tools can automate various processes within the Hyperledger ecosystem, leading to increased efficiency and faster transaction validation.

  2. Improved Security: AI can detect potential security breaches and vulnerabilities in the Hyperledger framework, thereby bolstering the platform's overall security.

  3. Data Analysis: AI tools can process vast amounts of data generated by the Hyperledger network, providing valuable insights and facilitating data-driven decision-making.

  4. Smart Contract Optimization: Incorporating AI can help optimize smart contracts, making them more robust, reliable, and self-adapting.


  1. Complexity: Integrating AI tools into the Hyperledger community might introduce complexities in the development and maintenance of the network, requiring skilled developers with expertise in both AI and blockchain.

  2. Resource Intensive: AI applications can be computationally intensive, potentially requiring more powerful hardware and increased resource allocation, leading to higher operational costs.

  3. Lack of Expertise: Finding individuals with a deep understanding of both blockchain and AI technologies could be challenging, limiting the pool of available talent for developing and maintaining AI-driven solutions.



  1. Enhanced Scalability: AI-driven optimization can lead to improved scalability of the Hyperledger network, accommodating a larger number of transactions and users.

  2. Advanced Consensus Mechanisms: AI can aid in developing more sophisticated consensus algorithms, potentially leading to increased transaction speed and network consensus efficiency.

  3. Predictive Analytics: AI tools can be utilized to analyze historical data and predict potential future trends, helping users and stakeholders make informed decisions.

  4. Community Growth: The incorporation of AI into Hyperledger can attract developers and researchers from the AI domain, fostering a collaborative and innovative community.


  1. Regulatory Challenges: The integration of AI into a blockchain-based system might raise regulatory concerns, as AI technologies are still evolving and their implications not fully understood.

  2. Privacy Concerns: AI tools dealing with large amounts of data may raise privacy issues, necessitating robust data protection measures to maintain user trust.

  3. Compatibility Issues: Ensuring seamless integration of AI tools with existing Hyperledger components and protocols may present technical challenges and potential compatibility issues.

  4. Competitive Landscape: Other blockchain platforms or AI-driven networks may emerge, offering alternatives and potentially drawing away developers and users from the Hyperledger community.

SWOT Analysis for Incorporating AI Tools into the Hyperledger Community:

In conclusion, incorporating AI tools into the Hyperledger community presents significant potential to enhance efficiency, security, and scalability. However, it also comes with challenges related to complexity, resource allocation, and finding the right expertise. Properly managing these aspects can help maximize the benefits and opportunities while mitigating the associated weaknesses and threats.

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Overall outcome: 

1. Workflows for Userguides

2. Templates and Graphics Libraries

3 API / Tokens System for updates


Strategies for incorporating AI into the current workflow
ID workflows
Identify Use Cases

Chat GPT



CoHere ConsulantsHelp Model an overall enterprise solution
Create a Whitelabel AI  dashboard for your company


AI (Artificial Intelligence) workflow

refers to the process or series of steps involved in developing, deploying, and maintaining AI systems or models. The specifics can vary depending on the complexity of the project, the tools and technologies used, and the team's preferences, but generally, an AI workflow includes the following steps:

  1. Problem Definition: The first step in any AI workflow is defining the problem. What is the task that the AI is meant to solve? This includes understanding the business or scientific context, setting the goals for the AI project, and specifying the metrics that will be used to evaluate the model's performance.

  2. Data Collection: Once the problem is defined, the next step is to gather the data that the AI will learn from. This could be from various sources like databases, APIs, web scraping, or even manually collected and labelled data.

  3. Data Preprocessing: Raw data often requires cleaning and formatting before it can be used for machine learning. This step might involve removing or filling missing data, handling outliers, normalizing numerical data, encoding categorical data, and splitting the data into a training set and a test set.

  4. Model Selection and Training: Choose an appropriate machine learning model or models for your problem. You'll then train the model on your training data set. The specifics of this step will vary depending on the type of problem you're solving and the kind of model you're using.

  5. Model Evaluation: After the model has been trained, it's time to test it on unseen data to evaluate how well it performs. This involves using the test data set and the metrics defined in the problem definition stage.

  6. Model Optimization: If the model's performance is not satisfactory, you might need to tweak its parameters, choose a different model, collect more data, or preprocess your data in a different way. This is often an iterative process that continues until the model's performance reaches a satisfactory level.

  7. Deployment: Once you're satisfied with your model's performance, it can be deployed to a production environment where it can start doing useful work. This could be on a server, in the cloud, or embedded in a device.

  8. Monitoring and Maintenance: After deployment, the model needs to be monitored to ensure it continues to perform as expected as new data comes in. It may need to be retrained or tweaked over time.

  9. Documentation and Explanation: Throughout this process, it's important to document your work so that others can understand it, reproduce it, and maintain it. Depending on the application, you might also need to provide explanations of the model's decisions or predictions.

In addition, depending on the project, there may be other steps, such as gathering business or user requirements, conducting ethical reviews, or complying with regulations and standards related to data privacy and AI systems.

Testing different generative AI engines that can propagate changes from GitHub repositories to other applications requires thorough planning and systematic steps.
Below are the steps you can follow:

  1. Identify Your Goals and Objectives: Clearly identify what you want to achieve with the AI engines. Which type of changes are you interested in and what other applications should these changes be propagated to?

  2. Research AI Engines: Research the available generative AI engines that are capable of the task at hand. Understand their functionality, strengths, weaknesses, and requirements. Some potential engines you might consider are GPT-3, GPT-4, BERT, or T5 from OpenAI and Google, respectively.

  3. Plan Your Project Structure: Before starting any coding, plan out your project. This includes planning the architecture of your project, identifying the necessary components, and deciding on the programming languages and tools you'll use.

  4. Set Up a GitHub Repository: Create a new GitHub repository for your project. This will be the place where you will be making changes that need to be propagated to other applications. Make sure to understand and configure the repository's settings to fit your project's needs.

  5. Set Up Your Development Environment: Install necessary tools, libraries, and dependencies needed for the project. This could include Python, TensorFlow, PyTorch, and specific libraries for the AI models you plan to use. Ensure you have access to the APIs of the AI engines you'll be testing.

  6. Code Integration with GitHub: Create a script that can monitor changes in your GitHub repository. This can be done by using GitHub's Webhooks or GitHub API. The script should be able to detect changes, categorize them (e.g., commits, pull requests), and parse necessary information.

  7. AI Model Training: Depending on your chosen AI engines, you might need to train your models to interpret the changes made in GitHub and generate appropriate responses. Consider using a large dataset of example GitHub changes and their corresponding actions in other applications.

  8. Propagate Changes to Other Applications: Develop scripts that can take the output of your AI engine and perform necessary actions on the target applications. This will heavily depend on the applications you are targeting and their respective APIs.

  9. Testing: Set up testing procedures to ensure your system works as expected. This could be unit tests for individual components and integration tests for the system as a whole.

  10. Debug and Refine Your System: Based on your test results, debug and refine your system to improve performance and accuracy.

  11. Documentation: Document your project properly, including the setup process, usage, results, and any potential issues or limitations. This will help others understand and potentially contribute to your project.

  12. Monitor and Update Your Project: Keep monitoring the performance of your project, especially in response to changes in the GitHub repo. Regularly update your AI models and scripts as necessary to adapt to any changes or improvements in the AI engines or APIs you're using.

Remember to consider ethical implications and privacy issues while working on this project, as you'll potentially be dealing with data that could be sensitive. Make sure to comply with all the necessary regulations and guidelines.

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