While executing multiagent system using CrewAI encounter "list index out of range" error

##Version of crewai
crewai==0.28.8
crewai_tools==0.1.6

Code implementation
from langchain_groq import ChatGroq
llm = ChatGroq(temperature=0, model_name="llama3-70b-8192")

from crewai import Agent,Task,Crew,Process
from crewai_tools import SerperDevTool,ScrapeWebsiteTool,WebsiteSearchTool

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()
website_search_tool = WebsiteSearchTool()

planner = Agent(role="Conetxt Planner",
                goal = "Plan engaging and factually accurate context on the {topic}",
                backstory="You are working on a planning to write a blogpost about "
                "the topic {topic} in 'https://medium.com/'.You collect information that helps the "
                "audience leran something new and make informed decisions.You have to prepare a detailed "
                "outline and the relevant topics and sub-topics that has to be a part of the"
                "blogpost.Your work is the basis for the Content Writer to write an engaging "
                "and detailed blogpost on the topic provided",
                allow_delegation=False,
                llm=llm,
                max_iter=5,
                max_rpm=15,
                memory=True,
                #max_rpm=None, # No limit on requests per minute
                #max_iter=15, # Default value for maximum iterations
                verbose=True)

content_writer = Agent(role="Content_Writer",
                       goal="Write insightful and factually accurate"
                       "opinion piece about the topic {topic}",
                       backstory="You are working on a writing to write an opinion piece about "
                       "the topic : {topic}.You base your writing based on the work of the "
                       "Context planer, who provides an outline and relevant context about the topic."
                       "You will follow the main objectives and direction of the outline"
                       ", as provided by the Content Planner.You also provide Objective,impartial "
                       "insights and a meaningful conclusion and bak them up with the information"
                       " provided by the Content Planner.You acknowledge in your opinion piece "
                       " when your statements are opinions as opposed to objective statements",
                       allow_delegation=False,
                       tools=[search_tool,scrape_tool,website_search_tool],
                       llm=llm,
                       max_iter=5,
                       max_rpm=15,
                       memory=True,
                       #max_rpm=None, # No limit on requests per minute
                       #max_iter=15, # Default value for maximum iterations
                       verbose=True,)

content_editor= Agent(role="Editor",
                      goal="Edit a given blogpost to align with the writing style ollowed in 'https://medium.com/'.",
                      backstory="You are an editor who recieves a blogpost form the Content Writer."
                      "Your goal is to review the blogpost to ensure that it follows journalistic best practices,"
                      "provides balanced view points when providing opinions or assertions and also "
                      "avoids major controversial topics or opinions when possible.",
                      allow_delegation=False,
                      llm=llm,
                      max_iter=5,
                      max_rpm=15,
                      memory=True,
                      #max_rpm=None, # No limit on requests per minute
                      #max_iter=15, # Default value for maximum iterations
                      verbose=True)

content_planner_task = Task(description=(
    "1. Prioritize the latest trends,key players and noteworthy news on {topic}\n"
    "2. Identify the targt audience,considering their interests and pain points"
    "3. Develop a detailed content outline including an introduction,key points,a call to actions, sample code if applicable.\n"
    "4. Include SEO keywords and relevant data or sources use dfor reffrence"),
    expected_output = "A comprehenive content plan documen with an outline,audience analysis,"
    "SEO keywords,Conclusion,referrence resources.",
    agent=planner,
    verbose=True

)
writer_task = Task(description=(
    "1. Use the content plan to craft a compelling blogpost on{topic}"
    "2. Incorporate SEO keywords naturally."
    "3. Sections or Subtitles are properly named in an engaging manner."
    "4. Ensure the post is structured with an engaging introduction,insightful body and a summarizing conclusion."
    "5. Proofread for grammatical errors and alignment with the brand's voice."
    ),
    expected_output = "A well written blogspost in markdown format,ready for publication,"
    "each section should have 2 or 3 paragraphs.",
    agent=content_writer,
    verbose=True

)
editor = Task(
    description=("Proofread the given blog post for "
                 "grammatical errors and "
                 "alignment with the brand's voice."),
    expected_output="A well-written blog post in markdown format, "
                    "ready for publication, "
                    "each section should have 2 or 3 paragraphs.",
    output_file="blogpost.md",
    agent=content_editor,
    verbose=True,
)

crew = Crew(agent=[planner,content_writer,content_editor],
    tasks=[content_planner_task,writer_task,editor],
            verbose=2,
            process=Process.sequential,
            full_output=True,
            share_crew=False,
            manager_llm=llm,
            max_iter=15,)
inputs = {"topic":"Comparative study of LangGraph, Autogen and Crewai for building multi-agent system"}
result = crew.kickoff(inputs=inputs

##Output
[DEBUG]: == Working Agent: Conetxt Planner
 [INFO]: == Starting Task: 1. Prioritize the latest trends,key players and noteworthy news on Comparative study of LangGraph, Autogen and Crewai for building multi-agent system
2. Identify the targt audience,considering their interests and pain points3. Develop a detailed content outline including an introduction,key points,a call to actions, sample code if applicable.
4. Include SEO keywords and relevant data or sources use dfor reffrence

> Entering new CrewAgentExecutor chain...
I now can give a great answer!

Final Answer:

**Content Plan Document: Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**

**Target Audience:**

* Researchers and developers in the field of artificial intelligence and multi-agent systems
* Students and academics interested in comparative studies of AI frameworks
* Industry professionals looking to implement multi-agent systems in their organizations

**Interests and Pain Points:**

* Understanding the strengths and weaknesses of different AI frameworks for building multi-agent systems
* Identifying the most suitable framework for specific use cases
* Staying up-to-date with the latest trends and advancements in AI research
* Overcoming challenges in implementing and integrating multi-agent systems in real-world applications

**Detailed Content Outline:**

I. Introduction

* Brief overview of multi-agent systems and their applications
* Importance of choosing the right AI framework for building multi-agent systems
* Thesis statement: This post will provide a comparative study of LangGraph, Autogen, and Crewai for building multi-agent systems.

II. LangGraph

* Overview of LangGraph and its features
* Strengths: [list specific strengths, e.g., scalability, flexibility]
* Weaknesses: [list specific weaknesses, e.g., complexity, limited documentation]
* Use cases: [list specific use cases, e.g., natural language processing, knowledge graph construction]

III. Autogen

* Overview of Autogen and its features
* Strengths: [list specific strengths, e.g., ease of use, rapid prototyping]
* Weaknesses: [list specific weaknesses, e.g., limited scalability, lack of customization]
* Use cases: [list specific use cases, e.g., autonomous systems, robotics]

IV. Crewai

* Overview of Crewai and its features
* Strengths: [list specific strengths, e.g., high-performance computing, distributed architecture]
* Weaknesses: [list specific weaknesses, e.g., steep learning curve, limited community support]
* Use cases: [list specific use cases, e.g., high-performance computing, scientific simulations]

V. Comparative Analysis

* Comparison of LangGraph, Autogen, and Crewai based on [list specific criteria, e.g., scalability, ease of use, customization]
* Discussion of the trade-offs between different frameworks
* Recommendations for choosing the most suitable framework for specific use cases

VI. Conclusion

* Recap of the key points and takeaways
* Final thoughts on the importance of choosing the right AI framework for building multi-agent systems
* Call to action: Encourage readers to explore each framework in more depth and share their experiences

**Sample Code (if applicable):**

* Provide sample code snippets or tutorials for each framework to illustrate their usage and implementation

**SEO Keywords:**

* Multi-agent systems
* AI frameworks
* LangGraph
* Autogen
* Crewai
* Comparative study
* Artificial intelligence
* Machine learning
* Natural language processing
* Knowledge graph construction
* Autonomous systems
* Robotics
* High-performance computing
* Distributed architecture

**Reference Resources:**

* Research papers and articles on LangGraph, Autogen, and Crewai
* Official documentation and tutorials for each framework
* Industry reports and market research on AI and multi-agent systems
* Online forums and communities for AI and multi-agent systems enthusiasts

**Conclusion:**

This content plan provides a comprehensive outline for a comparative study of LangGraph, Autogen, and Crewai for building multi-agent systems. The post will provide an in-depth analysis of each framework, highlighting their strengths and weaknesses, and provide recommendations for choosing the most suitable framework for specific use cases. The target audience will gain a deeper understanding of the latest trends and advancements in AI research and be able to make informed decisions when implementing multi-agent systems in their organizations.

> Finished chain.
 [DEBUG]: == [Conetxt Planner] Task output: **Content Plan Document: Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**

**Target Audience:**

* Researchers and developers in the field of artificial intelligence and multi-agent systems
* Students and academics interested in comparative studies of AI frameworks
* Industry professionals looking to implement multi-agent systems in their organizations

**Interests and Pain Points:**

* Understanding the strengths and weaknesses of different AI frameworks for building multi-agent systems
* Identifying the most suitable framework for specific use cases
* Staying up-to-date with the latest trends and advancements in AI research
* Overcoming challenges in implementing and integrating multi-agent systems in real-world applications

**Detailed Content Outline:**

I. Introduction

* Brief overview of multi-agent systems and their applications
* Importance of choosing the right AI framework for building multi-agent systems
* Thesis statement: This post will provide a comparative study of LangGraph, Autogen, and Crewai for building multi-agent systems.

II. LangGraph

* Overview of LangGraph and its features
* Strengths: [list specific strengths, e.g., scalability, flexibility]
* Weaknesses: [list specific weaknesses, e.g., complexity, limited documentation]
* Use cases: [list specific use cases, e.g., natural language processing, knowledge graph construction]

III. Autogen

* Overview of Autogen and its features
* Strengths: [list specific strengths, e.g., ease of use, rapid prototyping]
* Weaknesses: [list specific weaknesses, e.g., limited scalability, lack of customization]
* Use cases: [list specific use cases, e.g., autonomous systems, robotics]

IV. Crewai

* Overview of Crewai and its features
* Strengths: [list specific strengths, e.g., high-performance computing, distributed architecture]
* Weaknesses: [list specific weaknesses, e.g., steep learning curve, limited community support]
* Use cases: [list specific use cases, e.g., high-performance computing, scientific simulations]

V. Comparative Analysis

* Comparison of LangGraph, Autogen, and Crewai based on [list specific criteria, e.g., scalability, ease of use, customization]
* Discussion of the trade-offs between different frameworks
* Recommendations for choosing the most suitable framework for specific use cases

VI. Conclusion

* Recap of the key points and takeaways
* Final thoughts on the importance of choosing the right AI framework for building multi-agent systems
* Call to action: Encourage readers to explore each framework in more depth and share their experiences

**Sample Code (if applicable):**

* Provide sample code snippets or tutorials for each framework to illustrate their usage and implementation

**SEO Keywords:**

* Multi-agent systems
* AI frameworks
* LangGraph
* Autogen
* Crewai
* Comparative study
* Artificial intelligence
* Machine learning
* Natural language processing
* Knowledge graph construction
* Autonomous systems
* Robotics
* High-performance computing
* Distributed architecture

**Reference Resources:**

* Research papers and articles on LangGraph, Autogen, and Crewai
* Official documentation and tutorials for each framework
* Industry reports and market research on AI and multi-agent systems
* Online forums and communities for AI and multi-agent systems enthusiasts

**Conclusion:**

This content plan provides a comprehensive outline for a comparative study of LangGraph, Autogen, and Crewai for building multi-agent systems. The post will provide an in-depth analysis of each framework, highlighting their strengths and weaknesses, and provide recommendations for choosing the most suitable framework for specific use cases. The target audience will gain a deeper understanding of the latest trends and advancements in AI research and be able to make informed decisions when implementing multi-agent systems in their organizations.

 [DEBUG]: == Working Agent: Content_Writer
 [INFO]: == Starting Task: 1. Use the content plan to craft a compelling blogpost onComparative study of LangGraph, Autogen and Crewai for building multi-agent system2. Incorporate SEO keywords naturally.3. Sections or Subtitles are properly named in an engaging manner.4. Ensure the post is structured with an engaging introduction,insightful body and a summarizing conclusion.5. Proofread for grammatical errors and alignment with the brand's voice.

> Entering new CrewAgentExecutor chain...
I will start by researching the topic to gain a deeper understanding of the subject matter.

Action: Search the internet
Action Input: {"search_query": "LangGraph, Autogen, and Crewai for building multi-agent systems"}

Search results: Title: LangGraph: Multi-Agent Workflows - LangChain Blog
Link: https://blog.langchain.dev/langgraph-multi-agent-workflows/
Snippet: Two examples of third-party applications built on top of LangGraph using multi-agent workflows (GPT-Newspaper and CrewAI); Comparison to other ...
---
Title: Autogen vs. LangGraph : r/LangChain - Reddit
Link: https://www.reddit.com/r/LangChain/comments/1b7q44y/autogen_vs_langgraph/
Snippet: LangGraph. Which library comes out on top? I am building a multi-agent system in production but still have not decided which framework to use.
---
Title: Multi-Agent Orchestration & Conversations using Autogen, CrewAI ...
Link: https://medium.com/data-science-in-your-pocket/multi-agent-orchestration-conversations-using-autogen-crewai-and-langgraph-3ca1c7026eaf
Snippet: We will be discussing MAO in my 151st blog post ! My debut book →. LangChain in your Pocket: Beginner's Guide to Building Generative AI ...
---
Title: LangGraph Vs CrewAI which one best for building Multi Agent ...
Link: https://www.youtube.com/watch?v=oBemQQ5aPSY
Snippet: LangGraph Vs CrewAI which one best for building Multi Agent Application|AI Application. 2 ...
---
Title: Comparing Multi-agent AI frameworks: CrewAI, LangGraph ...
Link: https://www.concision.ai/blog/comparing-multi-agent-ai-frameworks-crewai-langgraph-autogpt-autogen
Snippet: AutoGPT. Ideal for developers who want to utilize visual design tools for easy construction and management of multi-agent systems, with a focus ...
---
Title: Sajal Sharma - An Overview of Multi Agent Frameworks - LinkedIn
Link: https://www.linkedin.com/posts/sajals_an-overview-of-multi-agent-frameworks-autogen-activity-7183058637009952772-ITGQ
Snippet: You've provided valuable insights into pioneering platforms like AutoGen, CrewAI, and LangGraph, each offering distinct advantages in addressing ...
---
Title: Langchain vs. Crew.ai vs. Microsoft Autogen: A Comparison - LinkedIn
Link: https://www.linkedin.com/pulse/langchain-vs-crewai-microsoft-autogen-comparison-deepak-devassykutty-8awoc
Snippet: Pros: Multi-agent Collaboration: Crewai's strength lies in its ability to build and manage systems with multiple AI agents working together.
---
Title: Summary of [Livestream] CrewAI vs LangGraph vs AutoGen
Link: https://www.summarize.tech/www.youtube.com/watch?v=S6MtNDIm3oc
Snippet: The speakers experiment with different models and encounter questions about assigning skills to specific agents and how the system determines which agent ...
---
Title: LangGraph: Multi-Agent Collaboration Explained - Medium
Link: https://medium.com/@kbdhunga/langgraph-multi-agent-collaboration-explained-c0500b0f2e61
Snippet: Multi-Agent Orchestration & Conversations using Autogen, CrewAI and LangGraph. Understanding with examples and code explanations. 3 min read ...
---
Title: Multi-Agent Interview Panel system using LangGraph - YouTube
Link: https://www.youtube.com/watch?v=or36qevjxGE
Snippet: This tutorial explains how we can create a team of interview AI agents taking multiple rounds ...
---

Thought: I now know the final answer
Final Answer:

**Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**
====================================================================

**Introduction**
---------------

Multi-agent systems have revolutionized the field of artificial intelligence, enabling the development of complex systems that can interact and adapt to their environment. With the increasing demand for AI-powered systems, the choice of the right framework for building multi-agent systems has become crucial. This post provides a comparative study of LangGraph, Autogen, and Crewai, three popular frameworks for building multi-agent systems.

**LangGraph**
-------------

LangGraph is a powerful framework for building multi-agent systems, known for its scalability and flexibility. Its strengths include:

* **Scalability**: LangGraph can handle large-scale systems with ease, making it an ideal choice for complex applications.
* **Flexibility**: LangGraph provides a flexible architecture that can be easily customized to meet specific requirements.

However, LangGraph also has some weaknesses:

* **Complexity**: LangGraph can be challenging to learn and implement, especially for developers without prior experience.
* **Limited documentation**: LangGraph's documentation is limited, making it difficult for developers to find the necessary resources.

LangGraph is suitable for use cases such as natural language processing and knowledge graph construction.

**Autogen**
------------

Autogen is a popular framework for building multi-agent systems, known for its ease of use and rapid prototyping. Its strengths include:

* **Ease of use**: Autogen provides a user-friendly interface that makes it easy to build and deploy multi-agent systems.
* **Rapid prototyping**: Autogen enables rapid prototyping, allowing developers to quickly test and refine their systems.

However, Autogen also has some weaknesses:

* **Limited scalability**: Autogen is not designed for large-scale systems, making it less suitable for complex applications.
* **Limited customization**: Autogen's architecture is not easily customizable, limiting its flexibility.

Autogen is suitable for use cases such as autonomous systems and robotics.

**Crewai**
------------

Crewai is a high-performance framework for building multi-agent systems, known for its distributed architecture and high-performance computing capabilities. Its strengths include:

* **High-performance computing**: Crewai provides high-performance computing capabilities, making it suitable for computationally intensive applications.
* **Distributed architecture**: Crewai's distributed architecture enables the development of scalable and fault-tolerant systems.

However, Crewai also has some weaknesses:

* **Steep learning curve**: Crewai requires a significant amount of expertise and knowledge to implement and maintain.
* **Limited community support**: Crewai's community support is limited, making it challenging to find resources and support.

Crewai is suitable for use cases such as high-performance computing and scientific simulations.

**Comparative Analysis**
-------------------------

When choosing a framework for building multi-agent systems, several factors must be considered, including scalability, ease of use, and customization. LangGraph is ideal for large-scale systems, while Autogen is suitable for rapid prototyping and development. Crewai is ideal for high-performance computing and distributed systems.

**Conclusion**
--------------

In conclusion, the choice of framework for building multi-agent systems depends on the specific requirements of the application. LangGraph, Autogen, and Crewai each have their strengths and weaknesses, and understanding these differences is crucial for making an informed decision. By considering the trade-offs between different frameworks, developers can choose the most suitable framework for their specific use case.

**Call to Action**
-------------------

We encourage readers to explore each framework in more depth and share their experiences. The development of multi-agent systems is a rapidly evolving field, and staying up-to-date with the latest trends and advancements is essential for success.

> Finished chain.
 [DEBUG]: == [Content_Writer] Task output: **Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**
====================================================================

**Introduction**
---------------

Multi-agent systems have revolutionized the field of artificial intelligence, enabling the development of complex systems that can interact and adapt to their environment. With the increasing demand for AI-powered systems, the choice of the right framework for building multi-agent systems has become crucial. This post provides a comparative study of LangGraph, Autogen, and Crewai, three popular frameworks for building multi-agent systems.

**LangGraph**
-------------

LangGraph is a powerful framework for building multi-agent systems, known for its scalability and flexibility. Its strengths include:

* **Scalability**: LangGraph can handle large-scale systems with ease, making it an ideal choice for complex applications.
* **Flexibility**: LangGraph provides a flexible architecture that can be easily customized to meet specific requirements.

However, LangGraph also has some weaknesses:

* **Complexity**: LangGraph can be challenging to learn and implement, especially for developers without prior experience.
* **Limited documentation**: LangGraph's documentation is limited, making it difficult for developers to find the necessary resources.

LangGraph is suitable for use cases such as natural language processing and knowledge graph construction.

**Autogen**
------------

Autogen is a popular framework for building multi-agent systems, known for its ease of use and rapid prototyping. Its strengths include:

* **Ease of use**: Autogen provides a user-friendly interface that makes it easy to build and deploy multi-agent systems.
* **Rapid prototyping**: Autogen enables rapid prototyping, allowing developers to quickly test and refine their systems.

However, Autogen also has some weaknesses:

* **Limited scalability**: Autogen is not designed for large-scale systems, making it less suitable for complex applications.
* **Limited customization**: Autogen's architecture is not easily customizable, limiting its flexibility.

Autogen is suitable for use cases such as autonomous systems and robotics.

**Crewai**
------------

Crewai is a high-performance framework for building multi-agent systems, known for its distributed architecture and high-performance computing capabilities. Its strengths include:

* **High-performance computing**: Crewai provides high-performance computing capabilities, making it suitable for computationally intensive applications.
* **Distributed architecture**: Crewai's distributed architecture enables the development of scalable and fault-tolerant systems.

However, Crewai also has some weaknesses:

* **Steep learning curve**: Crewai requires a significant amount of expertise and knowledge to implement and maintain.
* **Limited community support**: Crewai's community support is limited, making it challenging to find resources and support.

Crewai is suitable for use cases such as high-performance computing and scientific simulations.

**Comparative Analysis**
-------------------------

When choosing a framework for building multi-agent systems, several factors must be considered, including scalability, ease of use, and customization. LangGraph is ideal for large-scale systems, while Autogen is suitable for rapid prototyping and development. Crewai is ideal for high-performance computing and distributed systems.

**Conclusion**
--------------

In conclusion, the choice of framework for building multi-agent systems depends on the specific requirements of the application. LangGraph, Autogen, and Crewai each have their strengths and weaknesses, and understanding these differences is crucial for making an informed decision. By considering the trade-offs between different frameworks, developers can choose the most suitable framework for their specific use case.

**Call to Action**
-------------------

We encourage readers to explore each framework in more depth and share their experiences. The development of multi-agent systems is a rapidly evolving field, and staying up-to-date with the latest trends and advancements is essential for success.

 [DEBUG]: == Working Agent: Editor
 [INFO]: == Starting Task: Proofread the given blog post for grammatical errors and alignment with the brand's voice.

> Entering new CrewAgentExecutor chain...
I now can give a great answer

Final Answer:

**Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**
====================================================================

**Introduction**
---------------

Multi-agent systems have revolutionized the field of artificial intelligence, enabling the development of complex systems that can interact and adapt to their environment. With the increasing demand for AI-powered systems, the choice of the right framework for building multi-agent systems has become crucial. This post provides a comparative study of LangGraph, Autogen, and Crewai, three popular frameworks for building multi-agent systems.

In recent years, the development of multi-agent systems has gained significant attention, and the choice of framework plays a vital role in determining the success of these systems. In this post, we will delve into the strengths and weaknesses of LangGraph, Autogen, and Crewai, and explore their suitability for different use cases.

**LangGraph**
-------------

LangGraph is a powerful framework for building multi-agent systems, known for its scalability and flexibility. Its strengths include:

* **Scalability**: LangGraph can handle large-scale systems with ease, making it an ideal choice for complex applications.
* **Flexibility**: LangGraph provides a flexible architecture that can be easily customized to meet specific requirements.

However, LangGraph also has some weaknesses:

* **Complexity**: LangGraph can be challenging to learn and implement, especially for developers without prior experience.
* **Limited documentation**: LangGraph's documentation is limited, making it difficult for developers to find the necessary resources.

LangGraph is suitable for use cases such as natural language processing and knowledge graph construction. Its flexibility and scalability make it an ideal choice for complex applications.

**Autogen**
------------

Autogen is a popular framework for building multi-agent systems, known for its ease of use and rapid prototyping. Its strengths include:

* **Ease of use**: Autogen provides a user-friendly interface that makes it easy to build and deploy multi-agent systems.
* **Rapid prototyping**: Autogen enables rapid prototyping, allowing developers to quickly test and refine their systems.

However, Autogen also has some weaknesses:

* **Limited scalability**: Autogen is not designed for large-scale systems, making it less suitable for complex applications.
* **Limited customization**: Autogen's architecture is not easily customizable, limiting its flexibility.

Autogen is suitable for use cases such as autonomous systems and robotics, where rapid prototyping and ease of use are essential.

**Crewai**
------------

Crewai is a high-performance framework for building multi-agent systems, known for its distributed architecture and high-performance computing capabilities. Its strengths include:

* **High-performance computing**: Crewai provides high-performance computing capabilities, making it suitable for computationally intensive applications.
* **Distributed architecture**: Crewai's distributed architecture enables the development of scalable and fault-tolerant systems.

However, Crewai also has some weaknesses:

* **Steep learning curve**: Crewai requires a significant amount of expertise and knowledge to implement and maintain.
* **Limited community support**: Crewai's community support is limited, making it challenging to find resources and support.

Crewai is suitable for use cases such as high-performance computing and scientific simulations, where high-performance computing capabilities are essential.

**Comparative Analysis**
-------------------------

When choosing a framework for building multi-agent systems, several factors must be considered, including scalability, ease of use, and customization. LangGraph is ideal for large-scale systems, while Autogen is suitable for rapid prototyping and development. Crewai is ideal for high-performance computing and distributed systems.

**Conclusion**
--------------

In conclusion, the choice of framework for building multi-agent systems depends on the specific requirements of the application. LangGraph, Autogen, and Crewai each have their strengths and weaknesses, and understanding these differences is crucial for making an informed decision. By considering the trade-offs between different frameworks, developers can choose the most suitable framework for their specific use case.

**Call to Action**
-------------------

We encourage readers to explore each framework in more depth and share their experiences. The development of multi-agent systems is a rapidly evolving field, and staying up-to-date with the latest trends and advancements is essential for success.

> Finished chain.
 [DEBUG]: == [Editor] Task output: **Comparative Study of LangGraph, Autogen, and Crewai for Building Multi-Agent Systems**
====================================================================

**Introduction**
---------------

Multi-agent systems have revolutionized the field of artificial intelligence, enabling the development of complex systems that can interact and adapt to their environment. With the increasing demand for AI-powered systems, the choice of the right framework for building multi-agent systems has become crucial. This post provides a comparative study of LangGraph, Autogen, and Crewai, three popular frameworks for building multi-agent systems.

In recent years, the development of multi-agent systems has gained significant attention, and the choice of framework plays a vital role in determining the success of these systems. In this post, we will delve into the strengths and weaknesses of LangGraph, Autogen, and Crewai, and explore their suitability for different use cases.

**LangGraph**
-------------

LangGraph is a powerful framework for building multi-agent systems, known for its scalability and flexibility. Its strengths include:

* **Scalability**: LangGraph can handle large-scale systems with ease, making it an ideal choice for complex applications.
* **Flexibility**: LangGraph provides a flexible architecture that can be easily customized to meet specific requirements.

However, LangGraph also has some weaknesses:

* **Complexity**: LangGraph can be challenging to learn and implement, especially for developers without prior experience.
* **Limited documentation**: LangGraph's documentation is limited, making it difficult for developers to find the necessary resources.

LangGraph is suitable for use cases such as natural language processing and knowledge graph construction. Its flexibility and scalability make it an ideal choice for complex applications.

**Autogen**
------------

Autogen is a popular framework for building multi-agent systems, known for its ease of use and rapid prototyping. Its strengths include:

* **Ease of use**: Autogen provides a user-friendly interface that makes it easy to build and deploy multi-agent systems.
* **Rapid prototyping**: Autogen enables rapid prototyping, allowing developers to quickly test and refine their systems.

However, Autogen also has some weaknesses:

* **Limited scalability**: Autogen is not designed for large-scale systems, making it less suitable for complex applications.
* **Limited customization**: Autogen's architecture is not easily customizable, limiting its flexibility.

Autogen is suitable for use cases such as autonomous systems and robotics, where rapid prototyping and ease of use are essential.

**Crewai**
------------

Crewai is a high-performance framework for building multi-agent systems, known for its distributed architecture and high-performance computing capabilities. Its strengths include:

* **High-performance computing**: Crewai provides high-performance computing capabilities, making it suitable for computationally intensive applications.
* **Distributed architecture**: Crewai's distributed architecture enables the development of scalable and fault-tolerant systems.

However, Crewai also has some weaknesses:

* **Steep learning curve**: Crewai requires a significant amount of expertise and knowledge to implement and maintain.
* **Limited community support**: Crewai's community support is limited, making it challenging to find resources and support.

Crewai is suitable for use cases such as high-performance computing and scientific simulations, where high-performance computing capabilities are essential.

**Comparative Analysis**
-------------------------

When choosing a framework for building multi-agent systems, several factors must be considered, including scalability, ease of use, and customization. LangGraph is ideal for large-scale systems, while Autogen is suitable for rapid prototyping and development. Crewai is ideal for high-performance computing and distributed systems.

**Conclusion**
--------------

In conclusion, the choice of framework for building multi-agent systems depends on the specific requirements of the application. LangGraph, Autogen, and Crewai each have their strengths and weaknesses, and understanding these differences is crucial for making an informed decision. By considering the trade-offs between different frameworks, developers can choose the most suitable framework for their specific use case.

**Call to Action**
-------------------

We encourage readers to explore each framework in more depth and share their experiences. The development of multi-agent systems is a rapidly evolving field, and staying up-to-date with the latest trends and advancements is essential for success.

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
[<ipython-input-129-c2964984c620>](https://localhost:8080/#) in <cell line: 1>()
----> 1 result = crew.kickoff(inputs=inputs)

[/usr/local/lib/python3.10/dist-packages/crewai/crew.py](https://localhost:8080/#) in kickoff(self, inputs)
    264         ]
    265         self.usage_metrics = {
--> 266             key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
    267         }
    268

IndexError: list index out of range

I ran your code and found  that the metrics in line 266 was coming as an empty list ([]).

I made this PR https://github.com/joaomdmoura/crewAI/pull/742 to fix your issue.

Thank you @plaban1981 for submitting this issue! We were able to resolve the underlying issue in PR #844.
If you update to the latest version of crewAI, you won't run into this issue anymore.
Please let me know if you have any questions or need any additional help!
Closed by #844

Thank you @plaban1981 for submitting this issue! We were able to resolve the underlying issue in PR #844
If you update to the latest version of crewAI, you won't run into this issue anymore.
Please let me know if you have any questions or need any additional help!
Closed by #844


Fix: namespace in version header

Remember that thing in in the tentris PR where I needed to always write ::rdf4cpp (absolute namspace), that was caused by this namespace being wrong.

Nice catch. Feel free to merge it.


eval with Alpaca template

Do you have any PRs or examples that wrap around the Alpaca template?
I know this issue has been covered before (#896), but I was wondering if it has been updated since then.

#1873 will add support for arbitrary Chat Templates via Huggingface tokenizers soon, and you could try that PR out now!


2 Cameras C500 HD Stream failure

Description
I had installed at first one Tapo C500 on Home assistant and everything went ok. Next day, bought another C500 camera, add it to integration and also went ok.
Some hours later, first camera fails HD stream, only SD stream works. Removed it from integration, add it again, same problem.. 2nd camera working ok all this time.. Today, bought a 3rd camera, thinking 1st camera had some issue, add it to integration, 2 cameras streaming ok !! Great, i thought... some hours ago, this last camera bought today, fails HD Stream again :-( ...
Reproduction Steps
no errors
Expected behavior
2 or more cameras streaming HD or SD without issues.
If applicable, add error logs.
no logs - no errors
Device Firmware
1.0.4
Using stream component
Yes
Does camera work via official integrations?
Yes
Camera has all attributes filled out in developer tools
Yes
HASS Environment
Home Assistant
Search for similar issues
Yes
Additional information
No response

if I reboot Home Assistant camera works ok again

Hi, all this integration does regarding cameras is informs Home Assistant about the rtsp stream address. I recommend looking into camera limitations (there is a maximum number of concurrent streams), verifying the stream works and looking into logs. Closing as not an issue of integration.


Log email using Gmail fails with SMTP authentication error.

Dryadd can crash if log messages are sent using Gmail.
Specifically, the application will crash on attempting to send an email with:
smtplib.SMTPAuthenticationError: (535, b'5.7.8 Username and Password not accepte d. Learn more at\n5.7.8  https://support.google.com/mail/?p=BadCredentials u1-20 020a1709026e0100b0015e8d4eb24fsm16002976plk.153 - gsmtp')
Having a look at their documentation, there is this fun tidbit:

To help keep your account secure, from May 30, 2022, Google no longer supports the use of third-party apps or devices which ask you to sign in to your Google Account using only your username and password.

This means that emailing using just SMTPlib is not possible and alternate methods will be required to send email via gmail.
There are a number of possible solutions, none of which can be implemented in an afternoon.
https://support.google.com/accounts/answer/6010255?hl=en&pli=1#zippy=%2Cif-less-secure-app-access-is-on-for-your-account%2Cupdate-your-app-or-operating-system%2Cuse-more-secure-apps%2Cuse-an-app-password
Work on this can't commence for a few weeks: in the interim, don't use Google.

:(

v0.5.0 now utilizes and SSL email handler for logging/notification.


format code style

What is the purpose of the change
code style format to make code more standard

LGTM.


Update README.pod

Improve explanation of -s output of packages from AUR.
Fixed #691

Thank you. I'll do some adjustment, since <!> is specific to the non coloured output.

Done in fa32f24bb4c2a30b3a9bf905dee0dd13cd4fa085.


Need a animated blog  page page

in this blog page you can add some blogs as a example and page should be mobile responsive and animated too

Anyone wants to create a blog page ????

Hey! Can I work on this?

yes u can

Can u create a separate blog page for us

Hello I want to contribute to the issue. I am new to this repository, Can you please assign me this issue?


Improve performance of ImageList.from_files

Is your feature request related to a problem?
Currently, all files are loaded on one thread.
Desired solution
Add threading to improve loading time.
Possible alternatives (optional)
No response
Screenshots (optional)
No response
Additional Context (optional)
No response

:tada: This issue has been resolved in version 0.24.0 :tada:
The release is available on:

v0.24.0
GitHub release

Your semantic-release bot :package::rocket:


"connection is linked to another session" : connection ID partial match of session secret uuid

While running the server.go example, I experience this error:
2023/03/04 20:00:48 server is ready
2023/03/04 20:00:48 conn opened
2023/03/04 20:00:48 session opened
2023/03/04 20:00:48 announce request
2023/03/04 20:00:48 setup request
2023/03/04 20:00:48 conn closed (connection is linked to another session)
2023/03/04 20:00:48 session closed
2023/03/04 20:00:48 conn opened
2023/03/04 20:00:48 session opened
2023/03/04 20:00:48 announce request
2023/03/04 20:00:48 setup request
2023/03/04 20:00:48 conn closed (connection is linked to another session)
.
.

I added some session logging and also compared sxID to the sc.session.secretID.
As shown in the following output, the sxID value is a truncated version of the secretID uuid -- it is the first UUID subgroup:
./server
2023/03/04 20:08:25 server is ready
2023/03/04 20:08:25 conn opened
req: OPTIONS rtsp://<IP_ADDRESS>:8554/eagle8video RTSP/1.0
CSeq: 1
User-Agent: com.pedro.rtsp 2.1.7

req: ANNOUNCE rtsp://<IP_ADDRESS>:8554/eagle8video RTSP/1.0
CSeq: 2
Content-Length: 446
Content-Type: application/sdp
User-Agent: com.pedro.rtsp 2.1.7

v=0
o=- 0 0 IN IP4 <IP_ADDRESS>
s=Unnamed
i=N/A
c=IN IP4 <IP_ADDRESS>
t=0 0
a=recvonly
m=video 0 RTP/AVP 96
a=rtpmap:96 H264/90000
a=fmtp:96 packetization-mode=1; sprop-parameter-sets=Z2QAH6y0AoAt0rAQEBQD+UCs7msoADaFCag=,aO4G4sA=
a=control:streamid=0
m=audio 0 RTP/AVP 97
a=rtpmap:97 MPEG4-GENERIC/32000/2
a=fmtp:97 profile-level-id=1; mode=AAC-hbr; config=1290; sizelength=13; indexlength=3; indexdeltalength=3
a=control:streamid=1
2023/03/04 20:08:25 session opened
2023/03/04 20:08:25 announce request
req: SETUP rtsp://<IP_ADDRESS>:8554/eagle8video/streamid=0 RTSP/1.0
CSeq: 3
Transport: RTP/AVP/TCP;unicast;interleaved=0-1;mode=record
User-Agent: com.pedro.rtsp 2.1.7

2023/03/04 20:08:25 setup request
req: SETUP rtsp://<IP_ADDRESS>:8554/eagle8video/streamid=1 RTSP/1.0
CSeq: 4
Session: 95db3c63
Transport: RTP/AVP/TCP;unicast;interleaved=2-3;mode=record
User-Agent: com.pedro.rtsp 2.1.7

=============> sxID 95db3c63 didn't match session secret: 95db3c63-8473-4d47-aff8-695ab2eb0076

 2023/03/04 20:08:25 conn closed (connection is linked to another session)
2023/03/04 20:08:25 session closed

It appears that something is truncating the session id, most likely the client.  I don't have access to the client source.
So I'm adding a patch that will check the first group of the UUID:
func (sc *ServerConn) handleRequestInSession(
	sxID string,
	req *base.Request,
	create bool,
) (*base.Response, error) {
	// handle directly in Session
	if sc.session != nil {
		// session ID is optional in SETUP and ANNOUNCE requests, since
		// client may not have received the session ID yet due to multiple reasons:
		// * requests can be retries after code 301
		// * SETUP requests comes after ANNOUNCE response, that don't contain the session ID
		if sxID != "" {
			// the connection can't communicate with two sessions at once.
			if sxID != sc.session.secretID {
				fmt.Printf("=============> sxID %s didn't match session secret: %s\n ", sxID, sc.session.secretID)
				//Allow matching of first UUID group
				subu := strings.Split(sc.session.secretID, "-")
				if sxID != subu[0] {
					return &base.Response{
						StatusCode: base.StatusBadRequest,
					}, liberrors.ErrServerLinkedToOtherSession{}
				}
			}
		}

Does anyone else have any experience with this issue?  Is a submatch on first group UUID a worthy PR?
Thank you!

Hello, this is a bug of your RTSP client (com.pedro.rtsp).
There is no concept of sub UUID and decreasing controls on the session ID is a security issue.
Report the issue to the authors of the RTSP client.


Add support for NaiveDateTime in document encoding

This PR add support for NaiveDateTime in document encoding.
Since a lot of different structs in my project already use NaiveDateTime I believe that this would be a great improvement besides convert every NaiveDateTime into a DateTime.
This may be improved even more with protocol usage. That way anyone in need for a specific data type may implement it.
What you think?

I guess, we don't need this, because you can use the Mongo.Encoder protocol for that work. If you look at the code:
    {:ok, datetime} = DateTime.from_naive!(naivedatetime, "Etc/UTC")
     encode(datetime)

You are converting the NaiveDataTime by using UTC. But there is no way back to the NaiveDataTime. In my opinion you should not use the NaiveDataTime at all, but always DateTime with UTC time zone. By using DataTime with UTC you don't need to convert the struct.

Closing due to no response.


Failure signal handler should call writerfn with null buffer at the end

Describe the bug
The documentation for FailureSignalHandlerOptions in absl/debugging/failure_signal_handller.h says that writerfn may be called with null buffer to let the callee flush buffered data. But that never happens. It will help us if absl explicitly calls writerfn(nullptr, 0) just before crashing.
Our use case is that we want to caputure the crash message as one long string and log it specially marked.
I can create a PR if you think this is a good idea.
What version of Abseil are you using?
7f4d0ba
What operating system and version are you using
Ubuntu 20.04, x86-64.
What compiler and version are you using?
clang-10.0
What build system are you using?
bazel 3.5

I vaguely remember writing that comment in the documentation, but I have no idea why it seems like it was never implemented. I would take this as a PR.

The fix has been merged.


Issue converting Object with attribute [UInt8]

I have a class with the following attributes:
var Username: String? = nil;
var User: String? = nil;
var Password: String? = nil;
var Message: String? = nil;
var Value: Bool? = nil;
var SessionPassword: [UInt8]? = nil;
var SessionNum: Int? = nil;
var DateFormat: String? = nil;
var TimeFormat: String? = nil;

I implemented a mapping function on the class:
func mapping(map: Map) {
        self.Username <- map["Username"];
        self.User <- map["User"];
        self.Password <- map["Password"];
        self.Message <- map["Message"];
        self.Value <- map["Value"];
        self.SessionPassword <- map["SessionPassword"];
        self.SessionNum <- map["SessionNum"];
        self.DateFormat <- map["DateFormat"];
        self.TimeFormat <- map["TimeFormat"];
    }

And I have the following method to convert to json from object:
static func LoginResultToJson(loginResult: UserAccount) -> String{
        let JSONString = Mapper().toJSONString(loginResult, prettyPrint: true)
        return JSONString!
    }

The problem is the json string doesn't have the attribute SessionPassword, I checked the Object and the SessionPassword attribute have value.
Do any one have this issue before?
Is this a bug?

Disregard this issue, was my problem at the moment so create the object.
Thank you.


feat(controller): Automatically suspend a CronWorkflow after workflows repeatedly failed

Fixes https://github.com/argoproj/argo-workflows/issues/5659. Creating this PR in draft for design discussions.
Signed-off-by: terrytangyuan<EMAIL_ADDRESS>Checklist:

[x] My organization is added to USERS.md.

@simster7 Thanks for the suggestions! I have now implemented the two use cases and it should work when GC is enabled on the workflows too. Please take another look when you get a chance. I'll add some tests once we reach an agreement on the general design.

(PR is in draft mode, hence have not reviewed)


Node deletion and update API endpoints are not documented

The API docs do not cover DELETE or POST on /nodes/<id>. Only GET is documented.

Thanks! We should fix that (also see https://github.com/docker/docker/issues/23597)


Add example automation scripts.

Add startup/cleanup and bookinfo demo scripts.
Add example targets to the Makefile.
Add HelloWorld test automation changes
Add scripts to setup kubernetes environment for testing.
Add make examples to the before_deploy. Also, check the branch
name 'example_automation' as that's the current branch name for
this work.
Need to install the dependencies (docker-compose, a8ctl etc)
when doing the deploy since make examples needs them.
Automate the helloworld demo.
Add traffic-step and traffic-abort and update beta cli binary.
Tag images with the branch version number and support the cli
recipe-run command.
Push bookinfo examples to dockerhub.
Dump debug in bookinfo script after failures.
Reset the rules on failures to see if that resolves the issues
with the nginx service reporting as down.
Add additional retries to the remaining testcases.
Add k8s commands when dump debug info.
Seeing other failures during some of the other testcases that
will probably be fixed by retries.
Use kubectl commands for k8s demo runs when dumping debug data.
Add the start/stop makefile targets to set the version. Fix the
version variable name in the push to dockerhub script.
Add make targets to stop/start bookinfo.
Fix version tag for examples images.
Move push to after_deploy in travis.yaml.
The examples are running twice as the push script is considered
another deploy.  Move it to the after_deploy section.
Also, add stop/start makefile support for bookinfo.
Call push script in after_deploy.
Update push script and move versioned yamls to subdirs.
Merge in examples updates to change to k8s registry.
Update with elasticsearch changes.
Build the cli before running the tests.
Dockerize before running examples

@rshriram @ijsnellf
I have broken up the commit into smaller pieces so hopefully that makes reviewing a little easier.  I talked to @gicaceres about reusing code between examples and testing.  He's currently making a number of updates to testing so we thought we'd work together after he's done to see if there is a good way to share common code (in a separate pull request).  Sound good?


[BUG] Convert to HTML works for one scene, but does not work for multiple scenes

Terms

[X] Checked the existing issues and discussions to see if my issue had not already been reported;
[X] Checked the [frequently asked questions]](https://manim-slides.eertmans.be/latest/faq.html);
[X] Read the installation instructions;
[ ] Created a virtual environment in which I can reproduce my bug;

Describe the issue
When I use the convert to html feature with multiple scenes, the folder with all the .mp4 media is created correctly,. but the html file that plays from it is not working for some reason. It is simply black and I cannot advance slides (I can use "F" to make it fullscreen). When I do one scene at a time, it DOES work correctly. This issue was also reported on reddit https://www.reddit.com/r/manim/comments/1fzcv7z/manimslides_convert_gives_html_that_is_a_blank/ (which is what made me try rendering just one scene at a time)
Command
manim-slides convert Scene1 Scene2 output.html
Issue Type
Bug when presenting with HTML/PowerPoint/... format (manim-slides convert)
Python version
Python 3.9.12
Python environment
Manim Slides version: 5.1.10
Python executable: /Users/mnica/opt/anaconda3/bin/python3
Manim bindings:
/Users/mnica/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
	manim (version: 0.18.1)
	manimgl not found
Qt API: pyqt5 (version: 5.9.7)
(base) mnica@Mihais-MBP Downloads % manim-slides checkhealth
Manim Slides version: 5.1.10
Python executable: /Users/mnica/opt/anaconda3/bin/python3
Manim bindings:
/Users/mnica/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
	manim (version: 0.18.1)
	manimgl not found
Qt API: pyqt5 (version: 5.9.7)

What is your platform?
macOS
Other platform
No response
Manim Slides Python code
No response
Relevant log output
No response
Screenshots
No response
Additional information
No response
Recommended fix or suggestions
No response

Hi @mcnica89, thanks for reporting this bug!
I feel this is a duplicate of #496, did you check it?
If that is the case, a fix has already been merged, but was not yet released to PyPI. I just released manim-slides==5.2.0, which should contain the fix.
Let me know :-)


Update limits-and-config.md

In response discussion with CSS and @PriyaKodukula this morning...
Remove reference to exporting a flow to see its plan
Add limits column for entitlement vs interim
Help users figure out what their current plan is

@ChrisGarty : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@ChrisGarty : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@PriyaKodukula, I converted this to a draft to wait for your review

@PriyaKodukula, I converted this to a draft to wait for your review

@KumarVivek : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@KumarVivek : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@KumarVivek : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman

@MSFTMan : Thanks for your contribution! The author(s) have been notified to review your proposed change. @msftman


配置问题

what do these parameters mean？？？？

These are arguments of the DeepSpeed launcher.
NUM_WORKERS is used to set --num_nodes, which means the number of servers used for pretraining
NUM_GPUS_PER_WORKER is used to set --num_gpus, which means means the number of GPUs on each server
HOST_FILE_PATH is the path to an OpenMPI-style hostfile.
You can find more details from https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node

Thank you for your answer, I want to do model parallel experiment, but my checkpoint only has one model rank_00.pt, using MP_SIZE=4, it will show that the files rank_01.pt,rank_02.pt,rank_03.pt can not be found. This problem has been bothering me for a long time. If you know, I hope you can help me.

You need to divide the downloaded checkpoint with change_mp.py, following the instruction in https://github.com/THUDM/GLM#model-parallelism


[Backoffice] Throw generic error when create/edit product variant and unnecessary redirect to homepage

In backoffice, when creating new Product or editing Product, when try to add new product variation, it has checked for GTIN & SKU exist in database or not.

But instead of stay in page and notify user, it redirect to home page, and throw generic error " UPDATE FAILED " -> make user don't know which error caused this problem.

Expect: Throw detail error that API respone , and stay in create/edit page and let user continue modify.


Add several type-safe property retrieval methods

This change makes the document class much easier to use, and the
resulting code much easier to read since a lot of manual type casts
are now avoided. It also enforced type safety in the code, and puts
the onus on the developer to make it clear what the type
expectations are.

I share the same opinion as snej, IO cannot be trusted. Another app may be using the same database and for example emits doubles instead of int's or the user pokes in the database and bad data gets replicated. Either way we should gracefully handle the situation, specially when dealing with more than 1 document, like in a Map Reduce function.
This is what I use:
static <T> T getProperty(Map<String, ?> map, String key, Class<T> clazz) {
    Object value = map.get(key);
    if (value == null) {
        return null;
    } else if (clazz.isAssignableFrom(value.getClass())) {
        return (T) value;
    }
    String actual = value.getClass().getName();
    String expected = clazz.getName();
    Object docId = map.get("_id");
    Log.w(TAG, "Invalid value in key " + key + " of " + docId
            + ". Expected " + expected + " but got " + actual);
    return null;
}

static Integer getInteger(Map<String, ?> map, String key) {
    Number value = getProperty(map, key, Number.class);
    if (value == null) {
        return null;
    } else if (value instanceof Integer) {
        return (Integer) value;
    } else {
        return value.intValue();
    }
}

@MariusVolkhart,
In short term, we will not apply this change. So I will close this PR.
Thank you very much for your contribution!


[WIP] Add breadcrumb to the Request detail page

Merge after #24, #25
[WIP] Breadcrumbs for the Request tab

screenshots to follow...

Codecov Report

Merging #26 into master will decrease coverage by 3.82%.
The diff coverage is 11.59%.

@@            Coverage Diff             @@
##           master      #26      +/-   ##
==========================================
- Coverage   37.46%   33.64%   -3.83%
==========================================
  Files          45       47       +2
  Lines         379      428      +49
  Branches       38       46       +8
==========================================
+ Hits          142      144       +2
- Misses        212      251      +39
- Partials       25       33       +8

Impacted Files
Coverage Δ

src/App.js
0% <ø> (ø)
:arrow_up:

src/forms/request-comment-form.schema.js
33.33% <ø> (ø)
:arrow_up:

...rc/presentational-components/shared/top-toolbar.js
0% <0%> (ø)

src/smart-components/request/action-modal.js
25% <0%> (-13.47%)
:arrow_down:

src/helpers/request/request-helper.js
81.25% <0%> (+9.02%)
:arrow_up:

...rc/presentational-components/shared/breadcrubms.js
0% <0%> (ø)

...c/smart-components/request/request-detail/stage.js
11.11% <0%> (-1.39%)
:arrow_down:

src/smart-components/request/requests.js
27.27% <0%> (-2.73%)
:arrow_down:

src/helpers/shared/helpers.js
100% <100%> (ø)
:arrow_up:

src/redux/actions/request-actions.js
100% <100%> (ø)
:arrow_up:

... and 6 more

Continue to review full report at Codecov.

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Powered by Codecov. Last update b348ecb...8812947. Read the comment docs.

@Hyperkid123 - comment addressed and screenshots above were updated to reflect the updated headers.


How to Indicate BLE 'Service Changed' (0x2A05) (IDFGH-9551)

Under BLE standard 'Generic Attribute' (0x1801) there is a Characteristic 'Service Changed' (0x2A05) with 'indicate' property, which is supposed to be used to indicate that a range of handles in the BLE device have changed, so that they can be 're-discovered', updating any cached information.
How would I go about triggering this?
How do I specify the 'range of affected handles' that are affected by the change?
Does the client need to 'subscribe' to get the indication, or is this automatic (there is no 0x2902 descriptor to write to)?
Issue ( #1673 ) seems to indicate the 'service changed' notification is something other than what I interpret is is supposed to be.

@mainr gattc is automatic to 'subscribe'  the notification,  client will get ESP_GATTC_SRVC_CHG_EVT

Thanks for the response @Weijian-Espressif .
To clarify:
Say my 'gatt_server' is an ESP32, and the 'client' is an android phone.
Say my 'gatt_server' device has an attribute table:
SERVER 1
  +- service
  |  +- characteristic 1
  |  +- characteristic 2
  +- service
     +- characteristic 3

Now some conditions arise that cause the STRUCTURE of the attribute table to change, such as exposing a new service (or removing an existing one, etc.). If adding, say the attributes now look like:
SERVER 1
  +- service 1
  |  +- characteristic 1
  |  +- characteristic 2
  +- service 2
  |  +- characteristic 3
  +- service 3
     +- characteristic 4

I understand the function of the 'service changed' notification is to inform the client that changes have occured, and it should flush its internal cache (GAP/GATT) for this device, and re-discover services.
What I don't understand is what does the gatt server code running on the ESP32 need to do after modifying the GATT structure to trigger the notify/indicate to the client?
Surely the notification is not automatic so that EVERY change to the GATT triggers notification?
If so, the client-side discovery would be triggered every time a change was made?
What I expect is the server would make whatever changes are necessary, then issue some kind of 'commit' or 'update' or 'notify' command to trigger the 'service changed' notification.

service_change_demo.zip
please refer to the service change demo

service_change_demo.zip
please refer to the service change demo

Hi, is there the Arduino Framework version of this?

service_change_demo.zip
please refer to the service change demo

Hi, is there the Arduino Framework version of this?

Nothing?

@Sladerix I don't find anything specific either 'range of...' is ambiguous at best
but I 'guess' that in the end one sends a notify on this characteristic, which triggers the host side action.
I'm going to add that to my arduino code and see what happens...


App crashes unexpectedly when entering new segmentation

Prerequisites
Put an X between the brackets (without spaces [X]) on the following lines if you have done all of the following:

[X] Checked the wiki board for common solutions: https://github.com/CemrgAppDevelopers/CemrgApp/wiki
[X] Checked that your issue isn't already filed: https://github.com/search?q=+is%3Aissue+user%3ACemrgAppDevelopers
[] Are you providing the log? https://github.com/CemrgAppDevelopers/CemrgApp/wiki/Troubleshooting

Description
CemrgApp unexpectedly closes when making a new segmentation on Step 3 of the Morphological Measurements Pipeline. Specifically, the app closes on clicking 'OK' to enter the name for the new segmentation.
Steps to Reproduce

Load CT image in nii format via Open File
Select Morphological Measurements Pipeline
Click Step 3: Segment
Resize the split between the right hand side segmentation panel and the viewing 4-panels on the left hand side.
Click New on the segmentation panel
Enter name for segmentation
Click "OK" to confirm name

Expected behaviour:
Allow me to begin making my new named segmentation on the loaded CT image.
Actual behavior:
App closes
Reproduces how often:
Does not reproduce on immediately reopening the app. However, on reopening there is significant lag with cursor movement over the CT image. The lag is quite bad when trying to move the 3 different planes of viewing which makes it difficult to segment the image.
Additional Information
Any additional information, configuration or data that might be necessary to reproduce the issue.
´´´

A similar issue was also reported originally in #17.

This issue is due to the rotation of the MR image.
Cemrg doesn't seem to know how to deal with this. I was having the same issue just trying to reinit the image. Why this occurs with the segmentation tool as well is that when you create a new segmentation it reinits the image space to be in-plane with your image.
The rotation information in the nifti header (nii.hdr.hist) reads:
quatern_b: -0.0200
quatern_c: 0.4984
quatern_d: -0.7342
When I reset these values to [0,0,1], Cemrg no longer crashes. But we lose the original spatial information so this issue will still need to be dealt with.

The crash seems to be the same issue as MITK: https://phabricator.mitk.org/T25682. This was resolved in https://phabricator.mitk.org/source/mitk/branches/T25682-DominantAxesError/
So if we are building CemrgApp, we can use this branch of MITK2018 instead rather than checking out MITK2018.04.2
git checkout T25682-DominantAxesError

This should have been fixed on mitk2021. We just need to merge branch feature/update-mitk to development (and to master) whenever possible.

Made a new error #60. Closing this.


Acronym Inconsistencies

@brendt - I just noticed we have some inconsistencies with our acronyms, some of them my fault. For example:

Uuid
ORM
MACAddress
Regex

I think I am of the mind to capitalize class names where it would normally be capitalized. So these become:

UUID
ORM
MACAddress
RegEx

Any problem with this line of thinking?

Ah, yes. There's actually some kind of convention about this. I actually think it comes from .NET: https://learn.microsoft.com/en-us/previous-versions/dotnet/netframework-4.0/ms229043(v=vs.100)?redirectedfrom=MSDN

Casing of acronyms depends on the length of the acronym. All acronyms are at least two characters long. For the purposes of these guidelines, if an acronym is exactly two characters, it is considered a short acronym. An acronym of three or more characters is a long acronym.

I don't have a strong opinion about it 🤷♂
Whatever we choose, we should document it

I can get on board with this. Let's adopt the following per that article:
Do capitalize both characters of two-character acronyms, except the first word of a camel-cased identifier.
A property named DBRate is an example of a short acronym (DB) used as the first word of a Pascal-cased identifier. A parameter named ioChannel is an example of a short acronym (IO) used as the first word of a camel-cased identifier.
Do capitalize only the first character of acronyms with three or more characters, except the first word of a camel-cased identifier.
A class named XmlWriter is an example of a long acronym used as the first word of a Pascal-cased identifier. A parameter named htmlReader is an example of a long acronym used as the first word of a camel-cased identifier.
Do not capitalize any of the characters of any acronyms, whatever their length, at the beginning of a camel-cased identifier.
A parameter named xmlStream is an example of a long acronym (xml) used as the first word of a camel-cased identifier. A parameter named dbServerName is an example of a short acronym (db) used as the first word of a camel-cased identifier.

Cleaning up the existing classes and updating the contributing file with that will close this issue.

Seems good! Although I'm not sure I understand the first one 🤔 are camel-case identifiers things like properties, and Pascal-cased ones are eg. classes? So
PDO is the class name, but $pdo is the variable name?

Close! As a property it'd be pdo as a class it'd be Pdo since it is three characters.

Oh, why two instead of three?

Do capitalize both characters of two-character acronyms, except the first word of a camel-cased identifier.
_ Do capitalize only the first character of acronyms with three or more characters, except the first word of a camel-cased identifier._
Pdo would be a three letter acronym while IP would be a two letter acronym?

Because Bill Gates told me so!
In all seriousness, I'm fine adjusting that, but I think you just want to maintain all-caps ORM. 🙃
If someone happens to pick this off before me, just adjust the above accordingly to four letter acronyms being the point that only the first letter is capitalized.

Closed by #121.


binary: improve tests for invalid numbers

We should have separate tests for:

alphabetic characters at the beginning of a valid binary number
alphabetic characters at the end of a valid binary number
alphabetic characters in the middle of an otherwise valid binary number
invalid digits (e.g. 2)

If the test suite for binary has test cases that cover these edge cases, this issue can safely be closed.
See https://github.com/exercism/x-common/issues/95

Some of this was done here:
https://github.com/exercism/xswift/pull/68
Need to verify the others

We might want to just close this. https://github.com/exercism/x-common/issues/279

D'oh. Didn't mean to close it, just to comment :)


Release 1.4.1

There are many changes backported. I'm doing a patch release soon.

Changes

Fix(build): Default to routine build strategy for local IntegrationPlatform (#2243, @astefanutti)
Backport logging trait feature to fix GH issue #2000 (#2260, @orpiske)
Feat: Kamelet Binding error handler (#2266, @squakez)
Fix(cmd): run file system stat errors (#2277, @squakez)
fix(e2e): Fix OLM upgrade e2e test (#2281, @astefanutti)
fix cron trait issue (#2448, @liurui-1)


Player update

Why?
Player code needs improvement
What?

Camera was in awkward position
WIP Swimming code (note: liquid types implemented in another PR)

Can you elaborate on "Camera was in awkward position" ?
Your change seems to make the player looks shorter than the original.
Comparisons (can be observed at the position of the nails on the wall) :
The original (vkQuake)
https://github.com/gongpha/gdQmapbsp/assets/13400398/a218407c-9956-47de-81c6-cb854d882f0c
Your changes
https://github.com/gongpha/gdQmapbsp/assets/13400398/887e91fa-9322-41cf-9fa0-626937a93a86
Before the changes
https://github.com/gongpha/gdQmapbsp/assets/13400398/c44dd397-a6ef-4b14-bd27-04eb48abe7b2
And the swimming code you've noted as WIP looks OK. But it didn't work as expected yet.
https://github.com/gongpha/gdQmapbsp/assets/13400398/80a02c8a-f0ce-45ae-b93c-25a5f884dfe9

Reset the camera height or "around.

Updated Step and Ceiling checks to have the step checking collider align with bottom of player and top of player + step height. This fixes an issue with not detecting very small  offsets on ground surface as in the following screenshots

Fixed underwater movement to detect steps when in liquid
Added mechanism to play a variety of player sounds for each liquid type
TODO: detect level of underwater submersion in order to process amount of hurt to apply, and if the player is drowning (currently value is hardcoded to "fully submerged"

Added ability to detect player underwater submersion level: bellow mid-level = 1, above mid-level = 2, above eye-level = 3
Drowning / damage in liquid multiplied by submersion level
Coming up for air (gasping) sound added

Haven't you added water sounds, right ?
and it'll be better if you'd fix the collision box when a player is trying to swim up to a water surface. I would appreciate it ^^
https://github.com/gongpha/gdQmapbsp/assets/13400398/db21055d-7e53-4269-a1d4-eba9f515d9c5

No water enter sounds (splashing yet, but that would be easy to add).
The player is currently able to bounce on top of the water, by jumping or swimming up, which is not ideal, but no worse than before.
I think as an improvement, perhaps the player should not be able to swim/jump up more than half the bbox height out of water. But that would also mean that the there needs to be updates to how "getting out of water" when near a step or solid object -- perhaps by increasing the valid step height while in liquid to include the total height of the player?

After my investigation. Let's say there are 2 crucial height points to check :

Point A :

Approximately half of the player's bbox
Check if the player's upper half body is inside the water.

Point B :

Roughly at the player's feet
Check if the player's feet are inside the water

Both points can be a very short box or something similar and must touch the water to be considered as they're moving in water.
See how :
https://github.com/gongpha/gdQmapbsp/assets/13400398/d46d2587-b0da-4bd4-91d7-9c08d6ed1a51
In the clip, You can see the player would fall out immediately when their feet are out of the water.

Why needed Point B ? Point A alone will do stuff anyways.
Check out E4M2.
https://github.com/gongpha/gdQmapbsp/assets/13400398/b31b888b-11a5-4a54-bee4-23fbd4dcc5e7
The pool in E4M2 has a very thin water volume. The player couldn't even move in the water volume, despite Point A colliding with it. This is because their feet are already out of the water, making Point B doesn't collide.
Or even if Point B collides with water (their feet are inside the water) if Point A (their upper half body) doesn't collide with the water. It wouldn't be considered that they're moving in the water.
And sure, our implementation could do. which it shouldn't.
https://github.com/gongpha/gdQmapbsp/assets/13400398/d7c32266-a311-4ef7-b0d8-2ba9d513a171
This is my pure assumption. But should work in this situation :)

Also, I found a good source about the Quake metrics
https://book.leveldesignbook.com/process/blockout/metrics/quake
So, I will write it some of them here. And include Godot units too.

Metric
Quake unit
Godot unit (1/32)

Collision hull size
32 x 32 x 56
1 x 1 x 1.75 (current is 1.5)

camera height above floor
46
1.4375

Maximum run speed
320u/sec
10u/s

Fall damage height (for handling fall damage sound)
256+
8+

Underwater height (above floor)
29+
0.90625+

We can use func _get_submerged_level() to check 3 levels, bellow mid-point (bbox entered fluid volume) is 1, mid-point (half point of character bbox) is 2, and eye level (camera/around) is 3.

There's also something we can decide to do, is ask if we want to implement "all the features", or work in a more incremental way, where we can have more peace-meal player improvements across many PR's. I don't know if we will successfully implement all the player features at once.
Also I will be away this weekend, so won't touch this until later Monday or Tuesday.

To make it more like in the original behaviour func _get_submerged_level() can be modified to add another point collision check at the foot level which would represent A and return 1...
Personally I don't know if I would consider that an improvement, modifying behaviour to swim underwater when BBox touches water && mid-level (point B) touches water, would be good enough for me. But we can definitely try to be more like the original if we want...

Swimming should feel more like traditional game now (maybe not exactly, but closer)
Also getting out of water is easier now, as there's a separate "stairstep_liquid" which applies while in liquids, that is much higher than normal stairstep.

Looks better !
Also, I suggest you change the gravity as well. It seems a bit low. Compared with the original which falls faster.
it's around 800 or 25 in Godot unit.

Is there an issue with ramps in this PR? I'm testing it on e1m1 and this 45 degree ramp can not be walked up:


[ux/accessibility] Cmd + Click to open new tabs for links on the homepage

[Cmd + Click] should open links in new tabs. Now it doesn't work for most links on the home page.

Since this is more feature request than bug report, and the thread has been silent for multiple years, we'll go ahead and close this out. If people are still encountering this in current versions (currently 3.x) please open a new "Ideas" discussion for the feature request or open a PR to address the problem.


Body manipulation - possible refactoring opportunity?

Can you think of any refactorings to this method:
https://github.com/gedzubo/gp_surgeries_api_client/blob/master/data_manipulator.rb#L16
That might improve readability or maintainability?

We could create a method a method for "full_address += "#{surgery['address1']}, " if surgery['address1'] != '' " as all the lines are similar besides the key of the hash.
Other possible way I would approach it - would be to make a copy of existing body and try to append full_address to the existing hash using Hash method .merge().


copy connection for projection

During the tutorial I discovered that you can use the event store and subscription engine without Doctrine Bundle. But as soon as you want to write your projections with dbal, you have to install DoctrineBundle or set up the connection yourself.
(You cannot use the same connection as the one for event sourcing, otherwise the subscription engine will break)
So you currently have the following options:

You build a connection service yourself, which is cumbersome.

You install DoctrineBundle, but you immediately get an error message that the orm package is missing and you have to remove orm config first.

We recommend using DoctrineBundle if you want all the Dbal features like extended configuration options, logger and profiler. But for the start and tutorial it is unfortunate.
I also didn't want to duplicate the features of DoctrineBundle here. That wouldn't make sense.
I also looked to see if our bundle should require DoctrineBundle directly, but that isn't that easy to implement either, as we would have to manipulate the config. It could also be that you don't want to use dbal then we could have saved ourselves that.
So I came up with the simple solution of offering the option to build a second connection with the same URL, which you can then use in your projection. That is the simplest solution without having to go through DoctrineBundle.
The naming of the option is not good yet. Here are a few ideas:

use_for_projection
publish_connection

Regarding naming of the option: copy_for_projection could also be a good candidate.
I'll just toss the other possbility in here, where the user configures a different url and mark it as using for proejction:
connection:
    url: ''
projectionConnection:
    url: ''
    use_for_projection: true

instead of the current proposed:
connection:
    url: ''
    use_for_projection: true

I would go with provide_dedicated_connection. I think that describes it best. I have also expanded the documentation and tried to describe everything accordingly. Additionally I will set it to true in our flex recipe.

What do you think about extra_connection as naming?
connection:
    url: //...
    extra_connection: true


Is it possible to add the ability to use "ON DELETE RESTRICT" or "ON UPDATE RESTRICT"?

Currently, using System.Data.Rule limits us to so that we can't use RESTRICT via the API.  I am using Execute.Sql to get this, but is there a way to have it in the API?

Would it be possible to inherit the Rule and add more options, like RESRICT?

@scpeterson Wouldn't the ON DELETE RESTRICT act just the same as Cascade.NoAction ?

It depends on the database.  In some, NO ACTION is a deferred check.  In others they are the same.  In MySQL for example, foreign key constraints are checked immediately, so NO ACTION is the same as RESTRICT.  My understanding however, is that according to standards, NO ACTION is deferred and RESTRICT acts immediately.  It would be nice to have in in FluentMigrator so developers can decide for themselves which to use.

@scpeterson Maybe PostgreSQL is such a DBMS ? although the documentation is not that clear on the difference between NO ACTION and RESTRICTED (deferrable or not). What would be the outcome difference of an operation in either NO ACTION or RESTRICTED ?

@tommarien I'm not completely sure although I could look into it.  My initial reason for the comment was that I was trying to duplicate what we have using Fluent Migrator, and found that Data.Rule limited things.  My feeling is that if there is any difference between databases, then we need to make sure NO ACTION and RESTRICT can be defined separately.

@scpeterson agreed, but we need to justify the reason of existance of this specific Rule implementation.

@scpeterson Atm i consider this closed, as there seems to be no practical justification for this to change. If you still feel this is necessary, please give a practical use case


v2.0.1 token transfers not returned in record

See: https://github.com/hashgraph/hedera-sdk-java/blob/master/sdk/src/main/java/com/hedera/hashgraph/sdk/TransactionRecord.java
https://github.com/hashgraph/hedera-services/blob/master/hapi-proto/src/main/proto/TransactionRecord.proto

This field now exists on master https://github.com/hashgraph/hedera-sdk-java/blob/master/sdk/src/main/java/com/hedera/hashgraph/sdk/TransactionRecord.java#L74


Add performance alarms

Add alarms when we see slow performance on the build and run lambda for

cold boot time
initialization time
transition time
cleanup time

These alarms are all set up as anomaly detection for two reasons. Mainly because different lambda configurations create different performance benchmarks (namely theater, which has a higher memory size). In addition, anomaly detection gives a more accurate representation of what our expected performance is than I could get by manually looking at the data.
The alarms are very similar but I split them up individually instead of creating them via an array because we may want to tune the alarms individually. The general configuration is:

the metric value is set to p(95) for each minute
the threshold is greater than the anomaly band. Each anomaly band has a width--this is the 5 in ANOMALY_DETECTION_BAND(m1, 5). I gave a wider band to spikier metrics and vice versa, but these may need to be tuned.
we alarm if we hit 15 out of 15 minutes of poor performance. If we don't see a minute of data we ignore that minute, so if we saw 13 minutes of poor performance and 2 of no data we would still alert.
The alarms will go to #ap-csa-dev.

we alarm if we hit 15 out of 15 minutes of poor performance. If we don't see a minute of data we ignore that minute, so if we saw 13 minutes of poor performance and 2 of no data we would still alert.

LMK if I'm reading this correctly -- if we saw 1 minute of poor performance and 14 of no data, we alert?

@bencodeorg yes that's correct. Another option would be to treat missing data as good, but then if we saw 14 minutes of poor performance and 1 minute of no data we wouldn't alert, and we often having missing minutes of data. Not sure the best configuration to use here.

Woof the missing data section in CloudWatch docs is a doozy, I'm down to put them in and see how it goes 😆
https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/AlarmThatSendsEmail.html#alarms-and-missing-data


Unable to push/publish Visual Studio Code file into Power Apps. Lab 5, exercise 2, task 1.

Lab: 05
Task: 1
Step: 3
Description of issue: After completing the rest of this lab multiple times to ensure accuracy, we are unable to use the command "pac pcf push --publisher-prefix contoso" without facing errors.
Errors:

Error: Building temporary solution wrapper: failed. See obj\build.log for more details.
The OutputPath property is not set for project 'PowerAppsTools_contoso.cdsproj'.  Please check to make sure that you have specified a valid combination of Configuration and Platform for this project.

We also tried to run this before the publish command just to see if there were any steps missing that we could recommend adding but it didn't work for us: msbuild /p:configuration=Release
We were trying to see if there was anyone else having the same issue and how we could resolve it, but we were not successful and all posts we found were related to old versions that we're not using at the moment. All posts were about version 1.16 but we're on latest (1.21.8). We tried switching between versions just in case but that didn't work either so we think it's probably something else that we're missing.
Repro steps:

In Visual Studio Code, within the SRC directory in the terminal, I enter "pac auth create --url " and authenticate successfully.
The next step as mentioned above, fails with the errors listed.

Any chance I could get an update on this please? We have customers who are unable to follow the guides successfully in a lab environment due to this problem.

Having the same experience as a student on this course. Doesn't give a great impression of the product or how it is supported :-1:

Hi @skulthm-ms, is there any chance we can get an update on this soon, please? We are a training provider and have students currently taking part in MCT-lead classes and they are unable to complete the virtual lab environment based on the steps in this guide. As I'm sure you can understand, this leads to frustration from training providers and the students themselves who have paid to take part in these courses. We would greatly appreciate your assistance.

Modified the cdsproj file here:
obj\PowerAppsTools_contoso\PowerAppsTools_contoso.cdsproj
Added a new propertygroup with the OutputPath Property
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' "> <DebugSymbols>true</DebugSymbols>     <DebugType>full</DebugType>     <Optimize>false</Optimize>     <OutputPath>bin\Debug\</OutputPath>     <DefineConstants>DEBUG;TRACE</DefineConstants>     <ErrorReport>prompt</ErrorReport>     <WarningLevel>4</WarningLevel>     <TreatWarningsAsErrors>true</TreatWarningsAsErrors>   </PropertyGroup>

Move the PowerApps CLI install before developer command prompt is opened, or close and restart the developer command prompt
The PowerApps CLI install adds items to the PATH and this might be what you are missing

So I tried the following and got mine to work.
I merged the code with the first Propertygroup.

 $(MSBuildExtensionsPath)\Microsoft\VisualStudio\v$(VisualStudioVersion)\PowerApps
true
full
false
bin\Debug\
DEBUG;TRACE
prompt
4
true

We didn't encounter this issue in the recent test. Please follow julians advice and if that didnt work please let us know if DanePillay-nel solution or glissom is working for you. I really appreciate everyone's input here. Sometimes we can't reproduce the issue and your knowledge sharing is the only way to debug it.


Change the word create to creates

Not: create
But: creates
Then depending on that information the manager service create an instance of your actual contact-storage service just for that customer.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 9bb25acc-6868-7ae8-5b42-813eee29efa4
Version Independent ID: ba88e4b7-f27c-6d0a-7550-6e04c6f36c4c
Content: Scalability of Service Fabric services - Azure Service Fabric
Content Source: articles/service-fabric/service-fabric-concepts-scalability.md
Service: service-fabric
GitHub Login: @masnider
Microsoft Alias: masnider

@writex Thanks for the Feedback.
I have updated the document and changes will go live soon.


Produces empty title for year/month/day

jekyll-archives produces an empty "" element for year/month/day pages.  It seems this is intentional:
      # Returns a String (for tag and category archives) and nil for
      # date-based archives.
      def title
        @title if @title.is_a?  Firefox, for example, will display the URL if the title is not set. Why not set a title like "Archive for year yyyy", "Archive for month ... of year yyyy", etc.  Or easier to implement: "Archive for" and then append, year, month, day (whatever is available).

I’m generally not a fan of mixing Liquid and YAML, but in this instance it really would be very handy to just let the user specify an archives.title in _config.yml for us to parse as Liquid and use.
This would also allow users to format the date in a way that makes sense for their own site.

You can already specify a title using the layouts.
I do that for my yearly archives:
title: {{ page.date | date: "%Y" }}

This doesn't work. You can't use Liquid in the front matter.

This is a post drom 5 years ago. I no longer use this plugin.

There was a plugin which permitted what you said, but it doesn't work with newer releases of Jekyll. Anyway, sorry, my first comment might have sounded a little aggressive, I'm just researching this topic and I might come with a fork / PR fork for this repo which permits some sort of title generation for date based archives.


chore: use a better maintained and more flexible license checker tool

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Please upload report for BASE (main@e99c3eb). Learn more about missing BASE report.

Additional details and impacted files
@@           Coverage Diff           @@
##             main     #267   +/-   ##
=======================================
  Coverage        ?   90.64%
=======================================
  Files           ?       25
  Lines           ?     1603
  Branches        ?        0
=======================================
  Hits            ?     1453
  Misses          ?      107
  Partials        ?       43


Make "Submit" more explicit (or auto-save)

I found it not easy to find the "Submit" button and was close to lose all the data that I have entered by almost closing the browser tab.
One idea to mitigate this problem is making the "Submit" button bigger and more easy to find in the UI. Alternatively maybe create an auto-save logic.
My 2 cents only. Thanks for the work.

Auto-save would be maybe too much, but I think two things can improve the UX:

Add a Submit button under each category
Implement the logic that prevents closing the tab if there is unsaved data (and maybe mark the items that were changed)

I think the two UX fixes above solve the issue.  Feel free to reopen if you have better ideas.


Update the file/directory structure of the Twitter(X) connector

Description
This pull request refactors the file structure of the Ballerina Twitter (X) Connector to align with the new structured file organization.
Changes
Reorganized directories and files to follow the new structured file format.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

@vish-mv Lets keep the original .github/CODEOWNERS file.

Lets keep the ballerina/LICENSE file and move it to the project root.

@vish-mv pls rename .github/CODEOWNERS.txt to .github/CODEOWNERS.

@vish-mv shall we update the Build from the source section on README.md by referring to Ballerina Zendesk connector [1]
[1] - https://github.com/ballerina-platform/module-ballerinax-zendesk/?tab=readme-ov-file#build-from-the-source


[SymbolGraph] Fix a crash for member in invalid extension

Don’t record a memberOf relationship if we couldn’t look up the target, e.g. because the member is declared in an extension whose extended type doesn’t exist.

@swift-ci Please test


Set default prefetchRows to 0 and prefetchMemory to 4096

https://github.com/mattn/go-oci8/issues/38

Good to go

@mattn Travis just needs a restart for it to pass.

Hmm, test on travis is flaky

Yeah :/
I wonder if it has something to do with alexeiled/docker-oracle-xe-11g, maybe it has bugs in it?

@mattn What you think?

I hope to wait some consentration for changing prefetchMemory in #38.

@mattn FYI will be away from the computer for a couple of weeks. Will not be able to do any code changes.

@mattn https://github.com/mattn/go-oci8/issues/38 seems to have no new comments, could this be merged?


[BUG] Field is always false with the Postgresql driver

Bug Description [describe the bug in detail]
I have a table with a boolean field in a heroku postgres database (v12.2).
When a info field is added with db.Boolean or similar type with goadmin, the field always prints false. I have checked with:
info.AddField("Paused 0", "paused", db.Boolean)
info.AddField("Paused 1", "paused", db.Bool)
info.AddField("Paused 2", "paused", db.Int)
info.AddField("Paused 3", "paused", db.Tinyint)

@davidlcarrascal  Made a fix. 5dc13b

@chenhg5 Thanks!! Do I have to use the latest version?


Introduce Resolved dependencies for SLSA v1.0 predicate

Changes
This PR introduces resolved dependencies for the SLSAv1.0 predicate.
It addresses part of issue https://github.com/tektoncd/chains/issues/797
Related design.

Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[ ] Has Docs included if any changes are user facing
[x] Has Tests included if any functionality added or changed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including
functionality, content, code)
[x] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings)
[ ] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
Introduce resolved dependencies for SLSA v1.0 predicate.

/kind feature

Thank you @chitrangpatel for the refactor! Much cleaner now!
One general comment I have is that it would be great if we can have a consistent way to collect the list of materials/resolvedDependencies throughout the helper functions.
Currently, there are three categories:

some functions just receive []*common.ProvenanceMaterials as an argument and mutate it.
some receive the []common.ProvenanceMaterials but also return []common.ProvenanceMaterials
som functions just return a new  []common.ProvenanceMaterials without receiving a list of materials.

Thats a good idea. Since its a lot of refactoring, I suggest we do this in a followup PR since it does not fit well with this.

@lcarva could you please lgtm it again? I implemented the nits you mentioned. Thanks!

/lgtm
@wlynch, it looks like your comments have been addressed. Ok to merge this? (I'll hold off my approval for now)


Can't scan QR codes on 0.14.0 Mac DMG

Can't open webcam to scan QR code on MacOS release files. Work on development version (web (npm run dev) and electron (npm run start)

Just as a reference.  The NanoWallet build had the same issue too. https://github.com/NemProject/NanoWallet/issues/634#issuecomment-787484892

@bassemmagdy I think the permission should be asked to use camera by browser?

@NikolaiB Browser already asks for permission to open the camera


Omnibus: Known low-pri remaining bugs in Extract Method

[ ] Detect and disallow stranding of type parameters
[ ] Disallow extraction of LHS of assignmentlike exprs (e.g. do not allow extracting x in x += 3 or x = 10)
[ ] Need to sometimes insert whitespace before the substituted function call (e.g. when extracting [] from return[])
[ ] Disallow extracting the empty statement
[ ] Figure out what to do with decorators 😞
[ ] Allow extraction of code in class methods into local functions if that code doesn't reference this
[ ] Disallow extracting into a class scope if this doesn't bind to an instance of the class at the call site
[ ] getEditsForAction can, and probably should, return the location of a Rename session to kick off

All credit @amcasey

[ ] Detect and disallow stranding of type parameters

Good luck @amcasey ✋

Potential issue: variables that are only assigned (mutations are different) should not be passed in as parameters.

Potential improvement: can we use destructuring to expose declarations in the extracted range (assuming there are no writes).

FYI @billti

Improvement: Allow extraction of a span containing a variable declaration if we can initialize the declarations:
var x = 10 + 10;
~~~~~~~~~~~~~~~

return x;

should be come
var x = newFunction();
return x;
...
function newFunction() {
  return 10 + 10;
}

Improvement: same as above, but for multiple variables (using destructuring)

Example issue from the checklist above.  Try extracting 2 from
        // a
        var q = /*b*/ //c
            /*d*/ 2 /*e*/ //f
            /*g*/; /*h*/ //i


Add TransformedTargetClassifier

Adding TransformedTargetClassifier, which is a sklearn style class that wraps over a classifier and a transformer for the target. This is very similar to the existing https://scikit-learn.org/stable/modules/generated/sklearn.compose.TransformedTargetRegressor.html.
The main motivation of adding this class is that xgboost removes the label transform inside their sklearn interface since 1.6.0.
There is a discussion on adding https://github.com/scikit-learn/scikit-learn/issues/20952 to sklearn, and maybe in the future we can considering contributing our version to sklearn instead of maintain it in this automl runtime repo.

Codecov Report

Merging #78 (bf3ff01) into main (7bb7bb7) will decrease coverage by 0.14%.
The diff coverage is 96.42%.

@@            Coverage Diff             @@
##             main      #78      +/-   ##
==========================================
- Coverage   99.67%   99.53%   -0.15%
==========================================
  Files          19       21       +2
  Lines         614      639      +25
==========================================
+ Hits          612      636      +24
- Misses          2        3       +1

Flag
Coverage Δ

unittests
99.53% <96.42%> (-0.15%)
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Impacted Files
Coverage Δ

...atabricks/automl_runtime/sklearn/compose/target.py
95.65% <95.65%> (ø)

...time/databricks/automl_runtime/sklearn/__init__.py
100.00% <100.00%> (ø)

...abricks/automl_runtime/sklearn/compose/__init__.py
100.00% <100.00%> (ø)

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Warn on initialisation if the topic config does not match expected.

duplicate of https://github.com/creek-service/creek-kafka/issues/114