• The Apify Model Context Protocol (MCP) Server serves as an interface for AI applications to integrate with Apify's library of Actors for efficient web scraping and automation tasks. Users can access this server through secure HTTPS endpoints or the standard input/output method for local integrations. The MCP framework additionally allows dynamic actor management, facilitating real-time addition and execution of various Actors to enhance the capabilities of AI agents.

    Key Points
    - The Apify MCP Server connects AI applications with Apify's library of Actors for real-time web scraping and automation.
    - Users can connect to the MCP Server via HTTPS with OAuth or through standard input/output methods for local setups.
    - Before using the MCP Server, users must have an Apify account, API token, and an MCP-compatible client.
    - The server allows for dynamic actor management, enabling AI agents to discover, add, and execute Actors as needed.
    - Users can interact with the MCP Server over Server-Sent Events (SSE) for message sending and response receiving.
    - Troubleshooting tips include verifying the API token, ensuring the server runs the latest version of the MCP library, and ensuring a suitable Node.js environment.
    - Users can utilize a web-based Tester MCP Client for an accessible alternative to integrate and test capabilities without local setup.

    https://docs.apify.com/platform/integrations/mcp
    https://github.com/apify/actors-mcp-server

    #Apify #MCP #actors #webscraping #automation #AIagents #dynamicactormanagement #serversentevents #SSE #nocode #lowcode #rapiddevelopment #TesterMCPClient #webautomation #apifyactors #cloudcomputing #python #nodejs #portkey #airtable
    The Apify Model Context Protocol (MCP) Server serves as an interface for AI applications to integrate with Apify's library of Actors for efficient web scraping and automation tasks. Users can access this server through secure HTTPS endpoints or the standard input/output method for local integrations. The MCP framework additionally allows dynamic actor management, facilitating real-time addition and execution of various Actors to enhance the capabilities of AI agents. Key Points - The Apify MCP Server connects AI applications with Apify's library of Actors for real-time web scraping and automation. - Users can connect to the MCP Server via HTTPS with OAuth or through standard input/output methods for local setups. - Before using the MCP Server, users must have an Apify account, API token, and an MCP-compatible client. - The server allows for dynamic actor management, enabling AI agents to discover, add, and execute Actors as needed. - Users can interact with the MCP Server over Server-Sent Events (SSE) for message sending and response receiving. - Troubleshooting tips include verifying the API token, ensuring the server runs the latest version of the MCP library, and ensuring a suitable Node.js environment. - Users can utilize a web-based Tester MCP Client for an accessible alternative to integrate and test capabilities without local setup. https://docs.apify.com/platform/integrations/mcp https://github.com/apify/actors-mcp-server #Apify #MCP #actors #webscraping #automation #AIagents #dynamicactormanagement #serversentevents #SSE #nocode #lowcode #rapiddevelopment #TesterMCPClient #webautomation #apifyactors #cloudcomputing #python #nodejs #portkey #airtable
    Apify MCP server | Platform | Apify Documentation
    docs.apify.com
    Learn how to use the Apify MCP server to integrate Apify Actors into your AI agents or applications.
    0 Comments ·0 Shares ·386 Views
  • The Prompt Report: A Systematic Survey of Prompting Techniques

    "The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions.

    Key Points
    - A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document.
    - The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks.
    - In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques.
    - Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts.
    - Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated.
    - Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques.
    - The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs.

    https://learnprompting.org/blog/the_prompt_report
    https://huggingface.co/papers/2406.06608
    https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques

    #PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools
    The Prompt Report: A Systematic Survey of Prompting Techniques "The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions. Key Points - A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document. - The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks. - In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques. - Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts. - Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated. - Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques. - The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs. https://learnprompting.org/blog/the_prompt_report https://huggingface.co/papers/2406.06608 https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques #PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools
    The Prompt Report: Insights from The Most Comprehensive Study of Prompting Ever Done
    learnprompting.org
    Discover actionable insights from The Prompt Report, the most comprehensive study of 1,500+ papers and 58 prompting techniques ever done.
    0 Comments ·0 Shares ·265 Views
  • Apify's MCP Server enables integration with over 4,000 Apify Actors, allowing users to connect a vast range of data extraction and automation tools. This integration can be particularly useful for creating AI agents or incorporating web data into workflows. Apify seems to offer solutions for monetization related to these MCP Servers. The use of MCP Server allows for the easy integration of actors with different LLMs.

    #apify #mcpserver #dataextraction #webautomation #aiagents #llmintegration #webscraping #scrapy #beautifulsoup #selenium #puppeteer #playwright #automation #datamining #webdata #workflows #monetization #actors #integration #ai

    https://docs.apify.com/platform/integrations/mcp
    https://apify.com/integrations
    Apify's MCP Server enables integration with over 4,000 Apify Actors, allowing users to connect a vast range of data extraction and automation tools. This integration can be particularly useful for creating AI agents or incorporating web data into workflows. Apify seems to offer solutions for monetization related to these MCP Servers. The use of MCP Server allows for the easy integration of actors with different LLMs. #apify #mcpserver #dataextraction #webautomation #aiagents #llmintegration #webscraping #scrapy #beautifulsoup #selenium #puppeteer #playwright #automation #datamining #webdata #workflows #monetization #actors #integration #ai https://docs.apify.com/platform/integrations/mcp https://apify.com/integrations
    Apify MCP server | Platform | Apify Documentation
    docs.apify.com
    Learn how to use the Apify MCP server to integrate Apify Actors into your AI agents or applications.
    0 Comments ·0 Shares ·433 Views
Displaii AI https://displaii.com