Is using different AI models in same table affect my output result like is it good or bad.
Using different AI models in the same table can be beneficial as each model has its unique strengths: 1. Claygent Neon excels at: • Answer formatting and data extraction • Formatting results into multiple columns • Extracting various data points from a single run 2. GPT-4 and Claude Opus are better for: • Enhanced reasoning capabilities • Testing data accuracy • Handling complex analytical tasks It's recommended to experiment with different models to find the best fit for your specific needs. Each model processes prompts slightly differently, so you can select the most appropriate one for each task in your workflow.
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How to integrate phantom buster in clay
Here's how to integrate PhantomBuster with Clay: 1. Initial Setup: • Go to Settings > Connections • Click "+ Add Connection" and select PhantomBuster • Enter your PhantomBuster API key and name your account key 2. Using PhantomBuster in Clay: You can use PhantomBuster in two main ways: For New Tables: ** From Workspace Home, click "+ Create new" and search for "PhantomBuster" ** Select your PhantomBuster account ** Specify the Agent ID For Existing Tables: ** Open your table ** Select Actions > Import ** Configure PhantomBuster as your data source Available Actions: ** Pull Data: Retrieve data from PhantomBuster agents ** Push Data: Send data to PhantomBuster for processing You can optionally choose to fetch only the latest container or specify a particular Container ID for your data imports.
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How to push data while using phantom buster
Here's how to push data into PhantomBuster through Clay: 1. Select your PhantomBuster account 2. Specify the Agent ID you want to push data into 3. Set up necessary platform connections: • For Slack: Use the PhantomBuster browser extension to connect and retrieve your cookie (named "d") • For LinkedIn: Either use the PhantomBuster extension to connect or manually locate your "li_at" cookie • For Twitter: Provide profile URLs in one of these formats: • Single Twitter profile URL • Public Google Sheet with URL list • Public CSV file with URLs • For Google: Submit searches as: • Keywords • Google Maps search URL • Public Google Sheet with keywords/URLs • Public CSV with keywords/URLs 4. Fill out the required agent fields based on your specific needs 5. Configure your run settings This integration enables you to push data from Clay directly into PhantomBuster agent executions, allowing you to automate various scraping and processing actions within the PhantomBuster agent library.
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give result specific to For LinkedIn: Either use the PhantomBuster extension to connect or manually locate your "li_at" cookie
For LinkedIn data pushing to PhantomBuster, you have two options: 1. Using PhantomBuster Extension (Easier Method): • Simply click the "Connect to LinkedIn" button • The extension will automatically retrieve your "li_at" cookie 2. Manual Cookie Location Method: • Locate your LinkedIn cookie named "li_at" in your browser • Input this cookie value manually into the connection settings Both methods will establish the necessary LinkedIn connection for pushing data through PhantomBuster.
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I am passing the value but it shows invalid argument can
When you encounter a "Phantom argument is invalid" error while using the LinkedIn cookie, this typically means there's an issue with the argument format. Here's how to resolve it: 1. Make sure your "li_at" cookie value is: • Correctly copied from LinkedIn • Not containing any extra spaces or characters • Current and valid 2. For the most reliable method: • Use the PhantomBuster browser extension • Click the "Connect to LinkedIn" button • Let it automatically retrieve your cookie If you're manually entering the cookie, double-check that you're using the exact cookie named "li_at" from your LinkedIn session and that the argument structure matches PhantomBuster's requirements exactly.
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