Row 6059

Row ID: 6059 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 6059 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I Just finished an interesting exercise in AI. City Council meeting minutes dating back to January 2021 uploaded to 1.5 pro in the Google AI studio (over 100 .pdf documents condensed and converted into 4 text files, Over 200,000 words. Each text file contained 1 year of minutes in plain text format). **Instructions (very condensed)**: Only report on agenda items that had votes in conflict (no need to show consent items, or unanimous votes). Then format the text into outline format. This was to provide the residents of my dear city the voting record of their city official seeking reelection. I used about 800K of the 1 Million tokens including the source files to complete. End result: [https://www.thepalmbayer.com/p/palm-bay-city-council-voting-record](https://www.thepalmbayer.com/p/palm-bay-city-council-voting-record). \* It's a fun page to talk to with an AI extension also. Now that I have this large data set that knows each members voting pattern over 3+ years I wonder if it can predict........... \*\* Gemini 1.5 pro with a 1 Million token context window is a **beast**.

FieldValue
text I Just finished an interesting exercise in AI. City Council meeting minutes dating back to January 2021 uploaded to 1.5 pro in the Google AI studio (over 100 .pdf documents condensed and converted into 4 text files, Over 200,000 words. Each text file contained 1 year of minutes in plain text format). **Instructions (very condensed)**: Only report on agenda items that had votes in conflict (no need to show consent items, or unanimous votes). Then format the text into outline format. …
label r/artificial
dataType post
communityName r/artificial
datetime 2024-05-06
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url_encoded Z0FBQUFBQm5Lak9HNlJDNnowNHd0NF9ZV1d0OEdTWnp3ZFNuTU54LVBROWZMa3ctWWxYVnFETDlFZjcwZ3U0b0tUNElfWVNCN2J0ZDNCcFctQXdMLV9RRExUSnk2ZFF3OFcyZkdvdDBQdHNQY1JlTzNid2p4YjVubmNyRmFIZEJiam5fM2lQeTIzMVJWZEF5SE5pbjB0ZElNY2EzTnVrUjFsbk42V3hKTjhwMEs0V29XdlROTUdEbV9IMk5SMDF2Q0hLN1E2M0FERHJZdVJvOS1xYzE4ZEloemd6UlVsT1hZUT09

Raw Record

{
  "text": "I Just finished an interesting exercise in AI.    \nCity Council meeting minutes dating back to January 2021 uploaded to 1.5 pro in the Google AI studio (over 100 .pdf documents condensed and converted into 4 text files, Over 200,000 words. Each text file contained 1 year of minutes in plain text format).    \n**Instructions (very condensed)**: Only report on agenda items that had votes in conflict (no need to show consent items, or unanimous votes).  Then format the text into outline format.    \nThis was to provide the residents of my dear city the voting record of their city official seeking reelection.  \nI used about 800K of the 1 Million tokens including the source files to complete.    \nEnd result: [https://www.thepalmbayer.com/p/palm-bay-city-council-voting-record](https://www.thepalmbayer.com/p/palm-bay-city-council-voting-record).  \n\\* It's a fun page to talk to with an AI extension also.  Now that I have this large data set that knows each members voting pattern over 3+ years I wonder if it can predict...........  \n\\*\\* Gemini 1.5 pro with a 1 Million token context window is a **beast**.  ",
  "label": "r/artificial",
  "dataType": "post",
  "communityName": "r/artificial",
  "datetime": "2024-05-06",
  "username_encoded": "Z0FBQUFBQm5LakwyOUNjTzQ0VGl6NVZkOVd3OTRJSXZPY0g1dXFDaGE5encyc0FRcWc3ZzFnc0lBQXE4ZkxEdl9uVDRiZm9JME1hQlZDVkJLODYwczhBdHVlc19qYUxVRGc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9HNlJDNnowNHd0NF9ZV1d0OEdTWnp3ZFNuTU54LVBROWZMa3ctWWxYVnFETDlFZjcwZ3U0b0tUNElfWVNCN2J0ZDNCcFctQXdMLV9RRExUSnk2ZFF3OFcyZkdvdDBQdHNQY1JlTzNid2p4YjVubmNyRmFIZEJiam5fM2lQeTIzMVJWZEF5SE5pbjB0ZElNY2EzTnVrUjFsbk42V3hKTjhwMEs0V29XdlROTUdEbV9IMk5SMDF2Q0hLN1E2M0FERHJZdVJvOS1xYzE4ZEloemd6UlVsT1hZUT09"
}
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