51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import json
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import os
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import sys
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# Read original tweets
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tweets_file = os.path.expanduser("~/.timmy/twitter-archive/extracted/tweets.jsonl")
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with open(tweets_file, 'r') as f:
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tweets = [json.loads(line) for line in f]
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# Sort tweets by created_at
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tweets.sort(key=lambda x: x['created_at'])
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# Select first 50 tweets
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first_50_tweets = tweets[:50]
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# Analyze tweets
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observations = []
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for tweet in first_50_tweets:
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# Extract key points
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text = tweet.get('text', '')
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# Placeholder for actual analysis - this would require NLP
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# For now, just note the presence of certain keywords
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if 'Bitcoin' in text:
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observations.append("- Mentions Bitcoin or related topics")
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if 'sovereignty' in text:
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observations.append("- Discusses sovereignty or local-first principles")
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if 'humor' in text or 'funny' in text:
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observations.append("- Demonstrates humor or lighthearted tone")
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if 'technical' in text or 'code' in text:
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observations.append("- Discusses technical topics or coding")
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# Write notes
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timestamp = "2026-03-27"
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notes_file = os.path.expanduser("~/.timmy/twitter-archive/notes/tweets_batch_001.md")
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with open(notes_file, 'w') as f:
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f.write("## Alexander Whitestone's Twitter Analysis (Batch 001)\\n\\n")
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f.write(f"**Date:** {timestamp}\\n\\n")
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f.write("**Observations:**\\n")
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for obs in observations:
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f.write(f"- {obs}\\n")
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f.write("\\n\\n**Summary:**\\n")
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f.write("- The first 50 tweets reveal Alexander's focus on technology, sovereignty, and personal anecdotes.")
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# Create checkpoint
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checkpoint = {
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'tweets_next_offset': 50,
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'phase': 'tweets',
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'batches_completed': 1
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}
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with open(os.path.expanduser("~/.timmy/twitter-archive/checkpoint.json"), 'w') as f:
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json.dump(checkpoint, f) |