Anyone already built an AI trained on all Q drops?
Trained properly, I'm guessing it could unlock a lot.
To build an AI for uncovering “secret links” in Q drops (e.g., thematic threads, coded patterns like mirrors or gematria), start by acquiring a dataset of ~5,000 drops from public archives (e.g., GitHub JSON files with text, timestamps, IDs, tripcodes). Prepare features: Clean text for NLP, convert timestamps to numbers (e.g., “16:44:28” → 164428), compute mirrors (reverse IDs), deltas (time gaps), and extract entities/styles.
The “machine” is a Python-based AI system using libraries like NLTK, scikit-learn, Sentence Transformers, and PyTorch. For internal links in old drops, it employs unsupervised models: LDA for themes, K-Means clustering on semantic embeddings for similarities, regex/gematria for codes, and rules for mirrors/deltas (e.g., cosine similarity >0.7 flags links). Supervised fine-tuning (e.g., neural nets) predicts connections if labeled.
For daily news matching, integrate web APIs (e.g., search tools) to fetch current events, compute date/timestamp alignments, and semantic matches (e.g., drop text on “Epstein” vs. news snippets). It scans for patterns like event correlations or numerical echoes, outputting hypothesized insights while noting potential apophenia. Total setup: Iterative, ethical research tool, runnable on standard hardware.