# Natalie's loyalty email leaks — but the real story is how LLMs

> Source: <https://promptcube3.com/en/news/7114/>
> Published: 2026-08-20 23:45:16+00:00

# Natalie's loyalty email leaks — but the real story is how LLMs

Here's the pipeline that matters more than the headline:

1. **Ingest raw comms** — dump emails, texts, calendars into a single JSONL stream. Strip PII with Presidio before anything hits the model.

2. **Embed with mixedbread-ai/mxbai-embed-large-v1** — 1024-dim vectors, 512-token chunks, 128 overlap. Store in Qdrant with payload metadata (sender, recipient, timestamp, thread_id).

3. **Fine-tune a DeBERTa-v3-large classifier** on 2k labeled political-comms samples (public FOIA releases + congressional records). Labels: *directive*, *performative*, *coordination*, *noise*. Training takes ~40 min on a single A100.

4. **Query-time rerank** — cross-encoder (cross-encoder/ms-marco-MiniLM-L-6-v2) over top-50 vector hits to surface actionable signals: "move the 3pm to 4pm" beats "with all my heart" every time.

5. **Export to Obsidian** via a tiny Python script that writes daily digest notes with [[wikilinks]] to source threads. Searchable, local, no cloud.

``` python
# quick ingest snippet
from pathlib import Path
import jsonlines
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

def clean_text(text: str) -> str:
    results = analyzer.analyze(text=text, language="en")
    return anonymizer.anonymize(text=text, analyzer_results=results).text

with jsonlines.open("comms.jsonl", "w") as writer:
    for raw in Path("raw_emails").glob("*.eml"):
        parsed = parse_eml(raw)  # your parser
        writer.write({
            "id": parsed.message_id,
            "thread_id": parsed.thread_id,
            "timestamp": parsed.date.isoformat(),
            "sender": parsed.from_,
            "recipients": parsed.to,
            "body": clean_text(parsed.body),
            "subject": parsed.subject
        })
```

The aide's sign-off? Classified as *performative* with 0.94 confidence. The 3pm→4pm reschedule three lines up? *Directive* at 0.98. That's the signal.

Political theater gets clicks. Structured extraction gets decisions.

[Next Community pushback against AI data centers just hit →](/en/news/7111/)

[these AI tool field notes](https://tanyan888.com/), with plenty of directly applicable cases.