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Building systems that turn complex data into actionable insight.
πŸš€
Building systems that turn complex data into actionable insight.

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texasbe2trill/README.md

Chris Campbell

typing header

Real data. Measured outcomes. Shipped systems.


🎯 What I Do

I build data science and ML systems end to end, then ship them. Feature engineering, calibration, validation, the unglamorous work that decides whether a model is actually useful. By day, I'm a security engineer building Python forensic automation and applied LLM workflows over high-volume operational and behavioral data.

Currently exploring: regime-aware recalibration, LLM grounding, and time-aware validation patterns for production model bundles.

πŸ›‘οΈ Security

MITRE ATT&CK triage
Free-text incidents β†’ calibrated triage cards via hybrid classifier + LLM rationale

πŸ“Š Data Science

RΒ² 0.615 PRA holdout
Calibrated regressors, time-aware validation, regime-aware bundles (n_test 43K)

πŸ€– Applied LLMs

4 pluggable backends
OpenAI Β· Anthropic Β· Hugging Face Β· local llama.cpp β€” structured extraction & rationale

πŸš€ Featured Project: AlertSage

Open-Source SOC Console

Python Β· scikit-learn Β· sentence-transformers Β· Streamlit Β· LLMs Β· SQLite

Free-text security incident in, MITRE ATT&CK triage card out. A TF-IDF + sentence-transformer hybrid handles the fast first pass. An LLM (OpenAI, Anthropic, Hugging Face, or local llama.cpp) commits to the verdict and writes the rationale. SQLite backs case management, bookmarks, timelines, and batch processing up to 500 rows.


↓

TF-IDF + sentence-transformer
↓

OpenAI Β· Anthropic Β· HF Β· local llama.cpp
↓






🎯 MITRE ATT&CK-mapped output
πŸ”Œ 4 pluggable LLM backends
⚑ Fast classifier + LLM rationale
πŸ’Ύ SQLite case management
πŸ“Š Batch up to 500 rows



Β 

πŸ“¦ More Projects

πŸ€ Hooplytics

Python Β· scikit-learn Β· pandas Β· Streamlit

End-to-end NBA analytics. 60+ leakage-safe features, 8 calibrated regressors, regime-aware playoff bundle swap, live odds, printable PDFs.

PRA holdout β†’ RΒ² 0.615 Β· n_test 43K

πŸ“š KoNotes

Python Β· Streamlit Β· LLMs Β· NLP

Local-first AI-assisted knowledge analytics. Converts Kobo and Kindle annotations into structured, queryable insight with explainable, rule-based recommendations.

🍎 macos-trust

Python Β· CLI

Context-aware macOS trust assessment. Fast evaluation of apps, launch items, and system controls with low false-positive design.


πŸ› οΈ Stack

ML / Modeling scikit-learn, classification, regression, calibration, time-aware validation, residual diagnostics, threshold tuning
Python data stack Python, SQL, pandas, NumPy, statistical reasoning, EDA, reproducible Jupyter workflows
Applied AI LLM summarization, structured extraction, text classification, AI-assisted triage and case reporting
Query & detection SQL (incl. Snowflake), KQL, Splunk SPL, query optimization, detection-as-code (Panther)
Security domain telemetry analysis, forensic automation, investigation analytics, control monitoring
Delivery Streamlit dashboards, Typer CLIs, joblib model artifacts, ReportLab reports, REST APIs

πŸ“Š GitHub

Pinned Loading

  1. AlertSage AlertSage Public

    Open-source SOC console. Free-text security incident in, MITRE ATT&CK triage card out. TF-IDF + sentence-transformer classifier, multi-provider LLM dispatch, IOC enrichment, and case management.

    Jupyter Notebook 4 2

  2. hooplytics hooplytics Public

    Hooplytics turns NBA box-score data into player intelligence through machine learning, interactive analytics, and visual workflows for exploring trends, projections, and performance signals.

    Jupyter Notebook 4

  3. KoNotes KoNotes Public

    Interactive reading dashboard and CLI for Kobo and Kindle β€” library stats, AI-powered insights, smart recommendations, book cover art, reading activity, annotation exports, and Bluesky sharing.

    Python 4 2

  4. macos-trust macos-trust Public

    Intelligent macOS security scanner that identifies unsigned apps, Gatekeeper violations, and suspicious persistence mechanisms with context-aware risk assessment. No false positive fatigue.

    Python 2