My work involves making complex systems understandable by making their structure, assumptions, and tradeoffs explicit: whether that's how a language model predicts the next token, how an agent discovers what tools it has, or how a community weighs competing options in high-stakes decisions.

I like taking systems that are opaque, implicit, or fragmented and making them inspectable, structured, and decision-capable. RR

I sometimes work from my baby animal ♪ stress sanctuary - click in lower right to start ♪

• structural-explainability - designing information structures that remain reliable over time, even when interpretations differ. Explores ideas applicable to science, policy, law, civic infrastructure, digital twins, and AI explainability.

• Paper 1 SE 100: Neutral Substrates
• Paper 2 SE 200: Referential Regimes
• Paper 3 SE 210: Operational Identity with verification

Repository	Focus	Description
paper-100-neutral-substrate	Neutral substrates	Neutral-substrate design constraint for shared records under persistent interpretive disagreement
paper-200-identity-regimes	Referential regimes	Referential-regime structure required by neutral substrates, deriving six coarse families and nine refined profiles
paper-210-operational-identity	Operational identity	Finite audit comparing declared identity regimes with operational identity partitions induced by implementation mechanisms
se-verification-operational-identity	Operational identity	Reference-implementation check for finite mathematical core of SE-210, Operational Identity

A small ecosystem to build professional, inspectable, decision-explicit, and interoperable analytical projects. It includes:

• a grammar to express what and why
• reusable tools to do and see the work
• reproducible experiments to compare choices and evidence
• models demonstrating work on real data

The three organizations:

• composable-data - small, composable vocabularies and protocols for declaring analytical structure, decisions, rationale, experiments, evaluation, and assessment. The decision grammar: grain, target, problem type, selected features and why, split plan, model plan, comparison basis, evaluation, and experiment assessment.
• analytics-toolworks - small, reusable, open-source Python tools for applied analytics: visualization, privacy-preserving evidence logging, and other utilities that automate mechanics without reducing agency.
• applied-models - reproducible machine-learning applications on accessible datasets, organized by question (penguin species, customer churn, housing prices), each with a Data Card and Model Card. A home for contributed, comparable model experiments.

I design and maintain project-centered courses for applied computing, data analytics, streaming data, continuous intelligence, web mining, and natural language processing using professional Python workflows. The courses reflect the practices and tooling of leading data science and AI programs: GitHub-based projects, real-world datasets, reproducible workflows, marimo notebooks, public documentation sites, and modern professional tooling. Course repositories use repeatable module patterns designed to support independent progress, encourage reusable workflows, and build transferable technical skills.

Developed the PUP tool family to keep a series of academic, instructional, research, and applied Python repositories on a consistent professional baseline. The tools synchronize shared infrastructure while leaving project-specific content untouched.

• pup-up - PyPI - synchronizes professional repository infrastructure with maintained project templates
• pup-check - PyPI - checks repository structure and configuration for deterministic internal consistency
• pup-clean - PyPI - prepares instructor repositories for release by identifying and removing generated evidence and artifacts

Supporting infrastructure:

• pup-core - PyPI - shared repository inspection, metadata, path-safety, and project-structure primitives
• templates - opinionated template files for various project types

pup-check provides deterministic conformance checking for individual repositories, including structure, configuration, and policy.

• repo-census - collects and preserves repository activity and GitHub traffic data across a fleet of repositories to identify maintenance activity and continuing use. A worked example of delegating software-engineering work to an AI coding agent, then moving stable recurring execution to deterministic Python and GitHub Actions.

repo-census complements per-repository tooling with fleet-level information about:

• maintenance activity
• continuing use
• maintenance backlog
• operational health
• historical change over time

Created standards-based tooling and guides for professional Python analytics workflows, using uv, ruff, ty, Zensical, GitHub repositories, and modern project conventions.

• ⭐applied-computing-foundations - basics
• ⭐GUIDE: pro-analytics-02 - guide to professional Python
• ⭐EXPLAINER: professional projects - click to learn about each file you may see in a professional Python project

Pro-analytics-02 audio guides (watch videos, chat with a specially-trained bot, and more):

• Google NotebookLM: Set Up Machine
• Google NotebookLM: Apply Example Project

Python packages and project file templates:

• datafun-toolkit - PyPI package - privacy-aware logging and diagnostics
• datafun-streaming - PyPI package - streaming data analytics utilities
• templates - template files for repository infrastructure (with dc-up)

Many repository infrastructure files do not have native outlining capabilities, even though they are often organized into sections. This extension recognizes comment headers such as: # === Setup === and will show sections in the VS Code Outline view.

• Install from Marketplace
• View Source code

Designed and maintained a seven-module curriculum for professional Python fundamentals in data analytics. The curriculum progresses from project setup and Python foundations through automation, structured data processing, notebooks, SQL, applied analytics, and regression.

• IntroToPython - professional project setup for the textbook examples
• datafun-01-foundations - professional Python foundations
• datafun-02-automation - automation with repetition, ranges, list comprehensions, delays
• datafun-03-analytics - read, process, write text and structured data
• datafun-04-eda - explore/present EDA insights using Python and Markdown in notebooks
• datafun-05-sql - Python and SQL
• datafun-06-ml - explore ML and predictive analytics

• databases-for-analytics
• databases-for-analytics-app

Developed hands-on examples showing how GPT-style language models repeatedly predict the next token using training text, context windows, and learned patterns. Materials include an interactive text-prediction app and progressive examples comparing pre-trained models with different context-window sizes and different training-text structures. Used to explore how context, training data, and model design shape next-token predictions in language-model behavior.

Artifacts include:

• Interactive GPT-style next-token prediction app and source repo
• Progressive examples using 0, 1, 2, and 3-word context windows
• Comparisons using neutral and structured training text
• toy-gpt exploration materials
• App (Text Prediction) - interactive next-token prediction in GPT-style language models
• App (Text Prediction) Source Repo
• Introduction and Pre-Trained Models - progressive example training repos

Developed tools and conventions that help AI agents understand, navigate, and work reliably with external systems. The work uses explicit project conventions, including SKILL.md-based guidance, to support agent conformance, tool discovery, and project onboarding.

Artifacts include:

• Adaptive Conformance Specification (ACS) - behavioral protocol for observing, inferring, and conforming before generating artifacts
• Adaptive Tool Discovery (ATD) - capability mapping for external tool sets
• Adaptive Onboarding (AO) - context-building for team/project conventions, ownership, and norms
• Example repo

Developed a syllabus-generation workflow that builds syllabi from structured TOML data and a reusable template, supporting consistency, maintainability, and efficient updates.

• Syllabus Generator - build from TOML and a template

Designed and maintained a seven-module curriculum for professional streaming data pipelines in Python. The curriculum moves from simulated streams to Kafka producers and consumers, derived fields, validation, visualization, storage, and applied streaming scenarios.

• streaming-01-foundations - Python generator and simulated data streams
• streaming-02-kafka - Kafka producers and consumers
• streaming-03-analytics - adding derived fields and validation
• streaming-04-visualization - live charts and real-time updates
• streaming-05-storage - streaming data and databases
• streaming-06-scenarios - streaming scenarios
• kafka-producer-earthquake - Kafka producer for streaming earthquake data from USGS API
• dc-mailer
• dc-texter

Designed a seven-module curriculum for building, visualizing, and evaluating machine learning models in Python. The curriculum emphasizes characterization, feature engineering, classification, regression, ensemble methods, model serving, and professional ML projects.

• ml-01-intro - characterizing ml problems and datasets
• ml-02-features - feature engineering and data preparation
• ml-03-classification - classification models and evaluation
• ml-04-regression - regression models and prediction
• ml-05-ensembles - ensemble methods and model comparison
• ml-06-serving - serving models
• ml-07-applied - employing a deployed model

Apps

• ml-penguin-predictor - deployed on Render and HF
• confusion-matrix-explorer (app) PyShiny app
• confusion-matrix-explorer (repo)
• Why Do We Square Residuals in Linear Regression?

Designed a seven-module curriculum for building, analyzing, and communicating business intelligence insights using Python, Apache Spark, and PowerBI. The curriculum emphasizes data exploration, preparation, warehousing, OLAP reporting, storytelling, and an end-to-end BI pipeline with smart sales data.

• bintel-01-intro - introducing BI and smart sales data
• bintel-02-mining - mining and exploring raw data
• bintel-03-cleaning - cleaning and preparing data for the warehouse
• bintel-04-warehouse - building and populating a DuckDB data warehouse
• bintel-05-reporting - OLAP reporting with slice, dice, and drilldown
• bintel-06-storytelling - BI storytelling
• bintel-07-applied

Designed a seven-module curriculum for building real-time anomaly detection, signal monitoring, and drift-detection systems in Python. The curriculum emphasizes applied monitoring workflows, signal design, rolling analysis, drift detection, and continuous intelligence systems.

• cintel-01-getting-started
• cintel-02-static-anomalies
• cintel-03-signal-design
• cintel-04-rolling-monitoring
• cintel-05-drift-detection
• cintel-06-continuous-intelligence
• cintel-stock-response
• fetch-data-weather

Designed and maintained a seven-module curriculum covering text preprocessing, text exploration, API-based data collection, web document acquisition, and NLP pipelines.

• nlp-01-getting-started
• nlp-02-text-preprocessing
• nlp-03-text-exploration
• nlp-04-api-text-data
• nlp-05-web-documents
• nlp-06-nlp-pipeline
• toy-gpt exploration

GEDCOM-based family genealogy archives, with private builder repositories for source data processing and public redacted tree sites for family history access.

• genealogy-clusiau (org) & public tree
• genealogy-johnson (org) & public tree

• Harvard CS109
• Aurélien Géron's "Hands-On Machine Learning" notebooks - example code for O'Reilly 3rd ed.
• See Fig 2-14 from 02_end_to_end_machine_learning_project.ipynb for scatter_matrix / train_test_split / StratifiedShuffleSplit
• UC Berkeley Data 100
• Berkeley ds-modules
• Berkeley Data 8

• Quad9 DNS Resolver - free service
  • Firefox / Settings / Privacy & Security: DNS over HTTPS, enable Max and set to https://dns.quad9.net/dns-query OR add Mullvad VPN and set to Default.
  • Chrome / Settings / Privacy & Security / Security / Use secure DNS: toggle on, select Custom, enter https://dns.quad9.net/dns-query
  • Android / Settings / Network & Internet / Private DNS: Set Private DNS provider hostname to <dns.quad9.net> OR add Mullvad VPN and set to Automatic.
  • Starlink App / Settings / Router tab / Custom DNS: Toggle on, enter Primary: 9.9.9.9 and Secondary: 149.112.112.112 (allow 10 min to apply)
  • Windows / Settings / Network & internet / WiFi / Choose the network name / DNS server assignment: click Edit / Toggle on IPV4 / Preferred DNS: 9.9.9.9 DNS over HTTPS: On (automatic template) / Alternate DNS: 149.112.112.112 DNS over HTTPS: On (automatic template) / click Save.
  • Verify: https://on.quad9.net
• Proton Mail Plus - email provider, $48/yr
• Proton Pass - password storage (if using Proton Mail)
• BitWarden - password storage
• Firefox Browser with
  • Containers
  • uBlock Origin
  • Settings / Privacy & Security: Standard or Strict Tracking Protection
  • Settings / Privacy & Security: UNCHECK Ask to save passwords
  • Settings / Privacy & Security: DNS over HTTPS, enable Max and set to https://dns.quad9.net/dns-query
• FUTO Keyboard - mobile keyboard with swipe and voice that sends nothing (still in alpha)
• Mullvad VPN - VPN for 5 devices, $78/yr, Windows 11, Mac, Android
  • Verify at https://mullvad.net/en/check
  • Settings / Local network sharing / Allow LAN traffic ON for WiFi Printer (or use split tunneling)

• signin-sheet-builder - when you have too many columns to fit on a landscape page and you want 2 rows per member

• Web App - generate a QR code from a web link or text
• App Repo

• templates - favorite CI/CD GitHub actions, config files, MkDocs, etc.
• chrome_new_tab_ext

• datakit-lite

Uses pip and venv with requirements.txt:

• pro-analytics-01
• pro-analytics-01-audio-guides - repo used to generate podcasts with AI (crazy!)
• Listen <-- try the podcasts here

• applied-computer-organization

• RockSwap - match-3 game
  • (repo) - source code
• RockSwap-Vivid - match-3 game (red-green colorblind safe)
  • (repo) - source code
• RockFit - falling blocks game
  • (repo) - source code

• Life Expectancy at Birth (1960–2022) - interactive chart to compare life expectancy at birth by country, region, or economic classification.
• Life Expectancy By Location (Bar Chart Race) - YouTube video of the top 30 from 1960-2022
  • Progress Atlas: Source
• Minnesota GIS: Viewer
  • Minnesota GIS: Source
• Civic Interconnect (organization

Structured tools for making competing values and policy assumptions visible in high-stakes civic decisions. Multidimensional evaluation projects include:

• Interactive Explorer
• Repository

• Interactive Explorer
• Repository

Explorers are based on the Multidimensional Evaluation Engine: A domain-neutral engine for multidimensional evaluation under explicit policy assumptions.

• multidimensional-evaluation-engine
• On PyPI

• ☆Zig Bill Utils - high-performance toolkit for analyzing U.S. legislative bills.
• ☆Py Bill Utils - Python project for analyzing U.S. legislative bills. - uses Zig Bill Utils

• Zipline Estimator Visualizer
• Zipline Estimator: Source on GitHub

• Google NotebookLM - AI-generated summaries, Q&A, and podcasts from your own documents and sources.
• Meta Quest Virtual Reality - immersive games, learning, productivity, and creativity in mixed reality.
• Google Apps Script - automate workflows in Google Drive, Docs, Sheets, Gmail, and more using JavaScript-like scripting.
• Microsoft Quantum Simulator - Try quantum computing in your browser-no special hardware required.
• Learn with Quantum Katas - interactive notebook tutorials for quantum logic and computation.
• Viola the Bird - interactive experiment blending machine learning with cello-inspired play.
• BirdNET – Identify Bird Sounds with ML - Upload audio or use field device to identify birds by sound with bioacoustics AI.
• GitHub Copilot Workspace - AI-powered IDE that understands your repo, suggests tasks, writes code, and walks through implementation plans.
• GitHub Copilot Voice - use your voice to code (without spelling things out)
• More GitHub Next Projects - cutting-edge developer tools being prototyped by GitHub.

• 🆕Visit VR Forest
• 🆕See the code

• pro-analytics-ai - illustrates how LLMs like ChatGPT work by creating an AI assistant trained on pro-analytics-01. It uses APIs (OpenAI/OpenRouter) to process user prompts and generate natural language responses, but all retrieval and context selection is performed locally based on the source repository text.
• The AI assistant runs locally on a 2 TB SSD machine with 12/24 cores, using API calls (via a free OpenRouter API key).
• The frontend is lightweight, but the backend uses a local retrieval pipeline (ChromaDB + sentence-transformers) that depends on large Python ML libraries, making it too heavy for serverless platforms like AWS Lambda and significant even for AWS EC2.
• Even when calling external APIs (and not hosting full LLM models) generating embeddings and performing document retrieval requires a significant machine learning environment.
• When we build a brain by indexing our own documents, the storage, memory, and processing requirements expand dramatically.

Early reference work:

• 2020 May Research Paper introducing GPT-3: "Language Models are Few-Shot Learners"
• 2022 Nov OpenAI ChatGPT "research preview. Current link
• 2023 Nov: OpenAI DevDay - first developer conference (GPT-4 Turbo, and more).
• Conferences:
  • NeurIPS - Neural Information Processing Systems
  • ICML - International Conference on Machine Learning

• 🆕k8s-api-mosquito

• open-climate-disaster-dashboard

• App mandart-web
• Repo mandart-web

• test page
• mandart-engine-rust
• Comparing input to output
• Example input Frame25 and output Frame25

• smart-sales-analysis-goals
• smart-sales-raw-data
• smart-sales-example
• smart-sales-starter-files
• smart-sales-docs
• smart-sales-olap
• smart-sales-spark
• smart-sales-duckdb-sql
• smart-sales-duckdb-sql-python

World Diabetes Day (WDD) is the world’s largest diabetes awareness campaign reaching a global audience of over 1 billion people in over 160 countries. It is marked every year on 14 November, the birthday of Sir Frederick Banting, who co-discovered insulin along with Charles Best in 1922.

WDD was created in 1991 by International Diabetes Federation (IDF) and the World Health Organization and became an official United Nations Day in 2006 with the passage of United Nations Resolution 61/225.

• Insulin Affordability

• BWCA Entry Points
• Trygg History & Maps
• St Louis MN Homeowner Guide and Links

• MandArt (App Store) - runs on MacOS (v12+) - Released!
• MandArt-Docs
• MandArt-Discoveries
• Color Cube
• MandArt-Web

A SwiftUI app for creating custom art with the Mandelbrot set.

• MandArt: source repo
• MandArt: my fork
• MandArt deploy
• MandArt-Docs: source repo
• MandMath (w/Julia) - Julia enables 150 - 1500+ decimal places
• Color Cube: source repo
• GrandArt: source repo - explore exponents beyond 2
• MandArt3: source repo - explore MandArt with exponent 3

• digital-forensics-resources
• 386-site
• dfir-hash-1
• dfir-hash-2
• dfir-pwsh-forensic-copy
• dfir-pwsh-file-recovery
• df-mod6-mobile

• lkif-core -The LKIF Core Ontology of Basic Legal Concepts
• data-science-on-gcp
• Git-quick-reference-for-beginners
• Mojo 🔥
• oregon-trail

• awesome-for-beginners
• kafka-app
• big-data-dev-about-me
• big-data
• big-data-developer-1
• cp-client-hunt
• modules
• ZooGameApp
• ZooGroup10
• personal-website
• skill-bearcat-buddy
• sphero-edu-getting-started
• ape-1
• op-aml-csis44670
• cisagov-development-guide