Ai on Werk Space

AI Education

Tue, 03 Oct 2023 00:00:00 -0500

Overview

The effective integration of AGI into society requires an informed public. Given the complexity and potential impact of AGI, everyone — from schoolchildren to senior citizens — would benefit from a foundational understanding. Here’s a blueprint for such an educational effort. This is based upon a series of conversation with ChaptGPT 4.

Basic Digital Literacy: Before delving into AGI specifics, a foundational understanding of digital systems, the internet, and basic computing is crucial. This would cover concepts like software, hardware, networks, and digital security.

AI

Wed, 28 Jan 2026 00:00:00 +0000

Overview

This section is where I document how I use AI tools in my development workflow. The objective is to effectively use AI assistance inside the editor while keeping execution authority, cost, and context under control.

The currently used approach is the Cursor and VS Code hybrid workflow.

Cursor is used where deep multi-file reasoning and persistent AI context are required.
VS Code remains the authoritative execution environment connected to containers, remote servers, and runtime shells.

Using Cursor and VS Code as Hybrid

Wed, 28 Jan 2026 00:00:00 +0000

Introduction

This workflow is the one I use most of the time.
Cursor is where I do reasoning, planning, and high-level refactoring.
VS Code is where I edit, build, test, and run code.

Hybrid AI-Enhanced Development Workflow

This document describes a hybrid tooling strategy that combines:

VS Code as the long-term stable engineering platform
GitHub Copilot for inline coding and reliable patch application
Cursor as a high-power AI-native refactor and reasoning workbench
ChatGPT, Claude, and Gemini for deep reasoning, data engineering, and architectural critique

The objective is to achieve maximum AI leverage without sacrificing long-term stability, reproducibility, or toolchain reliability.

AI Multi-Engine Workflow in VS Code

Wed, 28 Jan 2026 00:00:00 +0000

AI Multi-Engine Workflow Introduction

This document summarizes the operational configuration for using GPT(ChatGPT), Gemini, Claude, and GitHub Copilot together in a coordinated research and engineering workflow for modeling and code development using VS Code.

Engine Roles

Engine	Model	Role
GitHub Copilot	GPT-4.1	Inline coding, refactors, reliable Apply
ChatGPT (OpenAI)	GPT-5.x	Deep reasoning, model design, refactor planning
Gemini Pro	Gemini 2.5 Pro	Data pipeline, schema reasoning, ETL logic
Claude	Sonnet / Opus	Architecture comprehension, conceptual critique, writing

Operational Principle

Copilot maintains continuous full-repo awareness and applies edits.
ChatGPT performs high-level reasoning and mathematical/system design.
Gemini engineers data ingestion and validation pipelines.
Claude critiques architecture and synthesizes narrative outputs.

Rule: Only Copilot performs direct code application. Other engines produce plans or diffs for manual application.

Optimizing Cursor and VS Code Hybrid Usage

Thu, 29 Jan 2026 00:00:00 +0000

Introduction

This page explains how I keep AI usage efficient and predictable.
The hybrid workflow can waste tokens or premium requests if conversations grow without rules. This is about managing context, access paths, and billing mechanics so I stay within plan limits.

The hybrid environment introduces three resources:

Model context window (tokens)
Model usage allowance (requests or plan limits)
Model access via web-based subscriptions

Efficient operation requires controlling all three. Each has advantages, and the goal is to leverage those advantages while controlling cost. Per-token and per-request consumption can become expensive quickly if left unmanaged.

Use of AI

Thu, 06 Aug 2026 00:00:00 +0000

Where AI sits in the system

I use AI as a drafting and management assistant inside the workflow. The ideas, the positions, and nearly all of the published language are mine. AI accelerates the parts of the work that are mechanical: gathering research, outlining, producing first drafts, restructuring the site, and building supporting material such as datasets and figures.

What AI does, and what I do

Work	AI does	I do
Research	Collects sources, summarizes dockets and reports, and organizes reference material in the private research repository (see the publishing workflow). For deep research this means multiple agents, sometimes ten at once, splitting search, analysis, and collation.	Direct the agents and triage the results. My reading queue is long, so I review the output and discard mercilessly. What survives gets verified against primary sources: every reference, figure, and dataset row. Nothing publishes with an open `[verify]` flag.
Argument	Assembles evidence for the positions I set.	Choose the thesis and review every argument closely. Drafts often frame positions I do not hold. The draft gets corrected, not the position.
Outlining	Develops a drafting outline interactively with me, often for more complex documents.	Write the document myself, from the outline.
Drafting	Produces first drafts against the voice guide. A draft is raw material.	Rewrite into my voice. I expect to rewrite a third of a draft or more, and for some pieces nearly all of it. My own edits are applied verbatim.
Editing	Works through a document with me, often line by line or paragraph by paragraph. Cleans rough passages I throw out, and makes spelling and punctuation corrections on my rewrites.	Supply the rough material, correct the cleaned version, and settle the final wording.
Reorganization	Restructures a document when I find it needs it: moves material within and between articles, and checks that a line in one article is not contradicted elsewhere.	Decide that a reorganization is needed, and review the moved result.
Site engineering	Restructures sections, builds redirects, edits configuration, and drafts documentation pages like this one, working directly in the repository.	Review every change before commit.
Code documentation	Reads code and documents what it does: template overrides, build scripts, and the procedures around them, in whatever language they are written. The Theme Overrides page is an example.	Ask the questions, and check the answers against the running site.
Supporting artifacts	Builds curated datasets, maps, and figures. These carry draft markings.	Verify every element before the draft marking comes off.
Publication	Nothing.	Decide what publishes, where, and when.

The reader might notice a pattern in that table. Every entry in the AI column is assembly: reading, collecting, drafting, moving. Every entry in mine is judgment against ground truth: the primary sources, my positions, my voice, the running site.

Wisconsin AI Governance

Thu, 13 Nov 2025 00:00:00 +0000

Framing Note

This analysis is guided by the companion document “The Benefits of a Forward-Thinking Technology Stance for Wisconsin,” which outlines the statewide advantages of embracing a partnership-oriented approach to data center development. That document articulates the long-term community, workforce, governance, and digital-resilience benefits that inform the recommendations and evaluation criteria used throughout this report. The present report should be read in conjunction with that guiding benefits overview, which establishes the strategic rationale for Wisconsin’s approach to hyperscale and AI-era infrastructure. [Go to The Benefits of a Forward-Thinking Technology Stance for Wisconsin](/policy/datacenters/forward-thinking.md)

Proposal: Wisconsin AI Governance & Safety Institute

A Public–Private Partnership Anchored by Data Center Investment

Purpose

To create a statewide center dedicated to AI safety, governance, digital resilience, public-sector modernization, and workforce training, leveraging partnerships with companies investing in Wisconsin data centers.