Bill Mongan

Bill Mongan

Professor, Author, Machine Learning Researcher, Educational Consultant, and Scholar of Teaching and Learning; Host: digitalsignature.fm

CSTA Philly

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Drexel is an institutional member of CSTA Philly, an active group in developing curriculum, sharing ideas, and broadening the reach of computing across educational disciplines.

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BetterWebUI: A Faculty-Friendly Agentic Front End for OpenWebUI

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Most large language model interfaces are designed for developers or for a general consumer audience. Faculty who want to use an AI assistant to help with grading, research, or course preparation either accept the limitations of a consumer chat interface or invest significant time learning to run and configure a developer-grade setup. BetterWebUI is an attempt to close that gap. It is a local Python/FastAPI server with a pure-HTML front end that connects to an existing OpenWebUI instance and layers on the features that make an agentic assistant genuinely useful in a higher-education context: workspaces, skills, MCP server management, CLI shortcuts, math rendering, and a suite of integrations with sibling agentic services.

AutoGUI: A Vendor-Neutral Desktop Automation Agent for LLMs

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Most LLM agents can read files, call APIs, and run shell commands, but they have no reliable way to operate a graphical desktop. They cannot click a button in a running application, verify that a dialog appeared, fill a form field, or observe what is currently on screen. AutoGUI is a research prototype that fills that gap. It connects any OpenAI-compatible LLM — including models served locally through OpenWebUI or directly through Ollama — to a full suite of OS-level desktop controls via a ReAct-style agentic loop.

OSScreenObserver: Giving AI Agents Eyes and Hands on Your Desktop

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Most AI agents, whether a large language model assistant running locally or a cloud-hosted agentic framework, have no reliable way to see or interact with the desktop applications running on the machine they are supposed to be helping with. They can read files, call APIs, and run shell commands, but they cannot observe that a dialog box appeared, that a form field is waiting for input, or that an application is in a specific state. OSScreenObserver is a prototype that changes that. It exposes the operating system’s UI accessibility tree, textual descriptions from multiple sources, and ASCII spatial sketches of the current screen layout through two simultaneous interfaces: a browser-based web inspector for humans and an MCP sees are always consistent.

A Private AI Knowledge Base: Obsidian, GitHub Sync, and Cross-Platform AI Context

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For the past year I have been building a knowledge management system with a specific design constraint in mind: every AI system I work with, whether a cloud-hosted assistant, a local agentic coding tool, or an automated GitHub Action, should be able to read the same authoritative description of who I am, what I am working on, and how I want to interact. More importantly, those systems should be able to write back into the knowledge base and have their work appear seamlessly in Obsidian on my local machine the next time I open the app. The proliferation of capable AI tools in 2025-2026 made both sides of this problem, reading and writing, tractable in a way they had not been before. This post documents the architecture I settled on: an Obsidian vault hosted on GitHub, synchronized via the Gitless Sync plugin, structured around three canonical files that any AI system can read and act on, and organized into a curated wiki that agents can query, extend, and maintain across platforms.