# Open WebUI Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/open-webui
> Markdown URL: https://aitinkerers.org/technologies/open-webui.md
> Technology record last updated: 2026-02-26T01:13:41Z
> Generated: 2026-09-21T22:52:07Z

Open WebUI is a self-hosted, extensible AI platform: it unifies Ollama and OpenAI-compatible LLMs under a single, feature-rich web interface.

Open WebUI delivers a powerful, self-hosted AI deployment solution, supporting both local models via Ollama and cloud-based models through OpenAI-compatible protocols. Deployment is streamlined using Docker or Kubernetes for effortless setup. Key features include Retrieval Augmented Generation (RAG) for document interaction, native Python function-calling, and granular Role-Based Access Control (RBAC) for managing user groups and model access. This provider-agnostic approach ensures a secure, private, and versatile environment for personal or enterprise-grade AI applications.

- Official technology site: https://openwebui.com
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Local hosting - sometimes joy can come in small packages](https://hong-kong.aitinkerers.org/talks/rsvp_cW9hxrT3uxI)

Often people consider AI inferencing as something that can only be done on cloud. And if it's to be done locally, it needs to be done in a large computer case - culminating in a hot and loud system that's sure to keep everyone at home in an annoyed state. I'll be presenting on how local AI inferencing doesn't need to be full of compromises, but also some of the different approaches to building out a system for local hosting. As somebody with a passion for building small form-factor PCs (SFFPCs), I've had a lot of experience building small and space-efficient desktops. Unlike building a SFFPC that can run games quietly and with good temperatures, I'll be showing how an AI-capable SFFPC is actually a simpler affair than building a gaming system. I'll also go through the different ways to host locally, with a particular focus on Windows applications, as well as Ubuntu via WSL. To meet the requirement of applied AI in finance, I will also be showing a new version of the Counsel Intelligence model that runs locally on my desktop.

- Event context: AI Tinkerers Hong Kong & GBA: Applied AI in Finance Demos — 2026-01-20 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_cW9hxrT3uxI

### [Building a university RAG LLM Middleware](https://nurnberg.aitinkerers.org/talks/rsvp_pvXhURuZ9O4)

I will showcase a RAG system I built and deployed at FAU, which serves as an AI-powered assistant over internal and public university data for students and staff. The talk will cover: Technical architecture: Python FastAPI server, hybrid retrieval (BM25 + dense embeddings via OpenAI + custom weight regression), and LLM generation pipeline. Performance tuning: Techniques used to reduce latency (asynchronous calls, caching), improve retrieval quality, and handle generation accuracy challenges in a real environment. Deployment learnings: Integrating with university systems, managing security and authentication, and monitoring user queries for iterative improvement. Live Demo Plan: 1- Query the assistant live with questions like “What is the process for thesis submission?” or “What events are happening this month?”. 2- Display generation output in real-time. 3- Walk through relevant parts of the code (retriever logic, query pipeline).

- Event context: AI Tinkerers Nürnberg First Meetup - July 3rd — 2025-07-03 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_pvXhURuZ9O4

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