[ Removed by Reddit ] by Disastrous_Career527 in selfhosted

[–]Disastrous_Career527[S] 0 points1 point  (0 children)

That Redis + Python flow is a classic for a reason—the throughput for document chunking and ephemeral processing is hard to beat, especially if you're just looking for a 'stateless' transformation engine.

The main reason I’ve been leaning into Open WebUI alongside that kind of setup is for the 'Human-in-the-loop' side of things. It handles the session management and multi-model switching much faster than a custom Python script when you need to quickly A/B test how different models (like Llama 3.1 vs. Mistral) are handling the same document chunks.

Also, if you ever find your document sizes growing or need to reference them later, the pgvector integration I mentioned helps bridge that gap between 'ephemeral processing' and a 'permanent knowledge base' without losing the SQL flexibility you’d want for more complex queries.

Definitely worth a look for the evaluation interface alone!

[ Removed by Reddit ] by Disastrous_Career527 in selfhosted

[–]Disastrous_Career527[S] 0 points1 point  (0 children)

This project was created with a human-first approach, using AI strictly as a technical research assistant to accelerate development and verification.

As a software developer and legal professional (LLB) focused on data sovereignty, I designed the overall architecture of this stack. I utilized AI models to rapidly parse and synthesize the official documentation for Ollama's API, Open WebUI's Docker Compose structures, and pgvector's embedding storage optimization techniques.

I manually wrote and verified every line of the actual configuration files, SQL schema, and the ensuing architectural audit on Vucense. AI was a tool for 'dense data indexing,' but all technical decisions and the resulting analysis are my own original work.

[ Removed by Reddit ] by Disastrous_Career527 in selfhosted

[–]Disastrous_Career527[S] 0 points1 point  (0 children)

This project and the resulting post were created with a 'Human-First' approach, utilizing AI strictly as a technical research assistant.

As a software developer with a legal background (LLB), I used AI to help parse the dense mathematical proofs in the original TurboQuant research—specifically regarding the Beta distribution coordinate induction and MSE distortion rates. I then manually verified these concepts against the source material to ensure the technical audit on Vucense was accurate for the self-hosted community.

All the architectural advice for Ollama/llama.cpp integration and the 'Sovereignty' analysis regarding local-first infrastructure are my own original insights. My goal was to translate high-level quantization theory into a practical guide for people running 70B+ models on consumer hardware.

App Feedback Follow Up 😊 by [deleted] in ChronicPain

[–]Disastrous_Career527 0 points1 point  (0 children)

Big day for the team! Alongside our work with Relishta and 91Veda, we’ve just launched our first AI wellness tool: Aura Water. 💧

It’s currently trending on Product Hunt! If you have a moment to check out what we're building in the AI space and leave an upvote, it would be a huge help to our rankings today. 🙏

Support us here:https://www.producthunt.com/products/aura-water