u/BBASecure

Made a desktop app for running AI models locally. Figured this crowd would appreciate the approach: zero cloud, zero telemetry, zero accounts. The app literally doesn't make network requests after you download a model.

- Scans your hardware and tells you what you can actually run

- Browses a model catalog with size/quant filters

- Manages your local models (detects existing Ollama installs too)

- Runs inference with a real-time HUD showing tok/s, GPU %, VRAM, temperature

- Workspace system so you can have different models/contexts for different tasks

- Engine start/stop/restart controls so you're not guessing if the model is running

Built with Tauri 2 (Rust backend, React frontend). It wraps llama.cpp for inference. No cloud, no telemetry, no accounts. I don't even have analytics on the website.

This is v0.1.1. It works, it's signed, but it's early. I'm a solo dev so feedback is genuinely useful. Things I know are coming: Mac support, model comparison view, fine-tuning support.

GitHub: https://github.com/BBALabs/athanor-lite

Download: https://github.com/BBALabs/athanor-lite/releases/latest

I made a desktop app for running AI models locally. Figured this crowd would appreciate the approach: zero cloud, zero telemetry, zero accounts. The app literally doesn't make network requests after you download a model.

It's a Windows app (Mac coming) that scans your GPU/VRAM, recommends models that'll fit, and handles inference locally via llama.cpp. Real-time stats on GPU utilization, VRAM usage, temperature, tokens/sec.

It's free, open source (GitHub), and signed. Not a SaaS play, not a funnel. I run an AI consultancy and built this because my clients needed something simpler than the terminal-first options out there.

https://github.com/BBALabs/athanor-lite

Been lurking here for a while and using local models for client work. Kept hitting the same wall, the people who would benefit most from local AI are the ones least equipped to set it up.

So I built Athanor Lite. Free, open source, Windows app. Here's what it does:

- Scans your hardware and tells you what you can actually run

- Browses a model catalog with size/quant filters

- Manages your local models (detects existing Ollama installs too)

- Runs inference with a real-time HUD showing tok/s, GPU %, VRAM, temperature

- Workspace system so you can have different models/contexts for different tasks

- Engine start/stop/restart controls so you're not guessing if the model is running

Built with Tauri 2 (Rust backend, React frontend). It wraps llama.cpp for inference. No cloud, no telemetry, no accounts. I don't even have analytics on the website.

This is v0.1.1. It works, it's signed, but it's early. I'm a solo dev so feedback is genuinely useful. Things I know are coming: Mac support, model comparison view, fine-tuning support.

GitHub: https://github.com/BBALabs/athanor-lite

Download: https://github.com/BBALabs/athanor-lite/releases/latest

I had a song I recorded in 2016 and never finished. Twenty five seconds of something that could've been a track. It sat on a drive for almost ten years.

So for the Hugging Face Build Small hackathon I built CODA, which takes an audio clip you upload and generates what comes next, in the same key and tempo, then splices it back seamlessly. Not text-to-music. It works on your actual waveform.

It uses Stable Audio 3 Small (0.6B params) and its inpainting sampler to do continuation in a single call at 44.1kHz stereo. Generates up to 5 candidates and auto-picks the cleanest one. The splice is loudness-matched with an equal-power crossfade.

The demo on the Space is literally my 2016 track getting finished. You can upload your own.

https://huggingface.co/spaces/build-small-hackathon/coda

Demo Video: https://vimeo.com/1201576373?share=copy&fl=sv&fe=ci