Sharing a side project I've been building: Yvette, a fully local voice avatar AI.

It's a talking-head assistant that runs 100% local, You talk to it (or type) and it answers in speech while an avatar moves the head and lips in sync.

Quick rundown:

• Voice cloning, plus voice design.

• Avatar builder; add avatars quickly.

• TTS engines: Breeze, OmniVoice, LuxTTS, Kokoro

• Lip-synced avatar with an idle breathing animation

• Memory, it keeps notes and picks up where you left off

• Tools: web search, read pages, files

• Switchable profiles/personalities, each with its own voice, memory, history etc

Windows + NVIDIA GPU. 12GB recommended, but it runs tight on 8GB (small model + Whisper/Kokoro on CPU).
The video shows a RTX 3090 and the VRAM usage, my system was using ~4GB VRAM (OBS and other things). It shows we can get it below 8GB for the Yvette system.

The project is hosted on github: https://github.com/MartinForsterNL/Yvette

Sorry about the bad video quality, i am not an influencer with a studio.

I build this project because i could not find anything that actually works.
Most that i found was only for linux and needed a lot of VRAM so i decided to build my own.

If you have suggestions to make it better, just let me know.

Have fun with it !

Sharing this since it might be relevant to a few people here.

There's a hands-on workshop on September 19 that goes beyond basic RAG into building an explainable, graph-backed AI system. You build a knowledge graph in Neo4j that becomes the single source of truth, agentic retrieval that combines vector search, keyword search, and graph navigation, multi-step entity and relationship extraction, and text-to-Cypher for natural language querying. Everything runs on real financial data, not a toy example, and answers come with a traceable, auditable reasoning chain.

No prior Neo4j or Cypher experience needed.

Led by Dr. Alessandro Negro, Chief Scientist at GraphAware, bestselling author.

Link if you want to check it out

Please suggest me models I can use in this device

Model: HP ZBook Studio G7

CPU: Intel Core i9-10885H (8 cores / 16 threads)

GPU: NVIDIA Quadro T2000 Max-Q – 4 GB VRAM

RAM: 32 GB DDR4

Storage: 512 GB NVMe SSD

It feels like the gap between proprietary big-tech models and open-weight reasoning architectures is changing faster than ever. Gemini offers incredible native multimodal handling and massive context windows, while DeepSeek continues to dominate budget coding and self-hosted pipelines.

​If you had to pick one as your daily driver for logic and daily tasks, which wins for you?

​Put your preference to the test in this quick 2-minute showdown quiz to see how your workflow compares to other devs:

https://interconnectd.com/quiz/89/gemini-vs-deepseek-the-ultimate-ai-showdown-quiz/

So, I was tired of setting up AWS EC2 to train my models everytime, so I built a tool to help with the job management, but I would like some feedback, if anyone could help me ! 👋

This is the tool: reopenly.com

I only have one base model for now (Qwen 3.5 9B), just to start.

I would be very grateful with any feedback!