[Release] Nexidion – A private knowledge vault with an autonomous local AI background worker.

Hello, After almost two years of on-and-off development, 5 complete architectural rewrites, and hitting a few brick walls, I’m finally open-sourcing a project I built to scratch my own privacy-paranoia itch: **Nexidion**. **GitHub Repo:** [https://github.com/HabermannR/Nexidion](https://github.com/HabermannR/Nexidion) There are a lot of "second brain" apps out there, but I didn't want to rely on a third-party cloud, and I definitely didn't want to send my sensitive notes to closed APIs. More importantly, I didn't just want a standard chat window tacked onto a text editor. ### The Local LLM Angle: Autonomous Background Worker Nexidion is a hierarchical Markdown note-taking app with a built-in, optional **autonomous background worker** designed specifically to plug into local OpenAI-compatible endpoints (llama.cpp, Ollama, LM Studio, etc.). Instead of just chatting with your notes, you can select a massive batch of nodes/folders and dispatch the agent to do actual work: * *"Reorganize these messy notes into hierarchical folders by topic."* * *"Summarize these subtrees."* * *"Extract all action items from these meeting notes."* **The safety net:** Letting an LLM autonomously organize your notes is terrifying if it hallucinates. Because of this, Nexidion has a built-in version control system. The AI works in the background and commits changes as a new version *under the AI's name*. Every single edit is fully traceable, and if your local model completely botches the organization, you can revert it with one click. No ruined databases. Zero external network calls. ### My "GPU Poor" Setup (2080 Ti) You don't need a massive multi-GPU rig for the agent to be useful. I am GPU poor and running this on a single RTX 2080 Ti (11GB VRAM). Right now, I am using the brand new **Qwen 3.6 35B-A3B with MTP** (specifically the `IQ3_XXS` quant) using a llama.cpp server backend. It works surprisingly well for the agent tasks! If anyone with constrained VRAM wants to replicate my setup, here is the exact Docker command I use to squeeze this 35B model onto my 2080 Ti (using flash attention, Q8 KV cache and speculative decoding): ```bash docker run --gpus all --rm \ -p 1234:1234 \ -v /mnt/c/.../models/unsloth/Qwen3.5-36B-A3B:/models \ havenoammo/llama:cuda12-server \ -m /models/Qwen3.6-35B-A3B-UD-IQ3_XXS.gguf \ --port 1234 --host 0.0.0.0 \ -n -1 --parallel 1 --threads 6 \ --ctx-size 100000 --fit-target 844 \ --mmap -ngl 18 --flash-attn on \ --temp 1.0 --min-p 0.0 --top-p 0.95 --top-k 20 \ --jinja \ --chat-template-kwargs '{"preserve_thinking":true}' \ --ubatch-size 512 --batch-size 2048 \ --cache-type-k q8_0 --cache-type-v q8_0 \ --spec-type ngram-mod,draft-mtp \ --spec-draft-n-max 3 ``` ### Getting Started I just finished Dockerizing everything, so spinning up the Postgres DB, backend, frontend, and the AI task runner takes a single command: ```bash docker compose --profile with-postgres --profile with-task-runner up -d ``` *(Full docs and setup instructions are in the repo).* I’d love to hear your feedback! I'm especially curious to hear from the local-AI crowd on how the background agent performs with different models/quants and what prompts you find work best for batch organization. Let me know what you think!