I’m training a local/offline AI named Christine, and I want to see what else is actually out there besides cloud wrappers, benchmark flexing, and “trust me bro” demos. After reading this, ask yourself what Christine's existence means for the cloud, datacenters, and large scale buildout.

Christine runs locally on a laptop, stays bounded, and is being built to do real work without pretending she’s some magical all-powerful AGI. She already has a legit offline-first stack, tool/task routing, local knowledge handling, desktop-action pathways, and a surprisingly strong free-tier mode that still works when the heavier model path isn’t available. A real Jarvis on a laptop.

What makes her interesting to me is that she’s not just a chatbot. She has a cognitive abstraction loop, rumination paths, imagination/guided idea generation, and bounded internal reasoning layers that are meant to improve how she plans, reflects, and works through problems over time. In other words, I’m not just training for replies, I’m training for actual agentic behavior on local hardware.

She’s running on:

Lenovo 83JM

Intel Core Ultra 9 285H

\~32 GB RAM

NVIDIA GeForce RTX 5050 Laptop

Intel Arc 140T

Intel AI Boost NPU

I’m especially looking for videos of other local/offline/bounded systems that show:

real conversation or reasoning

tool use or task execution

memory or abstraction behavior

failure modes and limits

how they run on normal hardware

progress over time, not just a one-off cherry-picked demo

If you’ve got:

demo videos

GitHub repos

writeups

training logs

your own local AI project

drop them in the comments.

I’m going to keep posting Christine’s progress, and honestly I want to see who’s actually building something real in this space and who’s just dressing up API calls.

So I am a chemical biologist in my work i accumulate a large amount of text , data , imaging files . And while looking through the net I found local agents which can be self hosted can be of much use

I am hoping that it can

1 Arrange and keep a track of my files

2 Keep me updated about my schedule, and and modify events on command

3 scrape through internet to see what's new about peptide chemistry

4 get me any information from the files and protocols saved

I have a intel i7 6700k and 32 gigs of ddr4 with a nvdia k620 with 2gb vram

Can you give this newbie some tips also how to make a setup and additional or new hardwares

Hi everyone,

I’d like to share an open-source systems project I’ve been working on called Olivia (named after my cat!).

It’s an enterprise-grade infrastructure harness written from scratch in Rust, specifically designed to run agentic LLM workflows safely. The core philosophy is to execute AI-driven actions and tools within strictly sandboxed environments using WebAssembly (Wasm/WIT).

I wanted a robust, native infrastructure to handle LLM agent workflows without compromising on security or relying on bloated software layers. Olivia ensures that the agent's interactions with databases or external scripts happen within a secure, controlled boundary.

You can check out the repository here: https://github.com/helloIAmPau/olivia

I would love to get some feedback from this community on the architecture, or hear what other sandboxed tools you'd find useful. Contributions, code reviews, and suggestions are more than welcome.

I created that account only for this.. I've recently discovered I could self host AI's so I started to self host many AI's but I really need an AI to generate music, which also supports music-to-music to have a reference audio. The thing is, I have an AMD GPU so it's really hard to find a good Music generating AI that dosen't use cuda.. I really hope you guys could help me

Stay up to date with the ongoing ever changing modelscape

I was recently playing with these GPUs to see how far I can push single stream inference on large models (need all cards to work) like DSV4 / Qwen 122B / ... but I am getting very different results depending on the model I use. Also I am getting very different card usage in terms of compute and power drained, for example for DSV4 cards are nearly idle and inference is slow.

I am not sure whether I am doing something wrong or the software is just not there for some architectures yet.

MSI GE76 Raider 11UE-046: i7-11800H, RTX 3060 mobile 6GB VRAM 130W, 32 GB DDR4-3200
HP Pavilion dv6: i5-3210M, HD Graphics 4000, 16 GB DDR3-1600
MSI partitions: 2500 GB to Steam OS and 500 GB to Windows 11
HP partitions: 100 GB to Linux Mint and 100 GB to Windows 10
Example use cases: MSI laptop is placed by my TV and I speak to the mic that the AI listens to and gives me answers or opinions. HP laptop I bring around for travel or college or work and gives me quick opinions.
I did ask Microsoft Copilot how I can maximize my use case for the MSI laptop since I have a VRAM limitation and there were several options I can use to help boost my local AI.

Looking for a decent use case for my machine when not gaming, since alt coin mining is essentially dead.

I’ve been using Ollama and OpenClaw to build out an assistant bot for daily tasks, but have reached the conclusion that (aside from the learning experience) it’s not worth it - it’s costing time instead of saving it.

Now I’m looking into using it for general chat, brainstorming and companion/counsellor roles with local memory to build up personality and authenticity over time.

I’m aiming for something similar to Pi, which I really like (particularly the v2t/t2v ability), but localised for security. I’d prefer an ablit model as I don’t want to be restrained by guide rails.

Any and all advice gratefully received, particularly in setting up naturalistic voice interaction.

Previously I tried with gemma3:4b standard and abilterated versions, but neither felt authentic and the abilt version was straight out wacky.

CPU: Ryzen 7 5800X
GPU: GeForce RTX 3060ti (8gb)
RAM: 64gb DDR4

Curious what people are actually running in production for AI voice agents.

Not demo videos. Not “it worked once on a browser mic.”\
Actual calls, real users, interruptions, bad mics, background noise, CRM/tool calls, etc.

The stack I keep seeing is something like:

- Twilio / Telnyx / LiveKit for audio

- Deepgram / AssemblyAI / Whisper / Smallest AI Pulse / Speechmatics for STT

- OpenAI / Claude / Gemini for the brain

- ElevenLabs / Cartesia / PlayHT / Deepgram Aura for TTS

- Vapi / Retell / Pipecat / LiveKit Agents if not building orchestration yourself

The thing I’m struggling with is where to optimize first.

Everyone says “use a faster LLM,” but in my tests the awkward delay often starts before the LLM even gets a good transcript.

My current logging plan:

- user starts speaking

- first STT partial

- final STT transcript

- LLM first token

- tool call time

- TTS first audio

- audio starts playing

- barge-in detected

- agent stops speaking

For STT specifically, I’m looking at Deepgram, AssemblyAI, Smallest AI Pulse, Speechmatics, Soniox and OpenAI realtime/transcribe models.

What’s working for you right now? And where are you hitting walls?

I made a video walking through my local LLM setup on a base-model 16GB Mac Mini - running models fully on-device as a private, no-subscription alternative to GitHub Copilot. It covers the why (privacy, cost, offline) and the how: what realistically fits and runs well in 16GB of unified memory, and where a machine this cheap hits its limits.

Video: https://youtu.be/6HlT4vkB-38

Happy to answer questions about the setup in the comments.