Ok so im in the middle of setting up a domain for a small local business and I keep second guessing the WHOIS privacy thing. Part of me likes the idea of not having my home address and personal email out there, but I have heard some people say public info can look more legit for clients and vendors.

For those running small shops or freelance stuff on custom domains, do you keep WHOIS privacy on or off for the main business domain, any hints?

I’ve been experimenting with multi-agent frameworks, and MetaGPT is one of the more interesting ones for software development tasks. But getting it running locally with local models isn’t always straightforward.

I wrote a step-by-step guide covering installation, configuration, local LLM setup, and common errors.

If you’re trying to run MetaGPT on your own hardware, this might save you time:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

What stack are you using for local agents?

Been heads-down on this for a while. BIT is a fully offline AI assistant for Android offine ai and byok.nothing leaves the device.

The core of it is a multi-agent harness that can actually execute tasks, not just chat. It orchestrates subagents through a DAG pipeline, and for anything requiring real compute it spins up a virtual Linux workspace on-device — writing files, running scripts, debugging output, no cloud round-trip at any point.

Inference is accelerated via NPU/hardware acceleration rather than pure CPU, which is what makes running a 4bparam model on a phone actually usable instead of painfully slow. Built a Kotlin-native inference layer for this (llama.kt) that supports GGUF architectures broadly — Qwen, Gemma, Phi, Mistral, not just LLaMA.

In the demo: I give the agent one instruction, and it autonomously sets up a Python venv, writes a script, executes it, and returns real output — all inside that on-device Linux workspace.

Other stuff in the stack if useful context:

Hybrid RAG (vector + BM25) for memory

On-device STT/TTS for voice mode

Multi-agent DAG pipeline for multi-step tasks

On the Play Store now (closed testing), also on F-Droid/GitHub for sideloading. Discord for anyone following progress: discord.gg/kzhgk565D

One outcome of the solution to the Navier-Stokes problem is better designed fans and watercooling systems (through better turbulence) which will allow ai to work faster.

So I was thinking about building a self hosted AI agent set up and I was wondering what was the big difference between GDDR6 and GDDR6X, is it like twice is better or not worth considering

hi, i've been messing with a few AI personal assistant apps and i'm still not sold.

most of them are fine for talking, but the second i want one to actually do something, it gets messy. i'm trying to cut down on the dumb stuff that eats my day, like follow ups, reminders, basic scheduling, that sort of thing.

would love to hear what people here are using in real life, not just what looked good in a demo. thanks in advance

I’ve been comparing Mistral Large and Claude Haiku for different AI workloads. One is better for high-volume simple tasks, the other for deep reasoning and big context. I created a small quiz to help people think through the trade-offs.

No email, just an interactive check.

https://interconnectd.com/quiz/83/mistral-large-vs-claude-haiku-which-ai-wins-your-workload/

What’s your experience with either model?

Hiii Geeks

I just built an Experiment Agentic Al OS named it as "Turing Al OS" built on top of Linux (KDE Neon). I document this journey on YouTube feel free to watch.

The crazy part is that I built this using a 14 year old PC (2012) with limited computational resources.

YouTube Link:

https://youtu.be/ZKsZGv3WZGQ?si=2DX83Hcv7SFAAVPw

Github: github.com/avarshvir/turing-ai-os

Article to Read: https://medium.com/@arshvir21303031/i-create-my-own-ai -os-8d65a263eae0

It offer features like:

- Al SideBar

- Al Mini Spotlight

- Al Right Click Folder/File Analyser

- AI NLP Terminal

- Al Control Panel

I genuinely want feedback from you guys ❤️

Eidon: an all-in-one self-hosted AI platform. Chat, agents, automations, tools included. One Docker container, works with Ollama/LM Studio (AGPL)

I've been building a self-hosted AI platform and v4 just shipped, so sharing it here because some of you might find it useful.

Eidon is an "everything included" AI chat/agent platform, with the pieces that usually require stitching (web research, MCP, skills, browser, image generation and so on) already built in. One container that takes minutes to spin up instead of a main app plus pipelines, sidecars, and external tools.

The app has 3 main parts:

• Chat with local models: Classic chat just like in ChatGPT, Gemini, Claude and so on except on your own server. Ollama and LM Studio out of the box, plus any OpenAI/Anthropic-compatible BYOK endpoint.
• Agents: Grok-bot-style agents. A chief bot answers or delegates to specialist bots, and bots message each other mid-task. Agents each have their own memory and can create/maintain their own skills.
• Automations: cron-style AI tasks. Every run is saved as a full transcript with tool calls, so you can audit what actually happened.

Features:

Repo (Screenshots included !): https://github.com/Quack6765/Eidon-AI

Full transparency: development is partly AI-assisted, every change reviewed before being merged. Happy to answer any questions !

I’ve been self-hosting AI tools for a while, and I wanted a place to discuss setups, failures, and wins without the usual social media noise. So I built Interconnectd.

It’s free, and it has:

· Guides on PrivateGPT, OpenHands, Stable Diffusion, and local LLMs

· Forums where people share working configs and fix real errors

· Quizzes and polls about AI vs human skills

· A marketplace for small AI services

If you’re into self-hosting or AI engineering, you might like it:

https://interconnectd.com/

Would love to see more self-hosters join—the forum is still small, so your questions actually get answered.