As we predicted, the tide is shifting to local LLM solutions and the benefit of hosting your own AI model is becoming easier.
Get a head start at https://t.co/gqcrtVOAXk
@MichaelDell We're building Kinward, the family layer on top of local AI. Per-kid profiles + guardrails, memory of your whole household, governance without surveillance. Model-agnostic, runs 100% on your hardware and never the cloud.
Free alpha now, Mac + Windows. 🛡️
Kinward is live. Navigate the setup of a local LLM with ease.
Your family's AI — on your own computer. No cloud. No accounts. No telemetry.
Everything you share with it stays inside your house. That's it.
Free alpha · Mac + Windows · https://t.co/3Oe4P0Sera
“If you want AI for your family, you shouldn't have to give your family to a tech company."
Built with that philosophy. Free alpha. Open source. https://t.co/9Hh5wDjLdA
@LottoLabs Tailscale will be packed on our install for non-dev families. A number of folks were asking about remote connection while protecting their data. This week overall has been huge for local LLM developments.
Kinward + Gemma 4 runs on a mid-range PC. New Alpha update live on our repo:
kinward-ai/kinward: kinward ai
If you've been waiting to setup a local LLM for your family but unsure of how to do it, we're here for you!
We just released Gemma 4 — our most intelligent open models to date.
Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows.
Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓
New COPPA rule goes into effect on 4.22. In 20 days, AI orgs face stricter rules.
Our approach: don't collect it in the first place. Run local. Keep everything on your own hardware. No children's data to regulate if it never leaves your home.
https://t.co/9Hh5wDjLdA
Google's TurboQuant: 6x memory compression, zero accuracy loss, no retraining, already being ported to llama.cpp and MLX.
If you're 🔨for local AI on consumer hardware, your users just got 6x more context instantly.
Households are getting more power...
@ShimazuSystems Very true, and those within dev circles can figure most of this out.
I do think there is something to be said for a packaged wizard to help non-devs get a local solution up and running.
And because LiteLLM is a transitive dependency in tons of AI agent frameworks, many teams got hit without ever running `pip install litellm` themselves.
I'm building an AI platform for households that runs entirely on local hardware via Ollama. No cloud calls, no API keys.