Local AI has moved from hobbyist experiments to production-ready tooling. You can now install, run, and tune open-weight models on most laptops and desktops with reliable performance. Local AI gives you three concrete advantages. • Privacy and data sovereignty. Your prompts and outputs never leave your machine. • Cost control at volume. No per-token fees after you own the hardware. • Guaranteed access. Your tools work offline, on planes and in environments where no data can leave the device. The Anthropic API outage in 2025 was a wake-up call for many developers. The community responded with Ollama, LM Studio, Jan, LocalAI, and llama.cpp-based runners that make local inference straightforward. Major model providers now ship smaller, open-weight models designed for local deployment. Qwen, Llama, Mistral, and Gemma families offer 1B to 12B parameter variants that run on consumer hardware and support fine-tuning with Unsloth, oMLX, and similar frameworks. This poll shows a couple of interesting projects to help you run models locally. Which have you tried?

Tell us what you are building right now.

Here is how I actually think about it. Most people hear "local AI" and picture a model running on a laptop or a device with no internet. That is not what we do here. What we care about is this: who controls the stack? At the AI Startup Foundry we run a hybrid model. That means: - An agent running locally on a local machine with M5 Silicon chip, using a local LLM as its brain. - The same agent architecture running remotely on a private server, using an LLM hosted on that same server or using a subscription with Open Weights. - Both talking to each other. Both under our control. No OpenAI. No Anthropic. No vendor deciding your pricing, your rate limits, or your deprecation schedule. The model can live on your machine, on your server, or on both. What matters is that you own the runtime and you decide where each task goes. And more importantly, you own and keep access to outputs at all times. Local for speed and privacy. Remote self-hosted for heavier workloads. Hybrid when you need both. That is the stack we build here. That is what the 30-day sprints are built around. If you are already running something, or about to ship something, on infrastructure you control, this is your room.

I focus on AI infrastructure as, in my view, getting it right early on saves a lot of hassle later. I do understand that most devs won't worry much until it's too late. I'm currently working on a couple of projects which need the right infra but there comes a time when infra should be sufficient to justify moving to the next step. One question for you:

Gave my first task to Hermes Agent with MiniMax M3 and it's seriously good. With a Token Plus plan it's such good value for the quality returned. Combine this with solid meta prompting and you are smiling as you see the tasks being developed and outputs being delivered. Give it a try and tell me what you think.