a few notes on local AI and our computers that run it around fifteen months ago, i led the pre-seed round for a weird local AI project it was called @actualinc led by @Tom_A_Lynch for the first time in the AI movement open models are now good enough for a large and growing set of workflows, especially where latency, privacy, bandwidth, and token cost are critical hermes agent from @nousresearch and ‘lab’ by @primeintellect have broken this idea wide open with killer products today, built on open rails we are no longer forced to assume that every useful task must route through one of a few centralized frontier APIs and so tom and the squad at @actualinc have painstakingly created a new type of proprietary inference engine for all types of computers maybe you are an owner of one of the tens of millions Apple macs or an owner of one of the millions of RTX-class machines or maybe you run a small team or company and you are starting to realize what many others across the world, have started to realize which is that sometimes centralized AI systems bring their own set of unique problems especially as our workflows move into an always-on, agentic era which might sometimes come with: - exorbitant costs - no privacy - lack of control but a problem remains running local AI is still too hard firing up one model on one machine is difficult enough but running models across different machines means navigating drivers, networking, model sizing, memory limits, API setups stitching together hardware is messy across Mac, Windows, Linux, NVIDIA, Apple, AMD heterogenous systems were never meant to work seamlessly together like this today, you'll find that most of the local inference software available is built around single users, for a single machine use case running inference capacity in bulk, across many users, and many kinds of computers that is a different animal altogether tools like llama.cpp made local models accessible, but they are not designed to coordinate tens of thousands of heterogeneous machines under a live supply network vLLM-style batching can be economical, but vLLM is limited in the machines it can run on the local AI movement requires a simple product experience, for a tough engineering problem that is the problem @Tom_A_Lynch pitched me fifteen months ago and the problem @actualinc has been engineered to solve now with the help of his cracked and growing team @skooookum, @somewheresy, @jack15032, and the rest of the crew starting with their first product go to actual.computer in under one minute, install the @actualinc client, authorize your machine, load a model, and @actualinc turns your personal device into a private inference endpoint in less than twenty seconds, add a second, or third, or fourth machine of any type and it will start behaving like a small private cluster from a technical POV actual runs as a lightweight daemon on each machine and turns local hardware into a routable inference endpoint each node reports its available memory, accelerator type, model state, and health back into the cluster, while the user interacts with a single API surface instead of managing ports, drivers, or SSH sessions a relay layer lets requests reach machines without forcing users to configure networking or expose their devices directly and as a result computer owners of all kinds of computers can get local inference clusters - when they want to control costs - when they want to control privacy - when they want to build on top of a neutral stack eventually, over time computer owners running these local clusters should be able to seamlessly contribute their unused capacity back to the @actualinc network for a market to price and for someone else to purchase you can think of @actualinc as building the engine and control plane for datacenter style throughput in homes, in offices, in small to medium sized businesses, and in very big sized businesses because there are hundreds of gigawatts already on the grid right now, that are waiting on desks, in storage closets to become actual computers