

Chris
9.9K posts

@Fiddleaboon
Dad of 4💜|Base Degen | Investing and connecting | Man of many talents | CEO of you|Stay humble!




what if you and 10 friends could pool your laptops and run the same AI models that cost model providers millions in GPUs? i built this. the model splits across every device in the group. they compute in parallel and the output is mathematically identical to running it on a single machine. not similar. identical. bit for bit. that's the breakthrough. the engine is fully deterministic so every device is interchangeable. nodes don't trust each other. they don't need to. if anyone computes wrong the math catches it instantly... inference passes consensus mesh-llm partitions experts across nodes. smart approach, but each node only sees part of the model. we shard the full model across any device with zero quality loss. every parameter active, every inference. 10 laptops, 50 phones, a gaming PC, whatever shows up. the output is mathematically identical to a single datacenter GPU. same model, same quality, owned by the people running it


I taught Claude to talk like a caveman to use 75% less tokens. normal claude: ~180 tokens for a web search task caveman claude: ~45 tokens for the same task "I executed the web search tool" = 8 tokens caveman version: "Tool work" = 2 tokens every single grunt swap saves 6-10 tokens. across a FULL task that's 50-100 tokens saved why does it work? caveman claude doesn't explain itself. it does its task first. gives the result. then stops. no "I'd be happy to help you with that." no "Let me search the web for you" no more unnecessary filler words "result. done. me stop." 50-75% burn reduction with usage limits getting tighter every week this might be the most practical hack out there right now














