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My latest articles and insights.
Running large models locally usually hits a hardware wall pretty fast. One machine has a decent GPU with limited VRAM, another has plenty of system memory, and a third is just sitting idle with spare disk space and CPU cycles. With RACoN (Reciprocal Agentic Compute Network), we wanted to see if we could pool these mixed machines together and expose them as one logical AI inference service. Designing a system like this means separating cluster management from raw tensor math. Here is how we laid out the architecture.
Data Meets Decisions serves as a bridge between the often-siloed worlds of project management and data analytics. It argues that project failure is rarely a lack of data, but a failure to translate that data into actionable, strategic decisions.