Category: Technical Publications

Earlier in this series we covered IRIDIUM ONE’s hardware baseline and its software stack, and promised a closer look at retrieval-augmented generation (RAG). This is that post: how the appliance grounds a local model in a client’s own documents without ever fine-tuning or retraining anything. Why Retraining Was Never on the Table Fine-tuning a model…

The last two posts covered why CODR routes across twelve providers instead of committing to one, and the dispatcher-versus-aggregator distinction that shapes its architecture. This post goes one layer deeper: before CODR can route a task anywhere, it has to decide what kind of task it’s actually looking at. This is the classification step, and…

In the last post we looked at IRIDIUM ONE’s hardware and software stack. This one zooms into a different piece of the ecosystem: IRIDIUM CODR, the Electron desktop app that sits between a developer and twelve different AI coding providers. CODR’s core premise is simple to state and harder to execute well: no single coding…

Most AI deployments today assume the cloud. For SMBs operating under DSGVO, that assumption is often the deal-breaker: data residency, vendor lock-in, and unpredictable inference costs make cloud-first AI a hard sell. IRIDIUM ONE was built to remove that trade-off entirely — a self-contained hardware appliance that runs the full AI stack on-premises, with no…