This post details the architecture behind DriftMind, a streaming forecasting engine we built to replace Transformers at the edge.The Context:We monitor high-frequency industrial sensor streams where the cost of round-tripping data to a GPU cluster (latency + bandwidth) often exceeds the value of the forecast itself. We needed a model that could run locally on a CPU, learn in a single pass (cold start), and adapt to concept drift instantly. The Core Mechanism: Instead of attention heads, we use a "Reflexive Memory" system: Online Clustering: Incoming data points are mapped to micro-clusters in $O(1)$.Temporal Graphs: We build a transition graph between these clusters to model state probabilities.Result: We achieve ~94% of Transformer accuracy with <1% of the training cost and 25ms inference latency on standard CPUs.The full mathematical breakdown and benchmarks are in the article. I’ll be around to answer questions about the trade-offs and the implementation!