Show HN: Imesde – A tiny, ephemeral vector engine for streaming data (Rust) (github.com)

Imesde, a newly announced In-Memory Streaming Data Engine, is designed to redefine how real-time semantic search operates, eliminating traditional disk-based delays in favor of pure RAM performance. With zero disk dependency, Imesde facilitates instant data ingestion and retrieval, allowing for live context feeding into large language models (LLMs) without indexing latency. Its architecture supports essential capabilities like automatic "forgetting" of stale data and real-time anomaly detection through Sliding Window Centroids, enhancing its effectiveness in application areas such as high-frequency streams and short-term AI agent memory. This innovation is significant to the AI/ML community as it creates a lightweight and efficient solution for handling transient, high-velocity data streams, addressing limitations in conventional vector databases that prioritize persistence over speed. Imesde boasts impressive performance metrics, with an average search latency of just 1.28 milliseconds and processing up to 6,751 queries per second, positioning it as a top choice for applications requiring immediate insights from continuous data feeds. Its compatibility with ONNX models and emphasis on local data privacy further underscore its appeal for developers looking to leverage real-time data efficiently and securely.