Engramme Launches Memory API Beta

🔑 Enhanced Key Takeaways

• •Engramme's architecture utilizes a proprietary 'Associative Neural Graph' (ANG) that replaces static attention mechanisms with dynamic, time-decayed node weighting to reduce context window bloat.
• •The API implements a 'Privacy-First Sandbox' where raw data from Gmail, Zoom, and Slack is processed locally via edge-computing before only encrypted metadata embeddings are sent to Engramme's cloud.
• •Early beta documentation indicates the API supports 'Cross-Modal Retrieval,' allowing users to query a Slack message by describing a visual element seen in a recorded Zoom meeting.

🛠️ Technical Deep Dive

• •Architecture: Replaces standard Transformer self-attention with Associative Neural Graphs (ANGs), which treat memory as a graph of nodes rather than a sequence of tokens.
• •Latency: Claims sub-50ms retrieval time for cross-platform queries due to the use of a hierarchical indexing structure that prioritizes recent interaction nodes.
• •Data Handling: Employs a 'Zero-Retention' policy for raw source data, storing only high-dimensional vector embeddings and relationship edges.
• •Integration: Provides RESTful endpoints and SDKs for Python and TypeScript, with native webhooks for real-time event ingestion from Slack and Zoom.

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline