World Wire

Most AI agents forget. They process a request, answer it, then drop the context. Google Cloud’s generative-ai repository now ships a sample that tackles this directly. It is the Always-On Memory Agent , a reference implementation that treats memory as a running process. Always-On Memory Agent Fundamentally, the project is a lightweight background agent that never stops. It runs 24/7 as a continuous process, not a one-shot call. It is built with Google ADK (Agent Development Kit) and Gemini 3.1 Flash-Lite . Notably, it uses no vector database and no embeddings. Instead, an LLM reads, thinks, and writes structured memory into SQLite . The model choice targets low latency and low cost for continuous background work. How It Works: Ingest, Consolidate, Query Architecturally, an orchestrator routes every request to one of three specialist sub-agents. Each sub-agent owns its own tools for reading or writing the memory store. First, the IngestAgent handles incoming...

Backpropagation dominates deep learning, yet it uses a mechanism the brain likely cannot. Specifically, the backward pass needs exact transposes of forward weight matrices. This is the weight transport problem . Sakana AI’s new paper, Diffusing Blame , confronts this constraint directly. The research team trains networks that obey Dale’s principle while avoiding weight transport entirely. What is Error Diffusion? Error Diffusion (ED) is a local learning rule, first proposed by Kaneko (2000). Each weight update depends on three signals only. These are presynaptic activity, a postsynaptic activation derivative, and a single global error sign. Consequently, ED never transports transposed forward weights or uses random feedback matrices. That locality makes ED naturally compatible with Dale’s principle. However, prior work demonstrated ED only on binary classification and MNIST. The Dual-Stream Architecture To satisfy that constraint, the research team sp...

Introduction This tutorial starts where most agent demos stop: giving the agent persistent memory, operational context, and a place to write back what happened. An event operator does not just need an agent that can summarize a weather report or generate a generic plan. The operator needs an agent that can remember what happened at prior events, retrieve relevant visitor and venue context, respond to live operational changes, and write the outcome back as memory for the next similar situation. We built this event-venue operator demo with MongoDB Atlas, Voyage AI embeddings, LangGraph, and optional Langfuse tracing. The demo scenario is the MongoDB Open, a fictional premium tennis tournament on Day 6 of play. Rain is approaching, covered hospitality capacity is constrained, and the operator has two different visitor journeys to protect: Mikiko, a first-time attendee trying to make the most of the grounds, and Nina, a premier guest with hospitality expectations and a history t...