Discover AgentSphere: a multi-agent AI architecture that enables multiple open-source AI models to work together using shared memory, automatically routes tasks to the right models, and optimizes AI costs on GreenNode AgentBase.

How to parallelize an LLM task-matching pipeline for Redmine/Jira using semantic retrieval and reconciliation, cutting p50 latency ~4x without breaking task hierarchy logic.

A hands-on tutorial: stop customer data and credentials from leaking into LLM prompts using Envoy AI Gateway and a custom DLP proxy with real latency and accuracy numbers.

Inside the leaked Claude Fable 5 prompt: 6 responsibility layers and a modular structure that cut token usage by 28.82% in real benchmarks. Includes code and guardrail mapping.

Break down the core architectural components of an AI cluster — including node, rack, and cluster-level design — and how an in-house team like ours managed to bring everything together and make it actually work

Prompting is often the first step in leveraging a pre-trained model, with techniques like prompt engineering helping maximize its potential. However, these techniques have limitations, and there comes a point where fine-tuning becomes essential to achieve more targeted results.