LLM Architecture

Learn how to move beyond simple Vector RAG by using Knowledge Graphs and LLMs like Gemini to build structured, persistent 'Deep Memory' for AI companions.

A comprehensive guide on moving LLM prototypes to production, covering multi-layer architecture, common pitfalls, and strategies for reliability and cost-efficiency.

Explore how to overcome context window limitations using recursive summarization and map-reduce patterns for processing millions of tokens with LLMs.

An in-depth analysis of the architectural breakthroughs in the Chinese open-source LLM landscape, exploring beyond DeepSeek to models like Qwen, Yi, and InternLM.

A deep dive into the technical trade-offs, costs, and implementation strategies for Retrieval-Augmented Generation (RAG) and Fine-tuning in LLM development.

An in-depth technical analysis of Differential Transformer V2, exploring how it eliminates attention noise and enhances model performance for developers using n1n.ai.

Discover the architectural secrets behind Tolan, the revolutionary voice-first AI companion powered by GPT-5.1, featuring real-time context reconstruction and sub-200ms latency via n1n.ai.

A deep dive into the Model Context Protocol (MCP), explaining how Large Language Models discover and execute external tools through a standardized architecture.

Moving beyond simple LLM calls to a deterministic 'Context as a Service' (CaaS) architecture for enterprise-level AI agents, focusing on safety, wisdom curation, and polymorphic output.