Google DeepMind’s DiffusionGemma 26B uses parallel text diffusion to generate text faster and remember more context. This open‑source model can write up to 1,100 tokens per second and supports a 256 K token window.
Google’s Gemini 3.1 AI model can read a million tokens at once, opening new possibilities for long‑form analysis, summarization, and smarter AI tools.
A step by step guide to llm self training that covers memory-first workflows, embeddings, safety checks, and tools like OpenCrabs and Neura ACE.
A clear guide on self adapting llms that explains how models learn at runtime, safety checks to add, and a step by step plan to run safe updates.
A clear guide to subquadratic models, why they matter, and how to test and build systems that handle very long AI inputs.
Qwen 3.7‑Max‑Preview introduces a 1M token context window, enabling large‑scale text processing and smarter AI workflows.
Fast‑Slow Training is a new training method that keeps AI models from forgetting old tasks while learning new ones. It uses slow weights for core knowledge and fast weights for quick adaptation.
Long Context Models let AI handle much larger text windows. This guide explains SubQ, mobile Gemini, Claude Dreaming, benchmarks, and clear steps to adopt long memory.
MIT’s SEAL framework lets large language models generate their own training data and edit themselves, making AI systems more autonomous and cost‑effective.
A simple, practical guide to Self Adapting LLMs, with examples, workflows, and safety checks for teams building smart apps.